A Comprehensive Review of Sensor Relocation

Shi, Wei; Corriveau, Jean-Pierre

doi:10.1109/greencom-cpscom.2010.42

Cited by 8 publications

(7 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(8) and (9) We also find that the ground-state energy (26) 14 Considering these quartic terms in the so-called Popov approximation, 29 we showed that the self-consistent equations admit solutions only for d d c0 = 0.4 . Here, we revisited the problem and perform more accurate numerical calculations.…”

Section: A Zero-momentum Becmentioning

confidence: 76%

Finite-momentum condensate of magnetic excitons in a bilayer quantum Hall system

Doretto

Smith²,

Caldeira³

2012

Phys. Rev. B

View full text Add to dashboard Cite

We study the bilayer quantum Hall system at total filling factor ν T = 1 within a bosonization formalism which allows us to approximately treat the magnetic exciton as a boson. We show that in the region where the distance between the two layers is comparable to the magnetic length, the ground state of the system can be seen as a finite-momentum condensate of magnetic excitons provided that the excitation spectrum is gapped. We analyze the stability of such a phase within the Bogoliubov approximation first assuming that only one momentum Q is macroscopically occupied and later we consider the same situation for two modes ±Q. We find strong evidences that a first-order quantum phase transition at small interlayer separation takes place from a zero-momentum condensate phase, which corresponds to Halperin 111 state, to a finite-momentum condensate of magnetic excitons.

show abstract

Section: A Zero-momentum Becmentioning

confidence: 76%

Finite-momentum condensate of magnetic excitons in a bilayer quantum Hall system

Doretto

Smith²,

Caldeira³

2012

Phys. Rev. B

View full text Add to dashboard Cite

show abstract

“…Such locations are often referred to as sensing holes. In order to fill these sensing holes, redundant sensors or previously deployed sensors [2] need to be moved to a particular location, a process that is called sensor relocation [3].…”

Section: Introductionmentioning

confidence: 99%

“…WSNs have attracted considerable research attention for a long period [4–6]. However, the deployment of a large number of mobile sensors is not cost-effective [3]. Moreover, mobile sensor self-relocation [7][8] is not suitable for all situations—for example, a hostile environment, where sensors cannot be manually deployed or air-dropped [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A one-commodity pickup-and-delivery traveling salesman problem solved by a two-stage method: A sensor relocation application

et al. 2019

View full text Add to dashboard Cite

In the carrier-based coverage repair problem, a single mobile robot replaces damaged sensors by picking up spare ones in the region of interest or carrying them from a base station in wireless sensor and robot networks. The objective is to find the shortest path of the robot. The problem is an extension of the traveling salesman problem (TSP). Thus, it is also called the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD). In order to solve this problem in a larger sensor distribution scenario more efficiently, we propose a two-stage approach in this paper. In the first stage, the mature and effective Lin–Kernighan–Helsgaun (LKH) algorithm is used to form a Hamiltonian cycle for all delivery nodes, which is regarded as a heuristic for the second stage. In the second stage, elliptical regions are set for selecting pickup nodes‚ and an edge-ordered list (candidate edge list, CEL ) is constructed to provide major axes for the ellipses. The process of selecting pickup nodes and constructing the CEL is repeated until all the delivery nodes are visited. The final CEL stores a feasible solution. To update it, three operations—expansion, extension, and constriction—are applied to the CEL . The experimental results show that the proposed method reduces the computing time and achieves better results in higher-dimensional problems, which may facilitate the provision of solutions for more complicated sensor networks and can contribute to the development of effective and efficient algorithms for the one-commodity pickup-and-delivery traveling salesman problem (1-PDTSP).

show abstract

“…Some papers assumed randomized deployment of sensors on the barrier and analyzed the probability of barrier coverage. Other papers have studied the case of relocatable sensors [20,21], which start at arbitrary positions and can move to final positions that achieve barrier coverage. Centralized algorithms for minimizing the maximum and average movement of sensors were studied in [5,6] and [7] respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimal Online and Offline Algorithms for Robot-Assisted Restoration of Barrier Coverage

Czyzowicz

Kranakis

Kriz̧anc

et al. 2015

Approximation and Online Algorithms

View full text Add to dashboard Cite

Cooperation between mobile robots and wireless sensor networks is a line of research that is currently attracting a lot of attention. In this context, we study the following problem of barrier coverage by stationary wireless sensors that are assisted by a mobile robot with the capacity to move sensors. Assume that n sensors are initially arbitrarily distributed on a line segment barrier. Each sensor is said to cover the portion of the barrier that intersects with its sensing area. Owing to incorrect initial position, or the death of some of the sensors, the barrier is not completely covered by the sensors. We employ a mobile robot to move the sensors to final positions on the barrier such that barrier coverage is guaranteed. We seek algorithms that minimize the length of the robot's trajectory, since this allows the restoration of barrier coverage as soon as possible. We give an optimal linear-time offline algorithm that gives a minimum-length trajectory for a robot that starts at one end of the barrier and achieves the restoration of barrier coverage. We also study two different online models: one in which the online robot does not know the length of the barrier in advance, and the other in which the online robot knows the length of the barrier. For the case when the online robot does not know the length of the barrier, we prove a tight bound of 3/2 on the competitive ratio, and we give a tight lower bound of 5/4 on the competitive ratio in the other case. Thus for each case we give an optimal online algorithm.

show abstract

A Comprehensive Review of Sensor Relocation

Cited by 8 publications

References 5 publications

Finite-momentum condensate of magnetic excitons in a bilayer quantum Hall system

Finite-momentum condensate of magnetic excitons in a bilayer quantum Hall system

A one-commodity pickup-and-delivery traveling salesman problem solved by a two-stage method: A sensor relocation application

Optimal Online and Offline Algorithms for Robot-Assisted Restoration of Barrier Coverage

Contact Info

Product

Resources

About