Mobility Aware Energy Efficient Clustering for MANET: A Bio-Inspired Approach with Particle Swarm Optimization

Mobility awareness and energy efficiency are two indispensable optimization problems in mobile ad hoc networks (MANETs) where nodes move unpredictably in any direction with restricted battery life, resulting in frequent change in topology. These constraints are widely studied to increase the lifetime of such networks. This paper focuses on the problems of mobility as well as energy efficiency to develop a clustering algorithm inspired by multiagent stochastic parallel search technique of particle swarm optimization. The election of cluster heads takes care of mobility and remaining energy as well as the degree of connectivity for selecting nodes to serve as cluster heads for longer duration of time. The cluster formation is presented by taking multiobjective fitness function using particle swarm optimization. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The results show the effectiveness of our proposed algorithm in terms of network lifetime, average number of clusters formed, average number of reclustering required, energy consumption, and packet delivery ratio.

[1]  Thomas Kunz,et al.  On the Selection of Cluster Heads in MANETs , 2011 .

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

[3]  Poojarini Mitra,et al.  A Brief Overview of Clustering Schemes Applied on Mobile Ad-hoc Networks , 2015 .

[4]  Muhammad Irfan,et al.  Mobile Ad-Hoc Networks Applications and Its Challenges , 2016 .

[5]  R. Sasikala,et al.  Investigation on Bio-Inspired Population Based Metaheuristic Algorithms for Optimization Problems in Ad Hoc Networks , 2015 .

[6]  Mohamed E. El-Hawary,et al.  A Survey of Particle Swarm Optimization Applications in Electric Power Systems , 2009, IEEE Transactions on Evolutionary Computation.

[7]  Amin Ziagham Ahwazi,et al.  MOSIC: Mobility-Aware Single-Hop Clustering Scheme for Vehicular Ad hoc Networks on Highways , 2016 .

[8]  Farrukh Aslam Khan,et al.  Weighted Clustering using Comprehensive Learning Particle Swarm Optimization for Mobile Ad Hoc Networks , 2010 .

[9]  M. Jiang,et al.  Cluster based routing protocol (CBRP) , 1999 .

[10]  Kevin R. Fall,et al.  The NS Manual (Formerly NS Notes and Documentation , 2002 .

[11]  Rahim Tafazolli,et al.  A survey on clustering techniques for cooperative wireless networks , 2016, Ad Hoc Networks.

[12]  Mohammad Mehdi Ebadzadeh,et al.  A novel particle swarm optimization algorithm with adaptive inertia weight , 2011, Appl. Soft Comput..

[13]  Supreet Kaur,et al.  Efficient Clustering with Proposed Load Balancing Technique for MANET , 2015 .

[14]  Leonard Barolli,et al.  Design and Implementation of a Simulation System Based on Particle Swarm Optimization for Node Placement Problem in Wireless Mesh Networks , 2015, 2015 International Conference on Intelligent Networking and Collaborative Systems.

[15]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[16]  Farrukh Aslam Khan,et al.  Clustering in Mobile Ad Hoc Networks Using Comprehensive Learning Particle Swarm Optimization (CLPSO) , 2009, FGIT-FGCN.

[17]  Fernando Tadeo,et al.  Algorithm for Optimum Sizing of a Photovoltaic Water Pumping System , 2015 .

[18]  Prasanta K. Jana,et al.  GAR: An Energy Efficient GA-Based Routing for Wireless Sensor Networks , 2013, ICDCIT.

[19]  N. Keerthipriya,et al.  Adaptive cluster formation in MANET using particle swarm optimization , 2015, 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN).

[20]  Sajal K. Das,et al.  An on-demand weighted clustering algorithm (WCA) for ad hoc networks , 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

[21]  Piotr Gajewski,et al.  An Enhanced Algorithm for MANET Clustering Based on Weighted Parameters , 2013 .

[22]  Lilian Adhiambo Omondi,et al.  Determining the influence of preoperative nursing Assessment on patients' surgical outcomes and anxiety at Kenyatta National Hospital, Kenya , 2015 .

[23]  Wonchang Choi,et al.  A Distributed Weighted Clustering Algorithm for Mobile Ad Hoc Networks , 2006, Advanced Int'l Conference on Telecommunications and Int'l Conference on Internet and Web Applications and Services (AICT-ICIW'06).

[24]  Shahram Jamali,et al.  An Energy-efficient Routing Protocol for MANETs: a Particle Swarm Optimization Approach , 2013 .

[25]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[26]  Charu Gandhi,et al.  Node Disjoint Multipath Routing Considering Link and Node Stability protocol: A characteristic Evaluation , 2010, ArXiv.

[27]  Gaurav Kaushik,et al.  An Clustering based AODV approach for MANET , 2013 .

[28]  Bokhari Hatem,et al.  A Review of Clustering Algorithms as Applied in MANETs , 2012 .

[29]  Abdelfettah Belghith,et al.  A Node Quality based Clustering Algorithm in Wireless Mobile Ad Hoc Networks , 2014, ANT/SEIT.

[30]  Abdul Hanan Abdullah,et al.  Efficient Cluster Head Selection Algorithm for MANET , 2013, J. Comput. Networks Commun..

[31]  Dacheng Yang,et al.  A Novel Enhanced Weighted Clustering Algorithm for Mobile Networks , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[32]  Charles E. Perkins,et al.  Ad hoc On-Demand Distance Vector (AODV) Routing , 2001, RFC.

[33]  Wim Lamotte,et al.  Quality of service in mobile ad hoc networks, carrying multimedia traffic , 2014 .

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

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