A Hybrid Fuzzy System Based Cooperative Scalable and Secured Localization Scheme for Wireless Sensor Networks

Kumar, Abhishek

doi:10.5121/ijwmn.2018.10305

Cited by 8 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to less necessity for hardware, the non-ranging position method is appropriate for largescale wireless adhoc networks and sensor networks [5]. Node localization (NL), coverage, the power consumption of sensor node (SN), data routing problems, etc., are the several problems in WSN applications [6,7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced seagull optimization based node localization scheme for wireless sensor networks

2022

IJATEE

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhanced seagull optimization based node localization scheme for wireless sensor networks

2022

IJATEE

View full text Add to dashboard Cite

show abstract

“…They achieved an average detection accuracy of 92.7% using KDD-99 in IoT-based WSNs. The proposed approach is further compared with Mohar et al [70], and Kumar et al [71] performed an average localization error of 0.43 with four beacon nodes using a hybrid scalable, secure collaborative localization technique for WSNs. Sajid et al [72] presented models based on genetic algorithms are presented for identifying malicious nodes, specifically the GA-SVM and GA-DT.…”

mentioning

confidence: 99%

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

Gebremariam

Panda

Indu

2023

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Wireless sensor networks are the core of the Internet of Things and are used in healthcare, locations, the military, and security. Threats to the security of wireless sensor networks built on the Internet of Things (IoT-WSNs) can come from a variety of sources. This study proposes secure attack localization and detection in IoT-WSNs to improve security and service delivery. The technique used blockchain-based cascade encryption and trust evaluation in a hierarchical design to generate blockchain trust values before beacon nodes broadcast data to the base station. Simulation results reveal that cascading encryption and feature assessment measure the trust value of nodes by rewarding each other for service provisioning and trust by removing malicious nodes that reduce localization accuracy and quality of service in the network. Federated machine learning improves data security and transmission by merging raw device data and placing malicious threats in the blockchain. Malicious nodes are classified through federated learning. Federated learning combines hybrid random forest, gradient boost, ensemble learning, K -means clustering, and support vector machine approaches to classify harmful nodes via a feature assessment process. Comparing the proposed system to current ones shows an average detection and classification accuracy of 100% for binary and 99.95% for multiclass. This demonstrates that the suggested approach works well for large-scale IoT-WSNs, both in terms of performance and security, when utilizing heterogeneous wireless senor networks for the providing of secure services.

show abstract

“…If any of these MAC fails to be authenticated, packet is dropped. Secured Localization [28] [29] [30] techniques are also an inherent requirement for many applications such as body area networks and RFID based location identification and verification.…”

Section: G Secured and Verifiable Data Aggregationmentioning

confidence: 99%

A Comprehensive Analysis of Security Issues and Cryptographic Paradigms in Wireless Ad hoc Sensor Networks

Kumar¹

2018

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract-The proliferation of Ad hoc sensor networks in various application scenarios has ostensibly called for effective security mechanisms. A lot of effort has been put and research has been carried out to tackle various threats that breach the security goals of the network. Since Wireless Sensor Network (WSN) does not rely on a concrete infrastructure and often deployed in hostile and irregular environments, its security requirement also differs vastly with that of traditional networks. It, thus, becomes imperative to design cryptographic algorithms that works efficiently keeping in mind the inherent limitations and constraints on resources, computational capability. In this paper, we present a survey on various security challenges of WSN and mechanisms to tackle the same. The aim is to present an extensive literature upon which research on security in WSN can be carried out.

show abstract

A Hybrid Fuzzy System Based Cooperative Scalable and Secured Localization Scheme for Wireless Sensor Networks

Abstract: ABSTRACT

Cited by 8 publications

References 4 publications

Enhanced seagull optimization based node localization scheme for wireless sensor networks

Enhanced seagull optimization based node localization scheme for wireless sensor networks

Blockchain-Based Secure Localization against Malicious Nodes in IoT-Based Wireless Sensor Networks Using Federated Learning

A Comprehensive Analysis of Security Issues and Cryptographic Paradigms in Wireless Ad hoc Sensor Networks

Contact Info

Product

Resources

About