QPSO-based energy-aware clustering scheme in the capillary networks for Internet of Things systems

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%.

[1]  Charalampos Tsimenidis,et al.  Energy-Aware Clustering for Wireless Sensor Networks using Particle Swarm Optimization , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[2]  Cassim Ladha,et al.  Dynamic clustering using binary multi-objective Particle Swarm Optimization for wireless sensor networks , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[3]  Tiankui Zhang,et al.  Multi‐relay selection schemes based on evolutionary algorithm in cooperative relay networks , 2014, Int. J. Commun. Syst..

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

[5]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[6]  Zheng Huang,et al.  A study on cluster lifetime in multi-hop wireless sensor networks with cooperative MISO scheme , 2012, Journal of Communications and Networks.

[7]  Jelena V. Misic,et al.  Extending LTE to support machine-type communications , 2012, 2012 IEEE International Conference on Communications (ICC).

[8]  Jonathan Loo,et al.  Duty cycle control with joint optimisation of delay and energy efficiency for capillary machine‐to‐machine networks in 5G communication system , 2015, Trans. Emerg. Telecommun. Technol..

[9]  Yousef S. Kavian,et al.  SEECH: Scalable Energy Efficient Clustering Hierarchy Protocol in Wireless Sensor Networks , 2014, IEEE Sensors Journal.

[10]  Tiankui Zhang,et al.  Interference-aware multi-user relay selection scheme in cooperative relay networks , 2013, 2013 IEEE Globecom Workshops (GC Wkshps).

[11]  Andrea J. Goldsmith,et al.  Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks , 2004, IEEE Journal on Selected Areas in Communications.

[12]  Lijuan Sun,et al.  Clustering protocol based on data aggregating scheme for wireless sensor networks , 2014 .

[13]  Zbigniew Michalewicz,et al.  Parameter Setting in Evolutionary Algorithms , 2007, Studies in Computational Intelligence.

[14]  Shuyuan Yang,et al.  A quantum particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[15]  Cheng-Yan Kao,et al.  Applying the genetic approach to simulated annealing in solving some NP-hard problems , 1993, IEEE Trans. Syst. Man Cybern..

[16]  Hongyuan Gao,et al.  A Simple Quantum-inspired Particle Swarm Optimization and its Application , 2011 .

[17]  Zhu Han,et al.  Lifetime maximization by cooperative sensor and relay deployment in wireless sensor networks , 2006, IEEE Wireless Communications and Networking Conference, 2006. WCNC 2006..

[18]  Robert Schober,et al.  User Association in 5G Networks: A Survey and an Outlook , 2015, IEEE Communications Surveys & Tutorials.