An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

Energy consumption is one of the important factors in wireless sensor networks (WSNs) design. As energy is a limited resource, energy consumption problem in WSNs has become a fast growing problem, and there is a need of efficient and robust algorithms for load balancing in WSNs. This energy is needed for sensor nodes operations. In order to maximize the network lifetime, energy consumption should be optimized. In cluster-based WSNs, cluster heads or gateways perform activities, such as data collection from its member nodes, data aggregation, and data exchange with the base station. Hence, load balancing of gateways in WSNs is one of the crucial and challenging tasks to maximize network lifetime. In order to address this problem, in this paper, shuffled frog leaping algorithm (SFLA) is improved by suitably modifying the frog’s population generation and off-spring generation phases in SFLA and by introducing a transfer phase. A novel fitness function is also designed to evaluate the quality of the solutions produced by the improved SFLA. We performed extensive simulations of the proposed load balancing algorithm in terms of various performance parameters. The experimental results are encouraging and demonstrated the efficiency of the proposed algorithm.

[1]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[2]  Chor Ping Low,et al.  Efficient Load-Balanced Clustering Algorithms for wireless sensor networks , 2008, Comput. Commun..

[3]  Jing Zhang,et al.  Clustering Model Based on Node Local Density Load Balancing of Wireless Sensor Network , 2013, 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies.

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

[5]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

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

[8]  Vaishali S. Gattani,et al.  Data collection using score based load balancing algorithm in wireless sensor networks , 2016, 2016 International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16).

[9]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[10]  Networks Shio,et al.  A Survey of Energy-Efficient Hierarchical Cluster-Based Routing in Wireless Sensor , 2010 .

[11]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

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

[13]  Abdul Wasey Matin,et al.  Genetic Algorithm for Hierarchical Wireless Sensor Networks , 2007, J. Networks.

[14]  Mohamed F. Younis,et al.  Fault-tolerant clustering of wireless sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[15]  Mark A. Shayman,et al.  Energy Efficient Routing in Wireless Sensor Networks , 2003 .

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

[17]  C. Christober Asir Rajan,et al.  A Modified Shuffled Frog Leaping Algorithm for Long-Term Generation Maintenance Scheduling , 2013, SocProS.

[18]  Mukesh Singhal,et al.  Security in wireless sensor networks , 2008, Wirel. Commun. Mob. Comput..

[19]  Koen Langendoen,et al.  An adaptive energy-efficient MAC protocol for wireless sensor networks , 2003, SenSys '03.

[20]  A. Sharma,et al.  A comparative study of modified crossover operators , 2015, 2015 Third International Conference on Image Information Processing (ICIIP).