Obstacle detection and estimation in wireless sensor networks

Wang, Wei-Tong; Ssu, Kuo‐Feng

doi:10.1016/j.comnet.2012.11.004

Cited by 17 publications

(13 citation statements)

References 15 publications

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“…The sensor nodes measure various parameters of the environment and transmit collected data to one or more sinks/base stations, using single or multi-hop communication (Wang & Liu, 2011). For commercially and technological point of view, WSN are used in every aspect of life like in military moncles in environment (Wang & Liu, 2011;Wang & Ssu, 2013). For the accuracy of gathered information sensors are required to be put on specific locations to cover the whole interested area in the context of energy, and coverage-hole minimization (Wang & Liu, 2011).…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

Liaqat

Chang

Gani

et al. 2016

International Journal of Information Management

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

Liaqat

Chang

Gani

et al. 2016

International Journal of Information Management

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“…A part from the latter fact, these protocols ( [10,[14][15][16][17]) succeed in avoiding obstacles while decreasing the length of resulting routes, but the real obstacle boundary or at least an approximation of this boundary remains unknown. This latter issue is addressed by Wang and Ssu in [9].…”

Section: Related Workmentioning

confidence: 88%

“…Authors of [9] proposed a scheme enabling detecting obstacles in WSNs. This solution approximates obstacle boundaries by marking the surrounding sensors nodes.…”

Section: Related Workmentioning

confidence: 99%

“…The validity of the routes of a given node is defined as the percentage of valid routes retained in its routing table. for each LocalDensityRadius ( ) from 3 to 9 step 3 do (6) for each MaxToleratedFarness ( ) from 5 to 45 step 5 do (7) for each threshold from 4 to 10 step 2 do (8) C N ← CN + 1 (9) for each run from 1 to 35 step 1 do (10) detectedObstcle ← detectObstcle(POI, Ang, , , threshold , ) (11) Precision[run] ← calculatePrecision(detectedObstcle, realObstcle) (12) Coverage[run] ← calculateCoverage(detectedObstcle, realObstcle) (13) end for (14) AveragePrecision ← average(Precision{1, ⋅ ⋅ ⋅ , 35})…”

Section: Routes Validitymentioning

confidence: 99%

“…Some obstacle detection approaches mark nodes lying on the obstacle boundaries instead of detecting its exact location [9,10]. After this marking phase, the routing process considers this piece of information to avoid selecting paths passing through marked nodes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomic Obstacle Detection and Avoidance in MANETs Driven by Cartography Enhanced OLSR

Belghith

Belhassen

Dhraief

et al. 2015

Mobile Information Systems

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The presence of obstructing obstacles severely degrades the efficiency of routing protocols in MANETs. To mitigate the effect of these obstructing obstacles, routing in MANETs is usually based on the a priori knowledge of the obstacle map. In this paper, we investigate rather the dynamic and autonomic detection of obstacles that might stand within the network. This is accomplished using the enhanced cartography optimized link state routing CE-OLSR with no extra signaling overhead. The evaluation of the performance of our proposed detection scheme is accomplished through extensive simulations using OMNET++. Results clearly show the ability of our proposed scheme to accurately delimit the obstacle area with high coverage and efficient precision ratios. Furthermore, we integrated the proposed scheme into CE-OLSR to make it capable of autonomously detecting and avoiding obstacles. Simulation results show the effectiveness of such an integrated protocol that provides the same route validity as that of CE-OLSR-OA which is based on the a priori knowledge of the obstructing obstacle map.

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Actualizing real‐time indoor positioning systems using plane models

Yeh

2015

Int J Communication

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SUMMARY Compared with other indoor positioning methods, pattern matching offers greater accuracy. However, its construction requires additional time and manpower cost, and larger positioning environments result in correspondingly greater construction costs and computing complexity. To reduce the cost and computing complexity of pattern construction, plane models featuring radio signals were adopted in conjunction with quick response codes to facilitate the simple and rapid construction of indoor environments in this study. The advantages of using plane equations instead of pattern matching radio maps for positioning computations include the following: (1) reducing the complexity of positioning computations; (2) eliminating the influences of positioning area size; and (3) providing reliable and accurate positioning with an average error of approximately 2.8 m. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Obstacle detection and estimation in wireless sensor networks

Cited by 17 publications

References 15 publications

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

WITHDRAWN: Towards sensor-cloud integration: A survey of enabling technologies and architectures

Autonomic Obstacle Detection and Avoidance in MANETs Driven by Cartography Enhanced OLSR

Actualizing real‐time indoor positioning systems using plane models

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