DOI:10.35833/MPCE.2021.000512 Optimal Network Partition and Edge Server Placement for Distributed State Estimation Page view: 0        Net amount: 153 Author: Lyuzerui Yuan,Jie Gu,Jinghuan Ma,Honglin Wen,Zhijian Jin Author Affiliation: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Foundation: This work was supported by the Shanghai Sailing Program (No. 19YF1423700), the National Key Research and Development Program of China (No. 2016YFB0900100), and the Key Project of Shanghai Science and Technology Committee (No. 18DZ1100303). Abstract: This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation. A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment, operation, and maintenance, avoid the difference in the partition sizes, reduce the level of coupling between connected partitions, and maximize the inner cohesion of each partition. Capacities of edge server are constrained against underload and overload. To efficiently solve the problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is developed, with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence. Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs, and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution. The optimized result contributes significantly to real-time distributed state estimation. Keywords: Network partition ; edge server placement ; distributed state estimation ; edge computing ; non-dominated sorting genetic algorithm (NSGA). Received:July 29, 2021               Online Time:2022/11/21 View Full Text       Download reader