Lifetime Improvement in Wireless Sensor Networks using Hybrid Differential Evolution and Simulated Annealing (DESA)

Abstract The major concerns in Wireless Sensor Networks (WSN) are energy efficiency as they utilize small sized batteries, which can neither be replaced nor be recharged. Hence, the energy must be optimally utilized in such battery operated networks. One of the traditional approaches to improve the energy efficiency is through clustering. In this paper, a hybrid differential evolution and simulated annealing (DESA) algorithm for clustering and choice of cluster heads is proposed. As cluster heads are usually overloaded with high number of sensor nodes, it tends to rapid death of nodes due to improper election of cluster heads. Hence, this paper aimed at prolonging the network lifetime of the network by preventing earlier death of cluster heads. The proposed DESA reduces the number of dead nodes than Low Energy Adaptive Clustering Hierarchy (LEACH) by 70%, Harmony Search Algorithm (HSA) by 50%, modified HSA by 40% and differential evolution by 60%.

[1]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[2]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[3]  Muddassar Farooq,et al.  Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions , 2011, Inf. Sci..

[4]  S. Shanmugavel,et al.  Hybrid Approach for Energy Optimization in Wireless Sensor Networks Using PSO , 2013 .

[5]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .

[6]  Ganesh K. Venayagamoorthy,et al.  Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[8]  Prasanta K. Jana,et al.  A novel evolutionary approach for load balanced clustering problem for wireless sensor networks , 2013, Swarm Evol. Comput..

[9]  Parikshit Yadav,et al.  A Robust Harmony Search Algorithm Based Clustering Protocol for Wireless Sensor Networks , 2010, 2010 IEEE International Conference on Communications Workshops.

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

[11]  S. Shanmugavel,et al.  Load Balancing and Optimization of Network Lifetime by Use of Double Cluster Head Clustering Algorithm and its Comparison with Various Extended LEACH Versions , 2013 .

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

[13]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[14]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[15]  René Thomsen,et al.  A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[16]  Vidushi Sharma,et al.  Cluster Head Selection in Wireless Sensor Networks under Fuzzy Environment , 2013 .

[17]  Amit Konar,et al.  Annealed Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Yu Zhang,et al.  Node Localization Method for Wireless Sensor Networks Based on HybridOptimization of Differential Evolution and Particle Swarm Algorithm , 2014 .

[19]  Anantha Chandrakasan,et al.  Energy-Scalable Protocols for Battery-Operated MicroSensor Networks , 2001, J. VLSI Signal Process..

[20]  Mohammad Ebrahim Banihabib,et al.  Monthly Inflow Forecasting using Autoregressive Artificial Neural Network , 2012 .

[21]  Daehee Kim,et al.  Energy-Efficient Adaptive Geosource Multicast Routing for Wireless Sensor Networks , 2013, J. Sensors.

[22]  Kah Phooi Seng,et al.  Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison , 2012, J. Netw. Comput. Appl..

[23]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[24]  Prasanta K. Jana,et al.  Energy-aware routing algorithm for wireless sensor networks , 2015, Comput. Electr. Eng..

[25]  Aliasghar Montazar,et al.  Sensitive analysis of optimized infiltration parameters in SWDC model , 2012 .

[26]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[27]  Ponnuthurai N. Suganthan,et al.  Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization , 2013, 2013 IEEE Congress on Evolutionary Computation.

[28]  Youyun Ao,et al.  Differential Evolution Using Opposite Point for Global Numerical Optimization , 2012 .

[29]  T. Aruldoss Albert Victoire,et al.  Dispatching a 19-Unit Indian Utility System Using a Refined Differential Evolution Algorithm , 2014 .

[30]  Aliasghar Montazar,et al.  Optimize of all Effective Infiltration Parameters in Furrow Irrigation Using Visual Basic and Genetic Algorithm Programming , 2012 .

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

[32]  V. Alamelumangai,et al.  Hybrid Approach for Energy Optimization in Wireless Sensor Networks , 2014 .

[33]  Curtis A. Siller,et al.  Recognizing Exceptional Accomplishment - The President's Page , 2005, IEEE Commun. Mag..

[34]  Prasanta K. Jana,et al.  A novel differential evolution based clustering algorithm for wireless sensor networks , 2014, Appl. Soft Comput..