Design of Radiation Detection System with Wireless Sensor Network

Menoufia University Faculty of Electronic Engineering- Menouf Department of Electronics and Electrical Communication Engineering

Design of Radiation Detection System with Wireless Sensor Network A Thesis Submitted for the Degree of Ph. D. in Engineering Science Electronics and Electrical Communication Engineering Signal Processing and Digital Photos Department of Electronics and Electrical Communication Engineering

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Eng. Amr Muhammed Abdel-Wahed Kishk B. Sc. In Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, 2003 M.Sc. In Electronics Engineering, Electrical Communication Engineering, Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, 2010 Lecturer Assistant, High Institute for Computers and Management Information Systems (HICMIS), Egypt

Supervisors Prof. Nagy W. Messiha Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Nawal A. El-Fishawy Department of Computer Science and Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Abdelrahman A. Alkafas Department of Reactors Atomic Energy Authority

Dr. Ahmed H. Madian Atomic Energy Authority

2017



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Supervisors Prof. Nagy W. Messiha

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Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Nawal A. El-Fishawy

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Department of Computer Science and Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Abdelrahman A. Alkafas Department of Reactors Atomic Energy Authority

Dr. Ahmed H. Madian Atomic Energy Authority

2017



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Approved by Prof. Nagy W. Messiha

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Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Nawal A. El-Fishawy

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Department of Computer Science and Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

Prof. Hany M. Harb Department of Systems and Computer Faculty of Engineering, Al-Azhar University, Egypt

Prof. Mona M. Shokeir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering, Menoufia University, Egypt

2017

ACKNOWLEDGEMENTS

First and foremost all praises and thanks to Allah who gave me the ability and bestowed me with perseverance to write this thesis. After that, I wish to express my sincere appreciation to my advisors Prof. Nagy W. Messiha, Prof. Nawal A. El-Fishawy, Prof. AbdelRahman, and Dr. Ahmed Madian for their guidance, encouragement, and support. In addition, I want to thank members of Faculty of Electronic Engineering for their help and support during all these last years of my life.

Finally, but not least, my deepest love and gratitude is devoted to my whole family. I would like to thank my father, my mother, and brothers. More words cannot express how much I love and appreciate them. This thesis is a dedication for their love.

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LIST OF PUBLICATIONS 1. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Hybrid Compression Algorithm for Wireless Sensor Network”, 2nd International Conference on Intelligent Information Networks (ICIIN 2014), China, Published in Journal of Advances in Computer Networks (JACN), vol. 2, no. 2, pp. 147-150, March 2014. 2. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Channel Encryption in Wireless Camera Sensor Network”, 2nd International Conference on Intelligent Information Networks (ICIIN 2014), China, Published in Journal of Advances in Computer Networks (JACN), vol. 2, no. 2, pp. 125-128, March 2014. 3. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Proposed Hierarchical Routing Protocol with Simple Nodes Locating Algorithm for Authenticated Nodes in Wireless Sensor Network”, 2nd International Conference on Intelligent Information Networks (ICET2014), Published in IEEE, March 2014. 4. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Haar Wavelet Image Compression Algorithm (HW-JPEG2000)”, 32nd National Radio Science Conference (NRSC2015), Published in IEEE, March 2015. 5. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Low Energy Lossless Image Compression Algorithm for Wireless Sensor Network (LELICA)”, Sensors & Transducers, 2015 IFSA Publishing, S. L., vol. 188, no. 5, pp. 102-106 , May 2015. (2013 Global Impact Factor: 0.705) 6. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Proposed Jamming Removal Technique for Wireless Sensor”, International Journal of Scientific Research in Network Security and Communication (IJSRNSC), vol. 3, no. 2, pp. 1-14 , May 2015. (2013 Global Impact Factor: 0.533) 7. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Performance Analysis of Multiple Key Management Schemes in Wireless

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Sensor Network”, International Journal of Computer Science and Network (IJCSN), vol. 4, no. 3, pp. 461-468, June 2015. (2015 Impact Factor: 0.417) 8. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Enhancements in the Security Level for Wireless Sensor Network”, Journal of Information Security (JIS), Scientific Research Publishing, vol. 6, no. 3, pp. 213-228, July 2015. (Google-based Impact Factor: 1.74) 9. A. Kishk, N. Messiha, N. El-Fishawy, A. Alkafs, and A. Madian, “Performance Analysis of Different Approaches Used in the Design of Hierarchical Routing Protocols for Wireless Sensor Network”, Minufiya Journal of Electronic Engineering Research (MJEER), vol. 25, no. 1, January 2016.

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ABSTRACT The fire in some building damages the building itself partly or entirely while the radiation leakage can damage the country partly or entirely. Therefore, the security should be built carefully in the countries. The objective of this thesis is the design of Wireless Sensor Network (WSN) having the ability to monitor the radiation leakage. The first problem discussed in this work deals with routing problem. A proposed routing protocol named by Long Lifetime Hierarchical Routing Protocol (LL-HRP) is presented to enhance WSN lifetime. LL-HRP is compared with the other routing protocols using some metrics which are Network clustering, energy consumption, number of lived nodes, number of lost sensed data, and the throughput. The comparison shows that LL-HRP succeeds in remedying the routing protocols drawbacks and enhancing both WSN lifetime and performance in comparison with the other protocols.

WSN is composed of huge numbers of sensor nodes. These sensor nodes are deployed randomly around a Base Station (BS). Therefore, the localization of these sensor nodes in WSN is very important in the nuclear plants to locate the radiation leakage problem accurately. This thesis introduces a proposed sensor nodes localization method named by Free-Cost Localization Method (FCLM). FCLM ignores the use of anchor nodes in comparison with the other methods. The use of anchor nodes increases WSN cost due to installation of additional modules in some of the sensor nodes. In addition, FCLM specifies the coordinates of the sensor nodes in WSN area depending on distances estimation only in comparison with the other methods which require the angel estimation beside distance estimation.

The active hacker of WSN in the nuclear plants means that this hacker is the devil himself or no mind person because of radiation leakage dangerous. The IV

data security is discussed by analyzing the different forms of the hackers. The analysis shows that the hackers can be faced by two approaches which are the key-updating and the frequency modulation. The key-updating approach is used in the authentication stage and the data transfer stage. The modulation technique approach is used to face the jamming threat. The proposed key management scheme is named by Key-Updating Authentication Protocol (KUAP). The proposed image encryption algorithm is named by New Image Encryption Algorithm for WSN (NEA-WSN). Moreover, this thesis introduces a proposed audio encryption algorithm named by Proposed Audio Encryption Algorithm (PAEA). The two proposed jamming defensive techniques are named by Inhomogeneous Carriers Modulation Technique (ICMT) and Hybrid Frequency Modulation with Amplitude Modulation (FM-AM), respectively. The jamming threat problem guides us to introduce an enhanced detection technique, named by Enhanced Jamming Detection Technique (EJDT), and a proposed jamming localization method named by Disturber Localization Method (DLM). In addition, the thesis introduces two merging techniques used to merge the audio samples and the image pixels in one signal. The merging techniques enhance the performance of the communication systems from many directions such as bandwidth utilization. The two merging techniques are named by Image and Audio Interpenetration Technique (IAIT) and Image and Audio Merging Technique (IAMT). Finally, the comparative study between the proposed techniques with the other techniques shows the success of the proposed techniques in the achievement of security requirements of each discussed threats type in the nuclear plants.

The last objective of the thesis is the data compression. The image is used as data. Thus, the thesis introduces five proposed image compression algorithms in comparison with the other image compression algorithms. These proposed algorithms are named by Zonal-DCT Image Compression Algorithm (Z-ICA), V

Enhanced Joint Photographic Experts Group 2000 (En-JPEG2000), Hybrid DWT with Zonal-DCT (DWT-Zonal) Image Compression Algorithm, Haar Wavelet Image Compression Algorithm (HW-JPEG2000), and Low Energy Image Compression Algorithm (LE-LICA). These proposed algorithms are enhancements in the quantization stage which has been shown in JPEG and JPEG2000. The tradeoff between the reconstructed image quality and the energy consumption is the main point of image algorithm design in WSN. The comparison results differentiate between the algorithms performance using some metrics such as energy consumption and Peak Signal-to-Noise Ratio (PSNR). The simulation results show the superiority of LE-LICA than the others. And also, the results show that LE-LICA is a proposed lossless image compression algorithm.

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TABLE OF CONTENTS ACKNOWLEDGEMENTS

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LIST OF PUBLICATIONS

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ABSTRACT

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TABLE OF CONTENTS

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LIST OF ABBREVIATIONS

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LIST OF FIGURES

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LIST OF TABLES CHAPTER 1 INTRODUCTION 1.1. Introduction

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1.2. Thesis Objectives

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1.3. Organization of the Thesis CHAPTER 2 CLUSTER AND LOCATING APPROACHES BASED ON HIERARCHICAL ROUTING PROTOCOLS 2.1. Cluster Approaches of Hierarchical Routing Protocol 2.1.1. Introduction 2.1.2. Related works 2.1.3. Proposed hierarchical routing protocol 2.1.4. Simulation results 2.2. Sensor Nodes Localization Methods 2.2.1. Introduction 2.2.2. Related works 2.2.3. Proposed sensor nodes localization methods 2.2.4. Performance analysis and discussions 2.3. Summary CHAPTER 3 ACHIEVEMENTS of DATA SECURITY REQUIREMENTS 3.1. Introduction 3.2. Key Management Schemes 3.2.1. Cryptanalysis 3.2.2. Proposed key management scheme 3.2.3. Results and discussions 3.3. Data Encryption Algorithms 3.3.1. Related works 3.3.2. Proposed encryption algorithms VII

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3.3.3. Results and discussions 3.4. Jamming Threat 3.4.1. Jamming detection techniques 3.4.1.1. Related works 3.4.1.2. Enhanced Jamming Detection Technique (EJDT) 3.4.2. Adversary localization methods 3.4.2.1. Related works 3.4.2.2. Proposed Disturber Localization Method (DLM) 3.4.2.3. Results of disturber localization methods 3.4.3. Defense techniques 3.4.3.1. Related works 3.4.3.2. Proposed defensive techniques 3.4.3.3. Two proposed merging techniques 3.4.3.4. Results and discussions 3.5. Summary CHAPTER 4 AUGMENTATION SENSOR NODE LIFETIME USING COMPRESSION ALGORITHMS 4.1. Introduction 4.2. Related Works 4.3. Proposed Image Compression Algorithms 4.4. Results and Discussions 4.5. Summary CHAPTER 5 CONCLUSIONS AND FUTURE WORK 5.1. Conclusions 5.2. Future Work APPENDIX A APPENDIX B REFERENCES

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LIST OF ABBREVATIONS AES AM AMBTC AoA APTEEN ASK BPR BPSK BS BTC BTC-CA CBCW CC CH CL CR CRC DCL DCT DEMUX DES DLM DoS DSSS DWT-Zonal EBS ECA EJDT En-JPEG2000 ERP-DCS Fast Zonal-DCT FCLM FCS FHSS FM FM-AM

Advanced Encryption Standard. Amplitude Modulation. Absolute Moment Block Truncation Coding. Angle of Arrival. Adaptive Periodic TEEN. Amplitude Shift Key. Bad Packet Ratio. Binary Phase Shift Keying. Base Station. Block Truncation Coding. BTC enhancement using Clifford Algebra. Chaos Block Cipher for Wireless Sensor Network. Correlation Coefficient. Cluster Head. Centroid Localization. Compression Ratio. Cyclic Redundancy Check. Double Circle Localization. Discrete Cosine Transform. De-multiplexer. Data Encryption Standard. Disturber Localization Method. Denial of Service. Direct Sequence Spread Spectrum. Hybrid DWT and Zonal-DCT Image Compression Algorithm. Exclusion Basis System. Energy Consumption Amount. Enhanced Jamming Detection Technique. Enhanced Joint Photographic Experts Group 2000. Efficient Key Management Scheme for Data-Centric Storage. Fast Zonal Discrete Cosine Transform. Free-Cost Localization Method. Frame Check on Sequence. Frequency Hopping Spread Spectrum. Frequency Modulation. Hybrid Frequency Modulation and Amplitude Modulation.

