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78
Joint Scheduling and Power Control for Wireless Adhoc Networks
, 2002
"... In this pape we introduce powe r control as a solution tothe multiple accel proble in conte tionbase wirenb adhocne works.The motivation for this study is two fold, limiting multiuse intej toincre single hop throughput, andrej powe r consumption to increj batte life We focus onne ne bor transmi ..."
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Cited by 189 (5 self)
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In this pape we introduce powe r control as a solution tothe multiple accel proble in conte tionbase wirenb adhocne works.The motivation for this study is two fold, limiting multiuse intej toincre single hop throughput, andrej powe r consumption to increj batte life We focus onne ne bor transmissions whes node are rej tose information packe  tothe re e e re e sub jej to a constraint on the signaltointealtoinjj ratio.The multiple acce  proble is solve via twoaltej phase name schej and powe r control.The sche algorithm isej tial to coordinate the transmissions ofinde ede t use inorde toejj strong intej (e.g selfinterference) that can not be ove by powe r control. On the othe hand, powe r control isej in adistribute fashion to dej the admissible powe r ve ifone ene that can be use bythe sche use to satisfy thei singlej transmissionrensmissi ts. This isdone for two type s ofne works, namej TDMA and TDMA/CDMA wire/CD adhocne works.
Capacity bounds and power allocation for wireless relay channels
 IEEE Trans. Inf. Theory
, 2006
"... Abstract—We consider threenode wireless relay channels in a Rayleighfading environment. Assuming transmitter channel state information (CSI), we study upper bounds and lower bounds on the outage capacity and the ergodic capacity. Our studies take into account practical constraints on the transmiss ..."
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Cited by 176 (6 self)
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Abstract—We consider threenode wireless relay channels in a Rayleighfading environment. Assuming transmitter channel state information (CSI), we study upper bounds and lower bounds on the outage capacity and the ergodic capacity. Our studies take into account practical constraints on the transmission/reception duplexing at the relay node and on the synchronization between the source node and the relay node. We also explore power allocation. Compared to the direct transmission and traditional multihop protocols, our results reveal that optimum relay channel signaling can significantly outperform multihop protocols, and that power allocation has a significant impact on the performance. Index Terms—Channel capacity, cooperative diversity, ergodic capacity, power allocation, relay channel, wireless networks. I.
Latency of wireless sensor networks with uncoordinated power saving mechanisms
 in Proceedings of Mobihoc, 2004
, 2004
"... We consider a wireless sensor network, where nodes switch between an active (on) and a sleeping (off) mode, to save energy. The basic assumptions are that the on/off schedules are completely uncoordinated and that the sensors are distributed according to a Poisson process and their connectivity rang ..."
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Cited by 61 (6 self)
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We consider a wireless sensor network, where nodes switch between an active (on) and a sleeping (off) mode, to save energy. The basic assumptions are that the on/off schedules are completely uncoordinated and that the sensors are distributed according to a Poisson process and their connectivity ranges are larger or equal to their sensing ranges. Moreover, the durations of active and sleeping periods are such that the number of active nodes at any particular time is so low that the network is always disconnected. Is it possible to use such a network for timecritical monitoring of an area? Such a scenario requires indeed to have bounds on the latency, which is the delay elapsed between the time at which an incoming event is sensed by some node of the network and the time at which this information is retrieved by the data collecting sink. A positive answer is provided to this question under some simplifying assumptions discussed in the paper. More precisely, we prove that the messages sent by a sensing node reach the sink with a fixed asymptotic speed, which does not depend on the random location of the nodes, but only on the network parameters (node density, connectivity range, duration of active and sleeping periods). The results are obtained rigorously by using an extension of first passage percolation theory.
A Framework for Crosslayer Design of EnergyEfficient Communication With . . .
, 2004
"... Efficient use of energy while providing an adequate level of connection to individual sessions is of paramount importance in multihop wireless networks. Energy efficiency and connection quality depend on mechanisms that span several communication layers due to the existing cochannel interference a ..."
