DTM&lt;sup&gt;2&lt;/sup&gt;: Adapting job market signaling for distributed trust management in vehicular ad hoc networks

Haddadou, Nadia; Rachedi, Abderrezak

doi:10.1109/icc.2013.6654786

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…The simulation results showed that the authors achieved higher energy efficiency and security level optimization based on QoS and energy demands 16 . Haddadou and Rachedi 17 proposed a method to detect and evict all malicious nodes in a network. Furthermore, the method significantly reduces the percentage of erroneous and misleading data delivered via a network to as low as 0% when increasing the participation ratio of selfish nodes by 20%.…”

Section: Related Workmentioning

confidence: 99%

k‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

Krishnan

A.H²,

Theerthagiri

2022

Int J Communication

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Summary MANETs (Mobile Ad‐Hoc Networks) have limited energy and are unable to energize themselves. This work aims to create a power management strategy for the MANET that saves energy. While routing based on the cluster, the cluster heads may fail or work improperly by the cause of power issues. As a result, the cluster heads experience instability when gathering information and interacting. The main focus of this research is identifying the unstable cluster heads and replacing them with other nodes that implement the self‐configurable cluster method. For defining the cluster heads successfully, we presented a k‐means protocol approach. The proposed k‐means technique identifies the abnormal or unnecessary variations of the cluster heads and the change in cluster count. A node is designated as a cluster head (CH) in MANET after the cluster value remains lesser than the existing routing threshold value. Information gathering, computing trust levels, and trust‐based configuration are the three key elements of the proposed trust management system, which communicates with each node to establish secure network connections. Finally, the testing results show that the proposed approach uses less energy and effectively manages trust in MANET.

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Section: Related Workmentioning

confidence: 99%

k‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

Krishnan

A.H²,

Theerthagiri

2022

Int J Communication

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“…A direct trust evaluation strategy that is inspired by the job market signalling model is proposed in [43], where the sender transmits a signal with its message to ensure the truthfulness of that message for the receiving vehicles. To use the signal, the sender is charged, and the cost depends on the value of the signal and its own behaviour.…”

Section: Direct Trust Modelmentioning

confidence: 99%

“…In another example [42], when a trustor receives a message from a potential trustee, the current trust value TR cur is calculated as

{ST}_{n}^{normalT} = γ \times {ST}_{cur} + false(1 - γ false) \times {ST}_{n - 1}^{normalT}, {ST}_{0}^{normalT} = 0.5

where

T {normalR}_{cur} = {ST}_{n}^{normalT}

. The satisfaction level ST is defined using the number of successful operations N suc and the number of failed operations N fail during multiple packet delivery as follows:

ST = \frac{N_{suc} + 1}{N_{fail} + N_{suc} + 2^{'}}

A direct trust evaluation strategy that is inspired by the job market signalling model is proposed in [43], where the sender transmits a signal with its message to ensure the truthfulness of that message for the receiving vehicles. To use the signal, the sender is charged, and the cost depends on the value of the signal and its own behaviour.…”

Section: Trust Evaluation Models For Vehicular Networkmentioning

confidence: 99%

Trust enforcement in vehicular networks: challenges and opportunities

El-Sayed

Chaqfeh

Kassabi

et al. 2019

IET wirel. sens. syst.

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A major objective of vehicular networking is to improve road safety and travel convenience. The experience of individual vehicles on traffic conditions and travel situations can be shared with other vehicles for improving their route planning and driving decisions. Nevertheless, the frequent occurrence of adversary vehicles in the network is unavoidable. These vehicles may engage in various malicious activities affecting the overall network performance. To control and monitor effectively security threats in vehicular networks, an efficient trust management system should be employed to identify the trustworthiness of individual vehicles and detect malicious drivers. This study provides a review of the research efforts aimed at enabling trust in vehicular environments. The major challenges are highlighted, and an edge-based architecture is proposed as a supportive platform. Furthermore, existing models proposed for trust evaluation, aggregation, propagation and decision making are reviewed. Finally, the current directions to enforce trust in vehicular environments are highlighted.

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“…Their work is featured by good scalability and thus potential in many applications. DTM 2 [13] is a distributed trust model inspired by Job Market model. With the help of third party hardware, system could incent good behaviors and punish malicious behaviors by changing each vehicle's signal value.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation

Yang

Liu

et al. 2016

Mobile Information Systems

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Anomaly detection is critical for intelligent vehicle (IV) collaboration. Forming clusters/platoons, IVs can work together to accomplish complex jobs that they are unable to perform individually. To improve security and efficiency of Internet of Vehicles, IVs’ anomaly detection has been extensively studied and a number of trust-based approaches have been proposed. However, most of these proposals either pay little attention to leader-based detection algorithm or ignore the utility of networked Roadside-Units (RSUs). In this paper, we introduce a trust-based anomaly detection scheme for IVs, where some malicious or incapable vehicles are existing on roads. The proposed scheme works by allowing IVs to detect abnormal vehicles, communicate with each other, and finally converge to some trustworthy cluster heads (CHs). Periodically, the CHs take responsibility for intracluster trust management. Moreover, the scheme is enhanced with a distributed supervising mechanism and a central reputation arbitrator to assure robustness and fairness in detecting process. The simulation results show that our scheme can achieve a low detection failure rate below 1%, demonstrating its ability to detect and filter the abnormal vehicles.

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DTM<sup>2</sup>: Adapting job market signaling for distributed trust management in vehicular ad hoc networks

Cited by 9 publications

References 14 publications

k‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

k‐means clustering based energy and trust management routing algorithm for mobile ad‐hoc networks

Trust enforcement in vehicular networks: challenges and opportunities

Anomaly Detection for Internet of Vehicles: A Trust Management Scheme with Affinity Propagation

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