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93
Optimal Energy Management Policies for Energy Harvesting Sensor Nodes
"... We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management ..."
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Cited by 132 (4 self)
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We study a sensor node with an energy harvesting source. The generated energy can be stored in a buffer. The sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time. We obtain energy management policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal energy management policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay.
Network Correlated Data Gathering With Explicit Communication: NPCompleteness and Algorithms
, 2005
"... We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we co ..."
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Cited by 72 (9 self)
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We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NPhard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
Dynamic algorithms for multicast with intrasession network coding
 In Proc. 43rd Annual Allerton Conference on Communication, Control, and Computing
, 2005
"... We establish, for multiple multicast sessions with intrasession network coding, the capacity region of input rates for which the network remains stable in ergodically timevarying networks. Building on the backpressure approach introduced by Tassiulas et al., we present dynamic algorithms for mult ..."
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Cited by 71 (11 self)
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We establish, for multiple multicast sessions with intrasession network coding, the capacity region of input rates for which the network remains stable in ergodically timevarying networks. Building on the backpressure approach introduced by Tassiulas et al., we present dynamic algorithms for multicast routing, network coding, rate control, power allocation, and scheduling that achieves stability for rates within the capacity region. Decisions on routing, network coding, and scheduling between different sessions at a node are made locally at each node based on virtual queues for different sinks. For correlated sources, the sinks locally determine and control transmission rates across the sources. The proposed approach yields a completely distributed algorithm for wired networks. In the wireless case, scheduling and power control among different transmitters are centralized while routing, network coding, and scheduling between different sessions at a given node are distributed. 1
Decentralized Erasure Codes for Distributed Networked Storage
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2006
"... We consider the problem of constructing an erasure code for storage over a network when the data sources are distributed. Specifically, we assume that there are n storage nodes with limited memory and k < n sources generating the data. We want a data collector, who can appear anywhere in the ne ..."
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Cited by 66 (3 self)
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We consider the problem of constructing an erasure code for storage over a network when the data sources are distributed. Specifically, we assume that there are n storage nodes with limited memory and k < n sources generating the data. We want a data collector, who can appear anywhere in the network, to query any k storage nodes and be able to retrieve the data. We introduce Decentralized Erasure Codes, which are linear codes with a specific randomized structure inspired by network coding on random bipartite graphs. We show that decentralized erasure codes are optimally sparse, and lead to reduced communication, storage and computation cost over random linear coding.
Separating Distributed Source Coding from Network Coding
, 2006
"... This correspondence considers the problem of distributed source coding of multiple sources over a network with multiple receivers. Each receiver seeks to reconstruct all of the original sources. The work by Ho et al. 2004 demonstrates that random network coding can solve this problem at the potenti ..."
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Cited by 61 (10 self)
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This correspondence considers the problem of distributed source coding of multiple sources over a network with multiple receivers. Each receiver seeks to reconstruct all of the original sources. The work by Ho et al. 2004 demonstrates that random network coding can solve this problem at the potentially high cost of jointly decoding the source and the network code. Motivated by complexity considerations we consider the performance of separate source and network codes. Previous work by Effros et al. 2003 demonstrates the failure of separation between source and network codes for nonmulticast networks. We demonstrate that failure for multicast networks. We study networks with capacity constraints on edges. It is shown that the problem with two sources and two receivers is always separable. Counterexamples are presented for other cases.
Powerefficient sensor placement and transmission structure for data gathering under distortion constraints
 in IPSN ’04
, 2004
"... We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for commun ..."
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Cited by 54 (4 self)
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We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use either joint entropy coding based on explicit communication between sensor nodes, where coding is done when side information is available, or SlepianWolf coding where nodes have knowledge of network correlation statistics. We consider both maximum and average distortion bounds. We prove that this optimization is NPcomplete since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem by first looking at the simplified problem of optimal placement in the onedimensional case. An analytical solution is derived for the case when there is a simple aggregation scheme, and numerical results are provided for the cases when joint entropy encoding is used. We use the insight from our 1D analysis to extend our results to the 2D case and compare it to typical uniform random placement and shortestpath tree. Our algorithm for twodimensional placement and transmission structure provides two to three fold reduction in
Distributed functional compression through graph coloring
 In 2007 Data Compression Conference
, 2007
"... Abstract We consider the distributed computation of a function of random sources with minimal communication. Specifically, given two discrete memoryless sources, X and Y , a receiver wishes to compute f (X, Y ) based on (encoded) information sent from X and Y in a distributed manner. A special case ..."
