Mobility Limited Flip-Based Sensor Networks Deployment

Chellappan, Sriram; Bai, Xin; Ma, Bin; Xuan, Dong; Cai, Xu

doi:10.1109/tpds.2007.28

Cited by 93 publications

(70 citation statements)

References 15 publications

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“…In the group of grid quorum based movement strategies [22][23][24][25][26][27][28], the area of the network is divided into a number of grid cells and each mobile sensor node must find a suitable cell as its final location and move to that cell.…”

Section: Self-regulated Deployment Strategiesmentioning

confidence: 99%

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Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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Section: Self-regulated Deployment Strategiesmentioning

confidence: 99%

“…After this initial placement, sensor nodes move around and cooperatively and gradually find their best positions within the area of the network [15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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“…Apart from the aforesaid use of mobile nodes their other significant functionality in sensor networks is coverage improvement (sensing operations). Various types of actuation mechanisms to move the mobile nodes between two given locations have been proposed in the literature and include: wheels, springs and wings [4]. Flying sensing nodes in the form of miniature aerial vehicles (MAVs) capable of sensing a given area, aerially, have been discussed in the literature [6].…”

Section: Introductionmentioning

confidence: 99%

Novel framework for data collection in wireless sensor networks using flying sensors

Mathur

Nielsen

Prasad

et al. 2014

2014 IEEE International Conference on Advanced Networks and Telecommuncations Systems (ANTS)

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Abstract-This paper proposes a novel framework for data collection from a sensor network using flying sensor nodes. Efficient data communication within the network is a necessity as sensor nodes are usually energy constrained. The proposed framework utilizes the various entities forming the network for a different utility compared to their usual role in sensor networks. Use of flying sensor nodes is usually considered for conventional purpose of sensing and monitoring. Flying sensing nodes are usually utilized collectively in the form of an aerial sensor network, they are not expected to function as a data collection entity, as proposed in this framework. Similarly, cluster heads (CHs) are usually expected to transfer the aggregated data to an adjoining CH or to the base station (BS) directly. In the proposed framework the CH transfers data directly to the flying sensor node, averting the need for energy intensive multi-hop inter-cluster communication to relay information to the BS. The flying sensor node is referred as sensor fly. The limitations of a conventional sensor network deployed on ground surface, in respect to the near ground path loss, and communication hindrance due to undulating terrain are avoided in this framework. The proposed framework is therefore highly suitable for sensor network deployment in inhabitable harsh terrain. Fuzzy logic based inference of the clusters (referred as cluster-hops) that could be covered by the sensor fly, governed by the input parameters has been presented.

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“…To illustrate the point, imagine the following scenario: where static sensors are deployed for monitoring wildfire applications, events such as winds, malicious attacks, and sensor failures are likely to cause network partitions. Thus, it is necessary to make use of mobile sensors, such as attaching sensors to robots or vehicles [9], or self-propel via springs [10] or wheels [11].…”

Section: Introductionmentioning

confidence: 99%

Distributed Movement Control for Building a Ring in Mobile Wireless Sensor Networks

Chen

Jia

Wen

et al. 2014

International Journal of Distributed Sensor Networks

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Many applications in wireless sensor networks require some sensors to form a ring or multiring-based shape in the target area, such as intrusion detection, border surveillance, routing overlay formation, and network full coverage. In this paper, we study the problem of sensor redistribution to build a ring-based shape for mobile sensor networks. We first give the theoretical analysis on what is optimal sensor movement with the given random deployment. Then, we propose a fully distributed movement control algorithm to achieve ring-based shape for mobile sensor networks. We formally prove that our algorithm can achieve a ring-based distribution within finite time. We also present the procedures of applying our algorithm to form multiring-based distribution. Finally, we present extensive simulations to verify that our approach outperforms other schemes in terms of both the moving distance and convergence time.

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Mobility Limited Flip-Based Sensor Networks Deployment

Cited by 93 publications

References 15 publications

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Novel framework for data collection in wireless sensor networks using flying sensors

Distributed Movement Control for Building a Ring in Mobile Wireless Sensor Networks

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