Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Sharma, Himanshu; Haque, Ahteshamul; Blaabjerg, Frede

doi:10.3390/electronics10091012

Cited by 108 publications

(44 citation statements)

References 92 publications

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“…Therefore, determination of the optimal distance according to which an RFID can send the data reliably to the base station is needed. Since the factors affecting transmission could change, the presence of a machine learning-based system helps to build an initial model that could then be updated whenever needed [ 34 , 35 ]. According to the analytical equations presented in Section 2.3 , in the proposed system the variables

, SINR th and

could affect the optimal distance

of reliable data transmission between the RFID reader and the base station.…”

Section: Methodsmentioning

confidence: 99%

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Osman

Saleh

et al. 2021

IJERPH

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Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system.

show abstract

, SINR th and

could affect the optimal distance

of reliable data transmission between the RFID reader and the base station.…”

Section: Methodsmentioning

confidence: 99%

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Osman

Saleh

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Thus, for example, artificial intelligence algorithms are one of the methods that can be used for this purpose. A node can develop skills to interact with nearby WSN nodes, detect viruses, analyze incoming and outgoing packets, authenticate between nodes, and maintain availability [13].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

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Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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“…Signal propagation in a wireless medium between a transmitter and a receiver basically involves location-dependent information, which can be utilized to locate the target of interest. This location-dependent information can be extracted from signal measurement metrics such as RSS, time of arrival (TOA), time difference of arrival (TDoA), angle of arrival (AOA), or combinations thereof [ 9 ]. Out of all these metrics, the RSS-based approach is the most preferred in WSN-based L&T, as unlike others, RSS-based localization systems do not involve the requirement of any additional hardware with the sensor node [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression for Mobile Target Localization in Indoor Environments

Jondhale¹,

Mohan

Sharma

et al. 2022

Sensors

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Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

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Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Cited by 108 publications

References 92 publications

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Support Vector Regression for Mobile Target Localization in Indoor Environments

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