An Improved Clustering with Particle Swarm Optimization-Based Mobile Sink for Wireless Sensor Networks

A group of sensors with limited energy is deployed in a region to collect useful information from the environment called as Wireless Sensor Networks. It is very difficult to collect the field information efficiently in terms of energy and it can be achieved from deployment phase to routing. Clustering and routing are the major phases to utilize the node energy efficiently. The fixed sink and existing clustering methods create energy hole problem and premature death of sensor nodes affects the data loss. In order to avoid these issues, a novel dynamic clustering approach with Particle Swarm Optimization based mobile data collector for information gathering is proposed. The methodology, implementation details, and the observed simulation results are showcased with the existing algorithms. The obtained simulation results show that the proposed approach increases the network lifetime extensively.

[1]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[2]  M. Marchese,et al.  An ant colony optimization method for generalized TSP problem , 2008 .

[3]  Dina S. Deif,et al.  Classification of Wireless Sensor Networks Deployment Techniques , 2014, IEEE Communications Surveys & Tutorials.

[4]  V. Geetha,et al.  Research Challenges in using Mobile Agents for Data Aggregation in Wireless Sensor Networks with Dynamic Deadlines , 2011 .

[5]  Bin Li,et al.  Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks , 2015, IEEE Transactions on Consumer Electronics.

[6]  Prasanta K. Jana,et al.  A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks , 2016, Wireless Networks.

[7]  Arun Kumar Sangaiah,et al.  Survey on clustering in heterogeneous and homogeneous wireless sensor networks , 2017, The Journal of Supercomputing.

[8]  Mubashir Husain Rehmani,et al.  Applications of wireless sensor networks for urban areas: A survey , 2016, J. Netw. Comput. Appl..

[9]  Ameer Ahmed Abbasi,et al.  A survey on clustering algorithms for wireless sensor networks , 2007, Comput. Commun..

[10]  K. Ramasamy,et al.  Optimized routing in wireless sensor networks by establishing dynamic topologies based on genetic algorithm , 2018, Cluster Computing.

[11]  Rathinasamy Sakthivel,et al.  Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs , 2018, Wirel. Networks.

[12]  Siba K. Udgata,et al.  Sensor Deployment and Scheduling for Target Coverage Problem in Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[13]  Cem Ersoy,et al.  Detection quality of border surveillance wireless sensor networks in the existence of trespassers' favorite paths , 2012, Comput. Commun..

[14]  Prasanta K. Jana,et al.  Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach , 2014, Eng. Appl. Artif. Intell..

[15]  Tin-Yu Wu,et al.  Low-SAR Path Discovery by Particle Swarm Optimization Algorithm in Wireless Body Area Networks , 2015, IEEE Sensors Journal.

[16]  C. Vasanthanayaki,et al.  Particle Swarm Optimization-Based Clustering by Preventing Residual Nodes in Wireless Sensor Networks , 2015, IEEE Sensors Journal.

[17]  Prasanta K. Jana,et al.  Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach , 2015, Wireless Personal Communications.