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GPRS GPS H.L. H.R. Haar DWT HD HPF HW-JPEG2000 IAIT ICMT ID IDCT IDS IDWT IEEE ISM JPEG JPEG2000 KUAP LEACH LE-LICA LF LL-HRP LPF MAC MAE MHWT MT NACK ND NEA-WSN NIES NSM O-QPSK PAEA PBKDF PDR PN PSNR PSR QoS QR

General Packet Radio Service. Global Positioning System. Horizontal Left. Horizontal Right. Haar Discrete Wavelet Transform. High Power Detection packet. High Pass Filter. Haar Wavelet Image Compression Algorithm. Image and Audio Interpenetration Technique. Inhomogeneous Carriers Modulation Technique. Identity. Inverse of DCT. Intrusion Detection System. Inverse DWT. Institute of Electrical and Electronics Engineers. Industrial, Scientific, and Medical. Joint Photographic Experts Group. Joint Photographic Experts Group 2000. Key-Updating Authentication Protocol. Low Energy Adaptive Clustering Hierarchy. Low Energy Image Compression Algorithm. Low Frequency. Long Lifetime Hierarchical Routing Protocol. Low Pass Filter. Media Access Control. Mean Absolute Error. Minimal Hardware Wait Time. Mersenne Twister. Negative Acknowledgement. Normal Power Detection Packet. New Image Encryption Algorithm for WSN. New Image Encryption Scheme based on a chaotic function. Network Security Manager. Offset-Quadrature Phase-Shift Keying. Proposed Audio Encryption Algorithm. Password-Based Key Derivation Function. Packet Delivery Ratio. Pseudo Noise. Peak Signal-to-Noise Ratio. Packet Send Ratio. Quality of Service. Query and Reply. XI

QUJDA RC6 RID RSA RSS RSSI SFD SHA-1 SN SSKM TDMA TDoA TEEN ToA UWB V.D. V.U. VFIL VLEACH WALEACH WALEACHenhanced WCL WSN Z-ICA

Query-based Jamming Detection Algorithm. Rivest Cipher 6. Radio Interference Detection. Rivest, Shamir and Adleman. Received Signal Strength. Received Signal Strength Indicator. Start of Framer Delimiter. Secure Hash Algorithm 1. Sensor Node. Secret Sharing-Based Key Management. Time Division Multiple Access. Time Difference of Arrival. Threshold Sensitive Energy Efficient Sensor Network. Time of Arrival. Ultra Wide Band. Vertical Down. Vertical UP. Virtual Force Iterative Localization. Vice-LEACH protocol. Energy Efficient Weight-Clustering Algorithm. Enhanced WALEACH. Weighted Centroid Localization. Wireless Sensor Network. Zonal-DCT Image Compression Algorithm.

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LIST OF FIGURES Figure (1.1) Figure (1.2) Figure (2.1) Figure (2.2) Figure (2.3) Figure (2.4) Figure (2.5) Figure (2.6) Figure (2.7) Figure (2.8) Figure (2.9) Figure (2.10) Figure (2.11) Figure (2.12) Figure (2.13) Figure (2.14) Figure (2.15) Figure (2.16) Figure (2.17) Figure (2.18) Figure (2.19) Figure (2.20) Figure (2.21) Figure (2.22) Figure (2.23) Figure (2.24) Figure (2.25) Figure (2.26) Figure (2.27) Figure (2.28) Figure (2.29) Figure (2.30)

Wireless Sensor Network (WSN) structure. Japanese sensor node components. Set-up phase (CHs selection) of LEACH. Cluster members’ election. Steady-state phase (data transfer). Vice-LEACH protocol (VLEACH). The presence of both RCH and CH in each cluster. WSN clustering using LL-HRP protocol. Repeating the procedures of figure (2.6) for the sensors that detect no RCH close to them. Clustering process for the second time. Final WSN clustering using LL-HRP. Sensor nodes distribution in WSN. The probability of all nodes become CHs. Number of CHs vs. round number r. The probability of no CH. Unfair distance between the sensor nodes and their CHs in comparison with BS. The probability of unfair distribution of CHs. The probability of CH death. Number of CHs in each round of Energy Efficient WeightClustering Algorithm (WALEACH). Number of CHs in each round of enhanced WALEACH. Number of CHs in each round of VLEACH. WSN clustering using LL-HRP. Number of CHs in each round of LL-HRP Radio energy dissipation model. Sensed data values used in our simulation. Energy consumption comparison in the case of set-up phase only. Energy consumption comparison in the case of both set-up phase and steady-state phase. Number of lived nodes per round in the case of set-up phase only. Number of lived nodes per round in the case of both set-up phase and steady-state phase. The anchor nodes beside the sensor node at (x, y). Time Difference of Arrival (TDoA). Angle of Arrival (AoA).

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Figure (2.31) Figure (2.32) Figure (2.33) Figure (2.34) Figure (2.35) Figure (3.1) Figure (3.2) Figure (3.3) Figure (3.4) Figure (3.5) Figure (3.6) Figure (3.7) Figure (3.8) Figure (3.9) Figure (3.10) Figure (3.11) Figure (3.12) Figure (3.13) Figure (3.14) Figure (3.15) Figure (3.16) Figure (3.17) Figure (3.18) Figure (3.19) Figure (3.20) Figure (3.21) Figure (3.22) Figure (3.23) Figure (3.24) Figure (3.25) Figure (3.26) Figure (3.27) Figure (3.28) Figure (3.29)

N1 coordinates using FCLM. N2 coordinates using FCLM. N3 coordinates using FCLM. Time adjustment. Time adjustment between two sensor nodes. Diffie Hillman encryption Algorithm. Rivest, Shamir and Adleman (RSA) encryption algorithm. RSA from two directions. ElGAMAL encryption algorithm. ElGAMAL drawback. Elliptic curve encryption algorithm. Elliptic Curve drawback. Keys generation process used to secure the channels using ERP-DCS scheme. Different keys used for authentication process in WSN using ERP-DCS. An Efficient Key Distribution scheme. Keys used to secure the communication channels using the Efficient Key Distribution scheme. Dynamic Key Management scheme. ID encryption process. WSN clustering via authorized sensor node using proposed hierarchical routing protocol. IDs validation using proposed authentication protocol. Authorized RCHs election and authentication keys renewal in addition to cluster key generation. Elected RCHs announce their IDs encrypted by Kd which is updated using S2-box. Sensor nodes discover the nearest RCH-ID decrypted by Kd which is updating using S2-box. KUAP procedures. NIES encryption algorithm pseudo code. NIES decryption algorithm pseudo code. CBCW encryption algorithm. CBCW decryption procedures. NEA-WSN encryption algorithm. NEA-WSN decryption procedures. Proposed Audio Encryption Algorithm (PAEA). PAEA decryption procedures. The original image and its histogram. The effectiveness of the change in the image pixels positions.

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Figure (3.30) Figure (3.31) Figure (3.32) Figure (3.33) Figure (3.34) Figure (3.35) Figure (3.36) Figure (3.37) Figure (3.38) Figure (3.39) Figure (3.40) Figure (3.41) Figure (3.42) Figure (3.43) Figure (3.44) Figure (3.45) Figure (3.46) Figure (3.47) Figure (3.48) Figure (3.49) Figure (3.50) Figure (3.51) Figure (3.52) Figure (3.53) Figure (3.54) Figure (3.55) Figure (3.56) Figure (3.57) Figure (3.58) Figure (3.59) Figure (3.60) Figure (3.61) Figure (3.62) Figure (3.63) Figure (3.64) Figure (3.65)

The histograms of the encrypted images shown in figure (3.33). The encrypted image of NIES and its histogram. CC Measurements for NEA-WSN and CBCW to ensure the success of the key updating process. Noise resistance comparison. Original audio samples. Histogram of original audio samples. Encrypted audio samples and its histogram. Decrypted audio samples and its histogram. Sensor nodes types in the presence of disturber. Radio Interference Detection (RID). Jamming announcement via jammed sensor nodes. Recording jammed sensor nodes IDs in groups. Results announcements. Results updating. Final updating. Sensor nodes types in the presence of disturber. di determination for sensor node i. xy-plane for a victim node at (xi, yi) and a disturber at (xj, yj). Three different disturber positions with different jamming area. Jamming effects on FHSS. The disturber effects on Tx(t) using Ja(t). Linking some sensor nodes pairs by wire connection. Frequency hopping pairs. Jamming effects on DSSS. Inhomogeneous Carriers Modulation Technique (ICMT). Jamming effects on the transmitted signal amplitude, phase, and frequency. FM-AM modulation technique. Bytes representation by FM signal. The merging process and jamming effects. Image and Audio Merging Technique (IAMT). Original image. Reconstructed images. The original image and its histogram. The original audio samples and its histogram. Decrypted jammed image and its histogram in the case of O-QPSK. Decrypted jammed audio samples and its histogram in the case of O-QPSK. XVI

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Figure (3.66) Figure (3.67) Figure (4.1) Figure (4.2) Figure (4.3) Figure (4.4) Figure (4.5) Figure (4.6) Figure (4.7) Figure (4.8) Figure (4.9) Figure (4.10) Figure (4.11) Figure (4.12) Figure (4.13) Figure (4.14) Figure (4.15) Figure (4.16) Figure (4.17) Figure (4.18) Figure (4.19) Figure (4.20)

Decrypted image and its histogram in the case of both IAIT and IAMT. Decrypted audio samples and its histogram in the case of both IAIT and IAMT. 1-D DWT. 1-D DWT procedures. Distributed JPEG2000. Applying 1D-DWT horizontally and vertically on the image. En-JPEG2000 Algorithm. DWT-Zonal Image Compression Algorithm. HW-JPEG2000 decomposition stage. HW-JPEG2000 reconstruction stage. Haar wavelet decomposition stage. Haar wavelet reconstruction stage. Flowchart of the details part. Flowchart of the reconstruction process. LE-LICA approach. Applying subtraction process on the rows and the last column. Original image. Distributed JPEG2000. Energy consumption comparison. Decompressed images and their PSNR values. PSNR comparison. Compression Rate (CR) comparison.

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LIST OF TABLES Table (2.1) Table (2.2) Table (2.3) Table (2.4) Table (2.5) Table (3.1) Table (3.2) Table (3.3) Table (4.1) Table (4.2) Table (4.3) Table (4.4)

The symbols definitions and values of radio energy dissipation model. The symbols definitions and values of the parameters used in the routing protocols. Round number comparison of first and last dead nodes. Number of times comparison where the sensed data is lost.

Comparison results. WSN security requirements. WSN layers threats and defense techniques. Measured PSNR (in dB) of modulation techniques. Energy consumption comparison. PSNR comparison. Compression Rate (CR) comparison. Correlation Coefficient (CC) comparison.

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Chapter 1

Introduction

Chapter 1 Introduction 1.1 Introduction Wireless Sensor Network (WSN) comprises a large number of sensor nodes that are densely deployed [1]. It aims to transfer the sensed data from the sensor nodes to the network observer. These sensor nodes are low-cost, low-power, and multifunctional sensor nodes [2]. They communicate with each other in short distances using wireless connections because of its dependence on the battery as an energy source [3]. Every sensor node mainly consists of four basic components [4]: a sensing unit, a processing unit, a communication unit, and a power unit as in Figure (1.1). The sensor unit is used to sense the data. The processing unit is used to manage the data processing in the sensor node before the transmission. The communication unit is used to manage the connection between the sender and the receiver. The power unit is used to feed the electronic circuits of the sensor node.

A sensing unit Sensor node A communication unit A power unit A processing unit

Figure (1.1): Wireless Sensor Network (WSN) structure

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Chapter 1

Introduction

1.2 Thesis Objectives WSN is used in many applications [4]. One of these applications is the reactors. The objective of the thesis is the use of WSN to monitor the radiation leakage in the nuclear plants. The design of radiation detection system with WSN has appeared clearly with Japanese people when their nuclear reactors have been affected by some disasters [5]. This action has caused radiation leakage. Japanese faced this problem hardly without using WSN. They scanned every citizen using radiation detectors to bound the problem [6]. After that, Japanese scientists have been suggested to monitor the radiation leakage using WSN [7]. This idea can bound the radiation leakage completely without hard efforts. In addition, WSN can specify the radiation sources, and also it can measure and monitor the radiation dose absorbed by each person body daily. The sensor nodes which designed by Japanese is shown in Figure (1.2) [7]. This sensor node consists of five components: radiation sensor board and Geiger tube, ZigBee Radio and Wasp mote, Global Positioning System (GPS), General Packet Radio Service (GPRS), and Lithium Battery 6600mA/h. Geiger tube is the sensing element used for the detection of ionizing radiation. Wasp mote is a sensor device used to save battery power in the case of no transmission. GPS is used to locate the sensor node to specify the position of the radiation leakage or the sensor node. GPRS is used to send the sensed data by the internet to the operator. In the case of no GPRS, the data will be sent through the network. Therefore, it should specify a routing protocol to guide the sent data to the sink. Most of the energy consumption in WSN comes from data reception and transmission. Therefore, the design of an efficient routing protocol is critical to prolong the WSN lifetime.

