Avoiding Void Hole Creation and Energy

Avoiding Void Hole Creation and Energy Conservation to Prolong Network Lifetime and Throughput in Underwater WSNs

By

Farwa Ahmed CIIT/FA15-REE-017/ISB MS Thesis In Electrical Engineering

COMSATS Institute of Information Technology Islamabad – Pakistan Spring, 2017

Avoiding Void Hole Creation and Energy Conservation to Prolong Network Lifetime and Throughput in Underwater WSNs A Thesis Presented to

COMSATS Institute of Information Technology, Islamabad

In partial fulfillment of the requirement for the degree of

MS (Electrical Engineering) By

Farwa Ahmed CIIT/FA15-REE-017/ISB

Spring, 2017

ii

Avoiding Void Hole Creation and Energy Conservation to Prolong Network Lifetime and Throughput in Underwater WSNs

A Graduate Thesis submitted to Department of Electrical Engineering as partial fulfillment of the requirement for the award of Degree of M.S (Electrical Engineering).

Name

Registration Number

Farwa Ahmed

CIIT/FA15-REE-017/ISB

Supervisor Dr. Khurram Saleem Alimgeer, Assistant Professor, Department of Electrical Engineering, COMSATS Institute of Information Technology (CIIT), Islamabad Campus. June, 2017

Co-Supervisor Dr. Nadeem Javaid, Associate Professor, Department of Computer Science, COMSATS Institute of Information Technology (CIIT), Islamabad Campus. June, 2017 iii

Final Approval This thesis titled

Avoiding Void Hole Creation and Energy Conservation to Prolong Network Lifetime and Throughput in Underwater WSNs By

Farwa Ahmed CIIT/FA15-REE-017/ISB has been approved For the COMSATS Institute of Information Technology, Islamabad

External Examiner: ___________________________________ Dr. Shafiq Shami Deputy Chief Manager, NTDC, Islamabad

Supervisor: _________________________________________ Dr. Khurram Saleem Alimgeer Assistant Professor, Department of Electrical Engineering, CIIT, Islamabad Co-Supervisor: ______________________________________ Dr. Nadeem Javaid Associate Professor, Department of Computer Science , CIIT, Islamabad

HoD: ______________________________________________ Dr. M. Junaid Mughal Professor, Department of Electrical Engineering, CIIT, Islamabad

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Declaration I Ms. Farwa Ahmed, CIIT/FA15-REE-017/ISB, hereby declare that I have produced the work presented in this thesis, during the scheduled period of study. I also declare that I have not taken any material from any source except referred to wherever due that amount of plagiarism is within acceptable range. If a violation of HEC rules on research has occurred in this thesis, I shall be liable to punishable action under the plagiarism rules of the HEC.

Date: ____________________________ Signature of the student:

_________________________ Farwa Ahmed CIIT/FA15-REE-017/ISB

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Certificate It is certified that Farwa Ahmed, CIIT/FA15-REE-017/ISB has carried out all the work related to this thesis under my supervision at the Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad and the work fulfills the requirements for the award of the MS degree.

Date: ____________________________ Supervisor:

____________________________ Dr. Khurram Saleem Alimgeer Assistant Professor, Department of Electrical Engineering, CIIT,

Islamabad Co- Supervisor:

____________________________ Dr. Nadeem Javaid Associate Professor, Department of Computer Science, CIIT, Islamabad

Head of Department:

____________________________ Dr. M. Junaid Mughal

Professor, Department of Electrical Engineering.

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DEDICATION This thesis is dedicated to my co-supervisor Dr. Nadeem Javaid and to my parents. I lovingly dedicate my thesis to my friends Ayesha Ahmed, Fozia Feroz, Saba Gul and Arshad Sher for their support throughout this session.

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ACKNOWLEDGMENT I am grateful to my supervisor Dr. Khurram Saleem Alimgeer for his insightful criticism and support. I would like to pay sincere thanks to my co-supervisor Dr. Nadeem Javaid for his endless efforts and unparalleled kindness throughout the thesis duration. He has been an inspiration, mentor and guide. I pay my profound gratitude for his support and encouragement. What I have learnt from him is a great asset that will be kept intact with me throughout my life. I would like to thank Department of computer science for providing us ComSens lab. Furthermore, my heartiest thanks to Arshad Sher, Saba Gul, Fozia Feroze and all other lab mates for their moral support and kind behavior. In addition to this, I respectfully acknowledge the efforts of my loving parents and siblings for their untiring help.

Farwa Ahmed CIIT/FA15-REE-017/ISB

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ABSTRACT

Avoiding Void Hole Creation and Energy Conservation to Prolong Network Lifetime and Throughput in Underwater WSNs Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs; as it severely affects the network performance. Avoiding void hole creation benefits for better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused in this thesis. Two hop adaptive hop by hop vector based forwarding (2hop-AHH-VBF) protocol aims to avoid void hole with the help of two hop neighbor node information. The other protocol quality forwarding adaptive hop by hop vector based forwarding (QF-AHH-VBF) selects optimal forwarder based on composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder section. Opting suitableness criteria for forwarder node selection significantly reduces probability of void holes. In addition to this, energy efficiency becomes more significant and challenging issue because of no easy replenish of energy supply in UWSNs. One of the proposed solutions for this problem is

ix

to introduce sleep-awake scheduling for energy conservation in the network. Regarding this, we have proposed schemes focusing on energy efficiency: geo-opportunistic asynchronous sleepawake scheduling routing scheme (GOASST) and coordinated mobility of multiple sinks based GOASST (CSM-GOASST). By exploiting geographic and opportunistic routing paradigms altogether, we find advantages like path diversity and improvement in terms of reliable communication. We then present a sleep-awake scheduling joint with geo-opportunistic routing scheme

for

energy

conservation.

Furthermore,

coordinated

sink

mobility

in multi-sink

architecture is introduced in CSM-GOASST that exhibits potential in reducing end-to-end delay in the network. In CSM-GOASST, optimal sink positions in the field help to find shortest path to deliver data towards destination. Linear programming based mathematical modelling is carried out to find feasible solutions for performance parameters. To evaluate performance of proposed works, extensive simulations are conducted. Simulation results demonstrate the effectiveness of proposed schemes.

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List of Publications Dr. Nadeem Javaid, Associate professor (Co-supervisor) Farwa Ahmed Journal Publications 1 N. Javaid, F. Ahmed, Z. Wadud, N. Alrajeh, M. S. Alabed and M. Ilahi, “Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs ”, Sensors 2017, 17, 1762; doi:10.3390/s17081762. (IF − 2.667). Download

Conference Proceedings 4 F. Ahmed, S. gul, M. A. Khalil, A. Sher, Z. A. Khan, U. Qasim and N. Javaid, “Two hop adaptive routing protocol for underwater wireless sensor networks”, 11th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2017. May 27, 2017. Download 3 F. Ahmed, N. Javaid, A. Sher, Z. Wadud and S. Ahmed, ”Geospatial division based geographic routing for interference avoidance in Underwater WSNs”, Conference: 2nd EAI International Conference on Future Intelligent Vehicular Technologies (Future5V-2017), Islamabad, Pakistan. Download. Download 2 Z. Ahmed, A. Sher, S. gul, F. Ahmed, U. Qasim, Z. A. Khan and N. Javaid, “Single hop selection based forwarding in WDFAD-DBR for under water wireless sensor networks”, 11th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), 2017. May, 2017. Download. Download 1 B. Ali, A. Raza, F. Ahmed, S. Islam, M. Imran and N. Javaid, “Forward Layered Multipath Power Control Routing Protocol for Underwater Wireless Sensor Networks”, IWCMC 2017, Conference 2017, in the Holiday Inn Express Hotel, Valencia, Spain. Download

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TABLE OF CONTENTS 1 Introduction 1.1 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . . . .

1 4

2 State of the art work 6 2.0.1 Synchronous Schemes . . . . . . . . . . . . . . . . . . . . . . 12 2.0.2 Asynchronous schemes . . . . . . . . . . . . . . . . . . . . . 13 3 Proposed Work: quality forwarding two hop adaptive vector warding for void hole avoidance in UWSNs 3.1 Network configuration . . . . . . . . . . . . . . . . . . 3.2 Problem definition . . . . . . . . . . . . . . . . . . . . 3.3 Proposed schemes . . . . . . . . . . . . . . . . . . . . . 3.3.1 Two hop adaptive routing scheme . . . . . . . . 3.3.2 Quality forwarding adaptive hop by hop scheme 3.4 Overview of forwarding algorithms . . . . . . . . . . . 3.4.1 Packet types . . . . . . . . . . . . . . . . . . . . 3.4.2 Forwarding algorithm for 2hop-AHH-VBF . . . 3.4.3 Algorithmic flowchart of QF-AHH-VBF . . . . . 3.5 LP based mathematical formulation . . . . . . . . . . . 3.5.1 Energy tax minimization using LP . . . . . . . 3.5.2 End to end delay minimization using LP . . . . 3.5.3 Throughput maximization using LP . . . . . . .

based for. . . . . . . . . . . . .

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4 Proposed work: design of joint geo-Opportunistic routing and sleep awake scheduling with multi-sink architecture in UWSNs 4.1 Design of GOASST . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Neighborhood Discovery . . . . . . . . . . . . . . . . . . . . 4.1.3 Asynchronous mode collaborated with geo-opportunistic routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Coordinated sink mobility in GOASST . . . . . . . . . . . . . . . . 4.3 LP based Mathematical Formulation . . . . . . . . . . . . . . . . . 4.3.1 Energy Consumption Minimization using LP . . . . . . . . . 4.3.2 Packet Delivery Ratio Maximization using LP . . . . . . . . 4.3.3 Minimization of Average Delay using LP . . . . . . . . . . . 5 Simulation results and discussion 5.1 Simulation setup for quality forwarding two hop adaptive based forwarding for void hole avoidance in UWSNs . . . . 5.1.1 Performance metrics . . . . . . . . . . . . . . . . . 5.1.2 Analysis of packet delivery ratio . . . . . . . . . . . 5.1.3 Analysis of end to end delay . . . . . . . . . . . . . 5.1.4 Analysis of Energy tax . . . . . . . . . . . . . . . . xi

14 15 16 17 17 18 19 20 21 21 21 22 25 27

29 30 30 31 33 34 35 35 38 39 44

vector . . . . . . . . . . . . . . . . . . . .

