WSN Protocols and Security Challenges for Environmental Monitoring Applications: A Survey

Adu-Manu, Kofi Sarpong; Engmann, Felicia; Sarfo-Kantanka, Godwin; Baiden, Godwill Enchill; Dulemordzi, Bernice Akusika

doi:10.1155/2022/1628537

Cited by 17 publications

(8 citation statements)

References 95 publications

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“…The practical implications of this research are substantial. Organisations responsible for deploying WSNs for various applications, including security and environmental monitoring, can leverage these findings to enhance their network efficiency and cost-effectiveness [ 27 ]. Moreover, the emphasis on data distribution characteristics underscores the importance of tailoring machine learning solutions to the specific requirements of the problem domain, thereby offering a more accurate and reliable predictive capability.…”

Section: Discussionmentioning

confidence: 99%

Optimising barrier placement for intrusion detection and prevention in WSNs

Reddy,

Kaza,

Anisha

et al. 2024

PLoS ONE

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This research addresses the pressing challenge of intrusion detection and prevention in Wireless Sensor Networks (WSNs), offering an innovative and comprehensive approach. The research leverages Support Vector Regression (SVR) models to predict the number of barriers necessary for effective intrusion detection and prevention while optimising their strategic placement. The paper employs the Ant Colony Optimization (ACO) algorithm to enhance the precision of barrier placement and resource allocation. The integrated approach combines SVR predictive modelling with ACO-based optimisation, contributing to advancing adaptive security solutions for WSNs. Feature ranking highlights the critical influence of barrier count attributes, and regularisation techniques are applied to enhance model robustness. Importantly, the results reveal substantial percentage improvements in model accuracy metrics: a 4835.71% reduction in Mean Squared Error (MSE) for ACO-SVR1, an 862.08% improvement in Mean Absolute Error (MAE) for ACO-SVR1, and an 86.29% enhancement in R-squared (R2) for ACO-SVR1. ACO-SVR2 has a 2202.85% reduction in MSE, a 733.98% improvement in MAE, and a 54.03% enhancement in R-squared. These considerable improvements verify the method’s effectiveness in enhancing WSNs, ensuring reliability and resilience in critical infrastructure. The paper concludes with a performance comparison and emphasises the remarkable efficacy of regularisation. It also underscores the practicality of precise barrier count estimation and optimised barrier placement, enhancing the security and resilience of WSNs against potential threats.

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Section: Discussionmentioning

confidence: 99%

Optimising barrier placement for intrusion detection and prevention in WSNs

Reddy,

Kaza,

Anisha

et al. 2024

PLoS ONE

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“…26 The EE methods for WSNs that collect energy and an "environmental monitoring applications" (EMAs) is examined in Ref. 27. As a result of the dynamic deployment and communication problems related to EMAs, it covers the concentrating on the physical layer, WSN protocol stack, MAC, as well as network layer.…”

Section: Literature Review Of Energy Efficient Protocols For Wsnsmentioning

confidence: 99%

“…This study's conclusion describes the safety problems with WSN for EMAs, including the risks just at network and nodal level as well as ways to mitigate and prevent them. 27 A variety of literature reviews on energy-optimized routing techniques are investigated in Ref. 28.…”

Section: Literature Review Of Energy Efficient Protocols For Wsnsmentioning

confidence: 99%

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

Bekal,

Kumar,

Mane

et al. 2024

F1000Res

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In this current era of communications and networking, The Internet of things plays the main role in the making of smart communication and networking. In this article, we have focused on the literature survey on wireless sensor networks which are energy efficient. Various standard protocols are reviewed along with some enhanced protocols which makes the network energy efficient. The comparison of the standard and enhanced protocols with respect to various applications in wireless sensor networks is thoroughly done in this article. The outcomes of the enhanced protocols are also briefly discussed. For easier analysis to future researchers, a comparative table which lists the enhanced protocols which are compared with standard counterparts along with the factors for energy efficiency of the protocols. This article also comments on the issues and challenges of the protocols which can be further analyzed for making the wireless sensor network more energy efficient.

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“…Despite our thorough investigation, it became evident that comprehensive reviews specifically addressing the integration of WSNs for pesticide detection in aquatic environments are notably scarce. While there exists a body of literature offering comprehensive reviews on WSNs employed for monitoring water quality [21][22][23][24], these reviews predominantly lack a focused examination of pesticide detection.…”

Section: Introductionmentioning

confidence: 99%

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

Mutunga,

Sinanovic,

Harrison

2024

Sensors

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Water constitutes an indispensable resource crucial for the sustenance of humanity, as it plays an integral role in various sectors such as agriculture, industrial processes, and domestic consumption. Even though water covers 71% of the global land surface, governments have been grappling with the challenge of ensuring the provision of safe water for domestic use. A contributing factor to this situation is the persistent contamination of available water sources rendering them unfit for human consumption. A common contaminant, pesticides are not frequently tested for despite their serious effects on biodiversity. Pesticide determination in water quality assessment is a challenging task because the procedures involved in the extraction and detection are complex. This reduces their popularity in many monitoring campaigns despite their harmful effects. If the existing methods of pesticide analysis are adapted by leveraging new technologies, then information concerning their presence in water ecosystems can be exposed. Furthermore, beyond the advantages conferred by the integration of wireless sensor networks (WSNs), the Internet of Things (IoT), Machine Learning (ML), and big data analytics, a notable outcome is the attainment of a heightened degree of granularity in the information of water ecosystems. This paper discusses methods of pesticide detection in water, emphasizing the possible use of electrochemical sensors, biosensors, and paper-based sensors in wireless sensing. It also explores the application of WSNs in water, the IoT, computing models, ML, and big data analytics, and their potential for integration as technologies useful for pesticide monitoring in water.

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WSN Protocols and Security Challenges for Environmental Monitoring Applications: A Survey

Cited by 17 publications

References 95 publications

Optimising barrier placement for intrusion detection and prevention in WSNs

Optimising barrier placement for intrusion detection and prevention in WSNs

A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks

Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater

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