This thesis is concerned with the design of radiation detection system using WSN on some objectives [8]. These objectives are routing protocols [9], sensor nodes localization methods [10], data security [11], and data compression [12].

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Chapter 1

Introduction

The sensor nodes are deployed randomly in WSN area. The data transfer uses the routing protocol to establish the communications in WSN to (BS) which communicates with the network operator. The operator takes his decision according to the received sensed data. But, the location of the sensor nodes should be known to follow the problem. Therefore, the localization methods are discussed to specify the coordinates of the sensor nodes in WSN area. The presence of the hackers [13] in the nuclear plants is the dangerous because the modifications in these data will cause false decision which it will damage the country partly or wholly. Thus, the presence of the hackers within WSN members should be faced as possible. Finally, the dependence on the battery as an energy source for the sensor nodes in WSN guides us to discuss the data compression to prolong the WSN lifetime.

Figure (1.2): Japanese sensor node components [7]

The thesis introduces 16 proposed approaches in the radiation detection system using WSN. These techniques are as follows:

1. Long Lifetime Hierarchical Routing Protocol (LL-HRP) 2. Free-Cost Localization Method for the Sensor Nodes in WSN (FCLM) 3. Key-Updating Authentication Protocol (KUAP) 4. New Image Encryption Algorithm for WSN (NEA-WSN) 5. Proposed Audio Encryption Algorithm (PAEA) 6. Enhanced Jamming Detection Technique (EJDT) 7. Disturber Localization Method (DLM)

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Chapter 1

Introduction

8. Inhomogeneous Carriers Modulation Technique (ICMT) 9. Hybrid Frequency Modulation with Amplitude Modulation (FM-AM) 10. Image and Audio Interpenetration Technique (IAIT) 11. Image and Audio Merging Technique (IAMT) 12. Zonal-DCT Image Compression Algorithm (Z-ICA) 13. Enhanced Joint Photographic Experts Group 2000 (En-JPEG2000). 14. Hybrid DWT with Zonal-DCT (DWT-Zonal) Image Compression Algorithm 15. Haar Wavelet Image Compression Algorithm (HW-JPEG2000) 16. Low Energy Image Compression Algorithm (LE-LICA)

1.3 Organization of the Thesis The rest of the thesis is organized as follows:  Chapter 2 gives a short survey on some Hierarchical Routing Protocols. It starts with the approaches used in the design of these protocols to transfer the data from the sensor nodes to the BS. The localization methods are the next to the routing protocols. They are used to locate the sensor nodes in WSN. Both proposed routing protocols, LL-HRP, and proposed localization method,

FCLM, are introduced after the survey of the routing protocols and the localization methods respectively. Comparison between the routing protocols and the proposed one will be made. In addition, comparison between the localization methods and FCLM will be done later.  Chapter 3 gives a short survey on key management schemes, encryption algorithms, and jamming detection techniques in addition its localization methods and defensive techniques. The proposed key management scheme, KUAP, is introduced in details. The drawbacks of these schemes are introduced with each protocol. The comparison results of the key management schemes with KUAP. The survey of encryption algorithms and the two proposed algorithms, NEA-WSN and PAEA, are presented.

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Chapter 1

Introduction

 Finally, the jamming threat is presented. Jamming is discussed from three directions: jamming detection techniques, jamming localization methods, and jamming defensive techniques. The drawbacks of detection techniques are introduced with their technique. The proposed detection technique, EJDT, is the next with its enhancement. The jamming localization methods and the proposed method, DLM, in addition their comparison are the next to jamming detection techniques. The defensive techniques are discussed in the end of this chapter. The enhancement in jamming defensive techniques is introduced by proposing a modulation technique used to face the jamming problem. Two proposed techniques are introduced as an enhancement to face the jamming. These two techniques are ICMT and FM-AM. And also, two merging techniques merge both the audio samples with the image pixels in one transmitted signal. The two merging techniques are IAIT and IAMT. The

comparison results of the defensive techniques are made.  Chapter 4 gives a short survey on image compression algorithms which enhance the WSN lifetime by compressing the data before the transmission. This chapter introduces five proposed image algorithms compared with seven image algorithms as related works. These proposed image algorithms are ZICA, En-JPEG2000, DWT-Zonal, HW-JPEG2000, and LE-LICA. Their comparison results are introduced at the end of the chapter.  The concluding remarks and the future work are presented in chapter 5.

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Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols

Chapter 2 Cluster and Locating Approaches based on Hierarchical Routing Protocols 2.1. Cluster Approaches of Hierarchical Routing Protocols 2.1.1. Introduction The sensor nodes are distributed randomly in the network area. Each sensor has a sensor unit. The sensed data should be routed to the network administrator to take a decision. This data route is specified by a routing protocol. One of the categories of routing protocols is hierarchical routing protocols. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of hierarchical routing protocols. It has introduced an approach to specify the data routes [14]. The other discussed protocols are Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN), Adaptive Periodic TEEN (APTEEN), Energy Efficient Weight Clustering Algorithm (WALEACH), and Vice-LEACH (VLEACH).

2.1.2. Related works A. Low Energy Adaptive Clustering Hierarchy (LEACH) LEACH has partitioned the network area into clusters. Each cluster has a CH and its members from the sensor nodes in its cluster. The presence of CHs manages the data transfer in WSN and it enhances WSN lifetime. CH collects the data of the sensor nodes in its cluster to send them to BS. Therefore, the CH lifetime will be decayed rapidly in comparison with its members in their cluster. Therefore, it has been thought to change CHs periodically to avoid CHs death during their operations. The choice of CH depends on a generated number compared with a threshold, Tn. LEACH steps can be explained in two stages or phases which are set-up phase (network clustering) and steady-state phase (data transfer). The two phases are reinstalled periodically or with each round. The steps of these two phases are shown as follows: 6

Chapter 2


 Set-up phase 1. Each sensor node generates a random number in [0, 1] range to send it to BS. 2. BS calculates Tn as shown in Eq. (2.1) [14] with each round where p and r are CH proportion and round number respectively as shown in Figure (2.1). Then, BS compares Tn with the received random numbers of the sensor nodes. 3. The node that its random number less than Tn will be elected as CH. Tn 

p 1  p r mod 1    p  

(2.1)

4. BS announces the results to the sensor nodes. 5. The elected CHs broadcast their IDs to all the sensor nodes to guide the sensor nodes to the nearest CH to join with it as shown in Figure (2.2). 6. Each sensor node announces its CH-ID which is the nearest one. 7. Each CH collects its member IDs to produce a Time Division Multiple Access (TDMA) schedule and notifies all its members in the cluster.

Each sensor node generates a random number in the [0, 1] range

BS calculates Tn with each round.

Figure (2.1): Set-up phase (CHs selection) of LEACH.

7

Chapter 2


 Steady-state phase All the sensor nodes will remain in the sleep mode after the reception of TDMA schedule. Each sensor node will use its time slot to send its sensed data to its CH. CH will collect all these sensed data of its members to send them to BS as shown in Figure (2.3).`

The elected CH announces its ID.

Figure (2.2): Cluster members’ election.

CH sends the sensed data of its member to BS.

Figure (2.3): Steady-state phase (data transfer). 8

Chapter 2


B. Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN) In LEACH, the sensor nodes sense and transmit any changing in its attribute. The sensed data of this changing may be unnecessary and the sensor nodes will spend more energy to send this unnecessary sensed data. Therefore, it has thought to put a condition to send the sensed data which is the approach of another protocol named by Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN) [15]. Both set-up phase and steady-state phase are the same as LEACH except the condition of data transfer. TEEN specified two thresholds:  Hard threshold (HT) This is a threshold value for the sensed attribute.  Soft threshold (ST) This is a small change in the value of the sensed attribute which triggers the node to switch on its transmitter and transmit. Therefore, the sensor nodes which used TEEN will send their initial sensed data, SD0, during round number one. And also, the senor nodes will send their sensed data, SDn, during round n if SDn≥(HT+ST) or SDn≤(HT-ST). These thresholds save the senor nodes energy more than the sensor nodes which used LEACH.

C. Adaptive Periodic TEEN (APTEEN) Both set-up phase and steady-state phase are the same as TEEN [15] except some enhancement in steady-state phase. The sensor node must transmit its last sensed data to its CH at the end of each round even if the sensor node did not send any sensed data along the current round.

D. Energy Efficient Weight-Clustering Algorithm (WALEACH) Both set-up phase and steady-state phase are the same as LEACH with some modification to the threshold Tn to be Tnn as shown in Eq. (2.2) [16].

9

Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols     E p  residual   K opt Tn n      1   Einitial   1  p r mod p       

(2.2)

Where:  p is CH proportion.  r is round number.  Eresidual is the residual energy of the sensor node at each round.  Einitial is the initial energy of the sensor node.  Kopt is optimal number of CH as shown in Eq. (2.3) [16].

K opt 

1

N fs

2 d toBS

2 mp

A

(2.3)

Where:  dtoBS is the distance between the sensor node and BS.  N is the number of sensor nodes in WSN.  Ɛ is the amplifier energy of the sensor node transmitter and denoted by Ɛfs for free space propagation and Ɛmp for multi-path propagation.  A is the area of WSN.

E. Vice-LEACH protocol (V-LEACH) Both set-up phase and steady-state phase are the same as LEACH with some modification to steady-state phase. VLEACH has suggested using a vice-CH in each cluster from its members [17]. The vice-CH is used instead of CH in the case of CH death to avoid lost data during the round as shown in Figure (2.4).

10

Chapter 2


CH death A sensor node sensed some data CH Vice-CH

Figure (2.4): Vice-LEACH protocol (VLEACH).

2.1.3. Proposed hierarchical routing protocol The two main points in the design of a new hierarchical routing protocol are the prolonging WSN lifetime more than the others and eliminating the shortages of the others. The proposed hierarchical routing protocol, named by Long Lifetime Hierarchical Routing Protocol (LL-HRP), suggests clustering the WSN once and no need to cluster the network periodically. Each cluster has two main points: a Cluster Head (CH) and a Recombination Cluster Head (RCH) as shown in Figure (2.5). RCH takes the functions of CH as in LEACH while CH in LLHRP manages the cluster in the case of RCH death. CH will manage the RCH election in the cluster in the case of RCH death. The presence of both RCH and CH enhances the security level, the lifetime of the cluster, and WSN performance.

Set-up phase 1. At the first time, the nodes are fixed and distributed around BS randomly. 2. The start of clustering process is at the sensor nodes by transmitting their IDs to the BS to share with the network.

11

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3. BS measures their Received Signal Strength Indicator (RSSI). Then, it ranks them in descending order to partition them into equal groups (3-5 groups). 4. The maximum RSSI from each group will be elected as RCH as in Figure (2.6). 5. After RCHs election, BS announces the elected RCHs IDs to all sensor nodes.

CH RCH

RCH

CH RCH

CH Figure (2.5): The presence of both RCH and CH in each cluster.

RCH 1st time RSSI Max

Group 1

Group 3 Min

Figure (2.6): WSN clustering using LL-HRP protocol.

12

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6. Each sensor node starts to detect the nearest RCH. 7. If the sensor node does not detect RCH, then the sensor node will send a query message to BS for RCH election by repeating the previous procedures as in Figure (2.7). 8. Repeat step 7 as shown in Figure (2.8) until all the sensor nodes join with a RCH as shown in Figure (2.9).

RCH 1st time

Figure (2.7): Repeating the procedures of Figure (2.6) for the sensors that detect no RCH close to them.

RCH 1st time RCH 2nd time

Figure (2.8): Clustering process for the second time. 13

Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols RCH 1st time RCH 2nd time RCH 3rd time

CH

Figure (2.9): Final WSN clustering using LL-HRP.

Steady-state phase The amount of radiation leakage varies from place to place. Therefore, it is suggested to use a soft threshold, ST. This threshold, ST, will trigger the sensor node to measure the radiation leakage. If SDn+1 and SDn are the current sensed data and the previous one respectively, then the sensor node will send its sensed data, SDn+1, when |SDn+1-SDn|>ST. The condition of steady-state phase of LLHRP is not the same as TEEN or APTEEN. TEEN modifies the condition of LEACH to avoid the unnecessary sensed data. TEEN does not solve the problem of unnecessary sensed data over its soft threshold. Therefore, LL-HRP suggests its condition |SDn+1-SDn|>ST to avoid the unnecessary sensed data. To avoid the CH death during steady state phase, LL-HRP suggests an approach to avoid the periodic CHs election process as in LEACH. LL-HRP approach remedies the drawbacks of the routing protocols and these enhancements will be discussed with the comparisons results. In LL-HRP, the reduction in RCH energy level to some level means another new RCH election process. RCH will send a notification message to CH in its cluster rather than BS to elect a new RCH. CH will test the energy level of each node in the cluster and select the highest one to be RCH. Finally, both CH and new elected RCH will announce this election. By the same way, the reduction in CH energy level to

14

Chapter 2


some level means new CH election process. CH will send a notification message to RCH and the members in its cluster to elect the highest energy level to be CH. Both RCH and new elected CH will announce this election.