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45 45 46 47 49

5.2

simulation set up and results for design of joint geo-opportunistic routing and sleep awake scheduling with multi-sink architecture in UWSNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.1 Topology-Related results . . . . . . . . . . . . . . . . . . . . 51 5.2.2 Sink utilization in CSM-GOASST . . . . . . . . . . . . . . . 53

6 Performance trade-offs between performance parameters of the proposed schemes 58 6.1 Performance trade-off between parameters of QF-2hop-AHH-VBF . 59 6.2 Performance trade-off for GOASST scheme . . . . . . . . . . . . . . 60 7 Conclusion

61

8 REFERENCES

63

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LIST OF FIGURES 1.1

Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8

Network architecture . . . . . . . . . . . . . . . . Problem identification . . . . . . . . . . . . . . . Illustration for holding time . . . . . . . . . . . . Neighbor ack packet format . . . . . . . . . . . . Algorithmic flow chart of QF-AHH-VBF scheme . Feasible region for energy tax minimization . . . . Feasible region for end to end delay minimization Feasible region for throughput maximization . . .

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15 17 20 21 22 25 26 28

4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8

Network architecture . . . . . . . . . . . . . . . . . . . . . . . Sleep-awake mechanism . . . . . . . . . . . . . . . . . . . . . . Feasible region (Energy tax minimization) for GOASST . . . . Feasible region (Energy tax minimization) for CSM-GOASST Feasible region (packet delivery ratio) for GOASST . . . . . . Feasible region (packet delivery ratio) for CSM-GOASST . . . Feasible region (end to end delay) for GOASST . . . . . . . . Feasible region (end to end delay) for CSM-GOASST . . . . .

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30 32 38 38 40 40 42 43

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12

Packet delivery ratio comparison . . . . . . . . . . . End-to-end delay comparison . . . . . . . . . . . . Accumulative propagation distance comparison . . Energy tax comparison . . . . . . . . . . . . . . . . Fraction of void nodes plots . . . . . . . . . . . . . Packet delivery ratio plots . . . . . . . . . . . . . . Energy consumption plots . . . . . . . . . . . . . . Average delay plots . . . . . . . . . . . . . . . . . . Energy consumption for varying number of sinks . . Packet delivery ratio under varying number of sinks End to end delay under varying number of sinks . . Sink positioning in CSM-GOASST . . . . . . . . .

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47 48 49 50 51 52 53 54 54 55 55 57

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5

LIST OF TABLES 2.1

Summary of state of the art work . . . . . . . . . . . . . . . . . . . 10

5.1 5.2

Parameter setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Sink position scenarios in CSM-GOASST . . . . . . . . . . . . . . . 56

6.1

Trade offs between performance parameters with respect to AHHVBF scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Performance trade offs with respect to GEDAR scheme . . . . . . . 60

6.2

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Chapter 1 Introduction

1

In recent years, underwater wireless sensor networks (UWSNs) have gained much attention due to their growing application horizons like ocean exploration, disaster prevention, oil and gas extraction, technical surveillance for defense, etc. UWSNs consist of randomly deployed sensor nodes to sense and gather data that is periodically reported to the sink. In underwater medium, acoustic waves are used as means of communication instead of radio waves that absorb in water within the very short distance from transmitter. Optical waves require alignment of transmitter and receiver for link establishment. Therefore, the acoustic communication is preferred for underwater wireless communication. However, the underwater acoustic communication faces unique challenges like limited available bandwidth, high end-to-end delay, severely impaired channel [1]. The network architecture of UWSNs comprises of randomly deployed sensor nodes equipped with acoustic links to communicate with each other and with the sink. Whereas sink is equipped with both an acoustic and a radio modems for the acoustic communication and the land communication, respectively. Sensor nodes report data to the sink which is further offload to the monitoring centre at the offshore. Propagation speed of the acoustic signal is five times less than radio frequency that results in higher end-to-end delays. Unstable network structure due to water currents in UWSNs is another challenge for traditional routing mechanisms to work in underwater environment. Other factor like low data rate is an outcome of limited available bandwidth of acoustic channel. Similarly, severe channel impairments cause high bit error rate. Both these factors altogether affect communication efficiency of UWSNs. Aforementioned reasons and constraints restrict performance of conventional terrestrial schemes in underwater environment. Moreover, dynamic nature of UWSNs, where sensor nodes are mobile due to water currents may create void holes. Void hole creation may also occur due to energy fatigue of a sensor node. In these scenarios, if sensor nodes send data towards void regions, it may result in packet drop that directly affects the packet delivery and energy consumption [2]. Therefore, throughput maximization has been extensively addressed in research because high packet drop leads towards high energy consumption. Hence, we need to imply throughput focused routing techniques for energy conservation and void hole avoidance for reliable data delivery. The proposed schemes:1) two hop adaptive hop by hop vector based forwarding (2hop-AHH-VBF) and Quality forwarding adaptive hop by hop vector based forwarding (QF-AHH-VBF) aim to maximize network throughput and minimize delay. Firstly, 2hop-AHH-VBF void hole avoidance scheme checks void hole status of forwarding nodes before sending data packets. Secondly, forwarding node selection 2

is optimized to ensure reliable data transmission with minimized end-to-end delay particularly in local sparse regions. The other scheme QF-AHH-VBF formulates a composite priority function (Fp ) after exchange of control packets in data packet forwarding stage. To carried out optimal forwarder selection at each hop throughout the routing path, every node calculates Fp . After relative comparison, node with high Fp value is selected as qualified forwarder. Afterwards, objective functions with linear constraints for either to maximize or minimize the performance parameters are defined under linear programming (LP) based problem formulation. As a final step, simulations are conducted to validate our schemes that perform better than counter vector based schemes in terms of successful transmissions and energy tax. Usually, sensor nodes perform sensing and processing much often as compared with communication tasks, this concludes that the transceiver of sensor nodes stays in idle listening mode most of the time. Therefore, it is effective to design power saving mechanisms for UWSNs. Regarding this aspect of energy conservation, existing sleep-awake scheduling schemes for sensor nodes have focused to minimize idle listening. When, there is no data communication, sensor node goes to inactive state and its communication module is turned off. For example, energy consumption of a micaZ node is 3µW and 60µW in sleep mode and idle mode, respectively [3]. It depicts the difference of energy consumption between sleep mode and idle mode. The two broad categories of sleep-awake scheduling are: synchronous wake up schemes and asynchronous wake up schemes. It is not preferred to use synchronous schemes because of high cost of maintaining clock synchronization in UWSNs [4]. In asynchronous sleep awake mode, sensor nodes wake up independently. Thus, it is easy to implement asynchronous mode that can be done locally without additional overhead cost. However, in dynamic topology change, frequent control packet exchange is performed to extract information of sensor nodes to keep connectivity in the network [5]. Hence, we have proposed two schemes: geo-opportunistic asynchronous sleepawake scheduling routing scheme (GOASST) and coordinated mobility of multiple sinks based GOASST (CSM-GOASST). 1) Description of GOASST is presented in Section ??, in which we have studied the effect of wake up duration and wake up rate of a node on its energy consumption. By jointly considering opportunistic routing and asynchronous sleep-awake scheduling, proposed scheme improves reliable data delivery in reduced energy cost. 3

2) In CMS-GOASST, sink mobility is introduced in GOASST to provide maximum coverage in the network to maximize data delivery in reduced end-to-end delay. Further in the paper, we have studied sink utilization ratio in GOASST by deploying multiple sinks in three dimensional field. 3) LP based mathematical modelling is performed to find feasible solutions for performance parameters in section 4.3. As a final step, we have conducted extensive simulations for performance evaluation.

1.1

Organization of Thesis

The thesis is structured as follows: state of the art work is categorized and tabulated in chapter 2, chapter 3 provides brief description of network architecture, problem definition and proposed schemes. In chapter 4, LP based mathematical formulation is presented. Simulation results in chapter 5 are followed by chapter 6 and chapter 7 concluding the thesis and discussing the trade-off between performance parameters and conclusion respectively. References are given at the end of the thesis. Thesis structure is presented in Fig. 1.1 providing organization of the thesis.

4

Problem definition

Introduction

Design of proposed schmes

State of the art

Thesis Structure

Network architecture

Packet Types

Proposed schemes

Overview of algorithms

Linear programming based mathematical formulation Simulation & results

Simulation setup & definition of performance parameters

Trade-off discussion Consclusion Figure 1.1: Thesis Structure

5

Chapter 2 State of the art work

6

From the existing works, routing protocols and schemes in WSNs are referred with brief descriptions of their features, advantages, and limitations in current section. Besides that routing protocols related to proposed schemes are also reviewed in the end of this section. For sake of simplicity, related works are summarized in Table 2.1. On the broader view, routing protocols in terrestrial WSNs (TWSNs) are categorized into three types: proactive routing, reactive routing, and location-based routing protocols. Due to multiple restrains in underwater environment, it is hard to imply terrestrial routing protocols in UWSNs. For example, proactive routing protocols OLSR [6] , DSDV [7] are not preferred in UWSNs because of large network overhead that results into high energy consumption. Moreover, reactive routing protocols (AODV [8], DSR [9], etc.) yield high latency consequently, more energy consumption. Routing protocols minimizing latency and energy consumption are encouraged in UWSNs because data communication is carried out over long routes with limited energy resources. Although location-based routing protocols find judicious applications in TWSNs where deployed nodes are aware of their coordinates at deployment time. In [10], the entitled mean square error algorithm (ECMSE) is made statistical assumption of Gaussian distributed location error Ricianly distributed distances between sensor nodes. ECMSE geographic routing technique has improved routing strategies to attain high PDR values while coping with location errors along with optimal energy consumption. Anyhow, it takes longer path while compared with other counterpart routing schemes. In [11], LMR energy efficient location based routing protocol for terrestrial sensor networks work on the principle of energy conservation by adjusting energy cost at each link. This scheme improves network throughput at the cost of large network overhead. Energy balanced data gathering (EBDG) protocol has achieved energy consumption both within one corona and different coronas. This corona based network division exploits both direct and multi-hop transmission. Due to hop-by-hop mode, data aggregation is performed in EBDG that improves network lifetime. However, this geographical scheme performs poor in large scale networks [12]. Anyhow, geographical routing techniques designed for TWSNs do not work up to the mark in underwater dynamic environments. While designing a routing protocol for UWSNs, better resource utilization needs to be considered due to limited resource availability. Keeping different considerations into account, routing protocols for UWSNs are classified into beacon based routing, clustering routing, 7

