Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks

Cluster based routing approaches have been researched extensively for saving energy of wireless sensor networks (WSNs). In a cluster based routing mechanism, cluster heads (CHs) cooperate mutually to forward their data to the base station (BS) through multi-hop fashion. Due to this process, CHs near to the BS loaded with huge relay traffic and tend to die quickly, which causes partition of the network is popularly known as a hot-spot problem. To tackle the hot-spot problem, in this paper, competitive swarm optimization (CSO) based algorithms have been proposed, jointly call these algorithms as CSO-UCRA (CSO based Unequal Clustering and Routing Algorithms). First, the CH selection algorithm has been presented which is based on CSO based technique, next assign the non-CH sensors to CHs based on the derived CHproficiency function. Finally, a CSO based routing algorithm has been presented. Efficient particle encoding schemes and novel fitness functions have been developed for these algorithms. The CSO-UCRA is simulated extensively with varying number of sensor nodes and CHs for various WSN scenarios, and the obtained results are compared with some recent devised algorithms and standard meta-heuristic based algorithm called PSO-UCRA to show the efficiancy in terms of various performance metrics. CSO-UCRA shows decreased energy consumption of 28.48%, 22.55%, 12.92%, and 3.81%, increased network lifetime of 56.92%, 46.02%, 26.2%, and 8.04% and increased data packets received 73%, 52.5%, 20.8%, and 6.18% over EBUC, EAUCF, EPUC and PSO-UCRA respectively.

[1]  Falko Dressler,et al.  On the lifetime of wireless sensor networks , 2009, TOSN.

[2]  Anand Nayyar,et al.  A Comprehensive Review of Cluster-Based Energy Efficient Routing Protocols in Wireless Sensor Networks , 2014 .

[3]  Gang Wang,et al.  An Energy-Aware Distributed Unequal Clustering Protocol for Wireless Sensor Networks , 2011, Int. J. Distributed Sens. Networks.

[4]  Prasanta K. Jana,et al.  Energy Efficient Clustering for Wireless Sensor Networks: A Gravitational Search Algorithm , 2015, SEMCCO.

[5]  Haider Banka,et al.  Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach , 2017, Wirel. Networks.

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

[7]  Yaochu Jin,et al.  A Competitive Swarm Optimizer for Large Scale Optimization , 2015, IEEE Transactions on Cybernetics.

[8]  Samayveer Singh,et al.  An energy aware clustering and data gathering technique based on nature inspired optimization in WSNs , 2020, Peer-to-Peer Netw. Appl..

[9]  Ajay K. Sharma,et al.  Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network , 2019, Appl. Soft Comput..

[10]  Haider Banka,et al.  Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks , 2017, Wirel. Networks.

[11]  S.K. Halgamuge,et al.  Particle Swarm Optimisers for Cluster formation in Wireless Sensor Networks , 2005, 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[12]  Sungryoul Lee,et al.  LUCA: An Energy-efficient Unequal Clustering Algorithm Using Location Information for Wireless Sensor Networks , 2011, Wirel. Pers. Commun..

[13]  Shigenobu Sasaki,et al.  An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks , 2016, Appl. Soft Comput..

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

[15]  Raffaele Cerulli,et al.  Maximizing lifetime in wireless sensor networks with multiple sensor families , 2015, Comput. Oper. Res..

[16]  Cauligi S. Raghavendra,et al.  PEGASIS: Power-efficient gathering in sensor information systems , 2002, Proceedings, IEEE Aerospace Conference.

[17]  Weiren Shi,et al.  Energy-balanced unequal clustering protocol for wireless sensor networks , 2010 .

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

[19]  Qin Gao,et al.  Resource allocation in two-tier small-cell networks with energy consumption constraints , 2020, Peer Peer Netw. Appl..

[20]  Prasanta K. Jana,et al.  A Gravitational Search Algorithm for Energy Efficient Multi-sink Placement in Wireless Sensor Networks , 2015, SEMCCO.

[21]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[22]  Yan Dong,et al.  An improved harmony search based energy-efficient routing algorithm for wireless sensor networks , 2016, Appl. Soft Comput..

[23]  Ciriaco D’Ambrosio,et al.  Extending Lifetime Through Partial Coverage And Roles Allocation in Connectivity-Constrained Sensor Networks , 2016 .

[24]  Prasanta K. Jana,et al.  PSO-Based Multiple-sink Placement Algorithm for Protracting the Lifetime of Wireless Sensor Networks , 2016 .

[25]  Wei Liu,et al.  Distance Measurement Model Based on RSSI in WSN , 2010, Wirel. Sens. Netw..

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

[27]  Anand Nayyar,et al.  A Comprehensive Review of Simulation Tools for Wireless Sensor Networks (WSNs) , 2015 .

[28]  M. Mehdi Afsar,et al.  Clustering in sensor networks: A literature survey , 2014, J. Netw. Comput. Appl..

[29]  Song Mao,et al.  Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO , 2011 .

[30]  Bara'a Ali Attea,et al.  Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks , 2011, Swarm Evol. Comput..

[31]  L. Malathi,et al.  Energy efficient data collection through hybrid unequal clustering for wireless sensor networks , 2015, Comput. Electr. Eng..

[32]  Anand Nayyar,et al.  Advances in Swarm Intelligence for Optimizing Problems in Computer Science , 2018 .

[33]  Tao Liu,et al.  An energy-balancing clustering approach for gradient-based routing in wireless sensor networks , 2012, Comput. Commun..

[34]  Mohamed F. Younis,et al.  An energy- and proximity-based unequal clustering algorithm for Wireless Sensor Networks , 2014, 39th Annual IEEE Conference on Local Computer Networks.

[35]  Samayveer Singh,et al.  A sustainable data gathering technique based on nature inspired optimization in WSNs , 2019, Sustain. Comput. Informatics Syst..

[36]  Francesco Carrabs,et al.  A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints , 2015, J. Netw. Comput. Appl..

[37]  Adnan Yazici,et al.  An energy aware fuzzy approach to unequal clustering in wireless sensor networks , 2013, Appl. Soft Comput..

[38]  Ossama Younis,et al.  HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks , 2004, IEEE Transactions on Mobile Computing.

[39]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[40]  Anand Nayyar,et al.  Introduction to Swarm Intelligence , 2018, Advances in Swarm Intelligence for Optimizing Problems in Computer Science.

[41]  Bara'a Ali Attea,et al.  A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks , 2012, Appl. Soft Comput..

[42]  Francesco Carrabs,et al.  Prolonging Lifetime in Wireless Sensor Networks with Interference Constraints , 2017, GPC.