Mobile Agent Based Wireless Sensor Network for Intelligent Maintenance

Wang, Xue; Jiang, Aiguo; Wang, Sheng

doi:10.1007/11538356_33

Cited by 18 publications

(7 citation statements)

References 6 publications

(7 reference statements)

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“…In such networks, data is not forwarded to an external sink, but instead, the processing application (the mobile agent), including its state variables, is sent to the node and executed locally [18]. As all process context data are contained within the agent, it can be supplied with input data at one node, while the processing can be performed at a different and more powerful system.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Data Processing Algorithm Based on Mobile Agent in Wireless Sensor Networks

Zhang

Sun

Fan

et al. 2012

International Journal of Distributed Sensor Networks

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Mobile agent (MA) systems provide new capabilities for energy-efficient data processing by flexibly planning its itinerary for facilitating agent-based data collection and aggregation. In this paper, we present a cooperative data processing algorithm based on mobile agent (MA-CDP), and considers MA in multihop environments and can autonomously clone and migrate themselves in response to environmental changes. MA accounts for performing data processing and making data aggregation decisions at nodes rather than bringing data back to a central processor, and redundant sensory data will be eliminated. The results of our simulation show that MA-based cooperative data processing provides better performance than directed diffusion in terms of end-to-end delivery latency, packet delivery ratio, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

Cooperative Data Processing Algorithm Based on Mobile Agent in Wireless Sensor Networks

Zhang

Sun

Fan

et al. 2012

International Journal of Distributed Sensor Networks

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“…A sink node is located in the center of the region to collect the data through the network and maintain the information of node positions and communication paths [5,6]. Wireless sensor nodes are densely placed, so that a number of nodes can potentially make simultaneous observations.…”

Section: Basic Modelsmentioning

confidence: 99%

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

Wang

et al. 2007

Sensors

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Abstract:Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

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“…We assume that WSN is composed of randomly deployed sensor nodes and one sink node in two-dimension sensing field. Sensor nodes work collaboratively for mobile target tracking with sensing period T , while the sink node gathers the information sensed by the sensor nodes [ 9 , 10 , 11 ]. A multi-sensor detection model will be discussed as well.…”

Section: Problem Formulationmentioning

confidence: 99%

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

Wang

et al. 2007

Sensors

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A primary criterion of wireless sensor network is energy efficiency. Focused on the energy problem of target tracking in wireless sensor networks, this paper proposes a cluster-based dynamic energy management mechanism. Target tracking problem is formulated by the multi-sensor detection model as well as energy consumption model. A distributed adaptive clustering approach is investigated to form a reasonable routing framework which has uniform cluster head distribution. Dijkstra's algorithm is utilized to obtain optimal intra-cluster routing. Target position is predicted by particle filter. The predicted target position is adopted to estimate the idle interval of sensor nodes. Hence, dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that the operation energy consumption of wireless sensor network can be reduced. The sensor nodes around the target wake up on time and act as sensing candidates. With the candidate sensor nodes and predicted target position, the optimal sensor node selection is considered. Binary particle swarm optimization is proposed to minimize the total energy consumption during collaborative sensing and data reporting. Experimental results verify that the proposed clustering approach establishes a low-energy communication structure while the energy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energy management.

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Mobile Agent Based Wireless Sensor Network for Intelligent Maintenance

Cited by 18 publications

References 6 publications

Cooperative Data Processing Algorithm Based on Mobile Agent in Wireless Sensor Networks

Cooperative Data Processing Algorithm Based on Mobile Agent in Wireless Sensor Networks

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

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