A Novel Approach for Selecting Effective Threshold Values in Ternary State Estimation Using Particle Swarm Optimization

被引:1
作者
Davar, Somayeh [1 ]
Fevens, Thomas [1 ]
机构
[1] Concordia Univ, Fac Comp Sci & Software Engn, Montreal, PQ H3G 1M8, Canada
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 21期
关键词
event-based state estimation; wireless sensor networks; ternary and hybrid event-based; multi-objective optimization; swarm optimization; SENSOR; TRACKING;
D O I
10.3390/app122110693
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Inspired by recent breakthroughs in cyber-physical systems (CPSs) and their applications, in this paper, we propose a novel multi-objective method to optimize the threshold values within the ternary event-based framework. To reduce communication overhead, the particle swarm optimization (PSO) approach is applied as an optimizer to identify Pareto optimal set values of the threshold. The proposed optimization technique is subject to constraints to ensure its feasibility. The simulation results confirm the efficiency of the recommended method. Furthermore, the simulation results demonstrate that the proposed framework is comprehensive and capable of finding a wide variety of Pareto optimal ternary event-based state estimations for each predefined threshold.
引用
收藏
页数:11
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