An efficient node localization and failure node detection in the MANET environment

Kailash P. Dewangan (), Padma Bonde (), Rohit Raja () and Jyotir Moy Chatterjee ()
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Kailash P. Dewangan: SSTC, SSGI Bhilai
Padma Bonde: SSTC, SSGI Bhilai
Rohit Raja: School of Studies (Engineering and Technology) Guru Ghasidas Vishwavidyala
Jyotir Moy Chatterjee: Graphic Era University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 85, issue 2, No 9, 313-329

Abstract: Abstract In general, a network that possesses numerous free or autonomous nodes is proffered as a Mobile Ad hoc Network (MANET). In this, to send along receive data, every single node acts as a router. The entire network’s performance is degraded with the existence of faulty Sensor Nodes (SNs); thus, to obtain better Quality of Service (QoS), the detection of faulty SNs is highly significant. Therefore, an Efficient Node Localization and Failure Node Detection in a MANET environment is proposed here. Here, first, the nodes are initialized. Next, distance estimation, position computation, and optimal localization are the ‘3’ steps utilizing which the SNs are localized. After that, via the network, the data packets are broadcasted. The best path is regarded for transmitting the data packet efficiently. It includes ‘2’ steps for this; first, Failure Probability (FP) calculation; second, estimation of error calculation. Lastly, by employing the Soft Taxicab Poisson Binomial-Reference Point Group Mobility Model (STPB-RPGM), the Faulty Nodes (FNs) are detected. In this, the Taxicab Distance (TD)-Fuzzy C-Means (TD-FCM) is utilized to detect the group members along with by utilizing the Poisson Binomial Distribution (PBD)-Emperor Penguin Optimization (PB-EPO), the group leader is selected. Consequently, the failure along with the non-failure node is detected effectually by the STPB-RPGM. Lastly, the proposed STPB-RPGM’s outcomes are analogized with the prevailing algorithms. The experiential outcomes displayed that the faulty SNs are detected with higher accurateness in the proposed methodology; thus, it surpassed the other state-of-the-art methodologies. The findings indicate that we were able to get 96.8% accuracy, 96.46% sensitivity, 96.88% precision, 97.12% specificity, 96.64% F-measure, and 96.46% recall using the STPB-RPGM. In 0.619 s, the TD-FCM finishes the clustering operation. The benefit of this investigation is that, compared to other clustering algorithms, the localization strategy decreases the number of dead nodes in data transmission, and the TD-FCM completes the clustering process quickly.

Keywords: Failure node detection; Multilateration technique; Rat swarm optimizer; Reference point group mobility model; Fuzzy C-means (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-023-01087-1

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