Node-Level Trust Evaluation in Wireless Sensor Networks

Desai, S. Sundeep; Nene, Manisha J.

doi:10.1109/tifs.2019.2894027

Cited by 37 publications

(21 citation statements)

References 47 publications

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“…Desai et al proposed a trust evaluation method that used node's internal resources to evaluate node-level trust [12]. Using the suggested self-test algorithm, this method helped nodes trust themselves after booting by ensuring reliable system memory.…”

Section: Related Workmentioning

confidence: 99%

“…Table 1 concludes about the features of the trust computing mechanism for WSNs mentioned. From Table 1, we can find that, in view of the security and trustworthiness of WSNs, some feasible and rich solutions were proposed [10,12,18,[34][35][36][37], but an efficient trust computing mechanism designed for clustered WSNs from the simultaneous achievement of overhead-saving and anti-collaborative attack ability is still necessary.…”

Section: Related Workmentioning

confidence: 99%

“…One is the trust relationship between two CMs. This is the most basic trust relationship in a clustered WSN environment [12]. The other is the trust relationship between two CHs, and this is a special trust relationship in the clustered wireless sensor network environment.…”

Section: Formal Definitions Of Trust Based On Cross-validationmentioning

confidence: 99%

“…Multiple CHs together form a higher level information transmission network. This layered network structure helps increase the speed of data collection and can limit network operations that consume large amounts of bandwidth [10,12]. Many applications in WSNs require coordination through wireless communications between participating nodes for interactive operations such as task collaborations and data transmissions [10,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…The ratings of these malicious nodes may produce erroneous results. Organized cooperative attacks are the most important threat to trust mechanisms in WSNs [10,12,18]. However, most previous studies lacked consideration of malicious cooperative attacks, which severely affected the security, availability, and reliability of the system.…”

mentioning

confidence: 99%

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Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks

Liu

2020

Sensors

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The trust computing mechanism has an increasing role in the cooperative work of wireless sensor networks. However, the computing speed, resource overhead, and anti-collaborative attack ability of a trust mechanism itself are three key challenging issues for any open and resource-constrained wireless sensor networks. In this study, we propose a fast, resource-saving, and anti-collaborative attack trust computing scheme (FRAT) based on across-validation mechanism for clustered wireless sensor networks. First, according to the inherent relationship among three network entities (which are made up of three types of network nodes, namely base stations, cluster heads, and cluster members), we propose the cross-validation mechanism, which is effective and reliable against collaborative attacks caused by malicious nodes. Then, we adopt a fast and resource-saving trust computing scheme for cooperation between between cluster heads or cluster members. This scheme is suitable for wireless sensor networks because it facilitates resource-saving. Through theoretical analysis and experiments, the feasibility and effectiveness of the trust computing scheme proposed in this study are verified.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Formal Definitions Of Trust Based On Cross-validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks

Liu

2020

Sensors

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SE‐TEM: Simple and Efficient Trust Evaluation Model for WSNs

Khan¹,

Singh²

2021

The Smart Cyber Ecosystem for Sustainable Development

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Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

Kale

Nene

2021

Int J Communication

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Summary In Sensor Networks (SNs), Network Lifetime (NL) enhancement using Data Aggregation Trees (DATs) is an extensively studied NP complete problem. The study in this paper investigates the existing DAT scheduling heuristics and proposes a metric termed as Brevis Factor ρ to evaluate the merit of the employed heuristics. Then, the ρ metric is applied to the state‐of‐art DAT scheduling heuristics which are typically based on Residual Energy (RE) parameter of the sensor nodes. Further, this paper proposes a new heuristics parameter for DAT scheduling based on the Number of Neighboring nodes (NN). Performance of NN‐based heuristics is evaluated with rigorous simulations, and the results demonstrate improvement in NL as compared with the state‐of‐art. The values of ρ are observed for the existing RE‐based heuristics as well as the proposed NN‐based heuristics. The results demonstrate how ρ metric values evaluate the DAT scheduling heuristics. In addition, the results facilitate to understand the trade‐off associated with the overheads to reconstruct DATs while improving NL.

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Node-Level Trust Evaluation in Wireless Sensor Networks

Cited by 37 publications

References 47 publications

Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks

Fast, Resource-Saving, and Anti-Collaborative Attack Trust Computing Scheme Based on Cross-Validation for Clustered Wireless Sensor Networks

SE‐TEM: Simple and Efficient Trust Evaluation Model for WSNs

Brevis factor ρ to evaluate data aggregation tree scheduling heuristics in sensor networks

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