Rigidity guided localisation for mobile robotic sensor networks

Wu, C. S.; Zhang, Ying; Sheng, Weihua; Kanchi, Saroja

doi:10.1504/ijahuc.2010.034324

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…In [17, 28], the authors discuss that static sensors can hardly cover the entire target region and cannot ensure the network connectivity. In this case, how to deploy mobile actuator to mitigate this network architecture is a challenge.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Scheduling for Mobile Sensors Using Connection Graphs in a Hybrid Wireless Sensor Network with Obstacles

Xie

Pan

2014

International Journal of Distributed Sensor Networks

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This paper considers the scheduling problem of mobile sensors in a hybrid wireless sensor network (WSN) with obstacles. In a WSN, static sensors monitor the environment and report where events appear in the sensing field. Then, mobile sensors are dispatched to these event locations to perform in-depth analysis. The sensing field may contain obstacles of any shape and size. A big challenge is how to efficiently dispatch the mobile sensor to find an obstacle-avoiding shortest path. To remedy this issue, we propose an efficient scheduling mechanism based on connection graphs in this paper. Specifically, the region of network is divided into grid cells with the same size. Consequently, the search space of the shortest path is restricted to the connection graphs composed of some grid cells. Through simulation, we verify the effectiveness of our method. The paper contributes to developing an energy-efficient dispatch solution in the presence of obstacles.

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Section: Related Workmentioning

confidence: 99%

Energy-Efficient Scheduling for Mobile Sensors Using Connection Graphs in a Hybrid Wireless Sensor Network with Obstacles

Xie

Pan

2014

International Journal of Distributed Sensor Networks

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“…Trilateration [1], [18] is commonly used as a means of localizing nodes, and often a variation of bilateration is used to find location of nodes. Moreover localization is assisted by a mobile anchor or mobile robot that help add missing distances between sensor nodes [15] [19].…”

Section: Introductionmentioning

confidence: 99%

Finding Maximal Localizable Region in Wireless Sensor Networks by Merging Rigid Clusters

Kanchi¹

2019

6th International Conference on Computer Science, Engineering and Information Technology (CSEIT-2019)

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Localization of Wireless Sensor Network (WSN) is the problem of finding the geo-locations of sensors in a sensor network deployed in various applications. Given the prolification of sensors in various applications, the localization and tracking of sensors have received considerable attention. Properties of rigidity and flexibility of the underlying graph of the WSN have been studied as a means of determining the localizability of the nodes in the WSN. In this paper, we present a new 3-merge technique for merging three rigid clusters of a network graph, into larger rigid cluster and we use this algorithm for finding maximal localizable regions within the WSN. We provide simulation results on random deployments of WSN to prove that this technique outperforms previously known algorithms for finding maximal localizable subregions. Moreover, simulation results show that the number of anchors needed to localize the entire WSN decreases due to finding large localizable regions.

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“…CMDS is a common method for dimensionality reduction and data visualization [2]- [5] and can also be applied in the fields of robotics and biomimetics [6]- [8]. CMDS routine is contained in MATLAB, SAS, SPSS, STATISTICA, S-Plus, etc.…”

Section: Introductionmentioning

confidence: 99%

A fast multidimensional scaling algorithm

Cai

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Classical multidimensional scaling (CMDS) is a common method for dimensionality reduction and data visualization. Aimed at the problem of slow speed of CMDS, a divide-and-conquer based MDS (dcMDS) algorithm is put forward in this paper. In this algorithm, the distance matrix between samples is divided along its main diagonal into several submatrices ， which are solved respectively. By isometric transformation, the solutions of the submatrices can be integrated to form the solution of the whole matrix. The solution of dcMDS is the same as that of CMDS. Moreover, when the intrinsic dimension of the samples is much smaller than the number of samples, the speed of dcMDS is significantly improved than CMDS. In this paper, a detailed theoretical analysis of dcMDS is presented, and its efficiency is verified by experiments.

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Rigidity guided localisation for mobile robotic sensor networks

Cited by 16 publications

References 33 publications

Energy-Efficient Scheduling for Mobile Sensors Using Connection Graphs in a Hybrid Wireless Sensor Network with Obstacles

Energy-Efficient Scheduling for Mobile Sensors Using Connection Graphs in a Hybrid Wireless Sensor Network with Obstacles

Finding Maximal Localizable Region in Wireless Sensor Networks by Merging Rigid Clusters

A fast multidimensional scaling algorithm

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