2.1.4. Simulation results WSN is composed multiple battery-operated sensor nodes distributed randomly as shown in Figure (2.10). Number of sensor nodes used in our estimations is 100 nodes and these nodes are in an area 50x50m2 because the transmission range of the sensor node is 50 m approximately. This area gives the sensor nodes the ability to communicate with BS which is located at the center or at (25, 25). The proposed routing protocol, LL-HRP, is compared with the other protocols using the following metrics [18]: A. Sensor nodes distribution during set-up phase (Network Clustering). B. Energy consumption: 1. in the case of set-up phase only. 2. in the case of both set-up phase and steady-state phase. C. Number of lived nodes in each round (First dead node and last dead node): 1. in the case of set-up phase only. 2. in the case of both set-up phase and steady-state phase. D. Number of lost sensed data.

BS Sensor node

Figure (2.10): Sensor nodes distribution in WSN 15

Chapter 2


A. Network clustering  LEACH, TEEN, and APTEEN suffer from some drawbacks which are: 1. The probability of all nodes become CHs can occur. All sensor nodes become CHs when all sensor nodes generates random numbers less than Tn or Tn=1 as shown in Figure (2.11) at certain values of r as shown in Figure (2.12).

BS CH

Figure (2.11): The probability of all nodes become CHs.

At some rounds, all sensor nodes become CHs and that occurs periodically.

At some rounds, most of sensor nodes become CHs and that occurs periodically.

Figure (2.12): Number of CHs vs. round number r. 16

Chapter 2


2. The probability of no CHs can occur. No CHs occurs when all sensor nodes generates random numbers greater than Tn only because Tn does not equal 0 as shown in Figure (2.13).

BS Sensor node

Figure (2.13): The probability of no CH.

3. The probability of unfair distribution. BS may elect all CHs in one side of the network and may leave the other sides without CHs. Therefore, the unfair distribution will reduce the sensors lifetime because the distance between some of the sensor nodes and their CHs become more than the distance between BS and these sensor nodes as shown in Figure (2.14). The unfair distribution gives a probability of a CH without members as shown in Figure (2.15). 4. The nodes with low remnant energy have the same priority to be a cluster head as the node with high remnant energy. The CH death can occur before the end of the round, periodic CHs election process, as shown in Figure (2.16) causing lost sensed data. And also, it exhausts the batteries of the members due to multiple reconnections process. Therefore, this drawback reduces the network lifetime.

17

Chapter 2


CH Some members suffer from long transmission to their CHs.

BS

Figure (2.14): Unfair distance between the sensor nodes and their CHs in comparison with BS.

CH BS Sensor node

CHs in one side of the network and the left side of the network without CHs and the sensors in left side are managed by a CH in the right side.

CH without members.

Figure (2.15): The probability of unfair distribution of CHs.

Figure (2.16): The probability of CH death. 18

Chapter 2


 Energy Efficient Weight-Clustering Algorithm (WALEACH) 1. Eresidual/Einitial term does not remedy the drawback of LEACH, TEEN, and APTEEN which is “The nodes with low remnant energy have the same priority to be a cluster head as the node with high remnant energy”. 2. Kopt can increase Tnn to be greater than 1 which increase the probability of “all nodes become CHs can occur” to be 1 as shown in Figure (2.17). This problem can reduce the lifetime of the network rapidly because of the communication with BS directly without clustering. Therefore, the probability of unfair distribution becomes 1.

The deletion of Kopt term from Tnn formula enhances the algorithm performance denoted by WALEACHenhanced. It reduces the probability of all sensors become CHs in comparison with LEACH, TEEN, and APTEEN as shown in Figure (2.18) in comparison with Figure (2.12), but the drawbacks of the LEACH, TEEN, and APTEEN still exist.

All sensor nodes become CHs nearly until round 200 which reduce the lifetime of the network rapidly.

Number of CHs become suitable to cluster the network at the end of the network lifetime.

Figure (2.17): Number of CHs in each round of Energy Efficient WeightClustering Algorithm (WALEACH). 19

Chapter 2


Figure (2.18): Number of CHs in each round of WALEACHenhanced.

 VLEACH VLEACH cares the probability of CH death before the round end during steady-state phase, but VLEACH still has the same drawbacks of LEACH, TEEN, and APTEEN at set-up phase. Figure (2.19) shows VLEACH still has the drawback of LEACH, TEEN, and APTEEN which is all sensor nodes become CHs at some rounds.

Figure (2.19): Number of CHs in each round of VLEACH.

20

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 Proposed routing protocol (LL-HRP) The proposed routing protocol, LL-HRP, overcomes the drawbacks of the previous protocols. It clusters the network once during set-up phase as shown in Figure (2.20). The change of both CH and RCH depends on their lifetime during steady-state phase only as shown in Figure (2.21). Therefore, there are not a probability of all sensor nodes become CHs, a probability no CHs exist, a probability unfair distribution, and a probability the nodes with low remnant energy have the same priority to be a cluster head as the node with high remnant energy.

Figure (2.20): WSN clustering using LL-HRP 12

Number of (Recombination) Cluster Head

11.8 11.6 11.4 11.2 11 10.8 10.6 10.4 10.2 10

0

500

1000

1500 2000 Round number

2500

3000

3500

Figure (2.21): Number of CHs in each round of LL-HRP

21

Chapter 2


B. Energy consumption The algorithms design for WSN is governed by the energy consumption which is a main metric used to differentiate and compare between different algorithms in WSN. The energy consumption based on the radio energy dissipation model of the sensor node as a transmitter and as a receiver as shown in Figure (2.22) and its symbols definitions and values are shown in Table (2.1) [19]. The energy consumption estimation depends on the propagation type between the transmitter and the receiver which specified by d and d0 where d0 can be calculated as in Eq. (2.4). For free space propagation: d≤do , γ=2, and ɛ=ɛfs=10 pJ/bit/m2 while multi-path propagation: d>do, γ=4, and ɛ=ɛmp=0.0013 pJ/bit/m4. And also, the energy estimation depends on the routing protocols parameter which its values are shown in Table (2.2). The sensed data used in our simulation is shown in Figure (2.23). The suggested sensed data values shown in Figure (2.23) are until 500 values only to be clear in the Figure, but the total number of the sensed data used in our simulation is until two millions. d

ETx(K,d)

K-bits

Transmit Electronics

Transmitter Amplifier

K*Eelec

K*ɛ*dγ

ERx(K)

Receive Electronics K*Eelec

Figure (2.22): Radio energy dissipation model.

Figure (2.23): Sensed data values used in our simulation. 22

K-bits

Chapter 2


do 

 fs  mp

(2.4)

Table (2.1): The symbols definitions and values of radio energy dissipation model. Symbol K Eelec

Definition

Value

Number of bits

4000 bits

Energy dissipation in Transmit and

50 nJ/bit

Receive Electronics stages

d

Distance between Tx and Rx

Variable

Eini

Initial Energy (Full Battery)

2J

γ ɛ EDA

Propagation constant

2 or 4

Energy dissipation in TX amplifier.

10 pJ/bit/m2 or 0.0013 pJ/bit/m4

Data Aggregation

5 nJ/bit

Table (2.2): The symbols definitions and values of the routing protocols. Symbol

Definition

Value

p

cluster proportion

0.07

N

Number of sensor nodes

100

A

Network Area

HT

50x50 m2

Hard Threshold for both TEEN and APTEEN Hard Threshold for proposed protocol

ST

Soft Threshold

M

Number of sensed data per round

(xBS, yBs ) dmax

0.5 1st sensed data 0.15

BS coordinates

4 (25, 25)

Maximum distance between RCH and its members

15 m

Figure (2.24) shows the comparison between different routing protocols without data transmission in each round to show how the periodic CH election process spends more energy consumption without data transmission. As expected, WALEACH exhausts the battery of the sensor nodes more than the other protocols causing the death of WSN quickly in comparison with the others.

23

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The deletion of Kopt term from Tnn formula as shown in Figure (2.24) prolongs the lifetime of the network more than the other protocols except LL-HRP. LLHRP does not suffer from the network death as the other protocols. The change of CH/RCH periodically spends more energy and delay time to send the sensed data, but LL-HRP does not obey to this rule. Therefore, we cannot estimate the network lifetime from the set-up phase for LL-HRP. The election of a new CH/RCH depends on their energy level. Therefore, the election of a new CH/RCH in the case of LL-HRP will be in steady-state phase rather than set-up phase in the case of the other protocols. The energy consumption rate of VLEACH is the same as LEACH, TEEN, and APTEEN. The last protocols exhaust the sensor nodes in WSN after WALEACH. VLEACH prolongs the network lifetime more than them as shown in Figure (2.24). Figure (2.25) shows the comparison in the case of both set-up and steady state phases. WALEACH scores the highest energy consumption rate while LL-HRP scores the least one. And also, the network lifetime is the shortest in the case of WALEACH and the longest in the case of LL-HRP. Both TEEN and APTEEN prolong the network lifetime in comparison with LEACH, VLEACH, and WALEACHenhanced. TEEN prolongs the network lifetime in comparison with APTEEN. And also, VLEACH prolongs the network lifetime in comparison with both LEACH and WALEACHenhanced. Finally, the WALEACHenhanced prolongs the network lifetime in comparison with both WALEACH and LEACH.

Energy consumption=0.0855 Joule (Set-up phase once)

Figure (2.24): Energy consumption comparison in the case of set-up phase only. 24

Chapter 2


Figure (2.25): Energy consumption comparison in the case of both set-up phase and steady-state phase.

C. Number of lived nodes per round Number of lived nodes per round is a metric used to show how many sensor nodes in the network can sense the data from the surroundings in each round. This metric indicates the rate of the sensor nodes death as shown in Figure (2.26). In the case of set-up phase only, LL-HRP keeps all sensor nodes without death which means that no lost data. WALEACH has higher rate of the sensor nodes death than the others while its enhancement, WALEACHenhanced, prolongs the sensor nodes more than the others except LL-HRP. In the case of both set-up phase and steady-state phase as shown in Figure (2.27), the routing protocols except LL-HRP are close to each other with respect to number of nodes per rounds as shown in Figure (2.27) while LL-HRP prolongs the sensor nodes to a high level. The round number of both first dead node and the last dead node are written down in Table (2.3). It is so clear from Table (2.4) that LL-HRP does not suffer from the problem of sensor nodes death in the case of set-up phase while the others suffer from this problem. And also, LL-HRP prolongs the sensor node lifetime more than the others in the case of both set-up and steady state phases.

25

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Table (2.3): Round number comparison of first and last dead nodes. In the case of set-up Routing Protocol

phase only

In the case of both set-up phase and steady state phase

First dead

Last dead

First dead

Last dead

node

node

node

node

LEACH

1319

1516

326

490

TEEN

1319

1516

434

772

APTEEN

1319

1516

434

676

WALEACH

1052

1347

299

453

WALEACHenhanced

1480

1597

418

523

VLEACH

1319

1581

326

660

LL-HRP

Inf

Inf

452

3359

All the nodes are lived

Figure (2.26): Number of lived nodes per round in the case of set-up phase only

26

Chapter 2


Figure (2.27): Number of lived nodes per round in the case of both set-up phase and steady-state phase.

D. Number of lost sensed data One of the metrics used in the comparison between routing protocols is number of lost sensed data. The lost data occur during steady-state phase because of CH/RCH death before the round end. Table (2.4) shows number of times of each routing protocol lost data. These numbers do not refer to number of bits, but they refer to how many times the network lost sensed data from the sensor nodes due to CH death before the round end. Both VLEACH and LL-HRP do suffer from this problem. VLEACH has remedied this problem by using another node to send the sensed data of the cluster in the case of CH death. LL-HRP has remedied this problem in the cluster as shown later. WALEACH lost few data in comparison with LEACH, TEEN, APTEEN, and WALEACHenhanced because all the sensor nodes become CHs most of the time and no members in their clusters. Although WALEACHenhanced is the enhancement of WALEACH, it suffers from higher number from lost data than WALEACH as shown in Table (2.4).

