A Grey-Wolf based Optimized Clustering approach to improve QoS in wireless sensor networks for IoT applications

Jaiswal, Kavita; Anand, Veena

doi:10.1007/s12083-021-01099-1

Cited by 43 publications

(25 citation statements)

References 43 publications

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“…The initial two optimum solution is stored and updated the locations of another searching agents grounded on the place of optimum searching agent. The teeming characteristics of the urochordates are distinct using Equation (10):…”

Section: Energy Aware Tsa Based Clustering Techniquementioning

confidence: 99%

“…This unit offers a detailed review of existing cluster‐based routing methods available in the literature for IoT assisted WSN. Jaiswal and Anand 10 proposed a gray wolf optimization (GWO) based clustering technique with the inclusion of diverse parameters such as distance, node degree, energy level, and priority factor. Besides, quality of service (QoS) aware routing process is also included to select effective relay nodes for efficient and trustworthy data transmission between CHs and BS.…”

Section: Literature Reviewmentioning

confidence: 99%

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Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

Neelakandan

Perumal

Kallimani

et al. 2022

Concurrency and Computation

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Because of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device.Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT-based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy-efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP-MS) technique for IoT supported WSN. The EACMRP-MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system.The suggested EACMRP-MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type-II fuzzy logic (T2FL) technique is used for the optimal selection of multi-hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP-MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

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Section: Energy Aware Tsa Based Clustering Techniquementioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

Neelakandan

Perumal

Kallimani

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

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“…Node energy > E th (11) Node degree ≤ ND th (12) D infro−cluster < T max (13) w 1 + w 2 + w 3 + w 4 = 1, w 1 , w 2 , w 3 , and w 4 ∈ (0, 1) (…”

Section: (D) Coverage Of Ch (Cch)mentioning

confidence: 99%

“…RISA could decrease power utilization as well as enhance the lifetime of network with a suitable data aggregation system. Jaiswal et al [13] propose a GWO based CH election method for WSN consider different aspects such as node degree, energy levels of the node, intracluster distance, priority factor, and sink distance. Also, this study addresses the routing via QoS aware relay nodes election for a reliable and effective intercluster routing from CH to BS.…”

Section: Introductionmentioning

confidence: 99%

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

Dutta¹,

Srinivasan²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

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In current days, the domain of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are combined for enhancing the sensor related data transmission in the forthcoming networking applications. Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks. In this view, this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy, distance to sink node, etc. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

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“…Remnant energy is applied to compute the utilisation of energy in WSN. A CH selection approach has been proposed in [29] which is based upon Grey wolf optimization technique for WSN. Authors consider many distinct factors such as node degree, energy level, intra cluster distance, sink distance and priority factor.…”

Section: Related Workmentioning

confidence: 99%

GWO-EFUCA: Grey Wolf Optimisation and Fuzzy Logic based Unequal Clustering and Routing protocol for sustainable WSN-based Internet of Things

Mehra

Jain

Chawla

et al. 2022

Preprint

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With the ever-growing application of Wireless Sensor Networks (WSN)-based Internet of Things (IoT), it has gained enormous attention in the public and research domains. The energy constraint of WSN limits its applicability. In the paper, we have proposed a hybrid of Grey Wolf Optimisation along with Fuzzy Logic (FL) for energy-efficient Cluster Head (CH) selection, cluster formation, and cost-effective routing which improves Quality of Service (QoS) that can enhance the applicability of WSN in the diverse domains of IoT. The proposed work is simulated, and performance assessment is done on the basis of QoS metrics namely FND, HND, throughput, average energy, average node load and latency. The obtained results exhibit significant enhancement in the network lifetime with an enhanced stability period, higher throughput, reduced latency, and lessen traffic load and load-balanced network towards its comparative making it suitable for wide applicability in WSN based IoT.

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A Grey-Wolf based Optimized Clustering approach to improve QoS in wireless sensor networks for IoT applications

Cited by 43 publications

References 43 publications

Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

Controlling energy aware clustering and multihop routing protocol for IoT assisted wireless sensor networks

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

GWO-EFUCA: Grey Wolf Optimisation and Fuzzy Logic based Unequal Clustering and Routing protocol for sustainable WSN-based Internet of Things

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