Deployment planning tool for indoor 3D-WSNs

Kouakou, Marc T.; Yamamoto, Shinya; Yasumoto, Keiichi; Ito, Minoru

doi:10.1145/1864431.1864440

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…[22] When deploying an indoor 3D WSN, it is important to be able to determine positions of the sensor nodes that achieve the fullcoverage of the target space and the connectivity between the sensor nodes with the minimum deployment cost. The sensor node deployment problem for 3D coverage and connectivity is NP-hard even without obstacles in the target field [23].…”

Section: Two-dimensional and Three-dimensional Deploymentmentioning

confidence: 99%

A Survey on Organization and Positioning of Nodes in Wireless Sensor Network

Tan

Zhang

Shi³

et al. 2016

IJFGCN

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Section: Two-dimensional and Three-dimensional Deploymentmentioning

confidence: 99%

A Survey on Organization and Positioning of Nodes in Wireless Sensor Network

Tan

Zhang

Shi³

et al. 2016

IJFGCN

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“…The deployment of wireless sensor networks (WSN) can be mainly divided into two types according to the type of sensors, the application background, and the environment condition: deterministic deployment method and stochastic deployment method. We mainly adopt the deterministic deployment method in the cases where locations of sensors have a great impact on the operation of the WSN, such as deployment of sensor nodes on the pipe [2,3], deployment of image/video sensors indoors [4][5][6], deployment of underwater sensors [7], and deployment of seismic sensors for volcano monitoring [8]. However, we adopt the stochastic deployment method in some special occasions [9,10], especially when the gas leakage happened in the chemical industrial parks.…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Sensor Deployment Method for Target Coverage

et al. 2018

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In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.

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“…The algorithm selects a node s c in the connected sensor nodes C that is the closest to s u and computes the new positions of s u and s c by moving each sensor node towards the other as long as the set of monitoring points covered by s u and s c remain unchanged (lines:[19][20][21]. If the sensor node s u is still unconnected after changing its position, extra nodes are put on the line segment with endpoints s u and s c (lines: 22-24) so that s u and s c are connected.…”

mentioning

confidence: 99%

Cost-efficient sensor deployment in indoor space with obstacles

Kouakou

Yasumoto

Yamamoto

et al. 2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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In this paper, we tackle the problem to achieve kcoverage of a target indoor space with obstacles, that is, any point in the target monitoring area has a line-of-sight to and is located in the sensing range rs of at least k sensors. We propose heuristic algorithms for computing a deployment pattern achieving the kcoverage in an arbitrary 3D target space with stationary and mobile obstacles, while minimizing the overall deployment cost. For the case with only stationary obstacles, we propose a greedy algorithm that puts sensor nodes one by one on a grid point of the deployable area in the descending order of the cost-performance value (i.e., how many monitoring points are covered by putting a sensor at the deployment point per unit deployment cost). In order to extend the algorithm for the case with a mobile obstacle, we define mobile k-coverage that guarantees k-coverage of a target space for arbitrary position of a mobile obstacle, then provide a sufficient condition for the mobile k-coverage: the halfsphere of radius rs centered at a monitoring point bounded by the vertical plane containing the point includes at least k sensor nodes. Based on this condition, we propose a heuristic algorithm that puts at least one sensor node in each π k+1 spherical wedge for each monitoring point. With the proposed algorithms, we have computed the sensor nodes position in an existing indoor environment and confirmed that the obtained WSN deployment in the real space accurately achieves k-coverage.

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Deployment planning tool for indoor 3D-WSNs

Cited by 6 publications

References 4 publications

A Survey on Organization and Positioning of Nodes in Wireless Sensor Network

A Survey on Organization and Positioning of Nodes in Wireless Sensor Network

A Deterministic Sensor Deployment Method for Target Coverage

Cost-efficient sensor deployment in indoor space with obstacles

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