GSA-RPI: GSA based Rendezvous Point Identification in a two-level cluster based LR-WPAN for uncovering the optimal trajectory of Mobile Data Collection Agent

Jayalekshmi, S.; Velusamy, R. Leela

doi:10.1016/j.jnca.2021.103048

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…A mobile agent-based improved protocol is established for creating an optimal cluster with mobile agents. 20 The objective of the anticipated framework is to enlarge the lifetime by adjusting the CH based on a threshold value. The number of live nodes in IWSN is increased about 5% from the existing I-LEACH protocol.…”

Section: Related Workmentioning

confidence: 99%

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Padmanaban¹,

Periasamy²,

Prasanth

2022

Concurrency and Computation

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The advancement of the Internet of Things (IoT) technologies will play a vital role in the evolution of the smart city, smart healthcare, and smart grid applications. Wireless sensor network (WSN) is one of the futuristic technologies utilized for sensing and data exchange processes in IoT-enabled applications. However, the hotspot problem, control packet overhead, and inefficient clustering are the most significant challenges to attaining an energy-efficient IoT-enabled WSN (IWSN) model. Most of the existing clustering schemes lagged to mitigate these aforesaid constraints since it consumes extra energy for data computation and forwarding tasks under any environmental conditions. In this article, a novel energy-efficient auto clustering (EEAC) framework has been proposed to develop an effective IWSN model with enhanced quality of service.The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as master, sub-master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01 J during a dense network and the network lifetime is prolonged up to 52% when compared with existing state-of-the-art clustering models.

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Section: Related Workmentioning

confidence: 99%

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Padmanaban¹,

Periasamy²,

Prasanth

2022

Concurrency and Computation

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show abstract

“…A Mobile Agent-based improved protocol is established for creating an optimal cluster with mobile agents [20]. The objective of the anticipated framework is to enlarge the lifetime by adjusting the CH based on a threshold value.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Energy-Efficient Auto Clustering Framework for Improving Quality of Service in IoT-enabled WSN Applications

S.P

PRASANTH³

2022

Preprint

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In today’s world, emerging Internet of Things (IoT) technology have the most potential to extend the influence of the internet through IoT-enabled devices in futuristic fields such as smart healthcare, commercial, and industrial applications. Wireless Sensor Network (WSN) is utilized for sensing and communication processes over IoT-based applications. However, the battery energy of the sensor nodes is restricted in IoT-enabled WSN (IWSN) owing to its irreplaceable ability. Most of the existing clustering schemes lagged to mitigate the control packet overhead problem since it consumes extra energy for data computation, gathering, and forwarding tasks at any environmental conditions. In this paper, a novel Energy-Efficient Auto Clustering (EEAC) framework has been proposed to develop the effective IWSN model with enhanced quality of service. The EEAC framework comprises three phases such as zone formation, node classification, and auto clustering phases. The objective of the first phase is to significantly form the different zones by alleviating the hotspot problem. Subsequently, the fuzzy logic algorithm is employed in the second phase to classify the nodes as Master, Sub-Master, and member nodes. Finally, the third phase of the proposed framework will accomplish the auto clustering mechanism based on hop information received from the reported packet. The performance results evident that the proposed EEAC framework obtains a lesser energy consumption of 0.01J during dense network and the network lifetime is prolonged up to 48% when compared with existing state-of-the-art clustering models.

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Data harvesting in wireless sensor networks using mobile sinks under real-world circumstances

Najjar-Ghabel

Farzinvash²,

Razavi³

2022

J Supercomput

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GSA-RPI: GSA based Rendezvous Point Identification in a two-level cluster based LR-WPAN for uncovering the optimal trajectory of Mobile Data Collection Agent

Cited by 5 publications

References 26 publications

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

An energy‐efficient auto clustering framework for enlarging quality of service in Internet of Things‐enabled wireless sensor networks using fuzzy logic system

Implementation of Energy-Efficient Auto Clustering Framework for Improving Quality of Service in IoT-enabled WSN Applications

Data harvesting in wireless sensor networks using mobile sinks under real-world circumstances

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