Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey

Lalama, Zahia; Boulfekhar, Samra; Fouzi, Semechedine

doi:10.1007/s11277-021-08945-8

Cited by 37 publications

(14 citation statements)

References 36 publications

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“…Genetic Algorithms [39] are stochastic search heuristics that mimics the natural selection and evolution process. As presented in Section 2, Genetic Algorithms have been widely adopted for optimization problems in WSNs among other areas [43][44][45][46]. In these algorithms, an initial population ( chromosomes) is randomly generated from the solution space.…”

Section: Genetic Algorithms (Ga)mentioning

confidence: 99%

Multi-objective Deployment of Wireless Sensor Networks in 3-D Environments using Metaheuristics

Barnawi

Hamanah

2023

Preprint

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Wireless Sensor Networks (WSNs) are expected to serve vast number of applications including environmental monitoring applications. In such networks, sensor nodes have limited energy supply and processing power. Hence, random deployments may result in initial communication gaps and waste of energy resources. These gaps may still exist even after structured manner deployments. Similarly, in hierarchical wireless sensor networks, deploying large number of sensor nodes in addition to relays and clusterhead (CH) could improve connectivity but may result in increasing the overall cost with no guarantees for enhancing network lifetime. Therefore, in order to achieve better connectivity, extend network lifetime and minimize cost, a careful deployment of nodes is required. Such deployment is challenging due to the conflicting nature of the aforementioned objectives. It is even more challenging when nodes are deployed in 3-D dimensional space. This paper proposes a 3-D multi-objective deployment of WSNs based on a heuristic optimization approaches, namely Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO). The proposed WSN has a multi-dimensional (3-D) structure with a two-layer hierarchy consisting of sensor nodes, clusterheads and a base station. The aim is to obtain an optimal or near optimal positioning for clusterheads to satisfy the desired objectives. Experimental results show that the proposed method can enhance the network deployment when the three objectives are considered.

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Section: Genetic Algorithms (Ga)mentioning

confidence: 99%

Multi-objective Deployment of Wireless Sensor Networks in 3-D Environments using Metaheuristics

Barnawi

Hamanah

2023

Preprint

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“…The authors in [55] presented a review of the most well-known localization methods used to minimize the localization error in WSNs. This paper gives a novel taxonomy of the different methods applied to solve localization issues in WSNs, including the mobility of anchor nodes, the techniques of machine learning, some mathematical models, and meta-heuristics.…”

Section: Han Et Al Have Proposed In [43] a Mobile Anchor-based Locali...mentioning

confidence: 99%

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

et al. 2022

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Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20%. We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm.

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“…Due to their short transmission ranges, sensor nodes usually cannot communicate directly with a base station, so they choose multi-hop communication. On the other hand, sensing data can be rendered useless if the location of the data collection node is unknown, making localization a defining issue in the WSN field [2]. Therefore, many of the proposed localization methods in the literature address the node localization problem [3].…”

Section: Introductionmentioning

confidence: 99%

A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks

Chakraborty

Kumar

2023

Telecommun Syst

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A node localization algorithm using the Jaya algorithm (JA) and range-free method of reliable anchor pair (RAP) selection approach is proposed to determine the position of target nodes or unknown devices in isotropic, O-shaped, and S-shaped anisotropic wireless sensor networks (WSNs). The reliable anchor selection method separates anchor nodes into prime anchor node pairs, subprime anchor node pairs, and unreachable anchor node pairs to determine the distance between anchor node pairs and target nodes. The nature-inspired Jaya algorithm is used to predict the position of the target node. The presented work is compared with the existing localization methods, including DV(Distance Vector)-maxHop, PSO (Particle Swarm Optimization), and QSSA (Quantized Salp Swarm Algorithm) based localization algorithms. The proposed approach increases localization accuracy regarding the number of anchor nodes and node density. The proposed algorithm also looks at how the degree of irregularity and computation time affect the performance.

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Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey

Cited by 37 publications

References 36 publications

Multi-objective Deployment of Wireless Sensor Networks in 3-D Environments using Metaheuristics

Multi-objective Deployment of Wireless Sensor Networks in 3-D Environments using Metaheuristics

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks

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