depth-based routing, and location-based routing protocols. Hop by hop dynamic addressing based (H2-DAB) protocol does not keep location information of nodes. Unique ID assignment to every node is performed based on hop counts from the sink. Reliable data delivery is achieved compromising the network overhead [13]. In cluster based routing [14], [15], cluster heads are selected based on residual energy or location. Features like efficient network management and communication overhead minimization are achieved. Due to longer end to end delays, it gives poor performance in time critical applications. In depth based routing (DBR) scheme, nodes greedily forward data packets to the upper nodes. If it does not find next hop forwarder node due to coverage hole or energy hole, packet drop occurs that affects packet delivery in DBR [16]. In Energy efficient depth based routing protocol (EE-DBR) energy conservation is obtained based on depth information. It incurs low end to end delay and less energy consumption compromising on the network overhead [17]. Weighting depth and forwarding area division DBR (WDFAD-DBR) protocol addresses the problem of void hole in UWSNs. In WDFAD-DBR, the extra energy expenditure and average end-to-end delay are reduced by suppressing duplicate packets in dense regions. Thus, enhanced reliability and reduced energy expenditure are achieved. In WDFAD-DBR, routing decisions are based on two metrics: firstly, depth of both current nodes and expected next hop forwarder are considered. Secondly, WDFAD-DBR adjusts forwarding area according to channel condition and node distribution density. WDFAD-DBR has achieved lessened probability of void holes; however, considering depth difference between two hops to avoid void holes does not eliminate it effectively and eventually packet drop affects network lifetime [18]. Similarly, in [19], interference-aware routing protocols (Intar: interference-aware routing and Re-intar: reliable and interference-aware routing) are presented. These protocols have adopted sender based approach to avoid void holes in UWSNs. Proposed protocols improve void hole avoidance along with collision probability at receiver side, thus, packet delivery ratio is improved. Intar scheme computes cost function (CF) based on multiple network parameters in forwarding phase. Sender node selects next forwarder on the basis of CF. Potential forwarding node with least number of neighbors, minimum number of hops from sink and maximum distance from the sender node computes maximum value of CF function. This approach reduces collision probability and avoids void holes by choosing potential forwarding node with maximum CF value. In Re-intar, CF is computed 8

using depth difference between source node and potential forwarding node along with other parameters taken by Intar protocol. For successful transmission, source node transmits one-hop backward in case of void region at the cost of increased accumulative propagation distance. In location aware vector based forwarding (VBF), forwarding area for transmission is confined within a virtual pipeline that is directional towards destination. Virtual pipeline direction and radius remains constant from a source to destination. In sparse networks, unavailability of forwarder nodes due to constant virtual pipeline radius causes retransmissions. Therefore, VBF scheme does not perform good in sparse networks [20]. Performance of VBF scheme is improved in hop by hop VBF (HH-VBF) protocol by changing direction of virtual pipeline hop by hop. Although, unchanged pipeline radius restricts the network performance in sparse node deployment because probability of void hole is not eliminated effectively. In case of small pipeline radius, current forwarding node does not find potential forwarders in its effective transmission range. In other case, when pipeline radius is large, packet duplication occurs due to large number of forwarders in the pipeline [21]. This problem is further optimized in adaptive hop by hop VBF (AHH-VBF). In [22], pipeline radius (PR) and transmission radius (TR) are adaptively changed hop by hop taking neighbor node distribution into account. Moreover, adaptive transmission radius is intended to avoid unwanted energy consumption. It is noteworthy that we have taken motivation from AHH-VBF scheme. 2hop-AHH-VBF and QF-AHH-VBF adopt adaptive PR and TR features of AHH-VBF, however, proposed schemes are different in following aspects. (1) There is inefficient void hole avoidance mechanism opted in AHH-VBF that is further modified in proposed scheme. (2) Forwarder selection in 2hop-AHH-VBF is based on potential neighbor number (PNN) of forwarder node in area towards destination (ATD). Hence, it aims to optimize forwarder selection criterion different from AHH-VBF adopted. (3) In QF-AHH-VBF, every node computes composite priority function (Fp ) to elect itself qualified node. Node that secures high Fp among the PNN of a sender node is assigned lowest holding time. Eventually, network throughput and end to end delay are achieved opting this mechanism. In traditional deterministic routing schemes over sensor based networks, each node selects a potential forwarder node based on a specific metric e.g. residual energy, 9

Table 2.1: Summary of state of the art work Techniques

Features

Proactive routing protocols (OLSR, DSDV, etc.) [6, 7] Reactive routing protocols (AODV, DSR, etc.) [8, 9] ECMSE [10], Geographic routing protocol for terrestrial sensor networks LMR [11], Energy efficient location based routing protocol for terrestrial sensor networks EBDG [12], Geographical routing protocol for terrestrial sensor networks Beacon-based routing protocols such as H2-DAB [13]

Routing tables are updated regularly to maintain routing information Route discovery, Route maintenance

Clustering routing [14, 15] in UWSNs

Parameters achieved Routes are always available

Bandwidth efficient, On demand approach

Limitations Large signalling overhead for establishing routing tables Higher latency, Energy consumption

Gaussian distributed location error, Forwarding decisions based on of energy cost information

High packet delivery while coping with location errors

Longer propagation distance

Energy conservation by adjusting energy cost at each link

High packet throughput

Large control overhead

Direct transmission, Multi-hop transmission, Corona-based network division

Improved network lifetime and Energy balancing

Poor performance in large scale networks

Without location information, Unique ID assignment to every node based on hop counts from sink Cluster head selection based on residual energy or location, Nodes report data to cluster heads

Improved Reliability

Infeasible for low node mobility speed, High Network overhead Longer end to end delay, Poor performance in time critical applications

Communication overhead minimization, Efficient network management

distance with the destination, direction of forwarding etc. Purpose of such selection is to relay data towards destination in minimum energy cost along with minimized average delay [23]. In contrast to deterministic routing schemes, opportunistic routing (OR) is a promising paradigm to deal with unreliable links in wireless medium because of involvement of multiple nodes. Some of the reviewed works have exploited OR for energy efficiency in sensor based networks [24], [25], [26]. In addition, geographic routing seems a definitive solution for providing efficient routing aided with location information in UWSNs. In [27] and [28], authors 10

Techniques

Features

DBR [16], Depth based routing protocol for UWSNs

Greedy forwarder selection, Constant forwarding region all the time

EE-DBR [17], Depth based routing protocol for UWSNs

Low end-to-end delay, Energy tax

HH-VBF [21], Location based routing protocol for UWSNs

Energy conservation based routing protocol, Depth based routing protocol for UWSNs Forwarder selection on the basis of two hop depth information, Adaptive changes the forwarding area One hop back transmission to avoid void hole, Forwarder selection based on cost function Confined forwarding area division to avoid packet duplication, Directional forwarding along the virtual vector Change in direction of pipeline hop-byhop, Directional forwarding

AHH-VBF [22], Location based routing protocol for UWSNs

Change in direction of pipeline, Adaptive transmission range hop by hop

High network throughput, Energy conservation

WDFAD-DBR [18], Depth based routing protocol for UWSNs Intar and Reintar routing protocol [19], For UWSNs VBF [20], Location based routing protocol for UWSNs

Parameters achieved Energy conservation, Network lifetime maximization

Void hole avoidance,Suppression of duplicate packets in local area network Void hole avoidance, High throughput

Limitations Inappropriate void hole avoidance mechanism, Inefficient forwarder selection Inefficient void hole mechanism, Large network overhead Unnecessary energy consumption due to packet drop High accumulated propagation distance

High packet delivery ratio

Poor performance in sparse netwroks

High network throughput

Inefficient void hole avoidance, Poor performance in sparse networks Inefficient forwarder selection

have utilized geographic information for data forwarding while selecting potential forwarder based on advancement towards destination. In [29], authors have proposed a geo-opportunistic routing scheme coping with communication voids by its depth adjustment recovery mode. A forwarder set is selected in which each node is prioritized according to its advancement towards destination. The node 11

that secures highest normalized advancement towards destination is chosen as a potential forwarder. To deal with communication voids, depth based recovery mechanism operates that ultimately provides better data delivery at the cost of high energy consumption. In [30], void aware pressure routing (VAPR) protocol utilizes two hop depth information and hop counts to select next hop forwarder. VAPR opts two fold procedure: enhanced beaconing and opportunistic directional data forwarding is performed. A node initiates a beacon containing information like its depth, data forwarding direction and hop count in the first phase of communication. In the second phase, sensor nodes forward data packets solely based on the direction and direction of next-hop data forwarding node, which ensure up that the packets can be forwarded upward to the water surface. Due to efficient beaconing, VAPR is robust against failures and node mobility. However, energy consumption at idle listening is not addressed in aforementioned related works. It is worth-noting that we have combined geo-opportunistic paradigm with the idea of sleep-awake scheduling. The primary objectives of duty cycling are minimization of energy consumption and the prolongation of network lifetime. Energy-efficient duty cycling schemes reported in literature are categorized into synchronous and asynchronous scheduling schemes. Some of them close to our proposed work are highlighted in following 2.0.1 and 2.0.2.

2.0.1

Synchronous Schemes

In this category of duty cycling schemes, sensor nodes keep common time references for exchange of information to achieve a certain degree of synchronization throughout the network. In global synchronous scheme, all nodes on or off their radios simultaneously, though it is hard to achieve it and synchronization error occurs up to some extent [31]. An UWAN-MAC (underwater wireless acoustic network-media access control) protocol proposed in [31] is an extension of S-MAC (sensor-media access control) and T-MAC [32], [33]. In S-MAC, sensor nodes usually stay in sleep mode and become active after detecting data packets. Sensor nodes exchange signalling information to learn schedules of their neighbors. This action is intended to schedule waking up periods at proper time intervals. In this way, S-MAC attains a balance between energy consumption and network throughput. S-MAC is further optimised in [34] in which an active state varies in different cycles. This is to make S-MAC adaptive according to different traffic conditions. These both mechanisms are extended for 12

the marine environment. Following the same mechanism as in S-MAC and TMAC, sensor nodes conserve energy by reducing idle listening and incentives are given to put in the sleep state. However, node mobility in underwater environment due to water currents is not considered in UWAN-MAC. Prior literature review focusing on synchronous scheduling methods exchange global information periodically to attain synchronization. However, global synchronization incurs high communication overhead that results into additional energy consumption. This problem is addressed in asynchronous duty cycling schemes presented in Section 2.0.2.

