An Energy-Efficient Motion Strategy for Mobile Sensors in Mixed Wireless Sensor Networks

Zhang, Zhenjiang; Fu, Junsong; Chao, Han Chieh

doi:10.1155/2013/813182

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…To further decrease the total moving distance, as our previous work in [6], we discussed how to match mobile sensors with coverage holes in order to obtain a shorter moving distance in a centralized way. In [7], the authors improved our approach.…”

Section: Figure 1 Deployment Of the Mobile Sensor Nodesmentioning

confidence: 99%

“…Given a set of two-dimensional nodes, we used a randomized incremental algorithm to triangulate them into many triangles [6]. In this way, we can obtain any point's temperature.…”

Section: Figure 5 Location Of the Firementioning

confidence: 99%

“…The properties of bipartite graphs have been researched for a long time. In our early work [6] on moving strategies for mobile sensor nodes in mixed WSNs, we proposed an algorithm to match the bipartite graph with a 2-exchange optimization algorithm. A better moving strategy was proposed in [7]; hence, we used that strategy in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Forest Fire Monitoring Based on Mixed Wireless Sensor Networks

Ma¹,

Liu²,

Fu³

et al. 2015

IJSH

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Forest fires can be fatal threats. Motivated by the need to detect fire early and locate forest fires clearly, in this paper, we propose a paradigm called the forest fire monitoring paradigm (FFMP). The purpose of the FFMP is for forest fire early detection and locating based on mixed wireless sensor networks (WSNs). Different from pure static and mobile WSNs, mixed WSNs are composed of both mobile sensor nodes and static sensor nodes. Mixed WSNs are a tradeoff between cost and coverage. In the FFMP, the mobile sensor nodes perform as cluster heads and they will construct a backbone network in which the mobile sensor nodes can connect with their neighbors and be capable of transmitting data to the base station. Each static sensor node chooses one neighboring mobile sensor node as its cluster head and uploads the generated messages to the cluster head. The mobile sensor nodes then fuses the information and transfers the fusion results to the base station, where the data were further processed to obtain the temperature distribution graph and locate the fires. The simulation illustrates that our approach performs well in early forest fire detection and locating. In addition, our approach can significantly prolong the lifetime of WSNs.

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Section: Figure 1 Deployment Of the Mobile Sensor Nodesmentioning

confidence: 99%

“…Given a set of two-dimensional nodes, we used a randomized incremental algorithm to triangulate them into many triangles [6]. In this way, we can obtain any point's temperature.…”

Section: Figure 5 Location Of the Firementioning

confidence: 99%

See 1 more Smart Citation

Forest Fire Monitoring Based on Mixed Wireless Sensor Networks

Ma¹,

Liu²,

Fu³

et al. 2015

IJSH

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“…The grid with minimum values is the goal of the mobile node. The authors in [18] proposed a novel, centralized algorithm to deploy a mixture of mobile and static sensors to construct sensor networks which used Delaunay triangulation rather than a Voronoi diagram to detect the coverage holes.…”

Section: Related Prior Workmentioning

confidence: 99%

Uncertainty-Aware Sensor Deployment Strategy in Mixed Wireless Sensor Networks

Chang

Zhang

Yang

et al. 2013

International Journal of Distributed Sensor Networks

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Deployment is a fundamental issue in wireless sensor networks (WSNs), which affects the performance and lifetime of the networks. Usually the sensor locations are precomputed based on "perfect" sensor detection model, whereas sensors may not always provide reliable information, either due to operational tolerance levels or environmental factors. Therefore, it is very important to take into account this uncertainty in the deployment process. In this paper, we address the problem of sensor deployment in a mixed sensor network where the mobile and static nodes work collaboratively to perform deployment optimization task. We consider the Gaussian white noise in the environment and present a centralized algorithm (FABGM for short) which discoveres vacancies by using detection model based on false alarm and moves the mobile nodes according to the method based on bipartite graph matching in this study. In this algorithm, the management node of the WSNs collects the geographical information of all of the static and mobile sensors. Then, the management node executes the algorithm to get the best matches between mobile sensors and coverage holes. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

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“…In [6] Voronoi polygon is exploited to find the number of coverage holes (with their positions) using a coverage enhancing algorithm. Similar to this, in [7] authors propose an algorithm which uses Delaunay triangulation to determine holes with the help of mobile and static sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network

Gosain

Snigdh

2015

IJECE

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This paper evaluates and ranks the suitability of routing algorithms for bipartite wireless sensor network topology. The network considered in this paper, consists of an irregular combination of fixed and mobile nodes, which leads to construction of a bipartite graph among them. A wireless sensor network is usually constrained by the energy limitations and processing capabilities. We therefore, consider the important metrics for analysis namely, carried load, energy consumption and the average delay incurred. We present the possibilities of employing the routing algorithms subject to the quality of service required by the wireless sensor networks applications

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An Energy-Efficient Motion Strategy for Mobile Sensors in Mixed Wireless Sensor Networks

Cited by 12 publications

References 25 publications

Forest Fire Monitoring Based on Mixed Wireless Sensor Networks

Forest Fire Monitoring Based on Mixed Wireless Sensor Networks

Uncertainty-Aware Sensor Deployment Strategy in Mixed Wireless Sensor Networks

Performance Comparison of Routing Protocols in Bipartite Wireless Sensor Network

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