Quantum Optimizer Using MOEAD for WSN’s

Optimization of Wireless sensor networks is done with respect to several parameters like energy efficiency, coverage, etc. A WSN is an inter-related collection of sensors. A WSN can be Homogeneous or Heterogeneous. All nodes in homogeneous WSNs have comparable characteristics like energy of the nodes or radius of sensing, etc. In Heterogeneous WSNs, some of these properties differ. MultiObjective Opimization (MOO) simultaneously optimizes more than one objective. The Multi Objective Evolutionary Algorithm with Decomposition (MOEAD) splits/decomposes a problem into subproblems and all these subproblems are simultaneously optimized. In classical computing, a bit is usually represented by 0 or 1. In Quantum Computing, a bit is 0, 1 or a superposition of 0 and 1. In our research, we use MOEAD with quantum computing to optimize the multiple goals of network lifetime along with coverage for WSNs. These WSNs can be homogeneous or heterogeneous. We contrast our methodology with some of the standard methodologies. Simulations show the upsides of our methodology over different techniques referenced here.