Lifetime Enhancement of Sensor Networks by the Moth Flame Optimization

Advancements in wireless communication technologies have facilitated the deployment of large-scale Wireless Sensor Networks (WSNs). Due to the constraint of associated battery power, various optimization structures have been proposed to enhance the lifetime of WSNs. In this article, the concept of supernodes is used along with Moth Flame Optimization algorithm to improve the lifetime of the heterogeneous WSNs. The Moth Flame Optimization algorithm is used to achieve the energy-efficient clustering and energy-aware routing. The performance of Moth Flame Optimization algorithm is compared with the other existing protocol, including Genetic Algorithm and Particle Swarm Optimization algorithm. The effects of varying populations of supernodes and sensor nodes on the network metrics are also analyzed here. The influence of the number of hops on the lifetime is also investigated considering two different positions of the base-station in WSNs.

[1]  Vinay Singh,et al.  GCRP: Grid-cycle routing protocol for wireless sensor network with mobile sink , 2018, AEU - International Journal of Electronics and Communications.

[2]  Xiang Cheng,et al.  Joint Power Allocation and Splitting (JoPAS) for SWIPT in Doubly Selective Vehicular Channels , 2017, IEEE Transactions on Green Communications and Networking.

[3]  K. M. Mridula,et al.  Three-dimensional sensor network connectivity considering border effects and channel randomness with application to underwater networks , 2018, IET Commun..

[4]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

[5]  Marco Parvis,et al.  Wireless Sensor Network for Distributed Environmental Monitoring , 2018, IEEE Transactions on Instrumentation and Measurement.

[6]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[7]  Abraham O. Fapojuwo,et al.  A centralized energy-efficient routing protocol for wireless sensor networks , 2005, IEEE Communications Magazine.

[8]  Azzedine Boukerche,et al.  DRINA: A Lightweight and Reliable Routing Approach for In-Network Aggregation in Wireless Sensor Networks , 2013, IEEE Transactions on Computers.

[9]  Guojun Wang,et al.  Secure VANETs: Trusted Communication Scheme Between Vehicles and Infrastructure Based on Fog Computing , 2019, Studies in Informatics and Control.

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Guolong Chen,et al.  A PSO-Optimized Real-Time Fault-Tolerant Task Allocation Algorithm in Wireless Sensor Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

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

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

[14]  Engin Masazade,et al.  A Proportional Time Allocation Algorithm to Transmit Binary Sensor Decisions for Target Tracking in a Wireless Sensor Network , 2018, IEEE Transactions on Signal Processing.

[15]  Jie Xu,et al.  Capacity Region of MISO Broadcast Channel for Simultaneous Wireless Information and Power Transfer , 2014, IEEE Transactions on Communications.

[16]  Cong Wang,et al.  An Optimization Framework for Mobile Data Collection in Energy-Harvesting Wireless Sensor Networks , 2016, IEEE Transactions on Mobile Computing.

[17]  Luis Alonso,et al.  Connectivity Analysis in Clustered Wireless Sensor Networks Powered by Solar Energy , 2018, IEEE Transactions on Wireless Communications.

[18]  S. Jeba Anandh,et al.  Energy Efficient Routing Technique for Wireless Sensor Networks Using Ant-Colony Optimization , 2020, Wirel. Pers. Commun..

[19]  Qing Ling,et al.  Distributed Sensor Allocation for Multi-Target Tracking in Wireless Sensor Networks , 2012, IEEE Transactions on Aerospace and Electronic Systems.

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

[21]  Xing He,et al.  Neural network optimization for energy-optimal cooperative computing in wireless communication system , 2018 .

[22]  Qianwei Zhou,et al.  A Novel Energy-Efficient Cluster Formation Strategy: From the Perspective of Cluster Members , 2013, IEEE Communications Letters.

[23]  S. Selvan,et al.  A hybrid soft computing: SGP clustering methodology for enhancing network lifetime in wireless multimedia sensor networks , 2019, Soft Comput..

[24]  B. S. Ramanjaneyulu,et al.  Optimized Energy Efficient Routing Protocol for life-time improvement in Wireless Sensor Networks , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[25]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[26]  Ridha Bouallegue,et al.  Opportunistic Routing Protocols in Wireless Sensor Networks , 2018, Wirel. Pers. Commun..

[27]  Nianxia Cao,et al.  Sensor Selection for Target Tracking in Wireless Sensor Networks With Uncertainty , 2015, IEEE Transactions on Signal Processing.

[28]  Wei Ni,et al.  Wireless Power Transfer and Data Collection in Wireless Sensor Networks , 2017, IEEE Transactions on Vehicular Technology.

[29]  Bülent Tavli,et al.  The Impact of Incomplete Secure Connectivity on the Lifetime of Wireless Sensor Networks , 2018, IEEE Systems Journal.

[30]  Shaojie Tang,et al.  Lossless In-Network Processing and Its Routing Design in Wireless Sensor Networks , 2017, IEEE Transactions on Wireless Communications.