2.0.2

Asynchronous schemes

In such schemes, predetermined schedule is followed by sensor nodes without time synchronization. To maintain connectivity between neighboring nodes, wake up period should overlap among the neighboring nodes. Quorum based energy conserving protocol working on the principle of asynchronous scheduling. This scheme ensures at least two overlapping active intervals between a pair of nodes in a cycle [35]. In [36], authors have proposed cyclic difference set based protocol in which total number of slots, number of active slots and minimum number of overlapping slots are determined by using difference set in one cycle. This scheme guarantees at least one active overlapping slot for a pair of nodes for any cyclic shift. In these schemes, sensor nodes fail to communicate with each other if they are not in the same duty cycle. Concerning to this above mentioned problem, authors have proposed wake-up asynchronous scheme for energy efficiency while not compromising on network connectivity. To achieve this, shortest path to the sink is selected by utilizing minimum relay nodes by keeping sleep nodes in inactive mode as much as possible [37]. Similarly, we present a scheme focusing on energy conservation in UWSNs. Our proposed work models wake-up rate of sensor nodes to reduce high energy consumption, however, asynchronous sleep-awake schemes incur high delays as in [37].

13

Chapter 3 Proposed Work: quality forwarding two hop adaptive vector based forwarding for void hole avoidance in UWSNs

14

This chapter describes proposed schemes (2hop-AHH-VBF and QF-AHH-VBF) following the network model and problem description.

3.1

Network configuration

In the network architecture, N=n1 , n2 , n3 , ..., nn sensor nodes are deployed randomly in three dimensional area. This network model comprises of anchored nodes, relay nodes, and a sink deployed as shown in Fig. 3.1. Anchored nodes are capable of sensing and collecting data that is further sent to relay nodes. Relay nodes are deployed at different depths which not only forward the relayed data but also sense and generate their own data. Sink node is the destination that is equipped with acoustic and radio modems. Acoustic links make it enable to connect with sensor nodes and radio links are used for land communication. Anchored nodes route the sensed data towards sink using relay nodes. Received data at sink node is further transmitted towards satellites using radio links. At the end of this forwarding process, data is reached at the data centre on the shore. Anchored node Relay node

Sink node Radio link Acoustic link Virtual vector

Sink Dense region

Sparse region

Figure 3.1: Network architecture

15

3.2

Problem definition

In UWSNs, frequent mobility of sensor nodes results into non-uniform distribution of nodes. There are some local regions with high node density and others are low node density regions. Low node density regions become sparse due to high node mobility and void regions create. That is caused due to two possibilities: either unavailability of sensor nodes in a region or due to energy hole that creates coverage hole. This problem as a whole is known as void hole problem in UWSNs. This problem leads towards high energy consumption due to packet drop. In AHH-VBF, forwarder node selection on the bases of relative position to virtual vector (desirableness factor) and maximum distance from source node does not avoid void holes effectively. For example, as in Fig. 3.2, node S is current forwarding node, a circle named as S1 Tx-Range is transmission range of node S. Two parallel lines shaped as virtual pipeline is to restrict transmissions within this area. Node A, B, C, D, E and F are above node S in its upper hemisphere of transmission range. Some nodes are suppressed neighbors below node S. When node S sends a packet, node A, B, C, D, E and F along with suppressed nodes receive the packet. Suppressed nodes discard the packet directly, whereas, node F also discards packet because it is outside the pipeline. In AHH-VBF, node S selects node A on the bases of relative position of node A with respect to virtual vector and its distance is maximum from node S. Whereas node B,C,D and E are distant from sink as compared to node A. Therefore, these nodes are not qualified for forwarding node selection. However, selection criteria for forwarding node within one hop neighbor range is not efficient enough to cater void hole problem. Void region is created due to unavailability of nodes above node A in A1 Tx-range. If node A continues to forward packet in void region, packet drop occurs. Hence, probability of unsuccessful transmissions in this scenario shows that forwarding node selection needs to be optimized to improve packet delivery.

16

D

D: Destination

SINK

S: Sender node A: Receiving node

la

lb

Void region

A1 Tx-Range A’ A

dA

B’




P D

dB B C

S E F

S R

S1 Tx-Range R

Figure 3.2: Problem identification

For this purpose, we have proposed two schemes that are further described in the next section.

3.3

Proposed schemes

In further subsections, we have described proposed schemes in detail.

3.3.1

Two hop adaptive routing scheme

In order to cater the void hole problem, 2hop-AHH-VBF selects forwarder node after checking enough number of potential neighbors in the ATD of current forwarding node. Due to sparse deployment, a predefined threshold for potential neighbors in ATD of a node is taken from AHH-VBF scheme. While, selecting forwarder node, 2hop-AHH-VBF checks potential neighbor number that satisfies this threshold value. Checking potential neighbor number up to two hops reduces

17

probability of void hole problem in local sparse regions. Forwarder node selection is based on following parameters: firstly, the selected forwarding node is at maximum distance from current forwarder. Secondly, its void status and number of potential neighbors are checked before transmitting data packet. To avoid duplication and collision at receiver, holding time calculation based on optimal forwarder selection is computed in Equation 3.10. PNN on the average are taken 2 to 4 nodes in the AHH-VBF schemes. 2hop-AHH-VBF scheme makes decision based on PNN of the sender node, that is why fN n is calculated corresponding to the upper and lower limit taken for PNN.   −1, N n < 2   sign(N n − 2) = 0, N n = 0    1, N n > 2.

(3.1)

This function computes PNN of the sender node before sending the data packet to check void status of the forwarder node. fN n =

1 + sign(N n − 2) . 2

(3.2)

Holding time calculation for nodes selected as potential forwarders depends on their void status and PNN. A node closer to the destination with no void status is selected as optimal forwarder. The lowest waiting time is assigned to the node selected as optimal forwarder and rest of the qualified nodes are assigned waiting time gradually increasing. T hold2hop

3.3.2

α × Tpre = + 2 × fN n

Pn

i=1

×Dist(ni , ni+1 ) V sound

(3.3)

Quality forwarding adaptive hop by hop scheme

For this proposed scheme, we have formulated a composite function based on residual energy, potential neighbor number, distance from sender node to forwarder, and distance of forwarder node with the virtual vector. f1 = Er ,

Er [Etx

Eo]

(3.4)

Fitness function f1 shows that it is directly proportional to residual energy of a node. Initial energy provided to every node is 100 J, therefore, residual energy of 18

a node is always equal or less than Eo . f2 = 1 + sign(N n − 2),

Nn =2 then Calculate fN n Calculate TR1, PR1, and T hold2hop elseChoose node at second priority if Distance(LasthopF orwarder, Dest) < TR Distance(CurrentF orwarder, LasthopF orwarder, Dest) > PR then Drop the data packet

3.4.3

||

Algorithmic flowchart of QF-AHH-VBF

Algorithmic flowchart of QF-AHH-VBF scheme provides the forwarding mechanism of QF-AHH-VBF in Fig. 3.5

3.5

LP based mathematical formulation

LP is an extensively used mathematical approach to attain an optimal outcome. Initially, an objective function either tends to maximize or minimize is formulated with linear constraints. We have used simplex method of LP to find out feasible 21

Start Dist(QF-PNN, dest) < TR && Dist(A, QFPNN, Dest) > PR

Node A broadcasts a ‘Pack’

Pack type is Yes Neighbor Query

Packet drop

No Send ACK

Calculate TR1 and PR1 for QF-PNN Calculate Tholding and set timer T

No

‘Pack’ type is ACK

Yes

Yes

Update Neigh_table

Tholding >=T

No

If QF-PNN overhears ‘Pack’

‘Pack’ type is DATA

Yes Packet drop

Transmission

Find PNNs of Node A No

Calculate Fp for all PNNs of node A

Packet transmission

Select PNN with max(Fp)

End

QF-PNN=max(Fp)

Get ‘Pack’ information

Figure 3.5: Algorithmic flow chart of QF-AHH-VBF scheme

regions for performance parameters. In this section, it is discussed that how LP based problem formulation helps in maximization or minimization of performance parameters in order to improve the network performance.

3.5.1

Energy tax minimization using LP

High energy consumption drastically affects network performance due to confined power resource in UWSNs. Many routing protocols have addressed this problem. For the sake of energy tax minimization, an objective function with multiple linear constraints is formulated. In QF-AHH-VBF, energy tax of sensor nodes mainly depends on packet transmission and packet reception between a source node and 22

the sink. Thus, keeping this in mind, we have formulated an objective function for energy tax minimization in Equation 3.11. M in

N X

ET ax (i)

∀iN

(3.11)

i=1

where ET ax is energy tax calculated for all number of nodes. Econsumed (ij) =

N X

(ET X × D(ij) + ERX × Nn )

∀ i,j N

(3.12)

i=1

where N n ≥ 0 and Dij ≥ 0. In Equation 3.12, Econsumed mainly depends on transmission energy of the sender node ET X consumed over the distance (D(ij) ) between sender node and receiver node. The receiving energy ERX is consumed due to number of neighbor nodes Nn of the sender node receiving the packet.

ETmax X = PT X × (HS + P L)/DR

(3.13)

max ERX = PRX × (HS + P L)/DR,

(3.14)

Equation 3.13 and Equation 3.14 give the maximum values of ET X and ERX . These values depend on transmission power required for data packet size (HS+PL) as per data rate (DR). Etotal = Einitial × N (3.15) Etotal is total energy provided to all the nodes in the network as initial energy in Equation 3.15. Energy tax is basically amount of energy consumed in all the simulation rounds that is stated in Equation 3.16 as ET ax =

rX max

(Econsumed (r)).

