EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs)

Ghani Ur Rehman, Muhammad Zubair*, Iqbal Qasim, Afzal Badshah, Zafar Mahmood, Muhammad Aslam, Syeda Fizah Jilani

*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

8 Dyfyniadau (Scopus)
44 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Since several Internet of Things (IoT) applications have been widely deployed on unstable wireless networks, such as the Delay Tolerant Network (DTN), data communication efficiency in DTN remains a challenge for IoT applications. Vehicular Delay Tolerant Network (VDTN) has become one of DTN’s potential applications, in which the network experiences connectivity interruption due to the lack of an end-to-end relay route. VDTNs focus on node cooperation to achieve this goal. As a result, it is essential to ensure that almost all network nodes adopt the protocol to preserve network performance. This is a challenging task since nodes may diverge from the basic protocol to optimize their effectiveness. This article presents an Efficient Monitoring System (EMS) to detect and respond to just selfish nodes to minimize their entire network and data communication efficacy. The scheme is based on a network-wide cooperative sharing of node reputation. It is also necessary to increase overall network efficiency by tracking selfish nodes. The NS-2 simulator is used to run this experimental setup. Simulation results indicate that the proposed scheme performs better in terms of probability of package delivery, package delivery delay, energy consumption, and amount of packet drops. For 80% selfish nodes in the network, the packet delivery of EMS is 37% and 31% better than SOS and IPS. Similarly, the average delivery delay of EMS is 22% and 18% lower than SOS and IPS when 80% selfish nodes are incorporated in the network.

Iaith wreiddiolSaesneg
Rhif yr erthygl99
Nifer y tudalennau20
CyfnodolynSensors
Cyfrol23
Rhif cyhoeddi1
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 22 Rhag 2022

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