A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

Abasıkeleş‐Turgut, İpek; Altan, Gökhan

doi:10.1002/ett.4355

Cited by 23 publications

(12 citation statements)

References 30 publications

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“…1 and Fig. 3 offer a comparative energy consumption (ECN) assessment of the IRSO-EAMHR model with recent models such as fully distributed energy aware multi-level clustering and routing (FDE), Energy-Efficient Optimal Multi-path Routing Protocol (EEOMPRP), Wolf optimization for multi-path routing protocol (WOMPR), CoAP congestion control for the internet of things (CoAP-IOT), and Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm (CAR-MOSOA)under distinct nodes [18][19][20][21][22]. The results indicated that the IRSO-EAMHR model has resulted in effectual outcome with lower ECN over the other algorithms under distinct count of nodes.…”

Section: Experimental Analysismentioning

confidence: 99%

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Manikandan¹,

Narasimhan²

2023

Intelligent Automation &Amp; Soft Computing

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Wireless sensor networks (WSNs) encompass a massive set of sensor nodes, which are self-configurable, inexpensive, and compact. The sensor nodes undergo random deployment in the target area and transmit data to base station using inbuilt transceiver. For reducing energy consumption and lengthen lifetime of WSN, multihop routing protocols can be designed. This study develops an improved rat swarm optimization based energy aware multi-hop routing (IRSO-EAMHR) protocol for WSN. An important intention of the IRSO-EAMHR method is for determining optimal routes to base station (BS) in the clustered WSN. Primarily, a weighted clustering process is performed to group the nodes into clusters and select cluster heads (CHs). Next, the IRSO-EAMHR approach derives a fitness function containing three input parameters (residual energy, distance, and node degree) for routing process. The IRSO technique was designed by the integration of Levy movement concepts into the traditional RSO algorithm. The experimental result analysis of the IRSO-EAMHR technique is carried out and the outcomes are examined in various aspects. The simulation outcomes demonstrate the promising performance of the IRSO-EAMHR technique over the recent state of art approaches.

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Section: Experimental Analysismentioning

confidence: 99%

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Manikandan¹,

Narasimhan²

2023

Intelligent Automation &Amp; Soft Computing

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“…Increasing the efficiency and scalability are the most important benefits of distributed algorithms. The ideas proposed in References 12,15,25,26,28,29,36,41,[54][55][56][57][58][59][60] are formed based on distributed clustering algorithm. Also, ideas proposed in References 13,14 are formed based on centralized clustering algorithm.…”

Section: Clustering Approachesmentioning

confidence: 99%

An energy‐aware and lightweight routing algorithm using clustering and mobile sinks in wireless sensor networks

Adabi

Mohammadifar

Nezhad

2022

Trans Emerging Tel Tech

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A wireless sensor network (WSN) consists of a number of small sensors which are powered by low-cost irreplaceable batteries. Therefore, one of the main issues in WSN design is to prolong the battery life of the individual sensors. Clustering and utilization of mobile sinks in collaboration with base station (BS) are two common solutions to address energy consumption challenge. Based on these solutions, a new algorithm in name "Energy-aware Routing algorithm using Clustering and Mobile Sinks (ERCMS)" is proposed in this article. The distinguishing features of ERCMS are as follows: (1) a geometric pattern is applied to divide entire network area into some parts and make cluster in each part. This pattern adjusts the cluster size based on the distance from the sink. Thus, hotspot problem is expected to be reduced, (2) computational load is distributed over sensors, (3) length of messages are controlled and each message is defined in the format with minimum required information, (4) simple mathematics approaches those impose lightweight computations are adopted, and (5) accuracy of mobile sink dispatching procedure is improved in critical conditions of the network. The simulation results show that ERCMS outperforms DFCR, MDFCR, and TEO-MCRP in terms of consumed energy, delivery ratio, delay, routing overhead, and throughput.

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“…Abasıkeleş‐Turgut and Altan 28 proposed a fully distributed energy‐aware multi‐level clustering and routing (FDEAM) approach to overcome the drawback associated with resource‐constrained sensor nodes in long‐distance communication. The communication distance that exists between the nodes is minimized via the multi‐level inter‐cluster and two‐level intracluster communications.…”

Section: Literature Surveymentioning

confidence: 99%

An optimized hybrid deep neural network architecture for intrusion detection in real‐time IoT networks

Shobana

Shanmuganathan

Challa

et al. 2022

Trans Emerging Tel Tech

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The Internet‐of‐Things (IoT) refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges like security, trustworthiness, reliability, confidentiality, and so on. To address those issues, we have proposed a novel GTBSS‐HDNN approach which hybridization of Group theory (GT), Binary Spring search (BSS) algorithm, and Hybrid deep neural network (HDNN). The proposed GTBSS‐HDNN approach effectively detects the intrusion in the IoT nodes. Initially, the privacy‐preserving technology was implemented using a Blockchain‐based methodology. Our proposed privacy‐preserving methods are divided into two parts. The first stage utilizes blockchain and the second stage involves Modified Independent Component Algorithm (MICA) to prevent intrusion attacks. The authentication of data is performed by blockchain‐based Enhanced Proof of Work (EPoW) and achieves better authentication. Furthermore, the experimental study is carried out using the ToN‐IoT dataset, which is used to evaluate the performance of our proposed work. To analyze the performance we have taken the performance metrics such as F1‐measure, Detection Rate, Precision, and Accuracy. The performance analyzes depict that the proposed method effectively preserves the accuracy and thereby avert the intrusion attacks. The proposed model achieved 95.3% accuracy, 96.54% precision, 95.23% recall, and 95.67% F‐score values on the ToN‐IoT dataset and 96.23% accuracy, 95.94% precision, 97.03% recall, and 96.70% F‐score results on the BoT‐IoT dataset.

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A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

Cited by 23 publications

References 30 publications

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

Improved Rat Swarm Based Multihop Routing Protocol for Wireless Sensor Networks

An energy‐aware and lightweight routing algorithm using clustering and mobile sinks in wireless sensor networks

An optimized hybrid deep neural network architecture for intrusion detection in real‐time IoT networks

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