Mobile sink-based energy efficient cluster head selection strategy for wireless sensor networks

Energy efficient routing protocol is the requirement of today’s wireless sensor networks. Various protocols have been developed in order to create an energy efficient wireless sensor network, but still some loopholes exist in this domain and energy hole is one of them. Energy hole refers to the early energy diminution of those nodes that are near to the sink. This study introduced a mobile sink based energy aware clustering mechanism to enhance the lifetime of the network by overcoming the issue of energy holes. In proposed work, the network is initially divided into the number of rectangular regions and each region is comprised of one cluster head (CH). The nature-inspired firefly optimization algorithm is used to select cluster heads where residual energy, average node to node distance and distance from the node to sink are the decisive parameters of the process. The sink moves in the observing field after estimating the centroid location of the CHs. The performance of the proposed work is compared with the LEACH, LEACH-GA, A-LEACH, MIEEPB, and MSIEEP by using Matlab simulation platform. The result section represents the proficiency of the proposed MSECA protocol over traditional techniques in term of network lifetime, packet delivery ratio and packet delay.

[1]  Jie Wu,et al.  An unequal cluster-based routing protocol in wireless sensor networks , 2009, Wirel. Networks.

[2]  Vishal Shrivastava,et al.  An Amend Implementation on LEACH protocol based on Energy Hierarchy , 2012 .

[3]  Dimitrios D. Vergados,et al.  Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[4]  Biao Fang,et al.  An Energy-Efficient Clustering Algorithm for Wireless Sensor Networks , 2013 .

[5]  Xin-She Yang,et al.  Firefly Algorithm: Recent Advances and Applications , 2013, ArXiv.

[6]  Mohammad Reza Zahabi,et al.  Optimizing LEACH clustering algorithm with mobile sink and rendezvous nodes , 2015 .

[7]  Janez Brest,et al.  A comprehensive review of firefly algorithms , 2013, Swarm Evol. Comput..

[8]  Jay Shankar Prasad,et al.  Performance enhancement by efficient ant colony routing algorithm based on swarm intelligence in wireless sensor networks , 2017, Int. J. Wirel. Mob. Comput..

[9]  Rajoo Pandey,et al.  An improved energy aware distributed unequal clustering protocol for heterogeneous wireless sensor networks , 2016 .

[10]  S. Shanmugavel,et al.  Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks , 2016, Swarm Evol. Comput..

[11]  Jonathan Cole Smith,et al.  A survey of optimization algorithms for wireless sensor network lifetime maximization , 2016, Comput. Ind. Eng..

[12]  Raffaele Cerulli,et al.  Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges , 2012, Eur. J. Oper. Res..

[13]  Mahmoud M. Salim,et al.  PR-LEACH: Approach for balancing energy dissipation of LEACH protocol for wireless sensor networks , 2014, 2014 31st National Radio Science Conference (NRSC).

[14]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[15]  Deepika Agrawal,et al.  An Energy Efficient Approach to Extend Network Life Time of Wireless Sensor Networks , 2016 .

[16]  Ramjee Prasad,et al.  Node Heterogeneity for Energy Efficient Synchronization in Wireless Sensor Network , 2016 .

[17]  Kyeong Hur,et al.  An intelligent agent-based routing structure for mobile sinks in WSNs , 2010, IEEE Transactions on Consumer Electronics.

[18]  T. S. B. Sudarshan,et al.  A genetic fuzzy system based optimized zone based energy efficient routing protocol for mobile sensor networks (OZEEP) , 2015, Appl. Soft Comput..

[19]  Boubaker Daachi,et al.  Optimization Techniques for Energy Consumption in WSNs , 2017 .

[20]  Elyes Ben Hamida,et al.  Strategies for data dissemination to mobile sinks in wireless sensor networks , 2008, IEEE Wireless Communications.

[21]  D. V. Ashoka,et al.  Validation of Multiple Mobile Elements Based Data Gathering Protocols for Dynamic and Static Scenarios in Wireless Sensor Networks , 2016 .

[22]  Ashok Kumar,et al.  Improving reporting delay and lifetime of a WSN using controlled mobile sinks , 2018, J. Ambient Intell. Humaniz. Comput..

[23]  Mohammed Abo-Zahhad,et al.  Mobile Sink-Based Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability Period of Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[24]  Hamid Sharif,et al.  Load-balanced energy efficient clustering protocol for wireless sensor networks , 2016, IET Wirel. Sens. Syst..

[25]  Nadeem Javaid,et al.  Maximizing the Lifetime of Multi-chain PEGASIS using Sink Mobility , 2013, ArXiv.

[26]  Lin Wang,et al.  An energy-efficient clustering algorithm for wireless sensor networks , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[27]  Chinya V. Ravishankar,et al.  LEACH-GA: Genetic Algorithm-BasedEnergy-Efficient Adaptive Clustering Protocolfor Wireless Sensor Networks , 2011 .

[28]  Rajoo Pandey,et al.  An Improved Energy Aware Distributed Clustering Protocol for Wireless Sensor Networks , 2016 .

[29]  J. Mohorko,et al.  Single-hop vs. Multi-hop - Energy efficiency analysis in wireless sensor networks , 2010 .

[30]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[31]  Krishna M. Sivalingam,et al.  Data Gathering Algorithms in Sensor Networks Using Energy Metrics , 2002, IEEE Trans. Parallel Distributed Syst..

[32]  Jie Zhang,et al.  A General Self-Organized Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network , 2012, IEEE Transactions on Nuclear Science.