2017 European Conference on Electrical Engineering and Computer Science
A New Cluster Head Replacement Protocol for Wireless Sensor Networks Khalid A. Darabkh1, Wala’a S. Al-Rawashdeh1, Raed T. Al-Zubi2, and Sharhabeel H. Alnabelsi3 1
Department of Computer Engineering, The University of Jordan Department of Electrical Engineering, The University of Jordan 3 Department of Computer Engineering, Al-Balqa Applied University 1,2Amman, 11942, Jordan 3Amman, 11134, Jordan Corresponding Author Email:
[email protected] Phone: +962-796969219 Fax: +962-65300813 2
design issues. It is noteworthy to mention that many factors have been taken in consideration while designing routing
Abstract—In this article, we propose a protocol to cluster a WSN through taking advantage of the shortcomings of known protocols (i.e., LEACH, T-LEACH, and MT-CHR), namely, Centralized Density- and Threshold-based Cluster Head Replacement (C-DTB-CHR) protocol that mainly aims at optimizing energy through minimizing the number of re-clustering operations, precluding cluster heads nodes premature death, deactivating some nodes located at dense areas from cluster’s participation, as well as reducing long-distance communications. Interestingly, our simulation results show impressive improvements over what are closely related in the literature in relation to the network lifetime. Index Terms— Sensor networks; Adaptive distribution; Activeness factor; Centralization
protocols including the number of deployed sensors in the field, nodes mobility, nodes density and placement, nodes energy, access media and radio model used, network size, security issues, and time constraints. Actually, these factors depend on the inherited architecture of WSNs along with the application requirements. It is worth stating that network clustering can be fixed (or static) where cluster heads are selected at the network setup-phase to work for the rest of the rounds [37, 40-41]. Accordingly, cluster head nodes will experience high data burden that will increase the power consumption in these nodes, resulting in a premature death of these nodes. Other protocols, under this category, perform dynamic clustering, where cluster heads are changed based on different parameters such as its energy and distance to base station or even it can be selected randomly [37-38].
data
I. INTRODUCTION
A popular protocol, in the context of dynamic clustering, is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [42]. LEACH protocol distributes cluster head selection randomly through all nodes, allowing more efficient power distribution, resulting in being one of the widely known protocols in WSNs.
Recently, networks underpin many substantial services everywhere, allowing abundant number of applications to emerge [1-8]. Owing to their lower installation costs, flexibility, portability and scalability, wireless networks become of a great interest and thus are widely deployed [917]. Interestingly, numerous researches in electromagnetics and electronic devices result in advancing the wireless communications as well as developing small-sized and lowprice devices coupled with sensing and radio frequency capabilities [18-27]. Sensors in wireless sensor networks (WSNs) are multifunctional and have sensing and processing capabilities enabling them to detect events and perform light computations [28-36].
The main shortcoming of LEACH protocol is that there is a network re-clustering every round. Threshold-based LEACH (T-LEACH) protocol utilizes threshold energy in cluster heads where dipping below this threshold triggers a re-clustering operation, decreasing control messages overhead and consequently increasing the network lifetime [43]. It is interesting to mention that keeping the threshold probability as that defined in LEACH protocol has a severe implication on the network performance. Taking advantage of this, the authors in [44] proposed a modified threshold-based cluster head replacement (MT-CHR) protocol where the threshold probability is proposed to be dependent on the number of re-clustering operations rather than the round number which results in reducing the number of cluster heads
Finding the appropriate routing protocol to send data through the network to the sink node (base station) has been widely pursued by the researcher community as it is extremely challenging [37-39]. Specifically, improving resource utilization including energy and bandwidth is one of the major
978-1-5386-2085-4/17 $31.00 © 2017 IEEE 978-0-7695-6213-1/17 DOI 10.1109/EECS.2017.93
472
more than that in T-ELACH protocol which thereupon leads to having a little burden on the system performance and prolonging the network lifetime.
ܦ ൌ
ே గכቀ
(1)
మ
σ ௗಹ ൗே ିଵቁ
where ܰ is the number of nodes per clusteri and ݀௧ு denotes for the distance between each node and its cluster head.
In this article, we have introduced a new dynamic clustering protocol for the sake of extending the network lifetime over that achieved in LEACH, T-LEACH, as well as MT-CHR protocols, namely, Centralized Density- and Threshold-based Cluster Head Replacement (C-DTB-CHR) protocol. In CDTB-CHR protocol, not all nodes will participate in data sending phase. In few words, a density-based activation method introduced to set the number of active nodes that will participate in data transmission.
The normalized density adopted in this protocol is the network density (D). If the cluster density is less than or equal to the network density, all nodes should be active, hence the active nodes ratio is equal to 1. While simulating C-DTB-CHR protocol, D is assumed to be equal to 0.01 node/m2. Nevertheless, if the base station finds that the cluster density value is beyond the normalized density, it calculates the ratio of extra sensors (RES) and sets the ratio of active sensors (RAS) of each node (i) as follows:
The rest of this manuscript is organized as the follows. Section II details our proposed protocol. Section III presents simulation results and discussions along with comparisons with LEACH, T-LEACH, and MT-CHR protocols. The last section concludes our work.
ܴ ܵܣൌ ͳ െ ܴܵܧ
(2)
where RES is found as shown below:
II. THE PROPOSED PROTOCOL
ܴ ܵܧൌ ܵܧൗܰ
Three important concerns are considered to develop C-DTBCHR protocol which were deficient in MT-CHR protocol. Firstly, mitigating the amount of control overhead required in every setup (i.e., reducing the control messages). Secondly, excluding nodes, that have been already served as cluster heads where their energy dips below the threshold energy, from being chosen cluster heads anymore. Lastly, making sure of employing fixed number of cluster heads per network as LEACH and MT-CHR consider the expectation of how many cluster heads should function in a network. Therefore, to prolong the network lifetime, two techniques are introduced in C-DTB-CHR: centralization, and density-based node activation.
(3)
where ES denotes for the number of extra sensors which can be expressed as: ܵܧൌ ܰ െ ܰேௗ௦
(4)
where ܰேௗ௦ symbolizes the normalized number of nodes required per cluster, which can be expressed by: ሺܰ ܦ כሻ ൗ ܦቓ ܰேௗ௦ ൌ ቒ
(5)
To this extent, we define the number of extra sensors that have to be deactivated in the current batch. What is the policy of deactivating these nodes? C-DTB-CHR protocol proposes a something called member node activeness factor (AFi). It considers three different factors. Firstly, the number of rounds that a node participated in sending data (PRi). As the number of participating rounds increases, the chance of being active decreases. In other words, PRi is inversely proportional to AFi. Secondly, the ratio of a node energy to the average residual energy. As the energy of a node gets higher and higher, the possibility of being active gets higher and higher. Lastly, the density of a cluster area which is defined by the ratio between the distance of a node to its nearest neighbor and the average distance between member nodes and their corresponding cluster head. III. S IMULATION RESULTS
In C-DTB-CHR protocol, the base station finds the average residual energy of the network and accordingly excludes those nodes that have been already served as cluster heads. Moreover, nodes will remain dependent on threshold probability to decide whether it will act as a cluster head or not. Thus, the base station will determine the cluster heads along with their member nodes based on a threshold probability formula. Interestingly, the base station is further capable of determining the number of active nodes that will participate in data sending throughout the batch. To select active nodes, the base station takes into consideration the node's energy level, number of rounds that a node has been participating in data sending, and node’s distance to the nearest node (i.e., how close the node to its neighbors).
The simulation parameters, used in the proposed protocol, are shown in Table I. The performance metric used to evaluate the performance of our proposed protocol is the number of alive nodes, which basically describes the number of alive nodes in any round.
To find the number of active nodes, the base station finds firstly the density of a cluster ܦ as follows:
473
TABLE I. SIMULATION PARAMETERS Parameter Network size Base Station Location Number of initial nodes ࡱࢋࢋࢉ ࣕ
Value 100 m X 100 m x=50 m, y=175 m 100 nodes ͷͲ ݊ܬȀܾ݅ݐ ͲǤͲͲͳ͵ ܬȀܾ݅ݐȀ݉ସ
ࣕࢌ࢙
ͳͲ ݊ܬȀܾ݅ݐȀ݉ଶ
Data message length Control message length Percentage of expected cluster heads nodes Network density
6000 bits 900 bits 0.05 0.01 node/m2
100
90
80
T-LEACH
70
MT-CHR
Number of Alive Nodes
C-DTB-CHR 60
50 LEACH 40
30
20
10
0
0
200
400
600 Number of Rounds
800
1000
1200
Figure 1. Number of alive nodes versus number of rounds considering LEACH, T-LEACH, MT-CHR, and C-DTB-CHR protocols T-LEACH, and MT-CHR). It is observed, in this figure, that an overarching common trend appears, that is, all curves have a decreasing behavior with respect to the number of rounds.
Fig. 1 shows the number of alive nodes has been investigated in relation to rounds, considering our proposed protocol (CDTB-CHR) and three other relevant protocols (i.e., LEACH,
474
This is expected as in each round, nodes definitely consume energy to send their data. Interestingly, C-DTB-CHR protocol extends the network lifetime up to 1134 rounds. Unlike MTCHR protocol, more crowded clusters, in C-DTB-CHR protocol, have lower active probability, hence, lower data rate is generated and nodes do not consume their power until later rounds of network lifetime. Additionally, centralization that is employed in C-DTB-CHR protocol diminishes the additional communication overhead that is incurred in the MT-CHR protocol, which certainly decreases the power dissipation and accordingly increases the network lifetime. Besides, C-DTBCHR protocol makes sure of employing optimal number of cluster heads, instead of using the randomness as used in the MT-CHR protocol, which basically mitigates the overall energy consumption which of course makes the network stay functioning longer. IV. CONCLUSION
[7]
[8]
[9]
[10]
We have proposed, in this manuscript, an energy-efficient clustering protocol, called, C-DTB-CHR. In C-DTB-CHR protocol, the probability of a node to act as cluster heads gets lower and lower as the cluster, it belongs to, becomes denser and denser. The reference point is the network density. Moreover, nodes will not serve cluster heads any longer if they have already taken this role. Additionally, there is an optimal number of cluster heads chosen every network batch, instead of using the expectation as in the former protocols, bearing in mind that the base station is in charge of setting up the clusters and taking care of aforementioned improvements. The performance of the proposed protocol is evaluated in terms of the average number of alive nodes per round. Results show that C-DTB-CHR protocol contributes significantly in extending the network lifetime over other counterparts.
[11]
[12]
[13]
REFERENCES [1] S. Tomic, M. Beko and R. Dinis, “RSS-based localization in
[2]
[3]
[4]
[5]
[6]
[14]
wireless sensor networks using convex relaxation: noncooperative and cooperative schemes,” IEEE Transactions on Vehicular Technology, vol. 64, no. 5, pp. 2037–2050, 2015. K. A. Darabkh and R. S. Aygün, “TCP Traffic Control Evaluation and Reduction over Wireless Networks Using Parallel Sequential Decoding Mechanism,” EURASIP Journal on Wireless Communications and Networking, vol. 2007, Article ID 52492, pp.1-16, November 2007. Mamoun F. Al-Mistarihi, Rami Mohaisen, Ashraf Sharaqa, Mohammad M. Shurman, and Khalid A. Darabkh, “Performance Evaluation of Multiuser Diversity in Multiuser Two-Hop Cooperative Multi-Relay Wireless Networks using MRC over Rayleigh Fading Channels,” International Journal of Communication Systems, vol. 28, no. 1, pp. 71-90, January 2015. Raed Al-Zubi, Mohammed Hawa, Ghazi Al-Sukkar, and Khalid A. Darabkh, “Markov-Based Distributed Approach For Mitigating Self-Coexistence Problem in IEEE 802.22 WRANs,” The Computer Journal, vol. 57, no. 12, pp. 1765-1775, December 2014. K. A. Darabkh, I. Jafar, G. Al Sukkar, G. Abandah, and R. AlZubi, “An Improved Queuing Model for Packet Retransmission Policy and Variable Latency Decoders,” IET Communications, vol. 6, no. 18, pp. 3315–3328, December 2012. Khalid A. Darabkh, Abeer M. Awad, and Ala’ F. Khalifeh, “New Video Discarding Policies for Improving UDP Performance over
[15]
[16]
[17]
[18]
475
Wired/Wireless Networks,” International Journal of Network Management, vol. 25, no. 3, pp. 181–202, May/June 2015. A. Khalifeh, K. A. Darabkh, and A. Kamel, “Performance Evaluation of Voice-Controlled Online Systems,” Proceedings of IEEE/SSD’12 Multi-conference on Systems, Signals, and Devices, Chemnitz, Germany, pp. 1-6, March 2012. Khalid A. Darabkh, Abeer M. Awad, and Ala’ F. Khalifeh, “Efficient PFD-Based Networking and Buffering Models for Improving Video Quality over Congested Links,” Wireless Personal Communications, vol. 79, no. 1, pp. 293-320, November 2014. K. A. Darabkh and R. S. Aygun, "Performance Evaluation of Sequential Decoding System for UDP-Based Systems for Wireless Multimedia Networks", Proceedings of 2006 International Conference on Wireless Networks (ICWN'06), Las Vegas, Nevada, pp. 365-371, June 2006. K. A. Darabkh and R. Aygun, “Improving UDP Performance Using Intermediate QoD-aware Hop System for Wired/Wireless Multimedia Communication Systems,” International Journal of Network Management, vol. 21, no. 5, pp. 432–454, September 2011. Ala F. Khalifeh, Abdel-Karim Al-Tamimi, and Khalid A. Darabkh, “Perceptual Evaluation of Audio Quality Under Lossy Networks,” Proceedings of 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017), Chennai, India, March 2017. K. A. Darabkh and W.D. Pan, "Stationary Queue-Size Distribution for Variable Complexity Sequential Decoders with Large Timeout," Proceedings of the 44th ACM Southeast Conference, Melbourne, Florida, pp. 331-336, March 2006. Mu'awiah M. Hlayel, Ali M. Hayajneh, Mamoun F. Al-Mistarihi, Mohammad Shurman, and Khalid A. Darabkh, “Closed-form Expression of Bit Error Rate in Dual-Hop Dual-Branch Mixed Relaying Cooperative Networks with Best-Path Selection over Rayleigh Fading Channels,” Proceedings of the 2014 IEEE International Multi-Conference on Systems, Signals & Devices, Conference on Communication & Signal Processing, Castelldefels-Barcelona, Spain, pp. 1-4, February 2014. K. A. Darabkh, A. Khalifeh, M. Naser, and E. Al-Qaralleh, “New Arriving Process for Convolutional Codes with Adaptive Behavior,” Proceedings of IEEE/SSD’12 Multi-conference on Systems, Signals, and Devices, Chemnitz, Germany, pp. 1-6, March 2012. K. A. Darabkh and B. Abu-Jaradeh, “Bounded Fano Decoders over Intermediate Hops Excluding Packet Retransmission,” Proceedings of IEEE 24th International Conference on Advanced Information Networking and Applications (AINA 2010), Perth, Australia, pp. 299-303, April 2010. K. A. Darabkh and B. Abu-Jaradeh, “Buffering Study over Intermediate Hops including Packet Retransmission,” Proceedings of IEEE International Conference on Multimedia Computing and Information Technology (MCIT-2010), Sharjah, U.A.E, pp. 45-48, March 2010. K. A. Darabkh and R. S. Aygun, "Quality of Service and Performance Evaluation of Congestion Control for Multimedia Networking", Proceedings of 2006 International Conference on Internet Computing (ICOMP'06), Las Vegas, Nevada, pp. 217223, June 2006. K. A. Darabkh and R. S. Aygun, "Quality of Service Evaluation of Error Control for TCP/IP-Based Systems in Packet Switching ATM Networks", Proceedings of 2006 International Conference on Internet Computing (ICOMP'06), Las Vegas, Nevada, pp. 243-248, June 2006.
[19] Ahlam K. Al-Dhamari and Khalid A. Darabkh, “Block-Based
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
Steganographic Algorithm Using Modulus Function and PixelValue Differencing,” Journal of Software Engineering and Applications, vol.10, no.1, January 2017. Khalid A. Darabkh, Iyad F. Jafar, Raed T. Al-Zubi, and Mohammed Hawa, “A New Image Steganographic Approach for Secure Communication Based on LSB Replacement Method,” Information Technology and Control, vol. 44, no. 3, pp. 315–328, September 2015. Khalid A. Darabkh, Ahlam K. Al-Dhamari, and Iyad F. Jafar, “A New Steganographic Algorithm Based on Multi Directional PVD and Modified LSB,” Information Technology and Control, vol. 46, no. 1, March 2017. K. A. Darabkh and W.D. Pan, "Queueing Simulation for Fano Decoders with Finite Buffer Capacity," Proceedings of the 9th Communications and Networking Simulation Symposium Conference (CNSS'06), Huntsville, Alabama, April 2006. K. A. Darabkh, B. Abu-Jaradeh, and I. Jafar, “Incorporating Automatic Repeat Request and Thresholds with Variable Complexity Decoding Algorithms over Wireless Networks: Queuing Analysis,” IET Communications, vol. 5, no. 10, pp. 1377–1393, July 2011. K. A. Darabkh, “Evaluation of Channel Adaptive Access Point System with Fano Decoding,” International Journal of Computer Mathematics, vol. 88, no. 5, pp. 916–937, March 2011. K. A. Darabkh, “Queuing Analysis and Simulation of Wireless Access and End Point Systems using Fano Decoding,” Journal of Communications, vol. 5, no. 7, pp. 551-561, July 2010. Khalid A. Darabkh, Huda Ibeid, Iyad F. Jafar, and Raed T. AlZubi, “A generic buffer occupancy expression for stop-and-wait hybrid automatic repeat request protocol over unstable channels,” Telecommunication Systems, vol. 63, no. 2, pp. 205-221, October 2016. Khalid A. Darabkh, “Fast and Upper Bounded Fano Decoding Algorithm: Queuing Analysis,” Transactions on Emerging Telecommunications Technologies, vol. 28, no. 1, pp. 1-12, January 2017. Mohammed Hawa, Khalid A. Darabkh, Raed Al-Zubi, and Ghazi Al-Sukkar, “A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks,” Journal of Sensors, vol. 2016, Article ID 9604526, pp. 1-18 pages, March 2016. Raed Al-Zubi, Marwan Krunz, Ghazi Al-Sukkar, Mohammed Hawa, and Khalid A. Darabkh, “Packet Recycling and Delayed ACK for Improving the Performance of TCP over MANETs,” Wireless Personal Communications, vol. 75, no. 1, pp. 943-963, March 2014. Mohammed Hawa, Khalid A. Darabkh, Loay D. Khalaf, and Jamal S. Rahhal, “Dynamic Resource Allocation Using Load Estimation in Distributed Cognitive Radio Systems,” AEÜ International Journal of Electronics and Communications, vol. 69, no. 12, , pp. 1833–1846, December 2015. M. Shurman, M. Al-Mistarihi, and K. Darabkh, “Merging Dynamic Address Autoconfiguration and Security Key Protocols in Mobile Ad Hoc Networks,” Proceedings of 36th IEEE International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2013), Opatija, Croatia, pp. 441-445, May 2013. Khalid A. Darabkh and Ola Alsukour, “Novel Protocols for Improving the Performance of ODMRP and EODMRP over Mobile Ad hoc Networks,” International Journal of Distributed Sensor Networks, vol. 2015, Article ID 348967, pp.1-18, October 2015.
