A Lightweight Trust Mechanism and Overhead Analysis for Clustered WSN

Singh, Moutushi; Sardar, Abdur Rahaman; Majumder, Koushik; Sarkar, Subir Kumar

doi:10.1080/03772063.2017.1284613

Cited by 29 publications

(27 citation statements)

References 21 publications

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“…Among the various existing trust management approaches for WSN [3], [7], [10], [19], [22]- [24], [20], [33]- [37], only the following researches in [10], [19], [22]- [24] were developed specifically for clustered WSNs that suffers from several limitations such as memory overhead, communication overhead and work under assumptions like dependency on specific platform or routing scheme that makes them unrealistic for large scale WSN [19]. To the best of our knowledge, only [10], [22], [23], [38] focused on both resource efficiency and dependability issues with TMS for WSN but still suffer from various issues like accuracy, attack resistant, convergence speed, complexity, and additional overheads. Moreover, in an open or hostile environment, malicious feedbacks can reduce the system dependability, availability and leads to incorrect trust decision.…”

Section: A Motivationmentioning

confidence: 99%

“…To remove the limitation of existing trust management schemes (TMS) [10], [19], [22], [23], we propose a novel and comprehensive TMS consisting the following unique features to improve cooperation among SNs in order to build a robust and reliable trust system. 1) Generate unique identity for each sensor node to make communication easier and secure from external attacks 2) Provide a robust and lightweight trust estimation scheme by employing clustering to improve cooperation among CMs and CHs.…”

Section: B Our Contributionmentioning

confidence: 99%

“…Although this method is suitable for clustered WSNs but it is not highly recommended because it is not robust against well-known on-off attack. Singh et al [23] proposed ''A lightweight trust mechanism (LWTS) and overhead analysis for clustered WSN.'' In this work, the author try to resolve the weight allocation problems by applying a self-adaptive weight allocation method at the cluster head level for both direct trust and indirect trust using ( 11) and ( 12) respectively as follows:…”

Section: Related Workmentioning

confidence: 99%

“…4) Trust establishment (TE) ensures that communication happens among trustworthy sensor nodes (SNs) during key management, authentication, and authorization. We visualize that exiting clustering approaches [4], [7], [10], [11], [19]- [23] is far better than individual SNs for effective collaboration in order to collect, aggregate and forward accurate data to base station. Moreover clustering approaches improve network throughput, scalability and gives flexibility to choose a cluster head (a sensor node with strong computing power or selected by election process [25]- [32] that will be responsible for detecting selfish (malicious) nodes and provide reliable route to cluster members within cluster to send their data.…”

Section: Introductionmentioning

confidence: 99%

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A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

et al. 2019

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With the wide applications of wireless sensor networks (WSNs) in various fields, such as environment monitoring, battlefield surveillance, healthcare, and intrusion detection, trust establishment among sensor nodes becomes a vital requirement to improve security, reliability, and successful cooperation. The existing trust management approaches for large-scale WSN are failed due to their low dependability (i.e., cooperation), higher communication, and memory overheads (i.e., resource inefficient). In this paper, we propose a novel and comprehensive trust estimation approach (LTS) for large-scale WSN that employs clustering to improve cooperation, trustworthiness, and security by detecting malicious (faulty or selfish) sensor nodes with reduced resource (memory and power) consumption. The proposed scheme (LTS) operates on two levels, namely, intra-cluster and inter-cluster along with distributed approach and centralized approach, respectively, to make accurate trust decision of sensor nodes with minimum overheads. LTS consists of unique features, such as robust trust estimation function, attack resistant, and efficient trust aggregation at the cluster, head to obtain the global feedback trust value. Data trust along with communication trust plays a significant role to cope with malicious nodes. In LTS, punishment and trust severity can be tuned according to the application requirement, which makes it an innovative LTS. Moreover, dishonest recommendations (outliers) are eliminated before aggregation at the base station by observing the statistical dispersion. The theoretical and mathematical validations along with simulation results exhibit the great performance of our proposed approach in terms of trust evaluation cost, prevention, and detection of malicious nodes as well as communication overhead.INDEX TERMS Trust management, data trust, communication trust, attack mitigation.

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Section: A Motivationmentioning

confidence: 99%

Section: B Our Contributionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

et al. 2019

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“…We assume that the communication range of an arbitrary node could cover its neighbor nodes so that these two neighboring nodes can transmit information with one another directly [ 24 , 25 , 26 ]. Each node calculates the coverage area of its own virtual cell and determines its cell membership according to the coverage area.…”

Section: Network Modelmentioning

confidence: 99%

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

Sun

Liu

et al. 2018

Sensors

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When the nodes in the network are deployed in the target area with an appropriate density, the effective aggregation and transmission of the data gathered in the monitoring area remain to be solved. The existing Compressed Sensing (CS) based on data aggregation schemes are accomplished in a centralized manner and the Sink node achieves the task of data aggregation. However, these existing schemes may suffer from load imbalance and coverage void issues. In order to address these problems, we propose a Compressed Sensing based on Fault-tolerant Correcting Data Aggregation (CS-FCDA) scheme to accurately reconstruct the compressed data. Therefore, the network communication overhead can be greatly reduced while maintaining the quality of the reconstructed data. Meanwhile, we adopt the node clustering mechanism to optimize and balance the network load. It is shown via simulation results, compared with other data aggregation schemes, that the proposed scheme shows obvious improvement in terms of the Fault-tolerant correcting capability and the network energy efficiency of the data reconstruction.

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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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A Lightweight Trust Mechanism and Overhead Analysis for Clustered WSN

Cited by 29 publications

References 21 publications

A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

CS-FCDA: A Compressed Sensing-Based on Fault-Tolerant Data Aggregation in Sensor Networks

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

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