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Cited by 33 (6 self)
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Abstract We consider the distributed computation of a function of random sources with minimal communication. Specifically, given two discrete memoryless sources, X and Y , a receiver wishes to compute f (X, Y ) based on (encoded) information sent from X and Y in a distributed manner. A special case, f (X, Y ) = (X, Y ), is the classical question of distributed source coding considered by Orlitsky and Roche (2001) considered a somewhat restricted setup when Y is available as side information at the receiver. They characterized the minimal rate at which X needs to transmit data to the receiver as the conditional graph entropy of the characteristic graph of X based on f . In our recent work In this paper, we consider a more general setup where X and Y are both encoded (separately). This is a significantly harder setup for which to give a singleletter characterization for the complete rate region. We find that under a certain condition on the support set of X and Y (called the zigzag condition), it is possible to characterize the rate region based on graph colorings at X and Y separately. That is, any achievable pair of rates can be realized by means of first coloring graphs at X and Y separately (function coding) and then using SlepianWolf coding for these colors (correlation coding). We also obtain a singleletter characterization of the minimal joint rate. Finally, we provide simulation results based on graph coloring to establish the rate gains on real sequences.
On optimal communication cost for gathering correlated data through wireless sensor networks
 in Proc. of ACM MobiCom
"... In many energyconstrained wireless sensor networks, nodes cooperatively forward correlated sensed data to data sinks. In order to reduce the communication cost (e.g. overall energy) used for data collection, previous works have focused on specific coding schemes, such as SlepianWolf Code or Expli ..."
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Cited by 32 (2 self)
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In many energyconstrained wireless sensor networks, nodes cooperatively forward correlated sensed data to data sinks. In order to reduce the communication cost (e.g. overall energy) used for data collection, previous works have focused on specific coding schemes, such as SlepianWolf Code or Explicit Entropy Code. However, the minimum communication cost under arbitrary coding/routing schemes has not yet been characterized. In this paper, we consider the problem of minimizing the total communication cost of a wireless sensor network with a single sink. We prove that the minimum communication cost can be achieved using SlepianWolf Code and Commodity Flow Routing when the link communication cost is a convex function of link data rate. Furthermore, we find it useful to introduce a new metric
A Distributed Framework for Correlated Data Gathering in Sensor Networks
, 2008
"... We consider the problem of correlated data gathering in sensor networks with multiple sink nodes. The problem has two objectives. First, we would like to find a rate allocation on the correlated sensor nodes such that the data gathered by the sink nodes can reproduce the field of observation. Second ..."
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Cited by 28 (0 self)
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We consider the problem of correlated data gathering in sensor networks with multiple sink nodes. The problem has two objectives. First, we would like to find a rate allocation on the correlated sensor nodes such that the data gathered by the sink nodes can reproduce the field of observation. Second, we would like to find a transmission structure on the network graph such that the total transmission energy consumed by the network is minimized. The existing solutions to this problem are impractical for deployment because they have not considered all of the following factors: 1) distributed implementation; 2) capacity and interference associated with the shared medium; and 3) realistic data correlation model. In this paper, we propose a new distributed framework to achieve minimum energy data gathering while considering these three factors. Based on a localized version of Slepian–Wolf coding, the problem is modeled as an optimization formulation with a distributed solution. The formulation is first relaxed with Lagrangian dualization and then solved with the subgradient algorithm. The algorithm is amenable to fully distributed implementations, which corresponds to the decentralized nature of sensor networks. To evaluate its effectiveness, we have conducted extensive simulations under a variety of network environments. The results indicate that the algorithm supports asynchronous network settings, sink mobility, and duty schedules.
Efficient energy management policies for networks with energy harvesting sensor nodes
 in Allerton Conference on Communication, Control, and Computing, 2008, Invited Paper
"... Abstract — We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node ..."
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Cited by 22 (5 self)
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Abstract — We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies.