Selecting Member Nodes in a Chain Oriented WSN

Mamun, Quazi; Ramakrishnan, Sita; Srinivasan, Bala

doi:10.1109/wcnc.2010.5506377

Cited by 15 publications

(20 citation statements)

References 19 publications

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“…EA-COSEN is an extension of existing COSEN protocol and it considers duty cycling of the sensor nodes to save the energy and prolong the network lifetime. Mamun et al 16 built a chain according to the coverage requirement of the network. They mitigate the constraints of sensor nodes by using a variety of logical topologies.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

Jiang

Zhao

2019

International Journal of Distributed Sensor Networks

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With the development of sensed technology, more and more sensor nodes carry multiple sensors in information collection wireless sensor networks. As a result, there are always a large number of correlated dynamic sensing data transmitted in the network. These data contain a lot of redundant information and errors, which leads to the resource waste and causes data congestion. Although various researches have focused on the sensing data collection and fusion, most of them do not consider the correlation of sensing data, and the network cannot adaptively collect data according to the accuracy required by users. Therefore, this article proposes a hierarchical data collection scheme for data-collecting wireless sensor networks. We combine the clustering and chain network structure and propose a probabilistic multimode sensing data selection method based on the characteristics of the sensors. Moreover, a data correlation analysis method based on gray correlation analysis is proposed to measure the similarity of the sensing data. Furthermore, we use the Bernoulli uniform sampling to estimate the approximate average value of data quality and make the approximation for the multi-mode sensing data on the basis of required data accuracy. Experimental results show the effectiveness of the proposed approach. And experiments prove that the proposed approach has better performance than state-ofthe-art approaches.

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Section: Related Workmentioning

confidence: 99%

An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

Jiang

Zhao

2019

International Journal of Distributed Sensor Networks

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“…We also assume that each node has an initial energy of 1 J (Joule). A very widely used energy consumption model (such as the one in [18]) is adopted, as described in…”

Section: Performance Evaluationmentioning

confidence: 99%

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

2016

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In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy.

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“…It further decreases the consumed energy by minimizing the transmission distance among sensor nodes. After PEGASIS, chain-based sensor networks have been used widely in recent works [14][15][16][17][18]. Jung et al proposed an enhanced PEGASIS [14], in which the sensing area is circularized into several concentric levels.…”

Section: Related Workmentioning

confidence: 99%

“…We adopt a very widely used energy model [1,4,18] as described in Figure 1. Tx ( , ) and Rx ( ) represent energy consumption of transmitting and receiving bits data over a distance :…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Robust and Energy-Efficient Data Gathering in Wireless Sensor Network

Feng

Lian

Zhao

2014

International Journal of Distributed Sensor Networks

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Robustness and energy efficiency are critical for sensor information system, in which an abundance of wireless sensor nodes collects useful data from the deployed field. The chain-based protocols (like PEGASIS (Lindsey and Raghavendra, 2002)) are elegant solutions where sensor node has high energy efficiency. Unfortunately, if one node in the chain is failed due to some reasons such as energy exhaust, then the information cannot be forwarded to the sink. To improve system robustness and balance the energy consumption, this paper proposes a robust and energy-efficient data gathering (REEDG) approach, which is an improvement over the chain-based and grid-based network structures, in sensor information collecting system. In REEDG, data gathering is executed by a data transmitting chain which is composed by a series of virtual grids. Each grid communicates only with its neighbor grid and takes turns transmitting the information to the base station. Furthermore, an adaptive scheduling scheme is proposed to trade off energy consumption on each node and data forwarding delay. Experimental results show that, when compared with state-of-the-art approaches, REEDG achieves network lifetime extension of at least 13% as measured in terms of 20% dead nodes and improves the data transmission ratio at lowest 24% as 20% nodes fail.

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Selecting Member Nodes in a Chain Oriented WSN

Cited by 15 publications

References 19 publications

An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

Robust and Energy-Efficient Data Gathering in Wireless Sensor Network

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