Weight Selection and PSO Based Three-Dimensional Localization for Wireless Sensor Networks

2013 ◽  
Vol 427-429 ◽  
pp. 2540-2544 ◽  
Author(s):  
Jia Liang Lv ◽  
Ying Long Wang ◽  
Huan Qing Cui ◽  
Nuo Wei
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Optimization Problem ◽  
Three Dimensional ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Localization Algorithms ◽  
Key Technologies ◽  
Simulation Results

Localization is one of the key technologies of wireless sensor networks, and the problem of localization is always formulated as an optimization problem. Particle swarm optimization (PSO) is easy to implement and requires moderate computing resources, which is feasible for localization of sensor networks. To improve the efficiency and precision of PSO-based localization methods, this paper proposes a novel three-dimensional PSO method based on weight selection (WSPSO). Simulation results show that the proposed method outperforms standard PSO and existing localization algorithms.

A novel approach of localization with Single Mobile Anchor using Salp Swarm Algorithm in Wireless Sensor Networks

2021 ◽  
Author(s):  
Shalli Rani ◽  
Pardeep Kaur ◽  
vinayakumar ravi ◽  
Gautam Srivast ◽  
Abu-Mahfouz A. M.
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Wireless Sensor ◽  
Swarm Optimization ◽  
Salp Swarm Algorithm ◽  
Mobile Anchor ◽  
Simulation Results ◽  
Swarm Algorithm

Abstract Wireless sensor networks (WSNs) have fabulous attributes to collect data by sensing the surrounding environment. WSNs have a large number of applications that are facing challenges of routing, security, deployment, prolonged lifetime, data computation, and localization. To achieve the high-level performance of WSNs, many researchers have proposed various computational Intelligence (CI) based algorithms for the above-mentioned challenges. The procedure to determine the location of the target node is called node localization. It is easy to determine the coordinates of static nodes accurately but challenging task for the mobile nodes. Localization accuracy directly affects the WSN’s performance. In this paper, a range-based and distributed method are proposed by using the application of the Salp Swarm Algorithm (SSA), and the simulation results are compared with existing approaches such as Particle swarm Optimization (PSO) and H-best Particle Swarm Optimization (HPSO). In this paper, a single mobile anchor node as a reference node traversing the entire network in the Hilbert path and localize the mobile target nodes that are randomly deployed in the networking area. The primary goal behind selecting the Hilbert trajectory is to reduce the issue of LoS. The simulation results show that the proposed method has low localization error and an approximate double number of localized nodes with less computing time as compared to existing methods.

scholarly journals Enhanced Particle Swarm Optimization for Effective Relay Nodes Deployment in Wireless Sensor Networks

2021 ◽  
Vol 13 (1) ◽  
pp. 53-73
Author(s):  
Bader Alshaqqawi ◽  
Sardar Anisul Haque ◽  
Mohammed Alreshoodi ◽  
Ibrahim Alsukayti
Keyword(s):  
Wireless Sensor Networks ◽  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Greedy Algorithm ◽  
Pso Algorithm ◽  
Optimization Process ◽  
Wireless Sensor ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
The Greedy Algorithm

One of the critical design problems in Wireless Sensor Networks (WSNs) is the Relay Node Placement (RNP) problem. Inefficient deployment of RNs would have adverse effects on the overall performance and energy efficiency of WSNs. The RNP problem is a typical example of an NP-hard optimization problem which can be addressed using metaheuristics with multi-objective formulation. In this paper, we aimed to provide an efficient optimization approach considering the unconstrained deployment of energy-harvesting RNs into a pre-established stationary WSN. The optimization was carried out for three different objectives: energy consumption, network coverage, and deployment cost. This was approached using a novel optimization approach based on the integration of the Particle Swarm Optimization (PSO) algorithm and a greedy technique. In the optimization process, the greedy algorithm is an essential component to provide effective guidance during PSO convergence. It supports the PSO algorithm with the required information to efficiently alleviate the complexity of the PSO search space and locate RNs in the spots of critical significance. The evaluation of the proposed greedy-based PSO algorithm was carried out with different WSN scenarios of varying complexity levels. A comparison was established with two PSO variants: the classical PSO and a PSO hybridized with the pattern search optimizer. The experimental results demonstrated the significance of the greedy algorithm in enhancing the optimization process for all the considered PSO variants. The results also showed how the solution quality and time efficiency were considerably improved by the proposed optimization approach. Such improvements were achieved using a simple integration technique without adding to the complexity of the system and introducing additional optimization stages. This was more evident in the RNP scenarios of considerably large search spaces, even with highly complex and challenging setups.

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