Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Punithavathi, R.; Selvi, R. Thanga; Latha, R.; Kadiravan, G.; Srikanth, V.; Shukla, Neeraj Kumar

doi:10.32604/iasc.2022.023344

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Security threats against WSN and IoT were analyzed in [26] to handle the security problems. Mobility-aware seamless handover method basis of the multipath transmission control protocol (MPTCP), metaheuristic clustering protocol, node localization with intrusion detection, and energy efficient protocols was developed in [27,[31][32][33] to deal with managing users' access and mobility concerns. But, the energy consumption was not considered.…”

Section: Related Workmentioning

confidence: 99%

Chapman Kolmogorov and Deep Recurrent Network Based Seamless Mobility for IOT Data Transmission in Wireless Network

Reddy,

et al. 2023

Preprint

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Next-generation wireless networks will be designed via all-IP-based network framework that ensures seamless mobility and universal access to Internet via wireless networks. Also over the past few years, wireless network has gained its popularity due to its exorbitant volume and reasonable access cost. Despite its familiarity owing to the constraint in WLAN coverage, handover between nodes may cause elevated amount of handover failures. Seamless mobility is an empirical framework that seizes prevailing circumstances of what user or node is doing, with the objective that the users inclined services are said to be optimized. To develop a smart decision-making mechanism for seamless mobility and reducing energy consumption, deep learning-enabled reconfigurable wireless network solutions are required. In this work, a new method called, Chapman Kolmogorov and Deep Recurrent Network-based (CK-DRN) IoT data transmission in wireless network is introduced to validate handoffs in pragmatic frameworks. First, with the raw data obtained from IoT device network logs, Chapman Kolmogorov Poisson Taylor Optimum Beacon-based Route Discovery algorithm is designed with the objective of ensuring seamless mobility in an energy efficient manner. Second, Deep Recurrent Network-based Data Transmission is proposed that with the aid of concurrent utilization of the numerous paths initiates numerous subflow associations covering disjoint routes and transmits via the subflows, therefore ensuring greater amount of throughput. Simulation of CK-DRN achieves Direction of Arrival (DOA) and ID oriented Socket Layer (IoSL) in terms of delay, energy saving, and throughput.

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Section: Related Workmentioning

confidence: 99%

Chapman Kolmogorov and Deep Recurrent Network Based Seamless Mobility for IOT Data Transmission in Wireless Network

Reddy,

et al. 2023

Preprint

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“…Then, the evolutionary algorithm, termed as a Dragon fly optimizer was utilized for localizing the node depends on the forecast. Punithavathi et al [28] examines a novel multi-objective manta ray foraging optimized (MRFO) related NL with intrusion detection (MOMRFO-NLID) approach for WSN. The objective of the MOMRFO-NLID system is to optimum localizing the unknown node and define the presence of intrusions from the network.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced seagull optimization based node localization scheme for wireless sensor networks

2022

IJATEE

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Wireless sensor network (WSN) has become an emergent paradigm of networking and computing. It uses several application domains like military, healthcare, surveillance, target tracking, etc. Despite the benefits of WSN, node localization (NL) remains a crucial problem. It intends to define the exact place of unknown nodes from the network depends upon the anchor nodes. NL issues may be processed as non-deterministic polynomial-time (NP) hard problems and can be resolved using meta-heuristic optimization techniques. An enhanced seagull optimization-based node localization (ESGOBNL) scheme for WSN was developed in this aspect. The ESGOBNL approach aims to determine the proper location coordinates of the sensor nodes (SNs) in WSN. The proposed ESGBONL technique has been derived by the inclusion of the levy movement (LM) idea into the classification seagull optimization (SGO) algorithm, which is based on the migration as well as the attacking nature of seagulls. A wide range of experiments was implemented in order to report the promising localization efficacy of the ESGOBNL approach. The extensive comparative outcomes highlighted the betterment of the ESGOBNL system on the recent approaches with minimal mean localized error (MLE) of 0.120214. In contrast, the elephant herding optimization (EHO), hybrid elephant herding optimization (HEHO), and tree growth algorithm (TGA) techniques have obtained maximumMLE of 0.793293, 0.333199, and 0.280031, respectively.

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“…WSN design issues include node localization and intrusion detection. The research in [29] provides a multi-objective manta ray foraging optimization (MRFO) based node localization with an IDS approach for WSN. The aim is to detect unknown nodes and detect network intrusions.…”

Section: Design and Implementation Of Intrusion Detection Systems Usi...mentioning

confidence: 99%

Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks

Kipongo,

Swart,

Esenogho

2022

International Journal of Electronics and Telecommunications

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Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

Cited by 6 publications

References 23 publications

Chapman Kolmogorov and Deep Recurrent Network Based Seamless Mobility for IOT Data Transmission in Wireless Network

Chapman Kolmogorov and Deep Recurrent Network Based Seamless Mobility for IOT Data Transmission in Wireless Network

Enhanced seagull optimization based node localization scheme for wireless sensor networks

Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks

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