Efficient algorithm for placing a given number of base stations to cover a convex region

Das, Gautam; Das, Sandip; Nandy, Subhas C.; Sinha, Bimal K.

doi:10.1016/j.jpdc.2006.05.004

Cited by 41 publications

(28 citation statements)

References 13 publications

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“…The number of BSs is a critical factor of the sensor network architecture that significantly affects the network performance. There exist several efforts in deploying a single BS [2,3] or multiple BSs [4][5][6][7][8][9]. Most of these studies assume that the number of available BSs is known a priori.…”

Section: Related Workmentioning

confidence: 99%

“…Second, since the locations of BSs are restricted, the ILP solution can only select the optimal locations among a limited set of possible locations. Several other efforts in BS deployment [4,5,7] employ iterative clustering algorithms such as kmeans algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…At every sensor node i, any valid routing solution must respect both flow balance defined in (4) and energy constraint defined in (5 (6) which is a quadratic function of BS coordinates. Since quadratic programming is NP-complete [13], so is BSD-M. We would like to point out that this optimal routing mechanism is limited to CL and is intended to provide a theoretical lower bound for evaluating the lifetime performance of any given BS deployment scheme.…”

Section: Problem Formulationmentioning

confidence: 99%

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On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

Lin

Cai

et al. 2010

International Journal of Distributed Sensor Networks

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Data transmission from sensor nodes to a base station or a sink node often incurs significant energy consumption, which critically affects network lifetime. We generalize and solve the problem of deploying multiple base stations to maximize network lifetime in terms of two different metrics under one-hop and multihop communication models. In the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution and a heuristic approach based on the minimal enclosing circle algorithm to deploy a base station at the geometric center of each cluster. In the multihop communication model, both base station location and data routing mechanism need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing paths for data transmission. Simulation results show the distinguished performance of the proposed deployment algorithms in maximizing network lifetime.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

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On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

Lin

Cai

et al. 2010

International Journal of Distributed Sensor Networks

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“…The algorithm, proposed by Das et al [7] is an iterative algorithm that makes use of the voronoi diagram and it is mainly effective for a small number of base stations and for square of triangular regions. The input parameters of this algorithm are 1) a two dimensional convex polygon, and the corresponding set of points P contained in it.…”

Section: Introductionmentioning

confidence: 99%

“…This is because half the coverage area of the base station would otherwise be wasted for points outside the polygon. The worst case time complexity is O(n + k log(k)) where n is the number of edges of [7]. The following figures depict this approach based on Voronoi disgram.…”

Section: Introductionmentioning

confidence: 99%

Load balancing in Wireless Sensor Networks by Optimal Placement of Base Stations using kd-Tree

2016

IJCCIE

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Abstract-In this paper, we explore an important problem in mobile and wireless sensor networks, namely the Base Station Placement Problem. In this problem, we place a given number of base stations in a two dimensional convex region. Each of the base stations have equal radius of coverage, and it is required that the base stations be placed in such a way as to minimize the common coverage radius of all the base stations, while having each point in the convex region to be covered by at least one base station. Simply put, the problem is to completely cover the given convex region with a given number of equal radius circles, while minimizing that radius. We then settle on the instance of non-uniform distribution of sensor nodes and further present a new method to place base stations in this kind of scenario, employing a k-dimensional tree and a density-distance minimum spanning tree, we discuss its implementation, followed by analysis of the both the time performance of the algorithm, and of the quality of results obtained when run on different data sets while varying the algorithm's parameters. The experimental results are encouraging.

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Joint location and power optimisation of femto base stations to improve indoor coverage: a geometric approach

Kundu¹,

Sanyal

Misra

2016

Trans. Emerging Tel. Tech.

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Femto base station (FBS) deployment with existing Macro Base Stations improves Quality of Service of end users in dead‐zone while simultaneously increasing co‐channel femto–femto interference. This necessitates judicious planning before FBS deployment. In this paper, a novel geometric approach to model any 3‐D deployment region along with two particle swarm optimisation based joint location and power optimisation algorithms: LOA‐POA and LPOA are proposed. The geometric plan of the deployment region (with multiple multistoried buildings) with 3‐D coordinates of serving‐MBS has been generated and provided as input to LOA‐POA and LPOA, to identify positions and transmission powers of the FBSs required to maximise coverage. For a significantly large deployment region of 1600 sq. m., the LPOA identifies locations and transmission powers of four co‐channel FBSs within 140 s while LOA‐POA identifies the same for five FBSs within 40 s. Simulation exhibits requisite coverage after FBS installation. Comparison of these algorithms has been carried out with existing works considering different wall materials. LPOA is observed to provide most economic solution but with higher convergence time than LOA‐POA. Copyright © 2016 John Wiley & Sons, Ltd.

show abstract

Efficient algorithm for placing a given number of base stations to cover a convex region

Cited by 41 publications

References 13 publications

On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

Load balancing in Wireless Sensor Networks by Optimal Placement of Base Stations using kd-Tree

Joint location and power optimisation of femto base stations to improve indoor coverage: a geometric approach

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