Sparse deployment scheme in mobile sensor networks with prioritized event area

Líu, Jùn; Cheng, Lianglun; Wang, Tao; Wang, Jianhua

doi:10.1002/dac.2954

Cited by 4 publications

(9 citation statements)

References 22 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then a deployment optimization algorithm is proposed based on the perception node coverage weight and the virtual force idea. Liu and coauthors [21] proposed a node deployment strategy that combined coverage weight and virtual force for the prioritized event area in mobile sensor networks with lowdensity sensors, in which the coverage completely cannot be realized. However, they do not deal with situations where all nodes are fixed static nodes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

et al. 2019

View full text Add to dashboard Cite

Data acquisition in large areas has issues of cost and data loss. When sensors are sparse in the physical field, it is critical to study the deployment methods to improve the accuracy of reconstructed data set and the precision of the recovery of lost data. It is desirable to place sensors at optimal locations to achieve higher precision of recovery. In this paper, we present a sparse sensor placement scheme for data interpolation reconstruction based on iterative four subregions using fractal theory. The results of our experiments demonstrate that the precision of our algorithm is higher than that with random placement in dispersion degree, coverage rate, and reconstruction accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

“…We select this moment with a timing of 33. The sensor set, whose sensor index numbers are {1, 3,7,9,14,15,18,19,21,22,24,26,28,29,30,31,36,38,39,41,43,46,47,48,50,51,54}, provides the acquisition data at time 33. We test it by means of reconstructing the temperature value.…”

mentioning

confidence: 99%

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Other non-ACO multi-objective node placement-related approaches are described in [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. Like us, the authors consider a deployment area characterized by the spatial irregularity of the sensed event.…”

Section: Related Workmentioning

confidence: 99%

An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying

López-Matencio

2016

Sensors

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) can gather in situ real data measurements and work unattended for long periods, even in remote, rough places. A critical aspect of WSN design is node placement, as this determines sensing capacities, network connectivity, network lifetime and, in short, the whole operational capabilities of the WSN. This paper proposes and studies a new node placement algorithm that focus on these aspects. As a motivating example, we consider a network designed to describe the distribution of helium-3 (3He), a potential enabling element for fusion reactors, on the Moon. 3He is abundant on the Moon’s surface, and knowledge of its distribution is essential for future harvesting purposes. Previous data are inconclusive, and there is general agreement that on-site measurements, obtained over a long time period, are necessary to better understand the mechanisms involved in the distribution of this element on the Moon. Although a mission of this type is extremely complex, it allows us to illustrate the main challenges involved in a multi-objective WSN placement problem, i.e., selection of optimal observation sites and maximization of the lifetime of the network. To tackle optimization, we use a recent adaptation of the ant colony optimization (ACOR) metaheuristic, extended to continuous domains. Solutions are provided in the form of a Pareto frontier that shows the optimal equilibria. Moreover, we compared our scheme with the four-directional placement (FDP) heuristic, which was outperformed in all cases.

show abstract

“…An efficient dynamic layered Voronoi scoping‐based routing protocol for mobile WSNs was presented in the study of Shi et alLayered Voronoi scoping‐based routing protocol adopts dynamic layered Voronoi scoping and dynamic anchor selection for each mobile sink. Further, Liu et alsuggested a deployment algorithm that makes mobile nodes move to a suitable place with effective deployment. Mobile sinks can reduce costs and decrease energy consumption and increase reliability and connectivity.…”

Section: Introductionmentioning

confidence: 99%

“…Other proposals such as the studies of Wichmann and Korkmaz, Shi et al, and Liu et al [5][6][7] used mobile sinks. Wichmann and Korkmaz 5 proposed a smooth path construction and adjustment for multiple mobile sinks and formulated the problem as a mathematical program and developed an algorithm that constructs a smooth path from a given traveling salesman problem tour.…”

Section: Introductionmentioning

confidence: 99%

Optimal load balanced clustering in homogeneous wireless sensor networks

Souissi

Meddeb

2016

Int J Communication

View full text Add to dashboard Cite

Summary Balancing the load among sensor nodes is a major challenge for the long run operation of wireless sensor networks. When a sensor node becomes overloaded, the likelihood of higher latency, energy loss, and congestion becomes high. In this paper, we propose an optimal load balanced clustering for hierarchical cluster‐based wireless sensor networks. We formulate the network design problem as mixed‐integer linear programming. Our contribution is 3‐fold: First, we propose an energy aware cluster head selection model for optimal cluster head selection. Then we propose a delay and energy‐aware routing model for optimal inter‐cluster communication. Finally, we propose an equal traffic for energy efficient clustering for optimal load balanced clustering. We consider the worst case scenario, where all nodes have the same capability and where there are no ways to use mobile sinks or add some powerful nodes as gateways. Thus, our models perform load balancing and maximize network lifetime with no need for special node capabilities such as mobility or heterogeneity or pre‐deployment, which would greatly simplify the problem. We show that the proposed models not only increase network lifetime but also minimize latency between sensor nodes. Numerical results show that energy consumption can be effectively balanced among sensor nodes, and stability period can be greatly extended using our models.

show abstract

Sparse deployment scheme in mobile sensor networks with prioritized event area

Cited by 4 publications

References 22 publications

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

Sparse Sensor Placement for Interpolated Data Reconstruction Based on Iterative Four Subregions in Sensor Networks

An ACOR-Based Multi-Objective WSN Deployment Example for Lunar Surveying

Optimal load balanced clustering in homogeneous wireless sensor networks

Contact Info

Product

Resources

About