27

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Cluster and Locating Approaches based on Hierarchical Routing Protocols Table (2.4): Number of times comparison where the sensed data is lost. Routing protocol

Number of lost data

LEACH

330

TEEN

404

APTEEN

238

WALEACH

181

WALEACHenhanced

344

VLEACH

0

LL-HRP

0

2.2. Sensor Nodes Localization Methods 2.2.1. Introduction The sensor nodes in WSN are used to sense some kind of data to send them to the Base Station (BS). The decision of the network observer depends on the location of the received sensed data. Therefore, the location of the sensor nodes in WSN is one of the main points in WSN design [20]. The localization methods are classified into two categories: range-based and range-free [21]. Range-based localization methods rely on the distance and/or angle estimation. Range-free localization methods depend on proximity sensing or connectivity information to estimate the node locations. Range-based localization methods are selected in the localization methods discussion to avoid the approximation of the range-free localization methods. The distance, d, between the sensor node and the anchor node can be estimated by one of the range-based methods such as Received Signal Strength (RSS), Time of Arrival (ToA), Time Difference of Arrival (TDoA), and Angle of Arrival (AoA).

28

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2.2.2. Related works A. Received Signal Strength (RSS) The free space model of the signal propagation is used to estimate d according to Frii’s free space transmission equation [21]. Therefore, d can be estimated as shown in Eq. (2.5) where both Ptx and Prx are transmitting power and receiving power respectively, both Gtx and Grx are the transmitter antenna gain and receiver antenna gain respectively, and λ is the signal wavelength. If Gtx=Grx=1, then d can be calculated as shown in Eq. (2.6). d

 4

d

Ptx Gtx Grx Prx

 4

Ptx Prx

(2.5)

(2.6)

The data transfer in WSN depends on environmental impacts which turn on the sensor unit to sense this change [21]. The localization of sensor nodes depends on the presence of anchor nodes besides them. The coordinates of the sensor node can be estimated by three anchor nodes whose coordinates are (xa, ya), (xb, yb), and (xc, yc) as shown in Figure (2.28). The distances, da, db, and dc, between the anchor nodes shown in Figure (2.28) and the sensor node located at (x, y) can be calculated as shown in Eq. (2.7-2.9), respectively. These three distance equations are used to calculate va and vb as shown in Eq. (2.10 and 2.11), respectively. Finally, the coordinates of the sensor nodes, x and y, can be extracted from va and vb as shown in Eq. (2.12 and 2.13), respectively.

29

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(xb, yb) (xa, ya)

db da (x, y) dc

(xc, yc)

Figure (2.28): The anchor nodes beside the sensor node at (x, y) d a2  x  xa    y  y a 

(2.7)

d b2  x  xb    y  yb 

2

(2.8)

d c2  x  xc    y  yc 

2

(2.9)

2

2

2

2





(2.10)



(2.11)

x  x c  xb   y  y c  y b  

1 2  d b  d c2   xb2  xc2   yb2  yc2   va 2

x  x a  xb   y  y a  y b  

1 2 d b  d a2  xb2  xa2  yb2  y a2  vb 2

y



 

 

vb  x c  x b   v a  x a  xb   y a  yb xc  xb    yc  yb xa  xb 

(2.12)

va  y y c  yb   x c  xb 

(2.13)

x

B. Time of Arrival (ToA) Time of Arrival (ToA) is one of the methods used to calculate the distance d based on both the transmitting time ttx and receiving time trx. Therefore, ToA can estimate d as shown in Eq. (2.14) where C is the light velocity or C=3x108 m/s [21].

30

Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols d  C t rx  t tx 

(2.14)

C. Time Difference of Arrival (TDoA) Time Difference of Arrival (TDoA) depends on two anchor nodes beside the sensor as shown in Figure (2.29) [21]. One of the anchor nodes, sensor node 1, sends a signal to both the other anchor node and the sensor node located at (x, y). The anchor node located at (x2, y2) estimates the distance, d21, as shown in Eq. (2.15) from the transmitting time of sensor node 1, ttx1, and the receiving time at sensor node 2, trx2. The sensor node can extract another distance equation from Eq. (2.15) and Eq. (2.16) as shown in Eq. (2.17). Therefore, the sensor node requires only d21 and trx2. to estimate d as shown in Eq. (2.17). The anchor node located at (x1, y1) will get d21 and trx2 from the anchor node located at (x2, y2) to send them to the sensor node located at (x, y) to estimate d shown in Eq. (2.17). (x2, y2) d21 (x1, y1) d

(x, y)

Figure (2.29): Time Difference of Arrival (TDoA) d 21  C t rx 2  t tx1 

(2.15)

d  C t rx  t tx1 

(2.16)

d  d 21  C t rx  t rx2 

(2.17)

31

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D. Angle of Arrival (AoA) Angle of Arrival (AoA) requires smart antennas to estimate the angles [21]. If the anchor node located at (x1, y1), as shown in Figure (2.30), transmits a signal to the sensor node located at (x, y), then the angle will be measured θ1. And also, the angle at (x, y) will be measured θ2. And also, both θ1 and θ2 can be calculated as shown in Eq. (2.18 and 2.19). Therefore, the coordinates of the sensor node located at (x, y) can be calculated as shown in Eq. (2.20 and 2.21) respectively. Therefore, AoA can be used to estimate the distance. And also, it can be used to locate the sensor node accurately. (x2, y2)

` (x1, y1)

(y2-y)

(y1-y)

θ2

θ1 (x, y)

(x2-x)

(x-x1)

Figure (2.30): Angle of Arrival (AoA)

x

tan1  

y1  y x  x1

(2.18)

tan 2  

y2  y x2  x

(2.19)

y1  y 2  x1 tan1   x2 tan 2  tan1   tan 2  y  y1  x  x1  tan1 

(2.20) (2.21)

2.2.3. Proposed sensor nodes localization methods The proposed localization method, named by Free-Cost Localization Method (FCLM), can estimate the sensor nodes coordinates in WSN area with their initial clustering steps. WSN operator will elect two sensor nodes beside the BS for the

32

Chapter 2


localization process. Both BS and the two sensor nodes will collect the sensor nodes IDs and they will estimate their distances using ToA shown later. Then, the two sensor nodes will share with BS to estimate the coordinates. FCLM procedures will be as follows: 1. BS will consider itself located at (0, 0) point as shown in Figure (2.31). 2. First node, N1, will be considered located at (DBS-N1, 0) as shown in Figure (2.31), where: DBS-Ni is the estimated distance via BS between node i and BS, i=1,2,…,n, and n is number of nodes in WSN area.

DBS-N1

BS(0, 0)

N1(DBS-N1, 0)

Figure (2.31): N1 coordinates using FCLM. 2 2 3. Second node, N2, is located at (x2, y2). Both D BS  N and D N  N are the 2

1

2

estimated distances of N2 via BS and N1 respectively. They can be calculated as shown in Eq. (2.22 and 2.23), respectively. y2 can be calculated from Eq. (2.22) as shown in Eq. (2.24). Therefore, x2 can be calculated from Eq. (2.23) using Eq. (2.24) as shown in Eq. (2.25). We note that y2 has two values as shown in Eq. (2.26) which is estimated from Eq. (2.24). The positive y2 value will be considered as shown in Figure (2.32). 2 2 2 DBS  N2  x2  y 2



2 D N2 1  N 2  x 2  DBS  N1



2

(2.22)  y 22

(2.23)

2 2 y 22  DBS  N2  x2

x2 

1 2 DBS  N1

D

2 BS  N1

(2.24)

2 2  DBS  N 2  D N1  N 2



2 2 y 2   DBS  N 2  x2

33





(2.25) (2.26)

Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols N2(X2, Y2) DBS-N2 DN1-N2

DBS-

BS(0, 0)

N1(DBS-N1, 0)

N1

N2 at negative y-direction

Figure (2.32): N2 coordinates using FCLM.

4. Third node, N3, will be located at (x3, y3) as shown in Figure (2.33) and its coordinates can be calculated as in Eq. (2.27 and 2.28), respectively as shown later. In this case, we will consider the two values of y3. The only way can be used to specify the correct one from the two values of y3 is DN2N3. First, N2 will calculate the distance between N2 and N3 using positive y3, y3+, to get Dc1-N2N3 as shown in Eq. (2.29). Second, N2 will calculate the distance between N2 and N3 using negative y3, y3-, to get Dc2-N2N3 as shown in Eq. (2.30). Finally, compare the estimated distance, DN2N3, with Dc1-N2N3 and Dc2N2N3.

If DN2N3 is the same as Dc1-N2N3, then positive y3 is the correct one. If

DN2N3 is the same as Dc2-N2N3, then negative y3 is the correct one.

x3 

1 2 DBS  N1

D

2 BS  N1

2 2  DBS  N 3  D N1  N 3



2 2 y3   DBS  N 3  x3





(2.27) (2.28)

Dc1 N 2 N 3 

x3  x2 2   y3  y2 2

(2.29)

Dc 2 N 2 N 3 

x3  x2 2   y3  y 2 2

(2.30)

34

Chapter 2


5. The coordinates of the other sensor nodes can be estimated as shown in step 4 by comparing the nodes with BS, N1, and N2 distances. N3(X3, Y3) DN2-N3

N2(X2, Y2)

DN3-N1 DBS-N3

DN1-N2

DBS-N1

BS(0,0)

N1(DBS-N1,0)

N3 at negative y-direction Figure (2.33): N3 coordinates using FCLM.

The time adjustment between the sensor nodes in WSN can be remedied by the following steps: Step 1: Adjust the distance between the sensor node and BS to be known distance, e.g. 1m, as shown in Figure (2.34). The distance is measured between their antennas as shown in Figure (2.34). ` d=1m

BS

Sensor node

Figure (2.34): Time Adjustment 35

Chapter 2


Step 2: Send a signal from BS to the sensor node. The content of the transmitted signal is the clock time of BS. Step 3: The sensor node estimates the receiving time, trx, as shown in Eq. (2.31). Step 4: Calculate Δtc which is the time error between the current clock time of the sensor node, trxc, and the estimated receiving time, trx, as shown in Eq. (2.32). Therefore, each sensor node will adjust its clock time by using known d and known ttx to get its Δtc. Step 5: The clock time of the sensor node can be adjusted, in the future, in the distance estimation by adding Δtc to its clock time, tSNc. Therefore, the distance estimation between the sensor node and BS can be estimated as shown in Eq. (2.33). Step 6: The distance estimation between two sensor nodes in WSN can be estimated as shown in Eq. (2.34) where tSNci and Δtci are the clock time and its adjusting time of the sensor node i where i=1 or 2 respectively as shown in Figure (2.35). t rx  t tx 

d C

(2.31)

t c  t rx  t rxc

(2.32)

d  Ct SNc  t c   t tx 

(2.33)

d  Ct SNc2  t c 2   t SNc1  t c1 

(2.34)

d

Sensor Node 1 t SNc1  t c1 

Sensor Node 2 t SNc2  t c 2 

Figure (2.35): Time adjustment between two sensor nodes

36

Chapter 2


2.2.4. Performance analysis and discussions The distance estimation is affected from many ways such as multipath effects, fading effects, the dependence on RSS rather than ToA in the presence of noise effectiveness and/or jamming effectiveness in the communication channels, and the synchronization between the sender and the receiver in the case of ToA. Here, we consider the free-space without multipath effects and fading effects. Until now, no accurate distance can be estimated in indoor. The comparison shows that:  The dependence on anchor nodes consumes energy for distance estimation and these anchor nodes should be close to the sensor node area. Therefore, if the desired number of anchor nodes in the sensor node area does not exist, then the distance estimation cannot be done.  The probability of the noise effects in WSN is expected. Therefore, the dependence on ToA instead of RSS enhances the distance estimation. Therefore, FCLM enhances the distance estimation in the presence of noise effects.  The use of the anchor nodes increases the cost of the WSN establishment as shown in Table (2.5).  FCLM remedies the drawbacks of the localization methods which depend on the anchor nodes. The two drawbacks of anchor nodes are the cost as shown in Table (2.5) and the absent of one or more anchor nodes from the desired number in the communication area of the sensor node.  FCLM depend on distance estimation without angle estimation to estimate the coordinates of the sensor nodes in WSN area.  The comparison results as shown Table (2.5) show the preference of the proposed method, FCLM, than GPS and the other localization methods.

37

Chapter 2

Cluster and Locating Approaches based on Hierarchical Routing Protocols Table (2.5): Comparison results The use of anchor nodes

FCLM

Number of sensor nodes

100

100

Number of Anchor nodes

30

0

Location error

0

0

Energy consumption (Joules)

0.0203

0

9600LE

0

Total cost (GPS modulo cost/nodes=320 LE)

2.3. Summary The data transfer uses the routing protocol to establish the communications in WSN. The network operator takes his decision according to the received sensed data. Therefore, the location of the sensor nodes should be known to follow the problem. The performance enhancement of hierarchical routing protocols prolongs WSN lifetime. The thesis introduces a proposed hierarchical routing protocol and a localization method. The two proposed approaches prolong WSN lifetime and performance in comparison with the others. The comparison depends on some popular metrics such as energy consumption and cost.

