Optimization by Genetic Algorithm in Wireless Sensor Networks Utilizing Multiple Sinks

Abstract

WSN (Wireless Sensor Network) comprises of small-sized and constraint-capability SN (Sensor Nodes) which record, send and receive data, sensed to a sink. The network lifetime and energy usability are important challenges to be dealt with. During the working of the SN, the maximum amount of energy is consumed than sensing and processing of data. Therefore, an efficient transmission of the data is required so that the energy can be saved. In this paper, a novel routing and scheduling method for WSNs using GA (Genetic Algorithm) is presented, where the sinks employed on four sides of the sensor field. These sinks collect the data from the SNs having the optimal distance towards the respective sink. The proposed scheme finds the optimized path using GA, during transmission of data from SN to the nearest sink. First, we run the GA for determination of routing paths, where a source SN finds the possible number of optimal hops. Second, the hops or intermediate relay SNs are assumed to relay the data towards the sink, efficiently. The performance is experimented and evaluated using MATLAB R2016b. The simulations have carried out through comparing the proposed scheme with TEEN (Threshold Sensitive Energy Efficient Sensor Network Protocol). The results of simulation comprise of 10 and 20 number of SNs, discretely. Additionally, the direct distance of each node is calculated and the distance through multiple hops from/to the nearest sink is also evaluated. The achievements of the proposed technique are to save both energy and distance for the sake of network longevity and optimal and precise data delivery by multiple hops.