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
476
Mohammad M. Shurman, Mamoun F. Al-Mistarihi, Khalid A. Darabkh, ”Dynamic Distribution of Security Keys and IP Addresses Coalition Protocol for Mobile Ad Hoc Networks,” Automatika Journal for Control, Measurement, Electronics, Computing and Communications, Ministry of Science, Education and Sports of the Republic of Croatia, In Press. Mohammad Shurman, Noor Awad, Mamoun F. Al-Mistarihi, and Khalid A. Darabkh, “LEACH Enhancements for Wireless Sensor Networks Based on Energy Model,” Proceedings of the 2014 IEEE International Multi-Conference on Systems, Signals & Devices, Conference on Communication & Signal Processing, Castelldefels-Barcelona, Spain, pp. 1-4, February 2014. Ala F. Khalifeh, Mahmoud AlQudah, and Khalid A. Darabkh, “Optimizing the Beacon and SuperFrame Orders in IEEE 802.15.4 for Real-time Notification in Wireless Sensor Networks,” Proceedings of 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017), Chennai, India, March 2017. Khalid Darabkh, Noor Al-Maaitah, Iyad Jafar, and Ala Khalifeh, “Energy Efficient Clustering Algorithm for Wireless Sensor Networks,” Proceedings of 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017), Chennai, India, March 2017. K. Darabkh, S. Ismail, M. Al-Shurman, I. Jafar, E. Alkhader, and M. Al-Mistarihi, “Performance evaluation of selective and adaptive heads clustering algorithms over wireless sensor networks,” Journal of Network and Computer Applications, vol. 35, no. 6, pp. 2068–2080, 2012. Khalid A. Darabkh, Wijdan Y. Albtoush, and Iyad F. Jafar, “Improved Clustering Algorithms for Target Tracking in Wireless Sensor Networks,” Journal of Supercomputing, vol. 73, no. 5, pp 1952–1977, May 2017. M. Shurman, M. Al-Mistarihi, A. Mohammad, K. Darabkh, and A. Ababnah,“Hierarchical Clustering Using Genetic Algorithm in Wireless Sensor Networks,” Proceedings of 36th IEEE International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2013), Opatija, Croatia, pp. 479-483, May 2013. S. S. Ismail, A. I. Al Khader, and K. A. Darabkh,” Static Clustering for Target Tracking in Wireless Sensor Networks,” Global Journal on Technology (Selected Paper of COMENG2014), vol. 8, pp. 167-173, 2015. K. A. Darabkh, Wala’a S. Al-Rawashdeh, Raed T. Al-Zubi, and Sharhabeel H. Alnabelsi, “C-DTB-CHR: Centralized Densityand Threshold-based Cluster Head Replacement Protocols for Wireless Sensor Networks,” Journal of Supercomputing, vol. 73, no. 12, pp. 5332–5353, December 2017. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” Proceedings of the International Conference on system sciences,” Hawaii, USA, pp. 1567-1576, January 2000. Jiman Hong, Joongjin Kook, Sangjun Lee, Dongseop Kwon, and Sangho Yi, “T-LEACH: The method of threshold-based cluster head replacement for wireless sensor networks,” Information Systems Frontiers, vol. 11, pp. 513–521, 2009. Khalid A. Darabkh, Wala’a S. Al-Rawashdeh, Mohammed Hawa, Ramzi Saifan, and Ala’ F. Khalifeh, “A Novel Clustering Protocol for Wireless Sensor Networks,” Proceedings of 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET 2017), Chennai, India, March 2017.
