Hierarchical Data Gathering Scheme for Energy Efficient Wireless Sensor Network

Chen, Jinping; Chang, Zinan

doi:10.14257/ijfgcn.2016.9.3.18

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…However, it is impossible to achieve a homogeneous power consumption among wireless nodes in a pipeline network due to its unique architecture [5]. The two major WSN drawbacks in a pipeline network are; (1) independent node energy optimisation and (2) network lifetime [1,[9][10][11][12].…”

Section: Factors Affecting the Power Consumption In A Pipeline Networkmentioning

confidence: 99%

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Active High Transmitter-receiver energy model for heterogeneous energy optimisation in a pipeline network

Subramaniam

Feroz

Ibrahim

et al. 2022

J. Phys.: Conf. Ser.

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A network energy management and optimisation are frequently associated to the network lifetime (maximum operation of nodes in a network) that is contributed by heterogeneous energy consumption pattern among nodes arranged in a pipeline layout. This scenario becomes even more critical in a remote monitoring application of an oil and gas pipeline network where a series of sensing points (commonly battery powered wireless nodes) are needed to communicate the measurements to a centralised monitoring station. This paper introduces the Active High Transmitter-receiver energy model (AHiT) which was designed as an adaptive sleep/wake for sensor nodes to optimise energy consumption in the long run. Implementing AHiT energy model on sensor nodes improves the energy consumption based on data transfer activity in a multi-hop pipeline layout wireless sensor network (WSN). In this research, the proposed AHiT energy model optimises node energy by the demand that is unlike to the conventional sleep and wake energy model that is operated on a predefined scheduling scheme that accommodates the data traffic pattern in a network. Generally, in a pipeline network where sensor nodes connectivity is considered critical among neighbouring nodes to support data transfer from one end to the other end of a network. Simulations results in NS2 has indicated node energy consumption is approximately 60% with extended network lifetime around 30% subjected to the data traffic pattern as compared to the conventional energy model.

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Section: Factors Affecting the Power Consumption In A Pipeline Networkmentioning

confidence: 99%

“…Unlike another network where an alternative route is formed during a nodes critical energy state. Many publications in WSN and energy models have emphasised improvements in communication protocols for better energy consumption and network lifetime [5,10,11,21].…”

Section: Network Lifetimementioning

confidence: 99%

Active High Transmitter-receiver energy model for heterogeneous energy optimisation in a pipeline network

Subramaniam

Feroz

Ibrahim

et al. 2022

J. Phys.: Conf. Ser.

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Clustering and Compressive Data Gathering for Transmission Efficient Wireless Sensor Networks

Pacharaney

Jain²,

Gupta

2021

Managing Resources for Futuristic Wireless Networks

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The chapter focuses on minimizing the amount of wireless transmission in sensory data gathering for correlated data field monitoring in wireless sensor networks (WSN), which is a major source of power consumption. Compressive sensing (CS) is a new in-node compression technique that is economically used for data gathering in an energy-constrained WSN. Among existing CS-based routing, cluster-based methods offer the most transmission-efficient architecture. Most CS-based clustering methods randomly choose nodes to form clusters, neglecting the topology structure. A novel base station (BS)-assisted cluster, spatially correlated cluster using compressive sensing (SCC_CS), is proposed to reduce number of transmissions in and form the cluster by exploiting spatial correlation based on geographical proximity. The proposed BS-assisted clustering scheme follows hexagonal deployment strategy. In SCC_CS, cluster heads are solely involved in data gathering and transmitting CS measurements to BS, saving intra-cluster communication cost, and thus, network life increases as proved by simulation.

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Hierarchical Data Gathering Scheme for Energy Efficient Wireless Sensor Network

Abstract: Compressive Data Gathering (CDG)

Cited by 2 publications

References 7 publications

Active High Transmitter-receiver energy model for heterogeneous energy optimisation in a pipeline network

Active High Transmitter-receiver energy model for heterogeneous energy optimisation in a pipeline network

Clustering and Compressive Data Gathering for Transmission Efficient Wireless Sensor Networks

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