Experimental study on persistent coverage control with information decay

Sugimoto, K.; Hatanaka, Takeshi; Fujita, Masayuki; Huebel, Nico

doi:10.1109/sice.2015.7285343

Cited by 12 publications

(30 citation statements)

References 9 publications

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“…Consider n = 1, to calculate D 1 (P, I Ω ), the rest of the sensors are divided into K 1 = 2 components U 11 = {2, 3, 4, 5, 6, 7} and U 12 = {8, 9, 10, 11, 12}. According to the definition of DR, the green overlap between the cyan region 7 j=2 B (p j , R c ) and the yellow region constraints (8) into account, and propose the concept of FR defined by…”

Section: B the Optimal Sensor Deploymentmentioning

confidence: 99%

“…In Appendix A, we show that Sensor n's optimal location should minimize its distance to the centroid c n (P) when the cell partition is fixed. In addition, by the analysis in Section III-B, Sensor n's movement should be restricted inF n (P, I Ω ) in order to guarantee both (i) energy constraints (8) and (ii) full-connectivity which has been proved (in Lemma 1) as a necessary condition for the optimum solution. Accordingly, Step (2) of CCML will not increase the distortion.…”

Section: Centralized Lloyd-like Algorithmsmentioning

confidence: 99%

“…The continuous sensor relocation in MWSNs is an interesting future work. dynamics, velocity, and/or second order dynamics, acceleration [8], [9], [16]. However, in the discrete time system, sensors only communicate with their neighbors at some discrete time instances, and their relocation is divided into multiple steps [12].…”

Section: A Problem Formulationmentioning

confidence: 99%

“…. , p * N ) be the initial and the optimal sensor deployment in a MWSN with performance function (4) and constraints (8), respectively. For convenience, let I Ω be the set of all sensors, S(P) be the set of sensors that can communicate with the AP when the sensor deployment is P, and N n (P) be Sensor n's neighbors given deployment P. S(P) is also referred to as the backbone network in Section II.…”

mentioning

confidence: 99%

“…, p * N ). Next, we check if P ′ satisfies the network lifetime constraints (8). First, since sensors m and n are neighbors, we have p 0 m −p 0 n ≤ R c .…”

mentioning

confidence: 99%

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Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range

Guo¹,

Jafarkhani²

2019

IEEE Trans. Wireless Commun.

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We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms.

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Section: B the Optimal Sensor Deploymentmentioning

confidence: 99%

Section: Centralized Lloyd-like Algorithmsmentioning

confidence: 99%

Section: A Problem Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

“…, p * N ). Next, we check if P ′ satisfies the network lifetime constraints (8). First, since sensors m and n are neighbors, we have p 0 m −p 0 n ≤ R c .…”

mentioning

confidence: 99%

See 3 more Smart Citations

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range

Guo¹,

Jafarkhani²

2019

IEEE Trans. Wireless Commun.

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Development of global optimal coverage control using multiple aerial robots

2019

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Coverage control has been widely used for constructing mobile sensor network such as for environmental monitoring, and one of the most commonly used methods is the Lloyd algorithm based on Voronoi partitions. However, when this method is used, the result sometimes converges to a local optimum. To overcome this problem, game theoretic coverage control has been proposed and found to be capable of stochastically deriving the optimal deployment. From a practical point of view, however, it is necessary to make the result converge to the global optimum deterministically. In this paper, we propose a global optimal coverage control along with collision avoidance in continuous space that ensures multiple sensors can deterministically and smoothly move to the global optimal deployment. This approach consists of a cut-in algorithm based on neighborhood importance of measurement and a modified potential method for collision avoidance. The effectiveness of the proposed algorithm has been confirmed through numerous simulations and some experiments using multiple aerial robots.

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