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Cited by 54 (0 self)
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Efficient use of energy while providing an adequate level of connection to individual sessions is of paramount importance in multihop wireless networks. Energy efficiency and connection quality depend on mechanisms that span several communication layers due to the existing cochannel interference among competing flows that must reuse the limited radio spectrum. Although independent consideration of these layers simplifies the system design, it is often insufficient for wireless networks when the overall system performance is examined carefully. The multihop wireless extensions and the need for routing users' sessions from source to the destination only intensify this point of view. In this work, we present a framework for crosslayer design towards energyefficient communication. Our approach is characterized by a synergy between the physical and the medium access control (MAC) layers with a view towards inclusion of higher layers as well. More specifically, we address the joint problem of power control and scheduling with the objective of minimizing the total transmit power subject to the endtoend quality of service (QoS) guarantees for sessions in terms of their bandwidth and bit error rate guarantees. Bearing to the NPhardness of this combinatorial optimization problem, we propose our heuristic solutions that follow greedy approaches.
On the power efficiency of sensory and ad hoc wireless networks
 in Proc. Asilomar Conf. Signals, Systems, and Computing
, 2002
"... Abstract—We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signaltonoise ratio (SNR) regime whe ..."
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Cited by 48 (3 self)
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Abstract—We consider the power efficiency of a communications channel, i.e., the maximum bit rate that can be achieved per unit power (energy rate). For additive white Gaussian noise (AWGN) channels, it is well known that power efficiency is attained in the low signaltonoise ratio (SNR) regime where capacity is proportional to the transmit power. In this paper, we first show that for a random sensory wireless network with users (nodes) placed in a domain of fixed area, with probability converging to one as grows, the power efficiency scales at least by a factor of. In other words, each user in a wireless channel with nodes can support the same communication rate as a singleuser system, but by expending only 1 times the energy. Then we look at a random ad hoc network with relay nodes and simultaneous transmitter/receiver pairs located in a domain of fixed area. We show that as long as, we can achieve a power efficiency that scales by a factor of. We also give a description of how to achieve these gains. Index Terms—Capacity, sensor networks, wireless communication systems and networks. I.
Minimum energy disjoint path routing in wireless adhoc networks
 in Proceedings of the 9th Annual International Conference on Mobile Computing and Networking
, 2003
"... We develop algorithms for finding minimum energy disjoint paths in an allwireless network, for both the node and linkdisjoint cases. Our major results include a novel polynomial time algorithm that optimally solves the minimum energy 2 linkdisjoint paths problem, as well as a polynomial time algor ..."
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Cited by 48 (1 self)
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We develop algorithms for finding minimum energy disjoint paths in an allwireless network, for both the node and linkdisjoint cases. Our major results include a novel polynomial time algorithm that optimally solves the minimum energy 2 linkdisjoint paths problem, as well as a polynomial time algorithm for the minimum energy k nodedisjoint paths problem. In addition, we present efficient heuristic algorithms for both problems. Our results show that linkdisjoint paths consume substantially less energy than nodedisjoint paths. We also found that the incremental energy of additional linkdisjoint paths is decreasing. This finding is somewhat surprising due to the fact that in general networks additional paths are typically longer than the shortest path. However, in a wireless network, additional paths can be obtained at lower energy due to the broadcast nature of the wireless medium. Finally, we discuss issues regarding distributed implementation and present distributed versions of the optimal centralized algorithms presented in the paper.
Packetostatics: deployment of massively dense sensor networks as an electrostatics problem
 In INFOCOM 2005
"... Abstract — We investigate the spatial distribution of wireless nodes that can transport a given volume of traffic in a sensor network, while requiring the minimum number of wireless nodes. The traffic is created at a spatially distributed set of sources, and must arrive at a spatially distributed se ..."
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Cited by 42 (6 self)
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Abstract — We investigate the spatial distribution of wireless nodes that can transport a given volume of traffic in a sensor network, while requiring the minimum number of wireless nodes. The traffic is created at a spatially distributed set of sources, and must arrive at a spatially distributed set of sinks. Under a general assumption on the physical and medium access control (MAC) layers, the optimal distribution of nodes induces a traffic flow identical to the electrostatic field that would exist if the sources and sinks of traffic were substituted with an appropriate distribution of electric charge. This analogy between Electrostatics and wireless sensor networks can be extended in a number of different ways. For example, Thomson’s theorem on the distribution of electric charge on conductors gives the optimal distribution of traffic sources and sinks (that minimizes the number of nodes needed) when we have a limited degree of freedom on their initial placement. Electrostatics problems with Neumann boundary conditions and topologies with different types of dielectric materials can also be interpreted in the context of wireless sensor networks. The analogy also has important limitations. For example, if we move to a three dimensional topology, adapting our general assumption on the physical and MAC layers accordingly, or we stay in the two dimensional plane but use an alternative assumption, that is more suited to Ultra WideBand communication, the optimal traffic distribution is not in general irrotational, and so can not be interpreted as an electrostatic field. Finally, the analogy can not be extended to include networks that support more than one type of traffic. Keywords: Electrostatics, Neumann’s boundary conditions, node placement, sensor networks, potential fields, sensor deployment, sensor networks, Thomson’s theorem, wireless ad hoc networks.
