Discharge Curve Backoff Sleep Protocol for Energy Efficient Coverage in Wireless Sensor Networks

More, Avinash; Raisinghani, Vijay

doi:10.1016/j.procs.2015.07.402

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…therefore, when the cluster number 4 k  , the maximum network lifetime is obtained. The simulation result is consistent with that obtained by (9). Fig.…”

Section: Simulation and Numerical Analysissupporting

confidence: 91%

“…At present many techniques have been proposed for the improvement of energy efficiency in energyconstrained, distributed wireless sensor networks. These techniques include energy optimization strategy based on node transmission power, such as common power (COMPOW) protocol [5]; energy optimization strategy based on routing protocol, such as low energy adaptive clustering hierarchy (LEACH) protocol [6]; energy optimization strategy based on medium access control (MAC) protocol, such as Sensor MAC (SMAC) protocol [7]; energy optimization strategy based on data fusion, such as maximum lifetime data gathering with aggregation (MLDA) algorithm [8]; energy optimization strategy based on node sleeping scheme, such as dynamic balanced-energy sleep scheduling scheme [9]. Among these techniques, energy efficiency routing protocol has been widely considered as one of the effective ways to save energy.…”

Section: Introductionmentioning

confidence: 99%

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Uneven Clustering Routing Algorithm Based on Optimal Clustering for Wireless Sensor Networks

Li¹,

Huo²

2016

jcm

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Hierarchical routing algorithm as an energy optimization strategy has been widely considered as one of the effective ways to save energy for wireless sensor networks. In this paper, we propose an uneven clustering routing algorithm based on optimal clustering. The algorithm considers the calculation of optimal cluster number, cluster head selection, cluster radius calculation, and isolated node management. Firstly, a new method to calculate the optimal cluster number is presented by considering data fusion rate and location adaptability. Secondly, we propose a cluster head selection algorithm by considering residual energy, initial energy, average energy consumption, and node degree. Thirdly, in order to solve "hot spot" problem, a self-adaptive uneven clustering algorithm is proposed, which takes node degree into consideration. Besides, we propose a solution to solve "isolated nodes problem". The isolated nodes join the nearest clusters according to the cost for joining near cluster and sending data to the base station through the nearest cluster head. The simulation results show the superiority in terms of network lifetime, the number of alive nodes, and the total energy consumption.

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“…therefore, when the cluster number 4 k  , the maximum network lifetime is obtained. The simulation result is consistent with that obtained by (9). Fig.…”

Section: Simulation and Numerical Analysissupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Uneven Clustering Routing Algorithm Based on Optimal Clustering for Wireless Sensor Networks

Li¹,

Huo²

2016

jcm

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show abstract

“…States are calculated from different parameters using back-off algorithm. The discharge curve back-off sleep protocol (DCBPS) [44] removes the drawback of RBSP and based on back-off sleep timing. The sleeping time of neighbors is calculated and based on the battery discharge curve.…”

Section: B State-based Modelmentioning

confidence: 99%

Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

Khan¹,

Rahman²,

Almaiah³

et al. 2020

IEEE Access

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Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as "The Internet of Things". WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network lifetime , but also affects the availability of these resources for other tasks. To increase lifetime and provide an energy-efficient WSN, here we have proposed a new scheme called "A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs". CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network lifetime by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

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“…The DCBSP protocol provides better coverage established on normalized standard battery discharge curve. This curve is used to find the back-off sleep-time for neighboring sensor nodes which are in sleeping mode [19]. In DCBSP, each sensor has three functioning modes that are likely to ACTIVE, RBSP SLEEP and FLOAT.…”

Section: J Discharge Curve Backoff Sleep Protocol (Dcbsp)mentioning

confidence: 99%

Coverage-Aware Protocols in Wireless Sensor Networks: A Review

Khan¹,

Mahmood²,

Shah³

et al. 2018

ijacsa

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Coverage plays a vital role in wireless sensor networks (WSNs), since it is used as one of the important measure to achieve the performance of the sensor network. The sensor nodes in WSN have limited power and energy resources. So, energy efficiency is an essential factor that should be considered along with coverage while designing the coverage protocols. During the past few years, many efforts have been made by the researchers on designing different coverage-aware protocols. Different coverage-aware protocols may impose different ways to solve the coverage and efficient utilization of energy among sensor nodes. In this paper, we present a review on coverage-aware protocols by highlighting their functionalities.

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Discharge Curve Backoff Sleep Protocol for Energy Efficient Coverage in Wireless Sensor Networks

Cited by 9 publications

References 11 publications

Uneven Clustering Routing Algorithm Based on Optimal Clustering for Wireless Sensor Networks

Uneven Clustering Routing Algorithm Based on Optimal Clustering for Wireless Sensor Networks

Improving Energy Efficiency With Content-Based Adaptive and Dynamic Scheduling in Wireless Sensor Networks

Coverage-Aware Protocols in Wireless Sensor Networks: A Review

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