An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks

Wang, Dandan; Wan, Jian; Wang, Meimei; Zhang, Qiang

doi:10.3390/s16071041

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…At the same time, the PPM can be used in the distributed cooperative scenario, which significantly reduces the pressure of information interaction between the two nodes. An outlier detection method was proposed by Wang et al [35] based on the maximum entropy principle and fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks

Tian

et al. 2019

Sensors

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Accurate position information plays an important role in wireless sensor networks (WSN), and cooperative positioning based on cooperation among agents is a promising methodology of providing such information. Conventional cooperative positioning algorithms, such as least squares (LS), rely on approximate position estimates obtained from prior measurements. This paper explores the fundamental mechanism underlying the least squares algorithm’s sensitivity to the initial position selection and approaches to dealing with such sensitivity. This topic plays an essential role in cooperative positioning, as it determines whether a cooperative positioning algorithm can be implemented ubiquitously. In particular, a sufficient and unnecessary condition for the least squares cost function to be convex is found and proven. We then propose a robust algorithm for wireless sensor network positioning that transforms the cost function into a globally convex function by detecting the null space of the relative angle matrix when all the targets are located inside the convex polygon formed by its neighboring nodes. Furthermore, we advance one step further and improve the algorithm to apply it in both the time of arrival (TOA) and angle of arrival/time of arrival (AOA/TOA) scenarios. Finally, the performance of the proposed approach is quantified via simulations, and the results show that the proposed method has a high positioning accuracy and is robust in both line-of-sight (LOS) and non-line-of-sight (NLOS) positioning environments.

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Section: Introductionmentioning

confidence: 99%

Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks

Tian

et al. 2019

Sensors

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“…When modified observations from attackers (or as we called them, the malicious observations) are unavoidable, the researchers propose detecting and eliminating the malicious observations and then using the remaining honest observations to achieve node localization [8,9,10]. This strategy can be called the secure localization strategy based on malicious node detection.…”

Section: Introductionmentioning

confidence: 99%

“…This strategy can be called the secure localization strategy based on malicious node detection. A typical work is the MEF-based localization algorithm proposed in Reference [8]. A common feature of this type of method is that the detection of malicious nodes usually needs to compare a large amount of data, thus causing a heavy calculation overhead.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks

Yang

Xiong

Wang

et al. 2019

Sensors

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Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer–Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion center (FC), we further define the suboptimal Byzantine attack and also find a way to realize such an attack. Finally, in order to alleviate the negative impact of attackers on the system performance, a modified sampling importance resampling (SIR) filter is proposed. Simulation results show that the tracking results of the modified SIR filter can be close to the true trajectory of the moving target. In addition, when the quantization level increases, both the security performance and the estimation performance of the tracking system are improved.

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Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

et al. 2021

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Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.

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An MEF-Based Localization Algorithm against Outliers in Wireless Sensor Networks

Cited by 6 publications

References 25 publications

Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks

Robust Least-SquareLocalization Based on Relative Angular Matrix in Wireless Sensor Networks

Analysis of Byzantine Attacks for Target Tracking in Wireless Sensor Networks

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

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