An Energy-Efficient Dynamic Clustering Protocol for Event Monitoring in Large-Scale WSN

Qu, Zhiyi; Xu, Huihui; Zhao, Xiaoxue; Tang, Hongying; Wang, Jiang; Li, Baoqing

doi:10.1109/jsen.2021.3103384

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…The authors in [ 14 ] proposed a dynamic clustering protocol (EEDC) for target tracking and continuous event monitoring. In EEDC, the overlapping cluster structure was dynamically reconstructed to adapt to the changing location of targets, and candidate cluster heads were selected according to rough fuzzy C-Means (RFCM) and genetic algorithm (GA).…”

Section: Related Workmentioning

confidence: 99%

“…We adopt the same radio model applied in [ 14 , 25 , 28 ] to calculate the energy consumption. As shown in Figure 3 , the energy consumption for transmission contains two parts: transmitter and receiver.…”

Section: System Modelmentioning

confidence: 99%

“…The energy

used for each node to transmit an

bits package over communication distance of

is given as follows [ 14 ]:

where

denotes the energy consumption to run the transmitter or receiver circuit,

and

denote the amplification coefficient for the free space model and the multi-path fading model, respectively, and

is a threshold distance calculated by [ 28 ]:

…”

Section: System Modelmentioning

confidence: 99%

“…In this section, we evaluate the performance of the proposed algorithm in the WSN, and the existing HCTT [ 12 ], EEAOC [ 13 ], and EEDC [ 14 ] algorithms are used in comparison in the simulation experiments. In HCTT, static clusters and on-demand dynamic clusters alternately perform the tracking tasks.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Simulation experiments were carried out using MATLAB 2016a (MathWorks, Natick, MA, USA). For the convenience of comparison, we refer to [ 12 , 13 , 14 ] to make a comprehensive experiment parameter setting. The parameters that appeared in simulations are listed in Table 1 .…”

Section: Performance Evaluationmentioning

confidence: 99%

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An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

2022

Sensors

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As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0–1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

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Section: Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

“…The energy

used for each node to transmit an

bits package over communication distance of

is given as follows [ 14 ]:

where

denotes the energy consumption to run the transmitter or receiver circuit,

and

denote the amplification coefficient for the free space model and the multi-path fading model, respectively, and

is a threshold distance calculated by [ 28 ]:

…”

Section: System Modelmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

2022

Sensors

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Hierarchical stable election protocol for WSN‐based IoT inhabitant and environmental monitoring applications

Jeevanantham

Rebekka

2022

Int J Communication

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Summary Wireless sensor networks (WSNs) play an essential role in IoT applications, especially for long‐term data gathering and monitoring applications. The deployment field and the limited energy sources are two critical constraints directly influencing WSN lifespan, and both must be considered while designing a routing protocol for WSN‐based IoT applications. Routing the data consumes a significant amount of energy in such an extensive network. Several hierarchical clustering protocols have been developed in recent years to address this purpose. We propose a new hierarchical stable election protocol (HISEP), an improved version of the Stable Election Protocol (SEP) equipped with the Dual CH election algorithm using a new enhanced threshold formula. Accepting nodes' heterogeneity and incorporating Voronoi‐based cluster formation ensures that the 2D/3D WSN network stays alive for an extended period and the HISEP algorithm will be suitable for habitat tracking and environmental monitoring applications. The simulation results show that the proposed algorithm outperforms other protocols in terms of routing efficiency and network lifespan.

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Energy-efficient smart architecture for fog-based WSN using NSGA-III and improved layer-wise clustering for enhanced building evacuation safety

Kaur,

Kaur

2024

Ann Oper Res

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An Energy-Efficient Dynamic Clustering Protocol for Event Monitoring in Large-Scale WSN

Cited by 24 publications

References 27 publications

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

Hierarchical stable election protocol for WSN‐based IoT inhabitant and environmental monitoring applications

Energy-efficient smart architecture for fog-based WSN using NSGA-III and improved layer-wise clustering for enhanced building evacuation safety

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