Improving network lifetime with mobile wireless sensor networks

Yang, Yibing; Fonoage, Mihai; Cardei, Mihaela

doi:10.1016/j.comcom.2009.11.010

Cited by 141 publications

(62 citation statements)

References 30 publications

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“…They separately used mobile sensors [14], mobile sinks [15], and mobile data collectors [16] to design related protocols and improve network performance of WSNs. Yang et al surveyed mechanisms into three categories [17]: mechanisms using mobile sinks, mechanisms using mobile sensor nodes redeployment, and mechanisms using mobile relays.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

Liu

Ruan

2013

International Journal of Distributed Sensor Networks

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Random mobility and energy constraint are two main factors affecting system performance in mobile sensor networks, which cause many difficulties to system design. It is necessary to develop high-efficiency algorithms and protocols for mobile sensor networks to adapt to dynamic network environment and energy limitation. In this paper, a new clustering algorithm based on residual energy difference ratio is presented to improve system performance. Firstly, it is an energy-efficient algorithm. The residual energy of sensor nodes and average residual energy of system are considered in the residual energy difference ratio, which effectively avoid the nodes with low residual energy being selected as cluster heads. An energy-optimal scheme is used in cluster formation phase to minimize energy consumption. Secondly, it is a dynamic algorithm. The system dynamically clusters the sensor nodes according to the data transmission delays. It makes the whole system adapt to the random mobility of sensor nodes. The NS2 software is used to simulate the new clustering algorithm. The simulation experiments can verify the validity of the proposed theory.

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Section: Related Workmentioning

confidence: 99%

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

Liu

Ruan

2013

International Journal of Distributed Sensor Networks

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“…However, Voronoi diagrams in these studies are constructed according to the position of mobile sensors, and thus need to be recomputed after each round of sensor movement. In [19], mobile sensors are used to improve energy efficiency of sensors in area coverage. In this work, when destinations have been determined, mobile sensors are designed to move along the shortest path to minimize the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

Liao

Wang

Zhang

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

175

114

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Abstract-Coverage of interest points and network connectivity are two main challenging and practically important issues of Wireless Sensor Networks (WSNs). Although many studies have exploited the mobility of sensors to improve the quality of coverage and connectivity, little attention has been paid to the minimization of sensors' movement, which often consumes the majority of the limited energy of sensors and thus shortens the network lifetime significantly. To fill in this gap, this paper addresses the challenges of the Mobile Sensor Deployment (MSD) problem and investigates how to deploy mobile sensors with minimum movement to form a WSN that provides both target coverage and network connectivity. To this end, the MSD problem is decomposed into two sub-problems: the Target COVerage (TCOV) problem and the Network CONnectivity (NCON) problem. We then solve TCOV and NCON one by one and combine their solutions to address the MSD problem. The NP-hardness of TCOV is proved. For a special case of TCOV where targets disperse from each other farther than double of the coverage radius, an exact algorithm based on the Hungarian method is proposed to find the optimal solution. For general cases of TCOV, two heuristic algorithms, i.e., the Basic algorithm based on clique partition and the TV-Greedy algorithm based on Voronoi partition of the deployment region, are proposed to reduce the total movement distance of sensors. For NCON, an efficient solution based on the Steiner minimum tree with constrained edge length is proposed. The combination of the solutions to TCOV and NCON, as demonstrated by extensive simulation experiments, offers a promising solution to the original MSD problem that balances the load of different sensors and prolongs the network lifetime consequently.

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“…Although such a scheme is able to prolong the lifetime of the WSNs, it might lead to additional energy consumption by the sensor nodes. In view of this, Literature [11][12][13][14][15] proposes a compromise scheme: Relocate sink node instead of any sensor node as shown in Figure 2. Figure 2.…”

Section: Introductionmentioning

confidence: 99%

A Network Lifetime-Oriented Energy-Aware Sink Relocation Scheme

Qiu¹,

Qin²,

Li³

2015

Int. J. Onl. Eng.

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Abstract-When sensor nodes are located around a sink node in a wireless sensor network (hereafter referred to as WSN), their energy will be consumed more quickly. Hence it's necessary to make a proper relocation of the sink node to prevent the surrounding sensor nodes from excessive energy consumption. In view of this, this paper proposes an energy-aware sink relocation (hereafter referred to as EASR) scheme, which is able to realize the adaptive adjustment on the transmission range according to the energy status of the sensor nodes in addition to the establishment of conditions for sink relocation. If the relocation condition is met, sink node will move toward the optimal direction based on the energy status of the surrounding sensor nodes. The simulation result shows that the EASR scheme proposed in this paper is able to prolong effectively the network lifetime.

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Improving network lifetime with mobile wireless sensor networks

Cited by 141 publications

References 30 publications

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

A Dynamic and Energy-Efficient Clustering Algorithm in Large-Scale Mobile Sensor Networks

Minimizing Movement for Target Coverage and Network Connectivity in Mobile Sensor Networks

A Network Lifetime-Oriented Energy-Aware Sink Relocation Scheme

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