M-SPOT: A hybrid multiobjective evolutionary algorithm for node placement in wireless sensor networks
Proceedings - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018
Energy, Hybrid Algorithms, Multiobjective Evolutionary Algorithms, Multiobjective Optimization, Relay Node Placement, Wireless Sensor Networks
© 2018 IEEE. We address the problem of the placement of static sensors and relays to monitor specific locations in an area assuming a single-tiered wireless sensor network model with limited communication and sensing constraints. We present a multiobjective optimization model with two conflicting objectives: total number of devices used in the placement and total energy dissipated by the placement. To optimize the model, we propose the Multiobjective Sensor Placement Optimizer (M-SPOT) algorithm, which is a hybrid evolutionary algorithm that combines the Non-Sorting Genetic Algorithm 2 (NSGA2) algorithm with local search heuristics. We evaluate the performance of M-SPOT by simulating the placement of sensors and relays. We found that the utilization of local search heuristics greatly contribute to find better placements when compared to the NSGA2 algorithm.
Perez, Alfredo J., "M-SPOT: A hybrid multiobjective evolutionary algorithm for node placement in wireless sensor networks" (2018). Faculty Bibliography. 2853.