38

Chapter 3

Achievements of Data Security Requirements

Chapter 3 Achievements of Data Security Requirements 3.1. Introduction The data security is an important objective in WSN. The hackers in WSN can eavesdrop and/or prevent the data to be transferred to BS safely and correctly. WSN security has been analyzed to track the network threats capabilities and their targets. The target of security analysis is the achievement of security requirements which are availability, authentication, confidentiality, integrity, nonrepudiation, freshness, forward secrecy, and backward secrecy [22]. These security requirements defined in Table (3.1) are exposed to threats. To protect WSN from these threats, we should discuss WSN threats. The active attacks analyze the traffic firstly to get/modify the sent data. The threats can be defined according to their effects on WSN layers which are physical layer, data link layer, network layer, and transport layer. The definitions of these threats are shown in Table (3.2) [22]. WSN threats can be categorized into three groups:  Threats interested in getting the sensor nodes identities (IDs) These threats affect on some of WSN security requirements which are authentication, confidentiality, freshness, forward secrecy, and backward secrecy.  Threats interested in getting/modifying the contents of the sent message partially and completely These threats affect only on the integrity as a WSN security requirement.  Threats interested in preventing the sensor nodes to send their data correctly regardless the message contents to exhaust the sensor nodes These threats affect only on availability and nonrepudiation as WSN security requirements. Jamming threat is an example of these threats.

39

Chapter 3


Finally, all the above threats can be faced by two approaches: key updating approach and frequency modulation approach. Key-updating approach is introduced in the design of key management schemes and encryption algorithms. Frequency modulation approach was introduced in the design of jamming defensive techniques. Therefore, WSN security requirements can be achieved by using: 1. A key management scheme and an encryption algorithm based on key updating approach. 2. A modulation technique based on frequency modulation approach which will be shown in jamming threat discussion.

Table (3.1): WSN security requirements. Security

Definitions

requirements Availability Authentication Confidentiality Integrity Nonrepudiation Freshness Forward secrecy Backward secrecy

It ensures that the communication with BS is available all the time or no denial of service. It ensures that the communication between both sensor nodes and BS are authenticated. It ensures that the trusted end-user is the only user can know the contents of the sensed data. It ensures that no modification occurs on the transmitted data. It ensures that no denial of the data sent before. It ensures that no repeated data from illegal sensor node after the successful of data reception. It ensures the inability to share in WSN after the disconnection from the network. It ensures the ability to connect with WSN again after the disconnection from the network.

40

Chapter 3

Achievements of Data Security Requirements Table (3.2): WSN layers threats

Layer Name

Threat Name

Definitions The sender prevention to transmit the sensed data to

Jamming

on the communication channel.

Physical layer Tampering Collision Data Link Layer

the receiver correctly by applying a jamming signal The modification in the sent data partially or completely. The transmission in the same time with another node in its time slot causing errors in the receiving bits. The exhausting the sender in the retransmission

Exhaustion

processes many times which causing the death of the sensor node.

Unfairness

The sensor node prevention to send in its time slot by using two successive time slots to send data.

Spoofed, Altered, or Replayed

Change the data route to disrupt traffic.

routing information Selective forwarding Network Layer

Sinkhole Sybil

The prevention of some data to reach the receiver completely. The appearance as a communication point between the sender and the receiver. Use

many

identities

in

WSN

during

the

communication with BS. The attacker copies the whole packet or message by

Wormholes

tunneling them to another network from the originator

Hello flood attacks

Transport Layer

Flooding De-synchronization

The attacker tries to communicate with the sensor nodes by sending Hello message to them. Attacker may request new connection repeatedly until the resources are exhausted. The try to damage the synchronization between the sender and the receiver.

41

Chapter 3


3.2. Key Management Schemes 3.2.1. Cryptanalysis WSN is frequently exposed to attackers. The data transmission in secure channel enhances the data confidentiality. The encryption keys used to encrypt the data are generated during the authentication process using a key management scheme. The key management scheme authenticates both the sensor nodes and BS with each other and manages the keys distribution between them. Attackers have the ability to analyze the traffic to get the encryption keys [23]. Hence, periodic updating of these keys enhances the security level and leads to increase the difficulties of the network cracking. Multiple schemes with different approaches will be discussed to analysis their performance. The main problem in the design of key management schemes is the exchange of the encryption keys safely without crack. Some of these schemes depend on using preloaded initial key and some of them based on using two different keys, public and secret keys. The third category of schemes considered the absence of attackers with the initial communication and no need to the initial key. The performance analysis of these schemes will show the best approach used to secure the communication channels in WSN. The drawback in the mathematical use of the modular function to generate a shared key is one of the thesis objects. This drawback is used in the modern schemes and it will be illustrated in a numerical example. The drawbacks of each scheme will be presented after the scheme immediately.

Scheme 1: Diffie Hillman Encryption Algorithm Algorithm: Diffie Hillman algorithm, key-exchange, had been used to establish a shared secret key [24]. BS announces two keys, pDH and gDH, and uses a secret key, xDH, as shown in Figure (3.1). Each sensor node in WSN has a secret key, yDH. The procedures of shared key generation are as follows: Step 1: BS announces the two keys, pDH and gDH. Step 2: As shown in Eq. (3.1), BS uses its secret key, xDH, to generate a new key, KBS. 42

Chapter 3


Step 3: BS sends KBS to the sensor node. Step 4: As shown in Eq. (3.2), the sensor node generates a shared key, KDH, used in identities encryption from the received key, KBS, and its secret key, yDH. Step 5: As shown in Eq. (3.3), the sensor node uses the two public keys, pDH and gDH, and its secret key, yDH, to generate a new key, KSN, to send it to BS. Step 4: As shown in Eq. (3.4), BS generates the shared key, KDH, from the received key, KSN, and its secret key, xDH. xDH K BS  g DH mod p DH

(3.1)

x DH y DH K DH  g DH mod p DH

(3.2)

y DH K SN  g DH mod p DH

(3.3)

y DH xDH K DH  g DH mod p DH

(3.4)

Authentication Process Sensor Node (SN)

Base Station (BS)

g DH , pDH  xDH K BS  g DH mod pDH y DH K SN  g DH mod pDH

    y

K BS y

    x K SN x

DH

DH

DH

DH

xDH y DH K DH  g DH mod pDH

y DH xDH K DH  g DH mod pDH

Figure (3.1): Diffie Hillman encryption algorithm.

Drawbacks: Diffie

Hillman

algorithm

has

considered

in

its

calculations

that

xDH x DH y DH ( K BS ) yDH  ( g DH mod p DH ) yDH = g DH mod p DH . This consideration is a mistake

which is used in some popular key management schemes. The sensor node x received KBS and did not receive g DH and p DH , separately. Thus, when the sensor DH

43

Chapter 3


x node wants to encrypt KBS, he will encrypt KBS and will not encrypt g DH alone. DH

x Y The two equations are not equal; the result of ( g DH mod pDH ) is always less DH DH

x than p DH while ( g DH mod pDH ) y is not always less than p DH . Moreover, at BS, DH

CH

xDH y DH x DH xDH y DH ( K SN ) xCH  g DH mod p DH . ( g DH mod p DH ) yCH = g DH mod pDH is correct only x y when g DH  pDH . Therefore, if DH DH

xDH y DH g DH  pDH , then

x DH ( g DH mod p DH ) yCH

≠

xDH y DH g DH mod pDH . We should know that, all the previous keys have the same

length. And also, both xDH and yDH are used in the power position. Therefore, it is x y impossible to get g DH

DH DH

 pDH . In addition, Diffie Hillman suffers from man-in-

the middle attack.

Scheme 2: Rivest, Shamir and Adleman (RSA) Encryption Algorithm Algorithm: In RSA algorithm, BS announces two keys and keeps two secret keys. The sensor node uses the two public keys to encrypt the message and BS uses one of the secret keys to decrypt the message. RSA steps are as follows [25]: Step 1: BS chooses two very large prime keys; pRSA and qRSA as shown in Figure (3.2). Step 2: It calculates nRSA=pRSA×qRSA. Step 3: It calculates ФRSA=(pRSA-1)×(qRSA-1). Step 4: It chooses a random prime key, eRSA, to determine dRSA as in Eq. (3.5). Step 5: BS announces eRSA and nRSA as public keys. Step 6: Sensor node can encrypt a plain text, PRSA, with two public keys, nRSA and eRSA, as in Eq. (3.6) to get a cipher text, CRSA. Step 7: BS decrypts CRSA by using dRSA and nRSA to get PRSA, as in Eq (3.7). (d RSA  e RSA ) mod  RSA  1

(3.5)

eRSA C RSA  PRSA mod n RSA

(3.6)

d RSA PRSA  C RSA mod n RSA

(3.7)

44

Chapter 3

Achievements of Data Security Requirements Authentication Process Sensor Node (SN)

Base Station (BS)

 pRSA , qRSA  nRSA  pRSA  qRSA

 RSA   pRSA  1 qRSA  1

     e

eRSA , nRSA  RSA

mod nRSA

d RSA  eRSA mod  RSA  1      d

RSA

mod nRSA

eRSA CRSA  PRSA mod nRSA

d RSA CRSA mod nRSA  PRSA

Figure (3.2): Rivest, Shamir and Adleman (RSA) encryption algorithm

Drawbacks: 1. The sensor node uses the two public keys, eRSA and nRSA, to encrypt his message. But, RSA does not consider the inverse direction. If BS wants to send a message to the sensor node, then BS will encrypt the message using both dRSA and nRSA and the sensor node will decrypt the encrypted BS message using both eRSA and nRSA. Both the sensor node and attackers know the two public keys, eRSA and nRSA. Therefore, attacker can crack the RSA easily from the inverse direction as shown in Figure (3.3). 2. Equation (3.6) can be written as PRSA  e (m  n RSA  CRSA ) where: m=0, 1, RSA

2, …. Both eRSA and nRSA are public keys and known. And also, CRSA is known where CRSA is the received encrypted message. PRSA is an integer value because each byte value in [0,255] range. Therefore, PRSA will be limited to some values because

eRSA

(m  n RSA  CRSA ) must be integer which

increases the probability to crack the system to high level. Therefore, the attackers can crack RSA from multiple encrypted packets from the sensor node to the BS.

45

Chapter 3



Base Station (BS)

Public keys (eRSA,nRSA)

     e

RSA

     d

mod nRSA

e PRSA | SN

RSA

mod nRSA

RS A

e CRSA |SN  PRSA |SN mod nRSA RS A

d CRSA | SN mod nRSA  PRSA | SN RS A

e PRSA | BS RS A

d CRSA |BS  PRSA |BS mod nRSA RS A

e CRSA | BS mod nRSA  PRSA | BS RS A

Figure (3.3): RSA from two directions

Scheme 3: ElGAMAL Encryption Algorithm Algorithm: In ELGAMAL encryption algorithm, BS chooses a secret key, xEEA, and broadcasts three public keys (gEEA, qEEA, and GEEA) to all sensor nodes [26] and x to all sensor nodes for authentication as shown in also broadcasts hEEA  g EEA EEA

y y Figure (3.4). The sensor node computes C1EEA  g EEA and S EEA  hEEA . Then, it EEA

EEA

encrypts its message mEEA by computing C2EEA=mEEA.SEEA. The sensor node sends C1EEA attached with C2EEA to BS. BS can recover mEEA by computing

C2 EEA

( C1 EEA ) X EEA

.

