Mohammad Shahiri - Master student of artificial intelligence and robotics
Arash Ghafori - Ph.D Candidate at Iran University of Technology (IUST)

Wireless sensor networks (WSNs) are nowadays powerful means of performing monitoring and surveillance tasks in both civil and military applications. However, it is challenging to strike compelling trade-offs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage and lifetime. In this paper, we will study a new optimization framework called multi-objective optimization (MOO) to address these compelling trade-offs. We also note that the use cases of these methods are not limited to WSNs and can be exploited in any problem having several conflicting objectives. Firstly, we seek to briefly shed light on the various parameters of WSN having trade-offs with each other. Then, we will study various MOO techniques used in the literature to address the conflicting objectives in WSNs. Furthermore, we will have a case study in which the problem of optimum deployment and power assignment is studied by the means of multi-objective evolutionary algorithm based on decomposition (MOEA/D). Finally, we have devised an optimum filter design problem for generalized frequency division multiplexing which aims to obtain optimum pulse shaping filter with maximum rate and minimum out of band (OOB) emission.

Multi-Objective Optimization, WSN, Evolutionary Algorithms, Genetic Algorithm