Energy efficient compression sensing-based clustering framework for IoT-based heterogeneous WSN

Manchanda, Rachit; Sharma, Kanika

doi:10.1007/s11235-020-00652-2

Cited by 22 publications

(11 citation statements)

References 39 publications

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“…Multicriteria decision analysis methods such as the weighted product model are used to solve the decision problem, i.e., sensor nodes with the largest P will be selected as CHs. Reference [19] also paid attention to CH selection by considering residual energy, distance to the sink, node density, and network average energy. Besides, they make the first attempt to introduce a heterogeneous energy model in CDG-based clustering.…”

Section: Clustering Schemes Of Cdgmentioning

confidence: 99%

“…A number of literature focused on the load balance problem. In traditional clustering methods, most of CH selection are random such as LEACH protocol or based on some competition criteria such as in [18,19]. However, these methods cannot guarantee even distribution of CHs, which can lead to better load balance of networks.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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Internet of Things (IoTs) are increasingly widespread in the field of health care, smart city and smart home application, industrial and agricultural monitoring, automation, etc. With its growing scale of networks, there are a large amount of data in IoTs needing to be sensed, transmitted, and processed. Resource-limited Wireless Sensor Networks (WSNs) as a perceptual layer of IoTs are hard to handle massive uncompressed sensing data. Compressive data gathering (CDG), which applies compressive sensing theory to data gathering, is a perfectly matching method for data compressing and gathering in WSNs. This promising method has attracted lots of researchers’ attention. In this paper, we attempt to survey substantial references about CDG in WSNs. According to their technology schemes, we classify the published references into three categories, i.e., routing protocol of CDG, clustering scheme of CDG, and CDG combined with other technologies. The merits and defects of each method are highlighted. Our work aims to provide an insight into CDG and promote improvements of this technology.

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Section: Clustering Schemes Of Cdgmentioning

confidence: 99%

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

A Survey of Compressive Data Gathering in WSNs for IoTs

Wang

Chen

2022

Wireless Communications and Mobile Computing

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“…In addition, within CS schemes, the focus is currently on its use in conjunction with clustering, where interesting works have been published [ 74 , 75 , 76 , 77 , 78 , 79 , 80 ]. More recently we must mention the works in [ 81 , 82 , 83 ].…”

Section: Energy Conservationmentioning

confidence: 99%

Current Trends on Green Wireless Sensor Networks

López-Ardao

Rodríguez-Rubio

Suárez-González

et al. 2021

Sensors

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The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

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“…The node density, that is, N D (i) of ith node is computed through the steps as follows. 49 a. The intracluster distance is computed through the following Equation 22.…”

Section: Setup Phasementioning

confidence: 99%

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

Manchanda

Sharma

2020

Int J Communication

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Summary Wireless sensor network (WSN) suffers from the energy‐limited sensor nodes which consume energy heavily depending upon the magnitude of data which is transmitted or received by the nodes in the network. In this paper, our primary aim is to reduce the quantity of data transmitted to the data‐collecting sink, which helps in the energy preservation and eventually leads to network longevity. To address this concern, in this paper, we propose a novel framework for energy‐efficient compressive data gathering (NFECG) for heterogeneous WSN. NFECG works in four following phases; in the first phase, the cluster head (CH) selection is performed by considering remaining energy, “distance within the nodes and the sink,” and node density; in second phase, sleep scheduling is done among the cluster member nodes; further, in third phase, the compression of the aggregated data is performed at the CH level, and equivalent compressed sparse signals are generated which are transmitted to sink. In the last phase, at the sink, decompression is applied to retrieve the original signals. The simulation of NFECG is performed using MATLAB under two cases of different network area and number of nodes. We examine its performance for various performance metrics and also inspect for its scalable characteristics. The results show that for one of the two cases, it improves stability period and network lifetime by 52.59% and 46.09%, respectively, as compared to energy‐adjusted high‐level data total tree (EHDT) protocol, and also for the other case of network configuration, it acquires supreme performance.

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Energy efficient compression sensing-based clustering framework for IoT-based heterogeneous WSN

Cited by 22 publications

References 39 publications

A Survey of Compressive Data Gathering in WSNs for IoTs

A Survey of Compressive Data Gathering in WSNs for IoTs

Current Trends on Green Wireless Sensor Networks

A novel framework for energy‐efficient compressive data gathering in heterogeneous wireless sensor network

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