Wireless coverage with disparate ranges

Wan, Peng‐Jun; Xu, Xiaoli; Wang, Zhu

doi:10.1145/2107502.2107517

Cited by 29 publications

(9 citation statements)

References 26 publications

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“…For every activated sensor in solution , assign transmission range level ( ) according to (16) Step 3. Evaluate each solution in using objective functions and connectivity constrain; Step 4.…”

Section: Resultsmentioning

confidence: 99%

“…Although all of the above-mentioned researches have achieved the coverage requirement, some of them have not considered the network lifetime [12,20,22,27] and the network connectivity [16,[21][22][23]. Some works such as [14,15,[17][18][19][20]24] require that the transmission range is at least twice the size of the sensing range; otherwise, the connectivity may not be guaranteed.…”

Section: Related Workmentioning

confidence: 99%

“…In each solution in the initial population, sensor nodes are activated uniformly and randomly ( is the number of grids in the cluster). To every activated sensor in the initial solution, a transmission range level is assigned as (16) such that it reaches the sensor which is its closest neighbor and is closer to cluster head compared to : …”

Section: Tascc Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

Jameii

Faez

Dehghan

2015

International Journal of Distributed Sensor Networks

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Coverage, connectivity, and network lifetime are important issues in wireless sensor networks (WSNs). Balancing the energy consumption in the network, reducing the transmission range of nodes, and density control of active nodes are approaches to extend the network lifetime. However, transmission range reduction and smaller number of active nodes can affect the network topology and may cause the network to be disconnected. So, there exist conflicts among lifetime, coverage, and connectivity. In this paper, these conflicting issues are considered and an evolutionary multiobjective optimization approach based on nondominated sorting genetic algorithm-II (NSGA-II) is proposed to optimize them. Simulation results demonstrate that the proposed algorithm can improve the network lifetime and coverage while maintaining the network connectivity.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Tascc Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

Jameii

Faez

Dehghan

2015

International Journal of Distributed Sensor Networks

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“…For the problem Minimum Disk Cover, there exists a polynomial (1 + )-approximation algorithm [24] by using a Local Search method as well.…”

Section: Minimum Strongly Dominating Setmentioning

confidence: 99%

Restricted coverage in wireless networks

Song

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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For wireless networks, coverage with different restrictions that can capture the practical requirements have received great research interests. We will study several restricted coverage problems. The first problem is about K-coverage , i.e., how to deploy wireless nodes such that each target is covered by at least K wireless nodes. We study the problem restricted to linear-K-coverage where there is a line, all targets lie in one side of this line and all wireless nodes lie in the other side. Assume each wireless node is associated with a weight, the objective is to select a minimum weighted subset of nodes such that each target is K-covered. We propose a 3-approximation for this problem by exploring geometric properties. The second problem is called Kroad-coverage. Given a road map in a two-dimensional area which contains a set of paths and a set of wireless nodes, the locations of nodes can either be arbitrary or fixed, the objective is to select a minimum number of wireless nodes such that each path can be K-covered. We will reduce the problem to K-coverage and apply the algorithmic results for K-coverage to solve it.Another line of this work is to investigate a well-motivated problem called strongly dominating set, which is intrinsically related to coverage. Given a wireless networking system represented by a digraph G = (V, − → E ). Each wireless node u has a covering disk centering at u with its radius equal to the transmission range of u. We then draw a directed edge − → uv in G if u's corresponding covering disk contains v. A subset U ⊆ V of wireless nodes is a strongly dominating set if every wireless node in V \ U has both an in-neighbor in U and an out-neighbor in U . The objective is to find a minimum size strongly dominating set. Our method can achieve an approximation factor of (2 + ).

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“…In wireless networks, discrete points coverage in the unit disc graphs is much studied, as unit disc graphs comply with real wireless model and have many favorable features. An overview of this problem can be found in the literature review of [21], [22]. An important branch of the above problem which attracts much attention is to find a dominating point set in unit disc graphs.…”

Section: Related Workmentioning

confidence: 99%

On wireless network coverage in bounded areas

Teng

Xuan

2013

2013 Proceedings IEEE INFOCOM

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Abstract-In this paper, we study the problem of wireless coverage in bounded areas. Coverage is one of the fundamental requirements of wireless networks. There has been considerable research on optimal coverage of infinitely large areas. However, in the real world, the deployment areas of wireless networks are always geographically bounded. It is a much more challenging and significant problem to find optimal deployment patterns to cover bounded areas. In this paper, we approach this problem starting from the development of tight lower bounds on the number of nodes needed to cover a bounded area. Then we design several deployment patterns for different kinds of convex and concave shapes such as rectangles and L-shapes. These patterns require only few more nodes than the theoretical lower bound, and can achieve efficient coverage. We have also carefully addressed and evaluated practical conditions such as coverage modeling and connectivity regarding our deployment patterns.

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Wireless coverage with disparate ranges

Cited by 29 publications

References 26 publications

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

Multiobjective Optimization for Topology and Coverage Control in Wireless Sensor Networks

Restricted coverage in wireless networks

On wireless network coverage in bounded areas

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