(3.16)

r=1

Subject to: all the linear constraints of the objective function are given in Equation 3.17, 3.18, 3.19 and 3.20. E(T X,RX) ≤ Ei initial ∀ i  N (3.17) E(T X,RX) is the energy required for transmission and reception collectively which should be less than the initial energy provided to a node. Meanwhile, Equation

23

3.17 states that E(T X,RX) should be greater or equal to residual energy of a node. E(T X,RX) ≥ Ei r

∀iN

(3.18)

In Equation 3.19, distance between a sender node and a receiving node should always be less or equal to the transmission range of the sender node RTmax X . Similarly, min this distance must not be equal to zero which is shown as RT X in Equation 3.20, Dij ≤ RTmax X

∀ i,j  N

(3.19)

Dij ≥ RTmin X

∀ i,j  N.

(3.20)

ETmin X = ET X /L

(3.21)

min ERX = ERX /L

(3.22)

Graphical Analysis: assuming a scenario in which transmission range is 2000 m that is divided into L levels i.e., L = 1, 2, 3, 4 and 5. Dividing the transmission range into levels is intended to observe the energy consumption according to these levels as written in Equation 3.21 and Equation 3.22. Where HS+PL = 888 bits, DR = 16 kbps, N = 500, PT X = 50W , and PRX = 0.158W respectively. From these parameters, ET X is 4.995 J calculated from Equation 3.21 at L = 1 and 0.999 J from Equation 3.21 at L = 5. By Equation 3.22, ERX is 0.555 J calculated at L = 1 and 0.111 J calculated at L = 5, respectively. 1 ≤ ET X + ERX ≤ 5.550

(3.23)

0.999 ≤ ET X ≤ 4.995

(3.24)

0.111 ≤ ERX ≤ 0.555

(3.25)

Corresponding to the bounds mentioned above, Fig. 3.6 depicts the intersection region in which all feasible solutions lie. This bounded region is named as feasible region. Any point laying within bounded region yields valid solution. As a next step, we test each vertex depicted in Fig. 3.6 as: at P1 : 0.999 + 0.111 = 1.110J at P2 : 0.999 + 0.555 = 1.554J at P3 : 4.995 + 0.111 = 5.120J 24

6

5 ETX+ERX= 5.51

ERX (J)

4

3 P4(4.995, 0.555)

2 P2(0.999, 0.555) P1(0.999, 0.111) 1

0

P3(4.995, 0.111)

Feasible region

0

1

2

3 ETX (J)

4

5

6

Figure 3.6: Feasible region for energy tax minimization

at P4 : 4.995 + 0.555 = 5.550J. Hence, this method provides valid solution satisfying these bounds. So, values of transmission and reception energy within the feasible region tends to minimize the energy consumption for optimal solution.

3.5.2

End to end delay minimization using LP N X

∀ i N

(3.26)

D = DT X + DP rop + Thold

(3.27)

D = DSD /Vsound + (HS + P L)/DR + Thold

(3.28)

Dtot = DDT + DM HT

(3.29)

DDT −min = DDT /L;

(3.30)

DM HT −min = Hn−min × D;

(3.31)

DM HT −max = Hn−max × D;

(3.32)

M in

Dtot (i);

i=1

where Thold is taken from Equation 3.10. at C1: 0 < DSD ≤ RT X at C2: Hn−min ≤ Hn ≤ Hn−max 25

12 P (1,888, 7.888) 2

10

P4(3.951, 7.888) ETX+ERX= 11.839

DMHT (s)

8

Feasible region

6

P3(3.951, 2.949) 4

2 P1(1.888, 2.949) 0

0

2

4

6 DDT (s)

8

10

12

Figure 3.7: Feasible region for end to end delay minimization

at C3: Thold−min ≤ Thold ≤ Thold−max Graphical analysis: Assuming a scenario in which if sender node is in transmission range of sink, it transmits directly. Whereas, if sink is few hops away from the sender node, data packet is sent over multiple hops. Considering the one-hop transmission delay as minimum and delay incurred by multiple hops maximum delay due to multiple hop. In direct transmission scenario, if we divide DSD into L levels i.e., L = 1, 2, 3, 4, and 5. 5.83 ≤ DDT + DM HT ≤ 11.83 1.888 ≤ DDT ≤ 3.951 2.949 ≤ DM HT ≤ 7.888 Each vertex of the region is shown as: at P 1 : 1.888 + 2.949 = 4.873s at P 2 : 1.888 + 7.888 = 9.776s at P 3 : 3.951 + 2.949 = 6.9s at P 4 : 3.951 + 7.888 = 11.839s Feasible region for end to end delay in this scenario is shown in Fig 3.7.

26

3.5.3

Throughput maximization using LP

The objective function formulated to maximize the network throughput considering the minimization of energy consumption. In our proposed schemes 2hop-AHHVBF and QF-AHH-VBF, data packets are relayed using multi-hoping mechanism. The network throughput is total number of data packets received successfully at the sink. Considering link quality for successful transmission, δ is defined as threshold value for neighbor number of a sender node. The link quality check is to ensure successful packet delivery. Moreover, energy required for transmitting a packet should be less than residual energy of a node participating in forwarding process as stated in C1. Distance between a pair of sender and receiver node must max min as in C4. All these linear constraints and less than Dij be greater than Dij are taken into consideration while formulating an objective function represented in Equation 3.33, N X M ax Tp (i); ∀ i  N. (3.33) i=1

where Tp (i) is network throughput contributed by each node. Furthermore, Tp (r) denotes total number of data packets received in r rounds in Equation 3.34. M ax

rX max

Tp × P ;

∀ i  N,

(3.34)

r=1

such that: C1:ET X,RX ≤ Er C2:Plink ≥ δ C3:ET X,RX ≥ Eth , where Eth is residual energy required for transmission and reception. max C4:0 < Dij ≤ Dij . Objective of throughput maximization is achieved under constraints C1, C2, C3 and C4. C1 and C3 restrictions on ET X and ERX are set to avoid unnecessary energy consumption. Feasible region for packet delivery ratio is shown as Fig. 3.8.

27

1 0.9 0.8 P3(0.93, 100)

P4(0.93, 500)

0.7

Feasible region

PDR

0.6 P2(0.45, 500)

0.5 0.4 P1(0.45, 100)

0.3 0.2 0.1 0

0

100

200

300 Node density

400

500

600

Figure 3.8: Feasible region for throughput maximization

28

Chapter 4 Proposed work: design of joint geo-Opportunistic routing and sleep awake scheduling with multi-sink architecture in UWSNs

29

Monitoring centre

Sleep node Sensor Node Sonobuoy Radio Link z-axis

Acoustic Link

xis

y-a

x-axis

Figure 4.1: Network architecture

4.1

Design of GOASST

We have considered multi-sink architecture in our proposed scheme. Asynchronous sleep-awake scheme is proposed in which opportunistic forwarding mechanism is opted.

4.1.1

System Model

We consider the network model as shown in Fig. 4.1, which comprises of sensor nodes and multiple sinks. Each sensor node is in charge of both sensing and forwarding data packets. Sensed data by a sensor node is processed and encapsulated into packets that are sent to any of the multiple sinks by multi-hop forwarding. Sink nodes receiving the data packets are regarded as super nodes having infinite energy and it outlasts throughout the network lifetime. Sink nodes have both radio and acoustic modems for land communication and acoustic communication, respectively. In addition, sink nodes can find its location by GPS services. We model our network as a graph G = (V, E), where V is the set of sensor nodes and E is the set of wireless links between them. Each node in this set has given identical initial energy and communication range Rc . For any pair of nodes (a,b), the wireless link between lab E, only if a and b are in communication range of each other. Let Pab be the link reliability between node a and node b. For multiple forwarders that constitute a forwarding set Fs in opportunistic routing, probability

30

of packet reception by at least one node in the set Fs is estimated as, Fs = ni Nk (t) : ∃Sn Ss (t)|D(ni , sn∗ ) − D(nk , sn ) > 0

(4.1)

Y

(4.2)

Ps = 1 −

(1 − Pab ).

∀aFs

Where Ps and Pab is the success probability and the reliability of the link between node a and node b, respectively. This equation shows success probability of onehop data transmission if a receives packets from b and a receives ACK from b.

4.1.2

Neighborhood Discovery

At the initialization stage, the neighbor discovery is performed in UWSNs in which a source node discovers neighbors in its vicinity. In real scenarios, sensor nodes are not always awake because this causes premature energy exhaustion of sensor nodes. In order to find neighbors in its vicinity, each node at least once exchanges messages based on its location information. To discover and notify the neighbors, neighbor discovery (ND) message is sent by a source node in its transmission range. Sensor nodes receiving the ND message respond to the query with NA message. Finally, a neighbor table consisting of neighbor information is formulated by each node in the network. Hence, neighbor discovery process completes after construction of neighbor tables. Moreover, operation of each sensor node keep alternating between sleep and awake mode. This operation follows predetermined number of active and inactive states. Taking the same duration for an active and inactive state denoted by T, the complete working cycle of a sensor node can be represented as, Tc = n × T,

(4.3)

where n Tmin−s n Tmin−s

(4.9)

Expected sleeping time of a node should be with in the constraint stated in Equation 4.10, n Texp−s [minDC/Vs − Twn , maxDC/Vs − Twn ], (4.10) in which, minimum and maximum limits of expected sleep time of a sensor node depends on distance covered by the sink and wake up period of a sensor node n. Further, Benefit function B(n) can be computed as, n n n n B(Texp−s , Tmin−s ) = 1 × P (Texp−s , Tmin−s ).

34

(4.11)

Hence, Cost function for a sensor node with different sleep times is, n Texp−s C(Texp−s ) = β × n . Tmin−s

(4.12)

β variable shows the trade-off between cost associated with minimum sleep and successful data transmission. For a sensor node with β = 1 denotes that minimum sleeping time benefits in successful transmission. While minimizing β in this relation decreases the probability of success and maximize the benefit associated with staying in sleep mode.

4.3

LP based Mathematical Formulation

LP is an extensively used mathematical approach to attain an optimal outcome. Initially, an objective function either tends to maximize or minimize is formulated with linear constraints. We have used simplex method of LP to find out feasible regions for performance metrics. In this section, it is discussed that how LP based problem formulation helps in maximization or minimization of performance metrics in order to improve the network performance.

