On optimal anchor placement for area‐based localisation in wireless sensor networks

Cheriet, Abdelhakim; Bachir, Abdelmalik; Lasla, Noureddine; Abdallah, Mohamed

doi:10.1049/wss2.12010

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…Wireless sensor networks (WSNs) are anticipated to enhance the ability to capture dynamic structural behaviors through dense instrumentation and a multihop communication protocol, while also facilitating the evaluation of structural conditions [14,[68][69][70]. However, the complexity of bridges introduces numerous monitoring parameters and structural degrees of freedom, rendering it impractical and unreasonable to deploy sensors for each one [71][72][73][74].…”

Section: Wireless Sensor Placement Optimizationmentioning

confidence: 99%

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Chen,

Shi,

et al. 2024

Buildings

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In recent years, wireless sensors have progressively supplanted conventional limited sensors owing to their attributes of small size, low cost, and high accuracy. Consequently, there has been a growing interest in leveraging wireless sensor networks for bridge structural health monitoring applications. By employing wireless sensor nodes to gather data from various segments of the bridge, information is relayed to a signal-receiving base station. Subsequently, the health status of the bridge is inferred through specific data processing and analysis, aiding monitoring personnel in making informed decisions. Nonetheless, there are limitations in this research, particularly pertaining to power consumption and efficiency issues in data acquisition and transmission, as well as in determining the appropriate wireless sensor types and deployment locations for different bridge configurations. This study aims to comprehensively examine research on the utilization of wireless sensor networks in the realm of bridge structural health monitoring. Employing a systematic evaluation methodology, more than one hundred relevant papers were assessed, leading to the identification of prevalent sensing techniques, data methodologies, and modal evaluation protocols in current use within the field. The findings indicate a heightened focus among contemporary scholars on challenges arising during the data acquisition and transmission processes, along with the development of optimal deployment strategies for wireless sensor networks. In continuing, the corresponding technical challenges are provided to address these concerns.

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Section: Wireless Sensor Placement Optimizationmentioning

confidence: 99%

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Chen,

Shi,

et al. 2024

Buildings

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“…Typically there is not one optimal anchor placement layout that optimizes HPHR properties for all target positions, but there are several possibilities that show similar results [2]. Optimal anchor placement is mostly conducted for area-based localization [3], [4]. To the best of our knowledge, optimal 3D anchor placement layout exploration has so far not been carried out, except in [5], focusing on a minimum dilution of precision (DOP) value for a certain number of beacons.…”

Section: Introductionmentioning

confidence: 99%

Techtile: a Flexible Testbed for Distributed Acoustic Indoor Positioning and Sensing

Delabie

Cox

Strycker

et al. 2022

2022 IEEE Sensors Applications Symposium (SAS)

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Indoor positioning applications are craving for ever higher precision and accuracy across the entire coverage zone. Optimal anchor placement and the deployment of multiple distributed anchor nodes could have a major impact in this regard. This paper examines the influences of these two difficult to approach hypotheses by means of a straightforward ultrasonic 3D indoor positioning system deployed in a real-life scenario via a geometric based simulation framework. To obtain an optimal anchor placement, a particle swarm optimization (PSO) algorithm is introduced and consequently performed for setups ranging from 4 to 10 anchors. In this way, besides the optimal anchor placement layout, the influence of deploying several distributed anchor nodes is investigated. In order to theoretically compare the optimization progress, a system model and Cramér-Rao lower bound (CRLB) are established and the results are quantified based on the simulation data. With limited anchors, the placement is crucial to obtain a high precision high reliability (HPHR) indoor positioning system (IPS), while the addition of anchors, to a lesser extent, gives a supplementary improvement.

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“…This was motivated by the GA's ability to solve complex engineering problems, and its ability to produce global optimal solutions 15 . See, for example, References 16‐18. The contributions of the GA in Reference 14 can be summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Wireless link scheduling via parallel genetic algorithm

Saad

El-Moursy

Alfawaz

et al. 2021

Concurrency and Computation

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With the advent of fifth generation (5G) systems and the Internet‐of‐Things (IoT), the number of interconnected wireless devices is increasing significantly. Protocols that allow these deceives to interconnect peer‐to‐peer through wireless links are becoming of interest. The major challenge is the inevitable interference among the simultaneously activated wireless links. Given a set of wireless links, this article addresses the non‐deterministic polynomial‐time (NP) hard problem of selecting the maximum subset of links that can be simultaneously activated at their respective signal‐to‐interference‐plus‐noise‐ratio (SINR) targets. The contribution of this article is two‐fold. First, we introduce a new genetic algorithm (GA) constraint‐handling mechanism, and prove analytically that finding optimal link schedules is guaranteed. Second, we develop a novel parallelized GA to solve the problem. Through serial algorithm analysis, we utilize data decomposition as well as exploratory decomposition in order to achieve significant running time speedup, which scales well with problem size. Our numerical results for openMP parallelization illustrate 6.5× and 5.4× reduction in computation time as compared to the serial versions of the GA and hybrid genetic algorithm (HGA), respectively. Moreover, the parallelization of the GA and HGA result in a speedup of 10.4× and 5.4×, respectively, using master‐slave multithreading.

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On optimal anchor placement for area‐based localisation in wireless sensor networks

Cited by 8 publications

References 22 publications

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Wireless Sensor Placement Optimization for Bridge Health Monitoring: A Critical Review

Techtile: a Flexible Testbed for Distributed Acoustic Indoor Positioning and Sensing

Wireless link scheduling via parallel genetic algorithm

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