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On the deployment of wireless data backhaul networks
 IEEE Trans. on Wireless Comm
, 2007
"... Abstract — We study the deployment of data backhaul nodes for wireless networks with energy constraints. We address the following problem: given the required lifetime of a sensor network, the energy constraint of backhaul nodes, and the area to be covered, what is the minimum number of nodes neede ..."
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Abstract — We study the deployment of data backhaul nodes for wireless networks with energy constraints. We address the following problem: given the required lifetime of a sensor network, the energy constraint of backhaul nodes, and the area to be covered, what is the minimum number of nodes needed to construct such a backhaul network and what is the corresponding deployment scheme? Finding an efficient deployment scheme involves location management, routing, and power management. We focus on linear networks and formulate a deployment optimization problem. We then propose and analyze a greedy deployment scheme that achieves close to optimal performance. We reveal the closedform relationship among different design parameters, namely, the number of sensor nodes, the desired lifetime, and the coverage distance. We also study the effect of miscellaneous power consumptions and nonuniform data density, and consider extensions to planar networks. Index Terms — Deployment, Sensor Networks, Lifetime. I.
An Algorithm for Wireless Relay Placement
"... Abstract—An algorithm is given for placing relays at spatial positions to improve the reliability of communicated data in a sensor network. The network consists of many powerlimited sensors, a small set of relays, and a receiver. For each sensor, the receiver receives a direct signal as well as an ..."
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Abstract—An algorithm is given for placing relays at spatial positions to improve the reliability of communicated data in a sensor network. The network consists of many powerlimited sensors, a small set of relays, and a receiver. For each sensor, the receiver receives a direct signal as well as an indirect signal from one of the available relays. The relays rebroadcast the transmissions in order to achieve diversity at the receiver. Both amplifyandforward and decodeandforward relay networks are considered. Channels are modeled with Rayleigh fading, path loss, and additive white Gaussian noise. Performance analysis and numerical results are given. Index Terms—Sensor network, cooperative communication, relay placement. I.
Impact of heterogeneity on . . .
"... In this paper, deployment of heterogeneous sensors in a field with preferential areas is studied. The problem is formulated using a mathematical program and solved optimally with an objective function that maximizes the coverage of the monitored field. The formulation considers several operation cap ..."
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In this paper, deployment of heterogeneous sensors in a field with preferential areas is studied. The problem is formulated using a mathematical program and solved optimally with an objective function that maximizes the coverage of the monitored field. The formulation considers several operation capabilities of the sensing devices including reliability, mobility, mobility cost, lifespan and power selfscheduling, as well as fields with preferential areas. For largescale problems, a twophase approach is proposed. A set of deployment patterns is first generated; and then assigned to the available devices considering their limited operational capabilities. Different sets of conducted experiments demonstrate the benefits of using heterogeneous sensors and fields with special monitoring requirements. In addition, the results show that the twophase approach is capable of producing nearoptimal coverage performance in a much shorter running time.
Research Article Optimal and Approximate Approaches for Deployment of Heterogeneous Sensing Devices
, 2006
"... A modeling framework for the problem of deploying a set of heterogeneous sensors in a field with timevarying differential surveillance requirements is presented. The problem is formulated as mixed integer mathematical program with the objective to maximize coverage of a given field. Two metaheurist ..."
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A modeling framework for the problem of deploying a set of heterogeneous sensors in a field with timevarying differential surveillance requirements is presented. The problem is formulated as mixed integer mathematical program with the objective to maximize coverage of a given field. Two metaheuristics are used to solve this problem. The first heuristic adopts a genetic algorithm (GA) approach while the second heuristic implements a simulated annealing (SA) algorithm. A set of experiments is used to illustrate the capabilities of the developed models and to compare their performance. The experiments investigate the effect of parameters related to the size of the sensor deployment problem including number of deployed sensors, size of the monitored field, and length of the monitoring horizon. They also examine several endogenous parameters related to the developed GA and SA algorithms. Copyright © 2007 Rabie Ramadan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.