4.3.1

Energy Consumption Minimization using LP

High energy consumption drastically affects network performance due to confined power resources in UWSNs. Many routing protocols have addressed this problem. For the sake of energy tax minimization, an objective function with multiple linear constraints is formulated. In proposed scheme, energy tax of sensor nodes mainly depends on packet transmission and packet reception between a source node and the sink. Thus, keeping this in mind, we have formulated an objective function for energy tax minimization in Equation 4.13. M in

N X

ET ax (i)

i=1

where ET ax is energy tax in the network.

35

∀iN

(4.13)

Initially, energy consumption in the network is due to transmission of packets and reception by the neighbors Econsumed (ij) =

N X

(ET X × D(ij) + ERX × Nn )

∀ i,j N

(4.14)

i=1

where N n ≥ 0 and Dij ≥ 0. In Equation 4.14, Econsumed mainly depends on transmission energy of the sender node ET X consumed over the distance (D(ij) ) between sender node and receiver node. The receiving energy (ERX ) is consumed due to number of neighbor nodes (Nn ) of the sender node receiving the packet.

ETmax X = PT X × (HS + P L)/DR

(4.15)

max ERX = PRX × (HS + P L)/DR,

(4.16)

Equation 4.15 and Equation 4.16 give the maximum values of ET X and ERX . These values depend on transmission power required for data packet size (HS+PL) as per data rate (DR). Etotal = Einitial × N (4.17) Etotal is total energy provided to all the nodes in the network as initial energy (Einitial ) in Equation 4.17. Energy tax is basically amount of energy consumed in all the simulation rounds that is stated in Equation 4.18 as ET ax =

rX max

(Econsumed (r)).

(4.18)

r=1

For GOASST scheme, energy consumption due to depth adjustment of void nodes has to be considered as well as shown in Equation 4.19, ET0 ax

=

rX max

(Econsumed (r) + EDA (r)).

(4.19)

r=1

EDA = Nvn × (EDA (nvn )).

(4.20)

Objective function defined in Equation 4.13 is defined under following linear constraints: E(T X,RX) ≤ Ei initial ∀ i  N (4.21) D(s,d) ≤ Rc 36

∀iN

(4.22)

EDA (nvn ) ≤ Eir

∀iN

(4.23)

All the linear constraints of the objective function are given in Equation 4.24, 4.25, 4.26 and 4.27. E(T X,RX) ≤ Ei initial ∀ i  N (4.24) E(T X,RX) is the energy required for transmission and reception collectively which should be less than the initial energy provided to a node. Meanwhile, Equation 4.24 states that E(T X,RX) should be greater or equal to residual energy of a node. E(T X,RX) ≥ Ei r

∀iN

(4.25)

In Equation 4.26, distance between a sender node and a receiving node should always be less or equal to the transmission range of the sender node RTmax X . Dij ≤ RTmax X

∀ i,j  N

(4.26)

Dij ≥ RTmin X

∀ i,j  N.

(4.27)

ETmin X = ET X /L

(4.28)

min ERX = ERX /L

(4.29)

The vertex points calculated are given below: 1 ≤ ET X + ERX ≤ 5.550

(4.30)

0.999 ≤ ET X ≤ 4.995

(4.31)

0.111 ≤ ERX ≤ 0.555

(4.32)

Corresponding to the bounds mentioned above, Fig. 4.3 depicts the intersection region in which all feasible solutions lie. This bounded region is named as feasible region. Any point laying within bounded region yields valid solution. As a next step, we test each vertex depicted in Fig. 4.4 as: at P1 : 0.999 + 0.111 = 1.110J at P2 : 0.999 + 0.555 = 1.554J at P3 : 4.995 + 0.111 = 5.120J 37

6 P (0.7, 1.5) 3

P (0.7, 4.55) 4

5

ETX+ERX= 5.25 J

ETX (J)

4

3

2

1

0

P2(0.24, 4.55) P1(0.24, 1.5) 0

1

2

3 ERX (J)

4

5

6

Figure 4.3: Feasible region (Energy tax minimization) for GOASST 3 P3(0.25, 0.85) P4(0.25, 2.5) 2.5 ETX+ERX= 3 J

ETX (J)

2

1.5

1

0.5

0

P2(0.09, 2.5) P1(0.09, 0.85) 0

0.5

1

1.5 ERX (J)

2

2.5

3

Figure 4.4: Feasible region (Energy tax minimization) for CSM-GOASST

at P4 : 4.995 + 0.555 = 5.550J. Hence, this method provides valid solution satisfying these bounds. So, values of transmission and reception energy within the feasible region tends to minimize the energy consumption for optimal solution.

4.3.2

Packet Delivery Ratio Maximization using LP

The objective function formulated to maximize the network throughput considering the minimization of energy consumption. In our 38

proposed scheme, data packets are relayed using multi-hoping mechanism. The network throughput is total number of data packets received successfully at the sink. Considering link quality for successful transmission, δ is defined as threshold value for neighbor number of a sender node. The link quality check is to ensure successful packet delivery. Moreover, energy required for transmitting a packet should be less than residual energy of a node participating in forwarding process as stated in C1. Distance between a pair of sender and receiver node must be min max greater than Dij and less than Dij as in C4. All these linear constraints are taken into consideration while formulating an objective function represented in Equation 4.33, N X M ax Tp (i); ∀ i  N. (4.33) i=1

where Tp (i) is network throughput contributed by each node. Furthermore, Tp (r) denotes total number of data packets received in r in Equation 4.34. M ax

rX max

Tp × P ;

∀ i  N,

(4.34)

r=1

such that: C1:ET X,RX ≤ Er C2:Plink ≥ δ C3:ET X,RX ≥ Eth , where Eth is residual energy required for transmission and reception. max . Objective of throughput maximization is achieved under C4:0 < Dij ≤ Dij constraints C1, C2, C3 and C4. C1 and C3 restrictions on ET X and ERX are set to avoid unnecessary energy consumption.

4.3.3

Minimization of Average Delay using LP N X

∀iN

(4.35)

Tw = DP roc + DP rop + Thold

(4.36)

M in

Dtot (i);

i=1

39

1.6 1.4

Packet delivery ratio

1.2

P4(0.83, 550)

1

L1 P2(0.55, 200) P3(0.60, 250)

0.8 0.6 0.4 0.2 0

P1(0.34, 150) 0

200

400 600 Node density

800

1000

Figure 4.5: Feasible region (packet delivery ratio) for GOASST 1.6 1.4

Packet delivery ratio

1.2

P4(0.86, 550) L1

1 P2(0.56, 200) P3(0.65, 250) 0.8 0.6 0.4 0.2 0

P1(0.36, 150) 0

200

400 600 Node density

800

1000

Figure 4.6: Feasible region (packet delivery ratio) for CSM-GOASST

TP rop = (Rc − D(ij))/s

Thold =

j X

D(ni , ni+1 )/s

(4.37)

(4.38)

i=1

Dtot = DDT + DM HT

40

(4.39)

DDT −min = Tw × Hn ;

(4.40)

Where Hn = 1 for direct transmission scenario when the sink is in transmission range of source node.

DM HT −min = Hn−min × Tw ;

(4.41)

DM HT −max = Hn−max × Tw ;

(4.42)

Subject to: ij ≤ Rc at C1: 0 < Dmax

at C2: 0 < Tw at C3: Hn−min ≤ Hn−max Graphical analysis: Assuming a scenario in which if source node is in transmission range of sink, it transmits directly. The delay caused by this transmission is denoted as DDT that is minimum delay in the network. Whereas, if sink is few hops away from the sender node, data packet is sent over multiple hops. Considering the one-hop transmission delay as minimum and delay incurred by multiple hops maximum. We can compute maximum and minimum delays caused in both direct transmission scenario and multi-hop scenario. 1.35 ≤ DDT + DM HT ≤ 3.45 0.45 ≤ DDT ≤ 0.6 0.9 ≤ DM HT ≤ 2.85 Each vertex of the region is shown as: at P 1 : 0.45 + 0.9 = 1.35s at P 2 : 0.45 + 2.85 = 3.30s at P 3 : 0.6 + 0.9 = 1.5s at P 4 : 0.6 + 0.285 = 0.885s Each vertex of the region is shown as: 41

3.5 P2(0.45, 2.85) P (0.6, 2.85) 4 3

DMHT (s)

2.5

DDT+DMHT= 4 s

2

1.5

1

0.5

0

P1(0.45, 0.9) 0

0.5

P3(0.6,0.9 )

1

1.5

2 DDT (s)

2.5

3

3.5

4

Figure 4.7: Feasible region (end to end delay) for GOASST

1.72 ≤ DDT + DM HT ≤ 3.71 0.50 ≤ DDT ≤ 0.65 1.22 ≤ DM HT ≤ 3.06 Each vertex of the region is shown as: at P 1 : 0.5 + 1.22 = 1.72s at P 2 : 0.5 + 3.06 = 3.56s at P 3 : 0.65 + 1.22 = 1.87s at P 4 : 0.65 + 3.06 = 3.71s

42

4

P3(0.65, 1.22) P4(0.65, 3.06)

3.5 3

DDT+DMHT= 3.71 s DMHT (s)

2.5 2 1.5 1 P2(0.50, 3.06) 0.5 0

P1(0.50, 1.22)

0

0.5

1

1.5

2 DDT (s)

2.5

3

3.5

4

Figure 4.8: Feasible region (end to end delay) for CSM-GOASST

43

Chapter 5 Simulation results and discussion

44

This chapter provides simulation analysis of proposed schemes along with simulation set up and discussion.

5.1

Simulation setup for quality forwarding two hop adaptive vector based forwarding for void hole avoidance in UWSNs

For simplicity, simulation parameters are tabulated in Table 5.1. 100-500 sensor nodes are deployed in three dimensional area of 10 km*10 km*10 km and a sink is fixed on the surface. Maximum power for transmission and reception is 150 watt and 10 watt respectively. Maximum transmission range is selected 2000 m because as taken because increase in transmission range does not affect network performance. To stop node movement outside the region, Random Walk 2D mobility model is used. Each sensor moves at the speed of 1-3 m/s. Vertical movement is considered negligible whereas, horizontal movement in X-Y plane is commonly considered in underwater environment. The header size and data size of data packet is 39 bytes and 72 bytes respectively. Neighbor Request and Neighbor Acknowledgement packets are of 66 bits and 114 bits respectively. In each round during simulations, simulation results are run over 50 times and each time network topology was random.

