A Majority Voting Scheme in Wireless Sensor Networks for Detecting Suspicious Node

Fan, Chou; Jin, Tan

doi:10.1109/isecs.2009.142

Cited by 7 publications

(3 citation statements)

References 8 publications

(14 reference statements)

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“…Implementing the cross-controller authentication of sensors and EV circuits [154] would protect against spoofing. In addition, there should be a majority sensor voting mechanism in case there exist redundant sensors to ensure data integrity [155].…”

Section: Sensor-based Attacksmentioning

confidence: 99%

Emerging Cybersecurity and Privacy Threats to Electric Vehicles and Their Impact on Human and Environmental Sustainability

et al. 2023

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With the global energy crisis, increasing demand, and a national-level emphasis on electric vehicles (EVs), numerous innovations are being witnessed throughout the EV industry. EVs are equipped with sensors that maintain a sustainable environment for the betterment of society and enhance human sustainability. However, at the same time, as is the case for any new digital technology, they are susceptible to threats to security and privacy. Recent incidents demonstrate that these sensors have been misused for car and energy theft, financial fraud, data compromise, and have caused severe health and safety problems, amongst other things. To the best of our knowledge, this paper provides a first systematic analysis of EV sustainability, digital technologies that enhance sustainability, their potential cybersecurity threats, and corresponding defense. Firstly, three robust taxonomies have been presented to identify the dangers that can affect long-term sustainability domains, including (1) life and well-being, (2) safe environment, and (3) innovation and development. Second, this research measures the impact of cybersecurity threats on EVs and correspondingly to their sustainability goals. Third, it details the extent to which specific security controls can mitigate these threats, thereby allowing for a smooth transition toward secure and sustainable future smart cities.

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Section: Sensor-based Attacksmentioning

confidence: 99%

Emerging Cybersecurity and Privacy Threats to Electric Vehicles and Their Impact on Human and Environmental Sustainability

et al. 2023

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“…Hierarchical Aggregate Classification (HAC) [ 21 ] is a method used to construct a hierarchical tree of nodes: focal points can aggregate all nodes’ classification results to achieve the overall assessment of the entire network of sensors. The majority node voting scheme [ 22 ] harnesses the cluster head nodes to make statistics of node classification results in the cluster to identify abnormal states in the cluster. In these methods, the classification results of majority nodes represent those of focal points or clusters.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Diagnosing Structural Behavior in Dam Safety Monitoring System

Wang

Mao

Cheng

et al. 2021

Sensors

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Collecting a myriad of prototype data through various types of monitoring sensors plays a virtual important role in many aspects of dam safety such as real-time grasp of safety state, exposure of hidden dangers, and inspection design and construction. However, the current methods of prediction are weak in the long-term sequence of nodes with missing and abnormal error value. Moreover, the limitation caused by the apparatus, environmental factors, and network transmission can lead to the deviation and inconsistency of diagnosis and evaluation of local region. In this paper, we consider the correlation of data on nodes in the entire monitoring network. To avoid the deviation caused by noise and missing value in the single-node data sequence, we calculate the correlation between the multiple sequences. A single-node assessment model based on multiple relevant sequence (SAM) is proposed to improve the accuracy of single node assessment. Given the different nodes of a local region have varying impacts on the evaluation results, a local region evaluation algorithm based on node credibility (LREA) is presented to model the credibility of nodes in order to alleviate inconsistent evaluation results in the local region of dam. LREA can assess the dam’s operation state by considering the variations in credibility and multiple nodes coordination. The experimental results illustrate the LREA can reveal the trends of the monitoring values change in a timely and accurate way, which can elevate the accuracy of evaluation results of dam safety.

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“…Examples of the reported non-fail-stop failures that occur in AAL environments include sensors that get blocked by furniture, get remounted by the user in wrong locations, get stuck at a value or get spurious signals due to air drafts, sunlight rays or pets [6,7]. The traditional fault diagnosis methods for wireless sensor networks [8][9][10] are designed to deal with homogeneous, time-driven and continuous-valued sensors. However, such methods do not suit the nature of sensors installed in non-intrusive AAL environments, which are often heterogeneous, event-driven and binary sensors.…”

Section: Introductionmentioning

confidence: 99%

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

ElHady

Jonas

Provost

et al. 2020

Sensors

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Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.

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A Majority Voting Scheme in Wireless Sensor Networks for Detecting Suspicious Node

Cited by 7 publications

References 8 publications

Emerging Cybersecurity and Privacy Threats to Electric Vehicles and Their Impact on Human and Environmental Sustainability

Emerging Cybersecurity and Privacy Threats to Electric Vehicles and Their Impact on Human and Environmental Sustainability

Deep Learning-Based Diagnosing Structural Behavior in Dam Safety Monitoring System

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

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