Testbed Evaluation of Self-Adaptive Trust Model for Cooperative Geographic Routing in Wireless Sensor Networks

Vamsi, P. Raghu; Kant, Krishna

doi:10.21307/ijssis-2017-922

Cited by 1 publication

(10 citation statements)

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“…To IJPCC 18,5 further detect neighbor nodes dropping or selectively forwarding packets, the sender overhears the wireless channel to check whether the packet is actually forwarded by its selected next hop node. Finally, the total trust value for node is generated by combining the location trusted information and direct trusted information (RaghuVamsi and Kant, 2016). Then, the routing metric is represented such that only node with lowest trust values are omitted from routing table.…”

Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

“…calculate the trust value for all nodes satisfying the condition and update the trust value and after each trust update interval (RaghuVamsi and Kant, 2016) for a secure and efficient communication between sender and destination node.…”

Section: Related Workmentioning

confidence: 99%

“…The BT-GPSR approach (RaghuVamsi and Kant, 2016) uses an integrated approach by using reputation and weight-based trust systems BT-GPSR. Hence, as an extension to these works, to design a TSGRP for detecting outsider attacks is proposed.…”

Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

“…From Section 3.3.3.2, it is observed that each node transmits a beacon to notify the neighboring node of its current position. Hence, based on the position updating beacon message, the trust value of other nodes are computed for a fixed time interval TUI (RaghuVamsi and Kant, 2016). The TUI value should be selected optimally such that a higher TUI value means the information about a malicious node can only be obtained after losing more packets, whereas a lesser value means frequently updating the trust value and hampering the network’s performance.…”

Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

“…The overhead introduced by PrivHabþ was evaluated using a proof-of-concept implementation, and its performance has been studied under the scope of a realistic application of podcast distribution. An integrated approach was suggested by P. RaghuVamsi and Kant (2016). It uses a reputation and weight-based trust systems backed by greedy perimeter stateless routing (BT-GPSR).…”

Section: Introductionmentioning

confidence: 99%

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Trusted Secure Geographic Routing Protocol: outsider attack detection in mobile ad hoc networks by adopting trusted secure geographic routing protocol

Shajin

Rajesh

2020

IJPCC

166

125

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Purpose This study aims to evaluate the direct trust value for each node and calculate the trust value of all nodes satisfying the condition and update the trust value and value each trust update interval for a secure and efficient communication between sender and destination node. Hence, a Trusted Secure Geographic Routing Protocol (TSGRP) has been proposed for detecting attackers (presence of the hacker), considering the trust value for a node produced by combining the location trusted information and the direct trusted information. Design/methodology/approach Amelioration in the research studies related to mobile ad hoc networks (MANETs) and wireless sensor networks has shown greater concern in the presence of malicious nodes, due to which the delivery percentage in any given network can degrade to a larger extent, and hence make the network less reliable and more vulnerable to security. Findings TSGRP has outperformed the conventional protocols for detecting attacks in MANET. TSGRP is establishing a trust-based secure communication between the sender and destination node. The evaluated direct trust value is used after the transmission of route-request and route-reply packets, to evaluate the direct trust value of each node and a secure path is established between the sender and the destination node. The effectiveness of the proposed TSGRP is evaluated through NS-2 simulation. Originality/value The simulation results show the delay of the proposed method is 92% less than PRISM approach and the overhead of the proposed TSGRP approach is 61% less than PRISM approach.

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Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

Section: Problem Identification With Proposed Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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