GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

Shad, Mozhdeh Norouzi; Maadani, Mohsen; Moghadam, Meisam Nesari

doi:10.1007/s11277-021-09051-5

Cited by 22 publications

(5 citation statements)

References 47 publications

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“…and then, a fuzzy c-partition V 0 is generated randomly and locates iteration number t = 0. Moreover, the membership values v ( t) hk , the cluster centers û( t) h (h = 1, … , c) are determined in Equation (13).…”

Section: Clustering Via a Fuzzy C-means Algorithmmentioning

confidence: 99%

“…The information obtained is subsequently evaluated and sent to BS. [12][13][14] CHs expressly convey data in base station (BS) to shorten the distance between them in IoT-WSN, lowering energy usage accordingly. Earlier versions used distributed clusters, in which all sensor nodes decided on communications, functions, as well as security.…”

Section: Introductionmentioning

confidence: 99%

“…The CH then collects data from non‐CHs, or cluster members, which are other nodes in the cluster. The information obtained is subsequently evaluated and sent to BS 12–14 . CHs expressly convey data in base station (BS) to shorten the distance between them in IoT‐WSN, lowering energy usage accordingly.…”

Section: Introductionmentioning

confidence: 99%

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A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

Gorikapudi,

Kondaveeti

2023

Concurrency and Computation

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SummaryThe Internet of Things (IoT) is a paradigm change in networking that aspires to connect almost everything on the globe. Clustering methods have been presented in the context of wireless sensor networks, and their application to the IoT could solve similar problems. However, existing clustering protocols have issues with the clustering structure that affect their performance, and some approaches pose a risk of privacy leakage during the data handling process. Therefore, this article aims to establish a novel secure and energy‐aware clustering model in the IoT, in which cluster head selection is performed via a proposed sandpiper optimization with cycle crossover process (SOCCP) model. Moreover, this cluster head selection includes certain constraints, including distance, energy, security, and cluster radius, respectively. The clustering is done by the optimized fuzzy C‐means algorithm. Here, the determination of the cluster head and cluster radius is tuned optimally via an adopted SOCCP model. Final results of the employed technique are calculated using a variety of metrics, such as alive node analysis, risk analysis, distance analysis, cluster radius analysis, as well as normalized energy analysis, respectively.

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Section: Clustering Via a Fuzzy C-means Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

Gorikapudi,

Kondaveeti

2023

Concurrency and Computation

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“…Reference [11] presented an IDSS-based clustering routing method called GAPSO-SVM for IoT, employing an SVM-based technique for assessing the node places and a hybrid GA-PSO-based process to optimize clustering. In [12], a deep reinforcement learning (DRL) based smart routing model was presented to IoT-enabled WSN, enhancing network lifespan and considerably decreasing delay.…”

Section: Introductionmentioning

confidence: 99%

Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

Basheri¹,

Ragab²

2023

Computer Systems Science and Engineering

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The Internet of Things (IoT) is one of the emergent technologies with advanced developments in several applications like creating smart environments, enabling Industry 4.0, etc. As IoT devices operate via an inbuilt and limited power supply, the effective utilization of available energy plays a vital role in designing the IoT environment. At the same time, the communication of IoT devices in wireless mediums poses security as a challenging issue. Recently, intrusion detection systems (IDS) have paved the way to detect the presence of intrusions in the IoT environment. With this motivation, this article introduces a novel Quantum Cat Swarm Optimization based Clustering with Intrusion Detection Technique (QCSOBC-IDT) for IoT environment. The QCSOBC-IDT model aims to achieve energy efficiency by clustering the nodes and security by intrusion detection. Primarily, the QCSOBC-IDT model presents a new QCSO algorithm for effectively choosing cluster heads (CHs) and organizing a set of clusters in the IoT environment. Besides, the QCSO algorithm computes a fitness function involving four parameters, namely energy efficiency, inter-cluster distance, intra-cluster distance, and node density. A harmony search algorithm (HSA) with a cascaded recurrent neural network (CRNN) model can be used for an effective intrusion detection process. The design of HSA assists in the optimal selection of hyperparameters related to the CRNN model. A detailed experimental analysis of the QCSOBC-IDT model ensured its promising efficiency compared to existing models.

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“…About 25 Billion objects are anticipated to be linked; hence the present Internet structure with TCP/IP protocols finds it difficult to deal with the network as large as IoT which requires a novel open structure to address several security as well as Quality of Service (QoS) issues. IoT is difficult to adopt without any proper privacy concerns [12]. Hence protecting data and users privacy are the major challenges in IoT.…”

Section: Introductionmentioning

confidence: 99%

Investigations on Iot Based Wsn With Swipt-noma Combination

Sixtus,

Muthu

2023

Preprint

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Wireless Communication provides the interconnection of different devices for the ubiquitous accessibility of intelligent capacity. Wireless Communication incorporates device interaction for the provision of sufficient capability in networking between intermediate devices. Conventionally, the Internet of Things (IoT) and Wireless Sensor Networks (WSN) offer sufficient information between intermediate devices. IoT-WSN devices are resource constraints (RC), compact devices, and limited resources of battery. The increase in the number of users leads to challenges with security in the IoT-WSN. The data transmission between the wireless communications uses the 5G communication-based NOMA communication. Due to limited RC features the computational complexity is higher with minimal consumption of space those are evaluated with embedded hardware features within the IoT – WSN. This paper aimed to develop an appropriate optimal routing simultaneous wireless information and power transfer based Long Short-Term Memory (ORSS). The proposed ORSS model uses the covariance matrix-based routing and security model. The performance of the proposed ORSS is evaluated for security analysis based on consideration of different attacks. With the ORSS model, the position of nodes is computed with the covariance matrix estimation. To identify the optimal route’s Particle Swarm Optimization (PSO) algorithm is implemented for the route scheduling for the data transmission. To minimize the energy consumption within the network SWIPT is implemented for effective energy harvesting. Based on the covariance estimation optimal routes in the network are computed for the detection of attacks. The attacks are computed based on the utilization of LSTM model for the detection and classification of attacks with use of CICIDS datasets. The comparative analysis stated that the proposed ORSS exhibits ~40% higher data transmission and ~21% reduced delay compared with state-of-techniques.

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GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

Cited by 22 publications

References 47 publications

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

A novel clustering model via optimized fuzzy C‐means algorithm and sandpiper optimization with cycle crossover process in IoT

Quantum Cat Swarm Optimization Based Clustering with Intrusion Detection Technique for Future Internet of Things Environment

Investigations on Iot Based Wsn With Swipt-noma Combination

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