Wireless sensor network deployment optimisation based on coverage, connectivity and cost metrics

Bouzid, Salah Eddine; Serrestou, Youssef; Raoof, Kosai; Mbarki, Mohamed; Omri, Mohamed Nazih; Dridi, C.

doi:10.1504/ijsnet.2020.109189

Cited by 7 publications

(5 citation statements)

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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]. The aim of a sensor optimization layout is to achieve comprehensive structural information for bridges using the minimum number of sensors possible [75][76][77][78].…”

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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“…This kind of machine learning incorporates a reward system for sensor nodes that improve their performance over time. The most popular kind of reinforcement learning algorithm, called Q-learning, has been shown to be successful in resolving routing problems [6].…”

Section: Reinforcement Machine Learningmentioning

confidence: 99%

“…In contrast to other approaches, in this one, the agents coordinate their calculations of the Q-values. According to [6], putting MRL-QRP into practicing be difficult. Some of the routing protocols now in use take networking LT into account.…”

Section: Review Of Routing Problemmentioning

confidence: 99%

“…According to testing results, communication consumes the greatest energy.There are various ways to transmit a message to the targeted node in such a dense network. As a result, choosing the optimal one, or the routing method, is a crucial decision that can offer an effective way to save energy [6].This challenge of route optimization while upholding service quality is regarded as an NP-Hard one [7].…”

Section: Introductionmentioning

confidence: 99%

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A Study on Energy-Efficient Wireless Sensor Network based on Machine Learning Techniques

Bhanu Priyanka Valluri

2023

IJRITCC

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Wireless sensor networks (WSNs) are advantageous when there is no existing infrastructure (such as in military applications, emergency relief efforts, etc.) and it is necessary to develop a network at a low cost. A predetermined routing protocol or intrusion detection system is not available to Wireless Sensor Networks because they are dynamic by nature and need separating the network's nodes to do this. Because nodes in the majority of WSN applications are mobile and rely on battery capacity and the availability of restricted resources, energy consumption is an important research area for carrying out a variety of activities in WSNs. Self-learning algorithms that function without scripting or human involvement can be effectively used to report this problem depending on the applications need. This study investigated different ML-based WSN systems and exploring the ML techniques for energy efficiency along with some open issues.

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“…Therefore, expanding the network lifespan is considered to be an important issue in Wireless Sensor Networks. Among the main aspects to be taken into consideration when increasing the lifespan are deploying sensors optimally [5][6][7], sleep scheduling (i.e.) changing the sensor mode from active to sleep [8][9][10][11], and maintaining the balance of load [12].…”

Section: Introductionmentioning

confidence: 99%

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

Hameed

Idrees

2021

Inventive Systems and Control

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The network topology control represents an essential factor during designing the Wireless Sensor Networks (WSNs) due to its primary role in the lifetime optimization of the WSNs. This paper proposes a Cuckoo Scheduling Algorithm (CSA) for lifetime optimization in Sensor Networks of Internet of Things (IoT). First, the sensor devices have clustered using the DBSCAN method, and then the CAS technique is applied at each cluster head. The CSA provides the best schedule of the sensor nodes inside each cluster to monitor the cluster region of with a minimum number of nodes while keeping a suitable level of coverage. The cluster head pooling and the CSA have executed periodically. The experimental results confirm that the CSA technique enhances the network lifespan and whilst preserving an acceptable ratio of coverage in WSNs.

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Wireless sensor network deployment optimisation based on coverage, connectivity and cost metrics

Cited by 7 publications

References 0 publications

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

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

A Study on Energy-Efficient Wireless Sensor Network based on Machine Learning Techniques

Cuckoo Scheduling Algorithm for Lifetime Optimization in Sensor Networks of IoT

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