Abstract
Sensing data by sensor nodes in wireless sensor network (WSN) is random both in space and time. Routing of sensed data to base station in energy constrained WSN become more challenging as batteries of sensor nodes got consumed with every round of routing. Data packets are routed in multihop wireless communication in Time Division Multiple Access mode to base station. In this paper, soft computing techniques are used to propose an intelligent algorithm which enhances network lifetime by providing energy efficient routing. This is a hybrid approach in which genetic algorithm with partially mapped crossover is applied to find optimal routes while fuzzy logic is used to determine link cost. In order to make routing optimal, the link cost between adjacent nodes is calculated that consider residual energy of node, distance from base station, and density of nodes in a cluster. Fuzzy logic mechanism is used to calculate this link cost of all adjacent nodes, and these costs are represented in a link cost matrix which is updated after every round. This algorithm is based on hierarchal routing concept, and K-Mean numerical approach is used for clustering of sensor nodes. The approach is successfully implemented in MATLAB, and the simulation results of the various scenario show that the number of rounds before which the first node dies is more than LEEACH, thereby it enhances network lifetime as compared to LEEACH.
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Uppal, R.S. (2018). Hybrid Intelligent Algorithm for Energy-Efficient Routing in WSN. In: Urooj, S., Virmani, J. (eds) Sensors and Image Processing. Advances in Intelligent Systems and Computing, vol 651. Springer, Singapore. https://doi.org/10.1007/978-981-10-6614-6_19
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DOI: https://doi.org/10.1007/978-981-10-6614-6_19
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