Modeling the Performance of Wireless Sensor Networks
 In IEEE Infocom
, 2004
"... A critical issue in wireless sensor networks is represented by the limited availability of energy within network nodes; therefore making good use of energy is a must. A widely employed energysaving technique is to place nodes in sleep mode, corresponding to a lowpower consumption as well as to red ..."
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Cited by 41 (1 self)
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A critical issue in wireless sensor networks is represented by the limited availability of energy within network nodes; therefore making good use of energy is a must. A widely employed energysaving technique is to place nodes in sleep mode, corresponding to a lowpower consumption as well as to reduced operational capabilities. In this work, we develop a Markov model of a sensor network whose nodes may enter a sleep mode, and we use this model to investigate the system performance in terms of energy consumption, network capacity, and data delivery delay. Furthermore, the proposed model enables us to investigate the tradeoffs existing between these performance metrics and the sensor dynamics in sleep/active mode. Analytical results present an excellent matching with simulation results for a large variety of system scenarios showing the accuracy of our approach.
Optimal deployment of large wireless sensor networks
 IEEE Trans. Inform. Theory
, 2006
"... Abstract—A spatially distributed set of sources is creating data that must be delivered to a spatially distributed set of sinks. A network of wireless nodes is responsible for sensing the data at the sources, transporting them over a wireless channel, and delivering them to the sinks. The problem is ..."
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Cited by 29 (3 self)
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Abstract—A spatially distributed set of sources is creating data that must be delivered to a spatially distributed set of sinks. A network of wireless nodes is responsible for sensing the data at the sources, transporting them over a wireless channel, and delivering them to the sinks. The problem is to find the optimal placement of nodes, so that a minimum number of them is needed. The critical assumption is made that the network is massively dense, i.e., there are so many sources, sinks, and wireless nodes, that it does not make sense to discuss in terms of microscopic parameters, such as their individual placements, but rather in terms of macroscopic parameters, such as their spatial densities. Assuming a particular interferencelimited, capacityachieving physical layer, and specifying that nodes only need to transport the data (and not to sense them at the sources, or deliver them at the sinks once their location is reached), the optimal node placement induces a traffic flow that is identical to the electrostatic field created if the sources and sinks are replaced by a corresponding distribution of positive and negative charges. Assuming a general model for the physical layer, and specifying that nodes must not only transport the data, but also sense them at the sources and deliver them at the sinks, the optimal placement of nodes is given by a scalar nonlinear partial differential equation found by calculus of variations techniques. The proposed formulation and derived equations can help in the design of large wireless sensor networks that are deployed in the most efficient manner, not only avoiding the formation of bottlenecks, but also striking the optimal balance between reducing congestion and having the data packets follow short routes. Index Terms—Capacity, electrostatics, node placement, physical layer, sensor networks, wireless ad hoc networks.
Transmission power control in wireless ad hoc networks: challenges, solutions, and open issues
 IEEE Network
, 2004
"... Recently, power control in mobile ad hoc networks has been the focus of extensive research. Its main objectives are to reduce the total energy consumed in packet delivery and/or increase network throughput by increasing the channel’s spatial reuse. In this article we give an overview of various powe ..."
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Cited by 22 (0 self)
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Recently, power control in mobile ad hoc networks has been the focus of extensive research. Its main objectives are to reduce the total energy consumed in packet delivery and/or increase network throughput by increasing the channel’s spatial reuse. In this article we give an overview of various power control approaches that have been proposed in the literature. We discuss the factors that influence the selection of the transmission power, including the important interplay between the routing (network) and the medium access control (MAC) layers. Protocols that account for such interplay are presented. obile ad hoc networks (MANETs) have recently been the topic of extensive research. The interest in such networks stems from their ability to provide temporary and instant wireless networking solutions in situations where cellular infrastructures are lacking and are expensive or infeasible to deploy