An Innovative Method for Load Balanced Clustering Problem for Wireless Sensor Network in Mobile Cloud Computing
Mobile Cloud Computing is a revolutionary way where global world is progressing in massive way. Connecting wireless sensor network with Mobile Cloud computing is a novel idea in this era. In this year several research has demonstrated to integrate wireles
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Abstract Mobile Cloud Computing is a revolutionary way where global world is progressing in massive way. Connecting wireless sensor network with Mobile Cloud computing is a novel idea in this era. In this year several research has demonstrated to integrate wireless sensor networks (WSNs) with mobile cloud computing, so that cloud computing can be exploited to process the sensory data collected by WSNs and allow these date to the mobile clients in fast, reliable and secured way. For rising lifetime of wireless sensor network, minimizing energy consumption is an important factor. In this case clustering sensor nodes is one of the effective solutions. It is required to gain some excessive load for cluster heads of cluster based WSN in case of collection of huge data, aggregation and communication of this respective data to base station. Particle Swarm Optimization or PSO is an efficient solution of for this problem.
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Keywords Mobile cloud computing (MCC) Wireless sensor network (WSN) Clustering Particle swarm optimization (PSO)
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1 Introduction Assembling of Data in wireless sensor networks (WSNs) as well as the data storage and processing in mobile cloud computing (MCC), WSN-MCC integration highly affected in two fields, such as academic and industry. Mobile users send requests for services to clouds through web browser then the cloud provider allocated resources according to request to establish connection. MCC will be distributed in a access outline heterogeneously with respect of Wireless Network Interfaces [1].
D. Sarddar ⋅ E. Nandi (✉) Department of Computer Science and Engineering, University of Kalyani, Kalyani, India e-mail: [email protected]; [email protected] A.K. Sharma ⋅ B. Biswas ⋅ M.K. Sanyal Department of Business Administration, University of Kalyani, Kalyani, India © Springer Nature Singapore Pte Ltd. 2017 S.C. Satapathy et al. (eds.), Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications, Advances in Intelligent Systems and Computing 516, DOI 10.1007/978-981-10-3156-4_33
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In cluster based WSN all sensor nodes are arranged in group into discrete clusters with a cluster leader, known as cluster head for each [2]. Cluster based WSN has several advantages as: [i] It can bring down energy consumption, so that cluster head per cluster required involving in routing process and data aggregation. [ii] It can preserve communication bandwidth as the sensor nodes want to communicate with respective cluster head and the exchange of extra message among them can be ignored [3].
2 Overview of Particle Swarm Optimization or PSO Particle swarm optimization is basically a population based stochastic optimization algorithm. The system is initialized with population of random solutions and searches for optima by upgrading generations. Here particles fly through problem space by observing the recent optimum particles [4]. The PSO algorithm is starting with a population of random candidate solutions,
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