On the Improvement of Positioning Accuracy in Wireless Sensor Network using Smart Antennas

Loh, Tian Hong; Lin, Haoyu

doi:10.1109/comnet47917.2020.9306083

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…-Situational awareness: There is no way for an intruder to spread malware from the compromised sensor node to the remainder of the network. So, to lessen the damage that may be done by unapproved users of a secure communication system, the location awareness method is used [45]. Moreover, the type of Attack on WSN Layers and Defense is presented in Table 1.…”

Section: Protocol-specific Attacksmentioning

confidence: 99%

A Comprehensive Analysis of Security Challenges and Countermeasures in Wireless Sensor Networks Enhanced by Machine Learning and Deep Learning Technologies

Saleh,

Marouane,

Fakhfakh

2024

IJSSE

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Wireless sensor networks (WSNs) play a crucial role in monitoring and capturing information in various domains, including the Internet of Medical Things (IOMT). However, WSNs face significant security challenges, such as intrusion and potential malicious activities, due to their distributed and resource-constrained nature. This research provides a comprehensive analysis of the security challenges in WSNs enhanced by machine learning and deep learning technologies within the context of the IOMT. The research addresses these challenges by proposing practical countermeasures to mitigate security concerns in WSNs. It discusses the complexities, critical security issues, and vulnerabilities within WSNs, offering insights into potential fixes based on various approaches and theories. Furthermore, the integration of machine learning and deep learning in WSNs enables efficient communication, data analysis, and control, supporting areas like the IOMT, which includes monitoring prescription orders, tracking patients' movements, and remotely managing patients with chronic illnesses. By addressing the security challenges specific to WSNs in the IOMT environment, this research contributes to the advancement of secure and reliable wireless sensing systems in critical domains. The utilization of machine learning and deep learning technologies facilitates the development of robust methods for detecting and mitigating network attacks. The practical implications of the research findings are demonstrated through tangible examples within the IOMT context, emphasizing the potential impact on improving the security and reliability of wireless sensing systems.

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Section: Protocol-specific Attacksmentioning

confidence: 99%

A Comprehensive Analysis of Security Challenges and Countermeasures in Wireless Sensor Networks Enhanced by Machine Learning and Deep Learning Technologies

Saleh,

Marouane,

Fakhfakh

2024

IJSSE

View full text Add to dashboard Cite

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“…The time-of-arrival (ToA) positioning method was proposed to reduce the effects of fading in NLOS environments using high-gain antennas in sensor radios [21]. It is necessary to specify the time, and a specific method for time acquisition has not yet been considered.…”

Section: Related Papersmentioning

confidence: 99%

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

et al. 2023

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Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the highspeed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

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“…However, many authors have recently defended the use of directional antennas. Directional antennas provide energy-saving or an extended transmission range, reduce collisions and interference, and can improve security against malicious attacks [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions

Azevedo

Santos

2022

Electronics

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Many authors suggest directional antennas to enhance the transmission performance of ZigBee networks. For line-of-sight propagation, directional antennas can extend the transmission range or reduce the transmit power. Directional antennas may also reduce interference between networks operating in the same frequency channel. However, these antennas may not perform similarly under non-line-of-sight propagation conditions. This work presents a study with ZigBee modules comparing the performance of a directional antenna with an omnidirectional one. The measurements were conducted on a university campus for different propagation outdoor environments. A deconvolution technique was applied to estimate the received signal as a function of the azimuth angle. The results demonstrated that the received power followed the gain difference between antennas only for paths with low attenuation. Considering the same Effective Isotropic Radiated Power (EIRP), the system with directional antennas started to lose packets at the same distance as the omnidirectional antennas. The directional antenna did not allow the increase in the link range compared to the omnidirectional antenna. The power consumption was also measured for different transmit power levels of the ZigBee radio. The study showed that the control circuits of directional antennas typically consume more power than omnidirectional antennas operating at a higher transmit power level.

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On the Improvement of Positioning Accuracy in Wireless Sensor Network using Smart Antennas

Cited by 3 publications

References 7 publications

A Comprehensive Analysis of Security Challenges and Countermeasures in Wireless Sensor Networks Enhanced by Machine Learning and Deep Learning Technologies

A Comprehensive Analysis of Security Challenges and Countermeasures in Wireless Sensor Networks Enhanced by Machine Learning and Deep Learning Technologies

Position Estimation of Radio Source Based on Fingerprinting With Physical Wireless Parameter Conversion Sensor Networks

Performance Evaluation of Directional Antennas in ZigBee Networks under NLOS Propagation Conditions

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