Optimal Network Partition and Edge Server Placement for Distributed State Estimation

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.

SOC-SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter

“…Consensus component (11) Figure 2 shows the architecture of the ET-DOKCF in BMS. Each sensor obtains the observed value z i l i k from its observed battery, and the sj k transmitted by the sensor of its neighbor node is processed by the consensus component and sent to the Kalman filter to obtain ŝi k , and send its own si k to the sensor of the neighbor node.…”

“…A SOCcorrected core temperature estimation technique finally moves forward. In [11], the authors proposed a fully distributed state estimation method for power systems based on weighted least squares and graph theory. In addition, unlike the existing methods, the method proposed in this paper is a bus-level DSE method, which does not require the power system to be divided into multiple regions.…”

Cloudlet Based Computing Optimization Using Variable-Length Whale Optimization and Differential Evolution

“…Consequently, instead of dealing with one optimal solution, we consider a set of non-dominated solutions that are provided to the decision maker or to an automated process for selecting one of them to be enabled according to certain criteria such as cost, time, and energy consumption. Some famous algorithms for multi-objective optimization are the non-dominated sorting genetic algorithm (NSGA-II) [9], NSGA-III [10], and the multi-objective evolutionary algorithm based on decomposition (MOEAD) [11].…”