Drawbacks: BS uses gEEA as a public key and sends hEEA to the sensor node. Both hEEA and gEEA are broadcasted to the sensor nodes. Therefore, the hackers can get xEEA using the relation xEEA=ln(hEEA)/ln(gEEA). And also, yEEA=ln(C1EEA)/ln(gEEA). Therefore, the attacker can get mEEA as shown in Eq. (3.13) where both C1EEA and

46

Chapter 3


C2EEA are unicasted while both gEEA and hEEA are broadcasted as shown in Figure (3.5). C

mEEA 

2 EEA ln  C1 EEA  ln  g EEA 

(3.13)

h

EEA


Base Station (BS)

g EEA , qEEA 

yEEA

hEEA  g

C1EEA  g

xEEA

xEEA EEA

y EEA EEA

y EEA S EEA  hEEA

C2 EEA  mEEA  S EEA

C1EEA , C2 EEA  C2 EEA  mEEA C1EEA  xEEA

Figure (3.4): ElGAMAL encryption algorithm


Base Station (BS)

g EEA , qEEA 

y EEA

C1EEA  g

y EEA EEA

xEEA hEEA  g EEA

xEEA

S EEA  h

y EEA EEA

C2 EEA  mEEA  S EEA C2 EEA  mEEA C1EEA  xEEA

C1EEA , C2 EEA  unicasted

mEEA  broadcasted

C2 EEA

ln  C1 EEA 

h

EEA

ln  g EEA 

unicasted

Figure (3.5): ElGAMAL drawback

47

broadcasted

Chapter 3


Scheme 4: Elliptic Curve Encryption Algorithm Algorithm: BS specifies the selected point, pE, in the curve. It preloads pE to all the sensor nodes. It generates a public key, QE, and QE= dE.pE. Therefore, QE is calculated from its private key, dE, to broadcast it to all the sensor nodes [27] as shown in Figure (3.6). If mE is the sensed data via some sensor node, then the sensor node will specify a point ME on the curve corresponding to mE. The sensor node will send two messages to the BS, CE1 and CE2, where CE1=KE.pE, CE2=ME+KE.QE, and KE is the private key of the sensor node. BS can recover ME from CE1 and CE2 using the relation CE2-dE.CE1 because CE2-dE.CE1=(ME+KE.QE)-dE.(KE.pE) = (ME + KE.dE.pE) - dE.(KE.pE) = ME. Authentication Process

Sensor Node (SN)

Base Station (BS)

pE

KE

dE

QE  d E  pE

mE  M E CE1  K E  pE

CE 2  M E  K E  QE

C

E1

, CE2  CE 2  d E  CE1  M E M E  mE

Figure (3.6): Elliptic Curve encryption algorithm.

Drawbacks: 1. The Elliptic curve is not suitable for authentication process in the inverse direction. For example: if BS tends to encrypt a message, it will use its secret key, dE, and send CE1 attached with CE2 where: CE1=dE.pE and CE2=ME+dE.QE. The sensor node will use its secret key, KE to recover ME by the same way but, CE2-KE.CE1≠ME because CE2-KE.CE1=(ME+dE.QE)KE.(dE.pE)=(ME+dE.dE.pE)-KE.(dE.pE) ≠ME as shown in Figure (3.7).

48

Chapter 3


2. Each sensor node can get the secret key, dE, of BS easily from the relation QE=dE.pE because both QE and pE are two public keys and known. In addition, BS can get the secret key, KE, of the sensor node from the received CE1 because pE is known.


Base Station (BS)

pE

KE

dE

QE  d E  pE mE | SN  M E | SN C E1 | SN  K E  p E

C E 2 | SN  M E | SN  K E  QE

C

E1

| SN , C E 2 | SN 

C E 2 | SN d E  C E1 | SN  M E | SN M E | SN  mE | SN M E | BS  mE | BS C E1 | BS  d E  p E

C

E1

| BS , CE 2 | BS 

C E 2 | BS  M E | BS d E  QE

C E 2 | BS  K E  C E1 | BS 

M M

E E

| BS  d E  QE   K E  d E  p E  

| BS  d E  d E  p E   K E  d E  p E   M E | BS

Figure (3.7): Elliptic Curve drawback.

Scheme 5: An Efficient Key Management Scheme for Data-Centric Storage Wireless Sensor Networks (ERP-DCS) Algorithm: In ERP-DCS, each sensor node in WSN is pre-loaded by some key materials into each node for other keys generating [28]. These materials include: 1. A unique master key 2. An initial key 3. A one-way hash function

49

Chapter 3


The sensor nodes use the hash function Hai (.) to send their cell ID to BS to join with the network. BS uses the master key, Kai, to secure the communications between the sensor nodes and BS for CHs election process as shown in Figure (3.8). And also, BS generates an initial key, Kainit, to secure the communications between the Cluster members to generate their keys: cell key and pairwise key. The elected CHs generate EBS keys to update the cell key depending on Kainit. The sensor nodes in the clusters generate their pairwise keys to communicate with each other in their cluster depending on the initial key Kainit as shown in Figure (3.9). The sensor nodes update their keys with each CHs election process and in the case of discovering a compromised node in WSN. The legal node updates its cell key depending on another key stored on it. The illegal node cannot communicate with CH. If BS discovers compromised CH, then it will broadcast a notification message to the cluster members using the respective master key. The sensor nodes will use their second stored key to elect a new CH without communication with BS. Therefore, the compromised CH will lose this election.

Sensor node

CHs generate Exclusion Basis System (EBS) to update the cell key.

Kainit CH Kainit is used to exchange the new cell key

Kainit

Sensor node

Kai

Kainit is used to exchange the new pairwise key

Kai is used to secure the channels between BS and the CHs, and also with the sensor nodes for CHs election process

BS

Figure (3.8): Keys generation process used to secure the channels using ERP-DCS scheme 50

Chapter 3


Sensor node

Cell key CH

Master key Kai

Pairwise key

Sensor node BS

Figure (3.9): Different keys used for authentication process in

WSN using ERP-DCS Drawbacks: Although the author refers to the use of a unique master key for future secure communications with the BS, BS does not update the master key. Therefore, the attackers can get a copy from all sensed data sent via CHs to BS communications. And also, they can work as a BS to deal with all the sensor nodes in WSN.

Scheme 6: An Efficient Key Distribution Scheme to Secure Data-Centric Routing Protocols in Hierarchical Wireless Sensor Network Algorithm: Each sensor node in WSN is preloaded by a common key, Global key, to secure the communication channels of the network [29]. The algorithm procedures are as follows: Step 1: BS generates a key for each cluster named by a Group key, KGR. Step 2: BS encrypts these Group keys and its Media Access Control address (MAC address) using the Global key, KGL, to send them to the clusters as shown in Figure (3.10). Step 3: After BS authentication via CHs, each cluster uses the Group key to secure the communications between CH and the cluster members.

51

Chapter 3


Step 4: Each sensor node generates its pairwise key, KPW, which is used to secure the communication channel between the sensor node and its CH. Step 5: The sensor node sends both its MAC address and its KPW encrypted by KGR to its CH. Step 6: CHs decrypt the sensor nodes messages using KGR to ensure from the sensor node MAC address and to get its KPW. Step 7: Each CH encrypts its MAC address by KPW of each sensor node. |Step 8: CH sends the encrypted message to the sensor nodes. Step 9: Each sensor node decrypts CH message by its KPW for authentication process. And also, it ensures the success of KPW reception as shown in Figure (3.10). If the sensor node is disconnected from the network, then all CHs will delete its KPW. Both BS and CHs shares to update KGR keys, and also both CHs and their members share to update KPW keys as shown in Figure (3.11). These keys are updated periodically to enhance the security level.

Drawbacks: The use of KGR key and KPW key enhance the security level in WSN. The KPW key adds difficulties to the hackers to follow each sensor node individually. But, in the case of CH death or sensor node death during the round, the sensor nodes cannot communicate with BS because the sensor nodes deleted KGL key which is used to communicate with the BS. And also, this problem will appear with periodic CHs election process. These two expected death problems can prevent the senor node to share in the key-updating time. The no sharing in the key updating can cause a problem with the network to share with it. This problem will appear in a huge number of sensor nodes and this matter is expected in the near time with the first dead node in WSN.

52

Chapter 3


Authentication Process


Cluster Head (CH)

Sensor Node (SN)

Base Station (BS)

EKGL MACBS  || EKGL KGR 

EKGL MACBS  || EKGL KGR  EKGR MACSN  || EKGR K PW  EK PW MACCH 

Figure (3.10): An Efficient Key Distribution scheme

Sensor node

Sensor node

CH

CH

K PW 3 K PW 1

K PW 4

K PW 2

K GR 1

K GR 2

Sensor node

Sensor node

BS

Figure (3.11): Keys used to secure the communication channels using the Efficient Key Distribution scheme

Scheme 7: A Dynamic Key Management Scheme Based on Secret Sharing for Hierarchical Wireless Sensor Networks Algorithm: The scheme depends on an initial key, KD, preloaded in each sensor node [30]. CHs secure their communication channels by using two different keys. The first key, CKi, is used to secure the communication channels with the sensor nodes. The second key, BK, to secure the communication channels with BS.

53

Chapter 3


a. The first key, CKi, generation 1. BS generates an encrypted key, rD1. 2. BS encrypts rD1 using the initial key, KD, stored in all sensor nodes EKD(rD1) as shown in Figure (3.12). 3. BS sends EKD(rD1) attached with its hashed code, h(rD1) to all the sensor nodes and CHs. 4. Both sensor nodes and CHs decrypt the received encrypted key using KD to get rD1. 5. Both sensor nodes and CHs ensure the validation of rD1 by comparing h(rD1)|received and h(rD1|decrypted) which tests the effects of hackers on h(rD1) in the communication channel. The equality means the absent of hackers from the communication channels. 6. Both sensor nodes and CHs generate their shared key, CKi, as shown in Eq. (3.8) where IDi is the identification of CHi. CKi is used to secure the communication channels with each others. b. The second key, BK, generation 1. BS sends EKD rD  || hrD  || EKD yD 2

2

i



to CHi where rD2 is another random

number and yD  f rD  IDi  is the response of a generated polynomial i

2

stored in BS. 2. CHi will decrypt the received message using KD to recover rD2 and yDi as shown in Eq. (3.9 and 3.10), respectively. It can calculate xDi as shown in Eq. (3.11) where IDi is the identification of CHi. 3. CHi sends BS request to reconstruct the master key, BK. 4. BS chooses t-1 CHs randomly and broadcasts to them the message of reconstruction and the identity of CHi, IDi. 5. These CHs, Cj|j=1,2,…,t-1, will receive BS message and encrypt their sub keys , xDj||yDj, using KD to send them to CHi attached with their identities, IDj. 6. CHi will decrypt CHj messages using KD to get their sub keys, xDj||yDj.

54

Chapter 3


7. CHi will use its sub keys, xDi||yDi, and CHj sub keys, xDj||yDj, to generate the master key, BK, as shown in Eq. (3.12). BS updates both CK and BK periodically to enhance the security level in WSN. The updating of the CK and BK is in

the updating rD1 and rD2

respectively. CK i  h(rD1  IDi )

(3.8)

    D E y   hr  ID 

rD2  DKD EKD rD2 yDi xDi

KD

KD

D2

t

(3.10)

Di

(3.11)

i

t

BK   y D i  i 1

(3.9)

j 1 j i

xD j xD j  xD i

mod q D

(3.12)

Drawbacks: 1. Both the sensor node and CH share to generate the authentication key between them, CK. This authentication key depends on a hash function. The hash function is not suitable to generate a key for encryption because it generates a signature code with few bits. 2. All CHs exchange their IDs in no secure channel to generate BK. Therefore, the attackers can use these IDs to crack and analyze the traffic. And also, these IDs can be used to detect the keys. 3. The sensor nodes do not use CK to communicate with BS, but it depends on KD. Both BS and sensor nodes do not update KD, but they update both CK and BK. This problem can give the hackers a chance to monitor the clustering process to get KD. If the hackers get KD, the hackers can share with the network. And also, the hackers can get ID of some sensor node to share with the others while the trusted node is in disconnected mode. The disconnection of the sensor node can be caused by hackers’ effects on it.

55

Chapter 3

Achievements of Data Security Requirements Authentication Process


Sensor Node (SN)

Secret key KD

Secret key KD

E





DK D EK D rD1   rD1 hrD1 Is

decrypted

Base Station (BS)

Cluster Head (CH)

  hr

D1

 received ?

E r , hr  r , hr  E r   r D1

KD

KD

D1

Secret key x0

D1

D1

DK D

KD

hrD1 Is

decrypted

D1

D1

  hr

D1

 received ?

CKi  h(rD1  IDi )

CKi  h(rD1  IDi )

   

CK i



EKD rD2 || h rD2 || EKD y Di

    r D E y   y hr  ID   x DKD EKD rD2 KD

KD

D2

Di

D2



i

Di

Di

BK Request BK Request to t-1 CHs selected randomly





EKD xD j || yD j



DKD EKD xD j || yD j t

t

i 1

j 1 j i

BK   y D i 

  x

xD j xD j  xD i

Dj



|| yD j

mod qD

BK Figure (3.12): Dynamic Key Management scheme

3.2.2. Proposed key management scheme The

proposed

key

management

scheme,

named

by

Key-Updating

Authentication Protocol (KUAP), faces the threats which interested in getting the sensor nodes identities (IDs). The face of these threats achieves the authentication, confidentiality, freshness, forward secrecy, and backward secrecy. KUAP suggests using two private keys for each sensor node without storing sensor nodes keys in RCH/BS memory. One of these keys is updated with each packet and the other is updated with each authentication process.