5.1.1

Performance metrics

Performance metrics used in this paper are: energy tax, packet delivery ratio (PDR), end-to-end delay and accumulated propagation distance (APD). Energy tax: average energy consumption per node while a packet successfully reaches the destination. Energy consumption includes energy consumed in transmission and reception of a packet and exchanging control packets. Mathematical notation for this metric can be written as:

EnergyT ax =

Et N odeN um × packets

(5.1)

where Et total energy consumption of the network is divided by number of nodes deployed and number of packets successfully reached at sink. Energy tax is measures in joules. 45

Table 5.1: Parameter setting

Parameter

Value

Nodes

100-500 Random deployment

Sinks

1, on the surface

Network area

3D region of 10km * 10km * 10km

Max power for transmission 150watt Max power for reception

10watt

Max Transmission range

2000m

Initial energy of each node 100 joules Node mobility

2

Data rate

16 kbps

Data packet size

111 bytes

Neighbor size

request

Acknowledgement size

packet 66 bytes packet 114 bytes

Centre frequency

12 kHz

Bandwidth

4 kHz

Mobility model

Random walk mobility mode

PDR: ratio of packets successfully received at sink to packets sent by sender node. End-to-end delay: Average time taken in transmitting packets from sender node to successfully receiving by sink node. It includes propagation delay, transmission and reception delay, holding time and calculation time taken in a successful transmission to sink. End-to-end delay is measured in seconds. APD: average propagation distance travelled at every hop traversed of all the packets successfully reached the sink. It is measured in km.

5.1.2

Analysis of packet delivery ratio

It can be seen in Fig. 5.1 that as node number increases, PDR begins to increase in all the schemes. Potential neighbor number per node increases as node number increases. Hence, void hole probability reduces due to availability of qualified 46

1

0.9

0.8

PDR

0.7

0.6

0.5 WDFAD−DBR AHH−VBF 2hop−AHH−VBF QF−2hop−AHH−VBF

0.4

0.3 100

150

200

250

300 350 Node number

400

450

500

Figure 5.1: Packet delivery ratio comparison

nodes in the RTD. PDR increases till a certain threshold, after that collision at the receiver results in reduction of PDR. Below 300 nodes, 2hop-AHH-VBF and QF-2hop-AHH-VBF perform better than AHH-VBF due to optimal forwarder selection in the ATD. This selection ensures successful packet transmission even in sparse regions in the network. As node number increase beyond 300 nodes, difference of PDR of all the schemes reduces due to the fact that increase in node number itself reduces the void hole probability. Although, QF-2hop-AHH-VBF outperforms than AHH-VBF because of optimal forwarders selection in terms of node position and residual energy. Hence, QF-2hop-AHH-VBF performs better than all other counter routing schemes in sparse and dense environment. In dense network regions, duplication of packets is handled with the constrained pipeline radius as set by AHH-VBF scheme. QF-2hop-AHH-VBF performs 5.6% better than AHH-VBF while 2hop-AHHH-VBF and WDFAD-DBR perform slightly better than AHH-VBF in sparse regions. In dense regions, duplication of packets is avoided by opting mechanism to restrict forwarding area in all the four schemes.

5.1.3

Analysis of end to end delay

It can be observed in Fig. 5.2 that all the routing schemes follow same decreasing trend in end-to-end delay. This is because in sparse network, there is less potential neighbors number. On the other hand, in dense region, a node finds more potential neighbors that is beneficial for the reduction of propagation delay. 47

10 WDFAD−DBR AHH−VBF 2hop−AHH−VBF QF−2hop−AHH−VBF

9

End−to−end delay (s)

8 7 6 5 4 3 2 1 0 100

150

200

250

300 350 Node number

400

450

500

Figure 5.2: End-to-end delay comparison

This eventually reduces end-to-end delay. End-to-end delay primarily comprises of propagation delay, holding time, transmission delay and calculation time. Accumulated propagation distance increases while considering two hops for routing decision in 2hop-AHH-VBF. That is why; 2hop-AHH-VBF bears more end-toend delay as compared with AHH-VBF and WDFAD-DBR. Up to node number 350, 2hop-AHH-VBF follows the same trend as AHH-VBF and WDFAD-DBR. After that as node number increases, number of hops taken to reach destination also increases. Holding time for packets is also introduced in both the schemes. Successful transmissions towards destination cause transmission delay that is also added in overall end to end calculations along with holding time. In AHH-VBF, qualified node selection criteria is based on distance with the virtual vector. Without considering residual energy and two hop information causes packet forwarding towards void region. Transmission failure increases end-to-end delay due to retransmissions of the same packet. Therefore, in QF-2hop-AHH-VBF scheme selection of qualified node and holding time calculation is based on composite function in Equation 3.3.9. This reduces propagation delay due to avoidance of coverage hole and efficiently minimizes holding time. Similarly, it can be seen that APD is also decreased in Fig. 5.3. Hence, QF-2hop-AHH-VBF performs 30(%) better than AHH-VBF scheme. Similarly, utilizing the two hop information along with node position with respect to virtual vector results in less hop traversing in QF-2hop-AHH-VBF. Hence, it performs 28(%) more efficient in reducing APD as compared to AHH-VBF scheme.

48

Accumulated Propagation Distance(km)

15 WDFAD−DBR AHH−VBF 2hop−AHH−VBF QF−2hop−AHH−VBF

10

5

0

100

150

200

250 300 350 Node number

400

450

500

Figure 5.3: Accumulative propagation distance comparison

5.1.4

Analysis of Energy tax

Energy tax tends to decrease with the increase in node number in all four schemes. It is due to the fact that increase in node density increases number of successful transmissions. In low node density regions, packet drop due to unavailability of forwarding nodes results in more energy consumption. As in Fig. 5.4, AHHVBF scheme takes more energy while node number is less. In AHH-VBF scheme, information of next hop forwarding node is not considered while transmitting packet. Therefore, it leads to packet transmission towards void region and unnecessary energy tax is consumed due to transmission failure. It can be seen that 2hop-AHH-VBF scheme consumes less energy than AHH-VBF scheme because unsuccessful transmissions are avoided here. While node number increases beyond 300 difference of energy consumption reduces. QF-2hop-AHH-VBF performs better in terms of PDR, hence it takes more energy consumption due to successful transmissions towards destination. Moreover, selection of quality forwarder in QF-2hop-AHH-VBF is on the based of two hop information and residual energy of forwarding nodes. Optimized selection of qualified node for forwarding process minimizes network overhead due to control packets. Hence, initially energy consumption of QF-AHH-VBF scheme is high because of high PDR while compared with other routing schemes. Gradually, energy tax difference is observed while node number increases beyond 400. Similarly, QF-AHH-VBF outperforms WDFAD-DBR because constrained pipeline radius avoids packet duplication and 49

0.35 WDFAD−DBR AHH−VBF 2hop−AHH−VBF QF−2hop−AHH−VBF

0.3

Energy tax(j)

0.25

0.2

0.15

0.1

0.05

0 100

150

200

250

300 350 Node number

400

450

500

Figure 5.4: Energy tax comparison

qualified node selection based on composite function provides optimal path towards ATD. WDFAD-DBR decides path on the basis of weighting depth difference that does not completely avoid packet duplication. Thus WDFAD-DBR takes unnecessary energy consumption.

5.2

simulation set up and results for design of joint geo-opportunistic routing and sleep awake scheduling with multi-sink architecture in UWSNs

In this section, we present the performance evaluation of proposed schemes by evaluating following performance parameters: PDR, fraction of local maximum nodes, energy per packet per node, latency and depth adjustment. In our simulations, the number of sensor nodes range from 150 to 450 and the number of sonobuoys is 45. They are randomly deployed in a region of size 1, 500 × 1, 500 × 1, 500m. In all experiments, the nodes have a transmission range Rc of 250 m and a data rate of 50 kbps. We consider that data packets have a payload of 150 bytes. Values of energy consumption associated with transmission, reception and idle state are Pt = 2W , Pr = 0.1W and Pi = 10mW as taken in [27]. Further in the section, analysis under varying sink number in three dimensional field is presented.

50

Fraction of local maximum nodes

0.35 GEDAR GOASST CSM−GOASST

0.3

0.25

0.2

0.15

0.1

0.05

0 150

200

250

300

350

400

450

Number of nodes

Figure 5.5: Fraction of void nodes plots

5.2.1

Topology-Related results

In this section, we have performed simulation analysis with respect to varying node density in the network. In order to investigate, how proposed schemes perform in the low and high network densities, we have conducted simulations for GOASST, CSM-GOASST and GEDAR. Forwarding strategies in all the schemes are different and it is worth-noting to observe their behaviours in different network conditions. In GOASST, forwarding set selection is based on residual energy of sensor nodes, whereas CSM-GOASST selects node based on advancement towards destination. Both the schemes follow geo-opportunistic mechanism, while selecting forwarding sets for data forwarding. The fraction of void nodes decreases with the increase in node density for all forwarding strategies. While, CSM-GOASST performs better than GOASST and GEDAR because more number of nodes are in the range of sinks in this scheme as compared with GOASST and GEADR schemes. GOASST avoids void holes by selecting the forwarding set with at least one awake node to receive the data packets. CSM-GOASST provides maximum coverage by deploying sinks at the optimal positions in the network. Whereas, GEDAR opts depth based recovery mechanism to tackle the local maximum problem. In addition to this, total displacement of void nodes is high in sparse network regions. This issue is resolved in CSM-GOASST by placing sinks at optimal positions to reduce distance between void nodes and sinks. Hence, fraction of void nodes decreases as shown in Fig. 5.5.

