A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Wang, Ya; Chen, Xinming; Shi, Lei; Cheng, Yuhua; Wang, Houjun

doi:10.1109/jas.2022.105881

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Compared with DoS attacks, bias injection attacks are more dangerous because they can tamper with the information transmission between sensors. Literature [7] studies the distributed localization of wireless sensor networks under bias injection attacks. In the face of the situation that the ranging information is tampered by the attacker during the localization process, a distributed localization algorithm based on trust evaluation is proposed, which has good detection effect and localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…In the face of the situation that the ranging information is tampered by the attacker during the localization process, a distributed localization algorithm based on trust evaluation is proposed, which has good detection effect and localization accuracy. However, Literature [7] only considers the case of injection attack with large bias, and it is easy to miss detection when the bias injection value is minuscule. Motivated on the above work, this paper proposes a distributed localization algorithm for wireless sensor networks under minuscule bias injection attacks.…”

Section: Introductionmentioning

confidence: 99%

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Distributed localization algorithm for wireless sensor networks under minuscule bias injection attacks

Chen,

Wang,

Shi

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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In this paper, the distributed localization problem of wireless sensor networks under minuscule bias injection attacks is studied. It is assumed that the attacker launches minuscule bias injection attacks on the distance measurement between sensors during the localization process of wireless sensor network, and the accumulated bias value has a great impact on the localization accuracy. To solve the above problems, a distributed localization algorithm based on adjacent detection and numerical fitting is proposed in this paper. The proposed algorithm realizes the detection of minuscule bias injection attacks by adjacent detection and improves the localization accuracy of the algorithm by numerical fitting. Finally, the correctness and superiority of the proposed distributed localization algorithm are verified by simulation experiment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed localization algorithm for wireless sensor networks under minuscule bias injection attacks

Chen,

Wang,

Shi

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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“…Park et al introduced a system integrating BIM with UWB for accurate indoor navigation and robot tracking, enhancing construction efficiency and safety [8]. Wang, et al [9] proposed a trust-assessment-based distributed localization algorithm that can achieve accurate localization.…”

Section: Introductionmentioning

confidence: 99%

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

Wang,

Zhang,

et al. 2024

Applied Sciences

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The ultra-wideband (UWB) technology has been increasingly recognized as an efficacious strategy for Indoor Positioning Systems (IPSs). However, the accuracy of the UWB system can be severely degraded by non-line-of-sight (NLOS) errors. In this study, we proposed a new method to reduce the UWB positioning error in such an indoor environment. We developed a system consisting of a Robotic Total Station (RTS), four UWB base stations, a moving target (including a prism and a UWB tag), and a PC. The observed coordinates of the moving target, captured using millimeter precision from an RTS device, served as the ground truth for calculating the positioning errors of the UWB tag. In a significant NLOS scenario, the UWB’s three-dimensional positioning error was identified to exceed the nominal value declared by the manufacturer by a factor of more than three. A detailed analysis revealed that each coordinate component’s error distribution pattern demonstrated considerable variance. To reduce the NLOS error, we designed a combined multilayer neural network that simultaneously fits errors on all three coordinate components and three separate multilayer networks, each dedicated to optimizing errors on a single coordinate component. All networks were trained and verified by benchmark errors obtained from the RTS. The results showed that neural networks outperform the traditional methods, attributed to their strong nonlinear modelling ability, thereby significantly improving the external accuracy by an average reduction in RMSE by 61% and 72%. It is evident that the proposed separate networks would be more suitable for NLOS positioning problems than a combined network.

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