Consensus Achievement of Decentralized Sensors Using Adapted Particle Swarm Optimization Algorithm

Abstract

This paper explores the possibility of enhancing consensus achievement of decentralized sensors by establishing cooperative behavior between sensor agents. To these ends, a novel particle swarm optimization framework to achieve robust consensus of decentralized sensors is devised to distribute sensing information via local fusing with neighbors rather than through centralized control; the new framework showed a 16.5 percent improvement in consensus achievement as compared to the classic majority rule method. Noteworthy enhancements in consensus achievement are also pertinent to the comparable situation of decentralized sensor systems.

Sensor placement optimization for critical-grid coverage problem of indoor positioning

“…13,19 To obtain optimal solutions of sensor placement, many researchers have to turn to the heuristic algorithms, such as genetic algorithm, 20,21 simulated annealing, 22 monkey algorithm, 23 ant colony algorithm, 24 and particle swarm optimization. 25 However, these studies do not emphasize critical area coverage problems of sensor placement for indoor positioning.…”

Adversarial Impacts on Autonomous Decentralized Lightweight Swarms

“…Regardless of location, a significant issue in autonomous swarms is decentralized coordination and control. Approaches to this problem include probabilistic density control of autonomous swarms [10], controllers for shape generation [11], sensor consensus achievement through particle swarm optimization [12], and non-homogeneous robotic swarm navigation [13]. Probabilistic density control of autonomous swarms has been achieved using Markov chain based approach using a decentralized density computation [10].…”

“…Decentralized controller methods enable swarms to create two dimensional shapes without requiring inter-agent communication [11]. Consensus algorithm for distributed sensors through data fusion of neighboring nodes that outperformed classic algorithms by 16.5% with little to no delay [12]. Finally, a leader-follower method for swarm navigation using only local data has been tested with physical drones [13].…”

“…Control over a drone swarm and location awareness through motion capture -+ --+ + [9] Review of swarm localization and positioning in closed environments -+ -+ + - [10] Markov chain based approach for the probabilistic density control of a swarm -+ + --- [11] Decentralized controller methods for swarm shape generation -+ + + -- [12] A novel particle swarm optimization framework for decentralized consensus --+ --- [13] A method for swarm navigation using only local data -+ + + -+ [14] Decentralized clustering and dispersion algorithms for autonomous swarms + + + --- [15] Survey on UAV swarms with inspiration from bird flocks -+ + --- [16] Optimization data collection technique for drone swarms and sensor networks…”

Sensor Distribution Optimization for Structural Impact Monitoring Based on NSGA-II and Wavelet Decomposition

“…This research involved in many modern heuristic algorithms such as genetic algorithm (GA) [5,6], simulated annealing (SA) [7], monkey algorithm (MA) [8], ant colony algorithm (ACO) [9] and differential evolution (DE). Kim established a novel particle swarm optimization framework to achieve robust consensus of decentralized sensors with neighbors rather than through centralized control [10]. An algorithm based on ladder diffusion and ACO is proposed to solve the power consumption and transmission routing problems in wireless sensor networks [9].…”