56

Chapter 3


BS generates ns for every sensor node in WSN. And also, BS generates the encryption key, Ks, of every sensor node from their ns. BS preloads the two keys in the sensor node, ns and Ks, which both vary from one sensor node to another. Both keys are generated in BS based on stored secrete key, Pv. Firstly; the sensor node starts its secure communication with the BS by transmission its encrypted message attached with ns. The transmitted message is encrypted by Ks. Secondly; BS detects Ks from ns based on the stored PV. Thirdly, BS decrypts the received encrypted message by using detected Ks. Ks is updated with each packet during the encryption process and also during the decryption process. The keys updating processes depend on two different S-boxes. The contents of these S-boxes are secret and stored in both BS and sensor nodes. Finally, BS uses the last updated Ks during decryption process to encrypt the reply message to send it to the sensor node. BS attaches new keys, ns and Ks, with the reply message before its encryption procedures. And also, it attaches a public key, Kd, for the authentication process between the sensor nodes and their elected RCH. The elected RCH and the sensor nodes can update their key, Kd, with the first transmission between them. KUAP depends on two generated S-boxes and two secret keys, ns and Ks, for authentication process. S-boxes, S1-box and S2-box, are 16x16 matrices. These SBoxes are used for key-updating processes. Their contents are secrets and random integer values between 0 and 255. For example, if the input value applied to S1-Box is 8D in a hexadecimal format, then the input value will be mapped to the value of row 8 and column D in S1-Box [31]. The procedures of KUAP scheme are as follows: 1. Generate a secret random key, Pv, and store it in BS only. 2. Generate a random key, ns, for each node separately, and each node has its ns 3. Follow the following equations to generate, Ks, for each node depending on their ns: 

nk  ns * Pv

57

Chapter 3




Qc  nk  ns



K s  Qc  Pv  mod 2b

 

, where b is the packet size.

4. Each sensor node encrypts its ID by Ks updated by S1-box as shown in Figure (3.13). Then, it sends the encrypted ID attached with ns to BS as shown in Figure (3.14). 5. BS generates Ks from the received ns as shown later to decrypt the encrypted ID by Ks updated by S1-box as shown in Figure (3.15). 6. The matching between decrypted ID with the stored ID in BS means an authorized node. 7. BS starts its normal operation to elect RCHs. It encrypts a message by updated Ks. The message contains the elected RCH-IDs attached Kd. Kd is a public key. It is used for authentication purpose between the elected RCH and sensor nodes as shown in Figure (3.16). Sensor node ID Ks

S1-Box

Encrypted ID Figure (3.13): ID encryption process.

Figure (3.14): WSN clustering via authorized sensor node using proposed hierarchical routing protocol. 58

Chapter 3


Figure (3.15): IDs validation using proposed authentication protocol.

Figure (3.16): Authorized RCHs election and authentication keys renewal in addition to cluster key generation.

8. RCHs encrypt their IDs using Kd. Kd is updated by using S2-box with each packet. Then, they broadcast their encrypted IDs as shown in Figure (3.17). 9. The sensor nodes decrypt the broadcasted RCH-ID by using the public key, Kd, which is updated via S2-box. This decryption process is done for clustering where the sensor nodes detect the nearest RCH as shown in Figure (3.18).

59

Chapter 3


Figure (3.17): Elected RCHs announce their IDs encrypted by Kd which is updated using S2-box.

Figure (3.18): Sensor nodes discover the nearest RCH ID decrypted by Kd which is updating using S2-box

10. If the sensor node does not detect RCHs in its communication area, then it will communicate with BS by its new ns and new Ks. Ks is updated by S1-box. 11. Repeat steps 7-10 without using new Kd. The sensor nodes will depend on the first Kd until the end of clustering process. 12. At the end of authentication process, the elected RCH will generate a random key, Kp, for authentication process in the cluster. Kp will be updated by using S2-box. Kp is different from cluster to cluster. Therefore, the authentication with BS will use ns and updated Ks using S1-box, and the authentication within the cluster will use updated Kp using S2-box. The replacement of CH or RCH means change Kp in the cluster. Figure (3.19) shows all the previous steps.

60

Chapter 3


13. Every elected RCHs generates a new key, KRCH. This key is used the RCH-BS communications in the inverse direction. The elected RCHs encrypt its KRCH by using its Ks attached with its ns. Then, BS replies by new Ks and ns. Therefore, KRCH is used to encrypt BS messages to the sensor nodes if BS requires some information from the sensor nodes after the end of clustering process. KRCH is known among cluster members. It updates with every RCH election. And also, KRCH uses the approach of key updating with every packet based on S1-box.

3.2.3. Results and discussions The discussed key management schemes showed different approaches to secure the communication channels. These different approaches can be classified the schemes into two groups. The first group includes schemes 5, 6, and 7. It has suggested to preload initial key(s). The second group includes schemes 1, 2, 3, 4, and the proposed one, KUAP. It is based on public key(s) and/or secret key(s). The keys of these schemes are updated periodically depending on the previous keys to secure the communication channel or with each packet and with each message as shown in the proposed scheme, KUAP. Through the discussion of these schemes, we note some drawbacks. These drawbacks enable attackers to crack most of these schemes. KUAP depends on preloaded two keys, ns and Ks, in each sensor nods to communicate with BS. And also, it preloaded two S-boxes which are common for all sensor nodes in WSN. BS does not require building database for all these keys, but it depends on some process to generate the authentication keys. The use of ns and Ks enhances the security level more than the others because of: 1. The dependence on an updated key, Ks, to encrypt the message. 2. The keys are updated with each packet, message, and CH election process. 3. The dependence on using a secret channel for each node separately without the dependence on database removes the problem of memory overload.

61

Chapter 3


4. No negative effects on the share of the sensor nodes with the BS after the disconnection. 5. No use of the same key to encrypt two successive packets because of key updating process. 6. The keys used with the CH are different than the keys used with the BS. 7. It avoids the drawbacks of the other schemes. It is suggested to embed ns key within the bits of the encrypted message rather than the attaching to enhance the security level. Authentication Process Sensor Node (SN)

Authentication Process Base Station (BS)

Cluster Head (CHi)

ns

nki  nsi * Pv

Pv

Qc i  nki  nsi

ns1 , K s1 

ns 2 , K s 2  EKS

2 updated

 

K s i  Qci  Pv  mod 2b

ID2  || ns 2 EKS

i updated

ID1  || nsi nki  nsi * Pv

Qc i  nki  nsi

 

K s i  Qci  Pv  mod 2b

IDi  DKS

i updated

IDi

decrypted

E

KSi

 IDi

updated

stored

yes : authentica ted

EupdatedKS

i updated

EK RCH _ ID1  DK

d u p d a ted

E

Kd

u p d a ted

d updated

RCH _ ID1 



RCH _ ID1 

EK

d updated

RCH _ ID1 || K d  RCH _ ID1 || K d  EupdatedKS i updated



RCH _ ID1 || IDn2 s1 , K s1 |updated



RCH _ ID1 || ID2  DK

d u p d a ted

E

Kd

u p d a ted



RCH _ ID1 || ID2 

RCH _ ID1 |decrypted  RCH _ ID1 |stored ? yes : authorized _ ID2

K p |updated 



EupdatedKd K p |updated 



DupdatedKd EupdatedKd K p |updated 

K p |updated

Kp

n

s2

, K s 2 |updated 

Figure (3.19): KUAP procedures 62

K RCH



IDi  ?

Chapter 3


3.3. Data Encryption Algorithms As the key updating approach is used in the proposed key management scheme, KUAP, it will be used in the design of two proposed encryption algorithms: data/image encryption and audio encryption. The encryption keys of the two encryption algorithms will be as shown in the end of Figure (3.19) or as follows:  If the sensor node sends a message to RCH or its cluster members, then it will depend on Kp as an encryption key.  If the sensor node receives a message from its RCH or its cluster members, then it will depend on Kp as a decryption key.  If RCH sends a message to its cluster members, then it will depend on Kp as an encryption key.  If RCH receives a message from its cluster members, then it will depend on Kp as a decryption key.  If the sensor node or RCH sends a message to BS, then it will depend on both its Ks and ns as shown later.  If the sensor node or RCH receives a message from BS, then it will depend on KRCH as a decryption key. The depending on data encryption algorithms faces the threats which are interested in data contents. Key updating approach enhances the security level to increase the difficulties to get the transmitted message contents during the transmission in the communication channel. It enhances the achievements of the WSN to get some of the security requirements. It achieves the integrity as a security requirement. The thesis discusses three different approaches used to encrypt the data. One of them depends on bytes positions exchange as a way to disturb the transmitted data. The second approach depends on bits positions exchange as another way to disturb the transmitted message contents. The last approach depends on an encryption key. The use of encryption key has two forms: fixed encryption key and updated encryption key. The fixed encryption key is one key used to encrypt all the packets all the time. The updated 63

Chapter 3


encryption key has two forms. One of them, the key can be updated periodically depending on BS. The other form, the encryption key is updated in the encryption algorithm itself during the encryption process. The updating process is independent on BS. The performance analysis of these approaches show the preference of the key updating in the algorithm than the others. Chaotic cryptosystems are examples for bytes positions exchange. New Image Encryption Scheme Based on a Chaotic Function (NIES) is an example for bits positions exchange. Data Encryption Standard (DES) is an example for fixed key encryption algorithm. Chaos Block Cipher for Wireless Sensor Network (CBCW) is an example for updated encryption key in the algorithm itself. The performance analysis of these encryption algorithms will be compared with two proposed encryption algorithms rather than key management schemes.

3.3.1. Related works A. Chaotic cryptosystems Chaotic cryptosystems are examples for bytes positions exchange. There are many different chaotic systems that have been used to construct chaotic cryptosystems such as: logistic map [32], chaotic piecewise map [33], and Arnold cat map [34]. These chaotic cryptosystems depend on a change in the image pixels positions as an encryption way to hide the image contents. Both logistic and piecewise maps depend on generated series as shown in Eq. (3.14) and Eq. (3.15), respectively where: both xn and m are in the range [0, 1] while λ is in the range (3.5699, 4]. The order of these series either in descending or ascending manner will specify the new pixel position in the encrypted image. Arnold cat map uses a different approach to change the pixels positions from (x, y) to (x', y'). It depends on an equation shown in Eq. (3.16) and the value of two variables, p and q, where Md is the image dimension. xn1  xn (1  xn )

64

(3.14)

Chapter 3


x n 1

 xn  m x m  n  1 m

 x' 1  y '  q   

0  xn  m m  xn  1

p  x mod M d pq  1  y 

(3.15)

(3.16)

B. New Image Encryption Scheme Based on a Chaotic Function (NIES) NIES encryption algorithm depends on bits positions exchange as an encryption way to hide the image contents [35]. It depends on a chaotic function shown in Eq. (3.17). The value S shown in Eq. (3.17) is initialized to L-1 and decremented after each iteration. L is number of bits. The seed, g, is in {1, . . . , L}. The initial value of Xn is X0 and X0=g while Xg=g2. Number of iterations/rounds, RA, is maximum value among RA1, RA2, and RA3 or RA=max(RA1, RA2, RA3). RA1 and RA3 can be calculated as shown in Eq. (3.18 and 3.19) respectively where ɛ2 is a fixed numerical tolerance, ɛ2=0.005, and t0 can be calculated as shown in Eq. (3.20) and nb=1 for the worst case. RA2 is estimated by a program as follows: 1. Let P0=0.5; ɛ1=0.001; R2=0;PR2=P0;dif=|0.5-PR2| 2. If dif> ɛ1 2  Calculate PR2 n  PR2

 1  PR2 n1 

2

n 1

 Let dif=|0.5-PR2|  Increment R2: R2|n=R2|n-1+1 3. If dif> ɛ1, repeat step 2. Otherwise, RA2=R2. X n1  X n2 mod S  X n  X g mod S

(3.17)

 128  RA1  Floor   1  log 2 L 

(3.18)

  ln 2     1 RA3  Floor log 2    lnt 0   

(3.19)

t0 

L  nb L

65

(3.20)

Chapter 3


NIES encryption procedures, as shown in Figure (3.20), are as follows: 1. Let I0 is the plain image; r=1; F=L-1;

I 0b =I0.

2. Generate random RA-values in {1, . . . , L} to get g-values. 3. If r

Design of Radiation Detection System with Wireless Sensor Network

Design of Radiation Detection System with Wireless Sensor Network

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