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Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT

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Abstract

Nowadays, the Internet of Things (IoT) plays a significant role in the Internet world. The IoT is a system which integrates the computing devices, digital machines provided with unique identifiers which have the ability to transfer the data over the network via the better route. IoT is also expected to generate large amounts of data, the consequent necessity for quick aggregation of the data and process such data more effectively. In this paper, a multi-objective fractional gravitational search algorithm is proposed to find the optimal cluster head for energy efficient routing protocol in IoT network. To extend the lifetime of the node, the Fractional Gravitational Search Algorithm (FGSA) is proposed to find out the optimal cluster head node iteratively in the IoT network model. The cluster head node is selected in FGSA that is evaluated by the fitness function using multiple objectives such as distance, delay, link lifetime and energy, termed as multi-objective FGSA (MOFGSA). The simulation results and performance is analyzed using MATLAB implementation. The performance is compared with existing algorithms like Artificial Bee Colony, Gravitational Search Algorithm and multi-particle swarm immune cooperative algorithm. Thus, the proposed MOFGSA algorithm ensures to prolong the lifetime of IoT nodes.

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Acknowledgements

The authors would like to thank to Dr. Arvind V. Deshpande, Principal, Smt. Kashibai Navale College of Engineering, Pune, Dr. Parikshit N. Mahalle, Head of Computer Engineering Department, Smt. Kashibai Navale College of Engineering, Pune and Dr. Mrs. Jayashree R. Prasad, Professor, Computer Engineering Department, Sinhgad College of Engineering, Pune, India for their constant support and motivation in our work.

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Correspondence to Amol V. Dhumane.

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Dhumane, A.V., Prasad, R.S. Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT. Wireless Netw 25, 399–413 (2019). https://doi.org/10.1007/s11276-017-1566-2

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