51

1

Packet delivery ratio (%)

0.9

GEDAR GOASST CSM−GOASST

0.8 0.7 0.6 0.5 0.4 0.3 0.2 150

200

250

300

350

400

450

Number of nodes

Figure 5.6: Packet delivery ratio plots

PDR seems consistent with the change in network density, it increases with the increasing network density for all the schemes. CSM-GOASST outperforms concerning to PDR results because of maximum coverage over the network assisted with controlled sink mobility. We observe the same performance of GOASST and GEDAR in sparse network regions. Both the schemes follow their respective forwarding mechanisms for data forwarding. Forwarder set selection with at least one awake node reduces the probability of unsuccessful data transmission in GOASST. In dense network regions, number of awake nodes available increases because of high node density. It reduces the fraction of void nodes as corroborated by the results in Fig. 5.5 and Fig. 5.6. Fig. 5.7 shows results concerning to energy consumption in the network. It is evident from the results that energy consumption in GEDAR is high for sparse network regions. Energy expenditure has also increased because of depth adjustment of void nodes in GEDAR. Whereas, CSM-GOASST bears high energy consumption in sparse network regions due to high displacement between nodes and sinks. When average displacement reduces between nodes and sinks because of increased network density, nodes transmit their data directly, otherwise, two or more hops are taken for relaying data towards destination. Anyhow, increase in node density reduces the energy consumption in CSM-GOASST. GOASST exploits sleep-awake scheduling of nodes for energy conservation in the network. It is certified from Fig. 5.7 that energy consumption is minimum as compared with CSM-GOASST and GEDAR because nodes stay in sleep mode for a large period

52

5 GEDAR GOASST CSM−GOASST

4.5

Energy consumption (J)

4 3.5 3 2.5 2 1.5 1 0.5 0 150

200

250

300

350

400

450

Number of nodes

Figure 5.7: Energy consumption plots

of time and avoiding idle listening in GOASST. When, there is high node density, GOASST finds more number of nodes in the forwarder set with the increased awake probability. Ultimately, it improves successful transmission probability of data packets in GOASST at the cost of more energy consumption in dense network regions. Concerning to end-to-end delay, as shown in Fig. 5.8, GOASST bears largest delay because of probation period dedicated for beacons messages along with propagation delay caused by forwarder to sink packet propagation. End-to-end delay increases with the increase in node density for all forwarding strategies. GEADR and GOASST follows opportunistic routing to improve data delivery, however, forwarder set selection instead of a single forwarder node causes higher delays. CSM-GOASST forwards data packets based on advancement taking minimum number of hops as compared with GOASST and GEDAR, thus, it bears lowest end-to-end delay.

5.2.2

Sink utilization in CSM-GOASST

In this section, we have carried out an analysis with different number of sinks under varying network density. We have investigated that how network scalability gets affected by different number of sinks at different positions in√the network. With a single sink placed at the centre of the network, dsink−centre = 53

d2 +d2sink−surf ace 2

nodes

3

End−to−end delay (s)

2.5

GEDAR GOASST CSM−GOASST

2

1.5

1

0.5

0 150

200

250

300

350

400

450

Number of nodes

Figure 5.8: Average delay plots 0.65 Energy consumption for Scenario 1 Energy consumption for Scenario 2 Energy consumption for Scenario 3

Energy Consumption (J)

0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 450

400

350

300

250

200

150

Number of nodes

Figure 5.9: Energy consumption for varying number of sinks

nearer to this sink forward data packets towards it, instead of surface sinks. The utilization of this sink increases because of nodes below the dcentre depth region forward their data to it. In second scenario, four sinks are deployed in order to provide maximum coverage in the network as shown in Table 5.2. Utilization ratio of a single sink is now divided into four sinks. Same data traffic generated in previous scenario is now divided into four sinks. Fig. 5.9, 5.10 and 5.11 show network performance as evident that energy consumption reduces due to reduced number of hops taken to forward data to the sinks consequently, it also reduces end to end delay. 54

1

Packet delivery ratio (%)

0.9

PDR for Scenario 1 PDR for Scenario 2 PDR for Scenario 3

0.8

0.7

0.6

0.5

0.4

150

200

250

300

350

400

450

Number of nodes

Figure 5.10: Packet delivery ratio under varying number of sinks 3

End−to−end delay (s)

2.5

Delay for Scenario1 Delay for Scenario 2 Delay for Scenario 3

2

1.5

1

0.5

0 150

200

250

300

350

400

450

Number of nodes

Figure 5.11: End to end delay under varying number of sinks

In third scenario, co-ordinates of deployed sinks are mentioned in Table 5.2 (Scenario 2). As the number of sinks increases, there are multiple paths available to forward data towards sinks. Taking network conditions into account, placement of sinks is at optimal distances to provide maximum coverage. Controlled sink mobility is introduced to cater connectivity problems in the network. The major objective is to provide maximum connectivity between nodes. It is certified by the results that multiple sinks in the field for data collection restrict the bottleneck created at the surface sinks. Taking multiple hops to relay data towards surface sinks ultimately result into drastic energy consumption of sensor nodes. Thus, multi-sink scenarios perform better in high traffic scenario and in large networks. 55

Table 5.2: Sink position scenarios in CSM-GOASST

Sink S1 S2 S3 S4

S1 S2 S3 S4 S5 S6 S7 S8

Scenario 1 Co-ordinates (x+500, y+500, z+1000) (x+1000, y+500, z+1000) (x+500, y+1000, z+500) (x+1000, y+1000, z+500) Scenario 2 (x+250, y+250, z+1250) (x+1250, y+250, z+1250) (x+750, y+1000, z+1000) (x+250, y+750, z+750) (x+1250, y+750, z+750) (x+750, y+500, z+500) (x+250, y+250, z+250) (x+1250, y+250, z+250)

56

S1 S3

S4

S

S2 S3

S4 S1

S5

S2 S6 S7

S8

Scenario 1

Scenario 2

Figure 5.12: Sink positioning in CSM-GOASST

57

Scenario 3

Chapter 6 Performance trade-offs between performance parameters of the proposed schemes

58

This chapter discusses the performance trade offs between performance parameters in the proposed schemes.

6.1

Performance trade-off between parameters of QF-2hop-AHH-VBF

In this section, we discuss comparative analysis of performance parameters of proposed schemes with respect to AHH-VBF scheme tabulated in Table 6.1. Proposed scheme 2hop-AHH-VBF performs better than AHH-VBF scheme in PDR and energy tax whereas end to end delay increases because of increase in propagation distance of the packet. 2hop-AHH-VBF has void hole avoidance mechanism that secures high PDR than AHH-VBF scheme. Energy tax reduces due to less packet drop. Packet forwarding decision based on two hop information has reduced void hole probability that increases PDR and energy tax of 2hop-AHH-VBF scheme. In QF-2hop-AHH-VBF scheme, forwarder is selected on the basis of composite function including multiple parameters. Efficient forwarder selection improves PDR that is 5.6 % more than AHH-VBF scheme. Holding time calculations in QF2hop-AHH-VBF reduces end-to-end delay. Similarly, APD has reduced that also minimizes end-to-end delay. Due to aforementioned reasons, end-to-end delay and APD in QF-2hop-AHH-VBF are reduced by 28.5% and 28% respectively. Energy tax increases due to more number of successful transmissions. Hence, end-to-end delay and PDR are improved at the cost of energy tax. Table 6.1: Trade offs between performance parameters with respect to AHHVBF scheme

Performance parameters

2hop-AHH-VBF QF-2hop-AHH- WDFAD-DBR scheme VBF scheme scheme

PDR(%)

2

5.6

2

Energy tax(%)

31.15

- 45

- 49.77

End-to-end delay (%)

- 21

28.5

- 56.13

APD (%)

- 25

28

- 53

59

Table 6.2: Performance trade offs with respect to GEDAR scheme

6.2

Parameters

PDR %

Energy tax % End-to-end delay %

GOASST

4 % Improved 50 % More effi- 35 % Increased PDR cient delay

CSM-GOASST

5 % Improved 52 % More effi- 12 % Reduced PDR cient delay

Performance trade-off for GOASST scheme

From the simulation results, we conclude that there is a trade-off between performance parameters. It exhibits that we have achieved certain performance parameters at the cost of a particular parameter. GOASST scheme has achieved energy efficiency at the cost of increased end-to-end delay. Whereas, CSM-GOASST has reduced end-to-end delay while compromising on energy cost in sparse network regions. Performance of GOASST scheme under coordinated sink mobility is also included in Table 6.2. We have relatively presented this trade off with respect to GEDAR routing scheme in percentage values. Energy efficiency achieved by GOASST is 50 % whereas end-to-end delay has increased up to 40 % in GOASST. It is observed that CSM-GOASST is 15 % more efficient in terms of energy cost and end to end delay has reduced up to 28 % as compared to GEDAR.

60

Chapter 7 Conclusion

61

In UWSNs, random deployment causes void hole creation that needs to be avoided for reliable data transmission. Our proposed schemes detect void hole efficiently before sending data packet towards destination. These schemes provide reliable data transmission along with low latency. Simulation results show that 2hopAHH-VBF outperforms in spare network. Whereas quality forwarding scheme performs better in terms of reliability and low latency in any network region. Holding time computation for qualified nodes helps in reducing end to end delay and to avoid collision at receiver. For theoretical analysis, we have formulated linear programming based objective functions for our performance parameters. Experimental results validate that proposed schemes outfox the existing counter part schmoes. Similarly, another proposed routing scheme is an asynchronous sleep-awake scheduling collaborated with geo-opportunistic routing that focused on energy efficiency and reliable data delivery. We have performed modelling of wake up rate and wake up period for energy efficiency in UWSNs. Collaborative design of sleepawake scheduling with geo-opportunistic routing has provided link reliability, consequently, high packet delivery is achieved. Simulation results have corroborated the performance of the proposed schemes. Additional overhead has been minimized by opting asynchronous sleep-awake scheduling locally in the network. In addition to this, we have evaluated performance of GOASST scheme in multi-sink architecture. Regarding this, incorporated coordinated sink mobility benefits in achieving reduced average delay in the network. Mathematical modelling has been carried out to find feasible solutions in order to minimize energy consumption and end-to-end delay. Through an extensive simulations, we showed the performance of proposed solutions for energy efficiency in UWSNS along with reduced end-to-end delay. In the future, we intend to work for void hole recovery in UWSNs. In order to cater void hole creation completely, void hole recovery can help to mitigate the problem. Furthermore, considering energy distribution on nodes, how balanced energy consumption for better energy utilization can be achieved will be focused. Furthermore, we aim to consider node mobility in proposed scenarios to inquire its influence on network performance.

62

Chapter 8 REFERENCES

63

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