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Bounds on the Gain of Network Coding and Broadcasting in Wireless Networks
 in INFOCOM
, 2007
"... Gupta and Kumar established that the per node throughput of ad hoc networks with multipair unicast traffic scales with an increasing number of nodes ¤ as ¥§¦¨¤�©�����¦����� � ¤�������¤� © , thus indicating that network performance does not scale well. However, Gupta and Kumar did not consider the p ..."
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Cited by 62 (5 self)
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Gupta and Kumar established that the per node throughput of ad hoc networks with multipair unicast traffic scales with an increasing number of nodes ¤ as ¥§¦¨¤�©�����¦����� � ¤�������¤� © , thus indicating that network performance does not scale well. However, Gupta and Kumar did not consider the possibility of network coding and broadcasting in their model, and recent work has suggested that such techniques have the potential to greatly improve network throughput. Here, for multiple unicast flows in a random topology under the protocol communication model of Gupta and Kumar [1], we show that for arbitrary network coding and broadcasting in a �� � random topology that the throughput scales as ¥§¦¨¤�©�����¦�����¤���¦�¤�©� © where ¤ is the total number of nodes and ��¦¨¤� © is the transmission radius. When is set to ensure connectivity, ¥§¦¨¤�©�����¦��� � � ¤�������¤� © , which is of the same order as the lower bound for the throughput without network coding and broadcasting; in other words, network coding and broadcasting at most provides a constant factor improvement in the throughput. This result is also extended to other dimensional random deployment topologies, where it is shown ¥§¦¨¤�©�����¦�����¤� © that for �� � the ¥§¦¨¤�©����� ¦ � � topology, networks ¥���¦¨¤�©����� ¦ � � �����
On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks
, 2009
"... Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, th ..."
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Cited by 41 (6 self)
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Compressive Sensing (CS) shows high promise for fully distributed compression in wireless sensor networks (WSNs). In theory, CS allows the approximation of the readings from a sensor field with excellent accuracy, while collecting only a small fraction of them at a data gathering point. However, the conditions under which CS performs well are not necessarily met in practice. CS requires a suitable transformation that makes the signal sparse in its domain. Also, the transformation of the data given by the routing protocol and network topology and the sparse representation of the signal have to be incoherent, which is not straightforward to achieve in real networks. In this work we address the data gathering problem in WSNs, where routing is used in conjunction with CS to transport random projections of the data. We analyze synthetic and real data sets and compare the results against those of random sampling. In doing so, we consider a number of popular transformations and we find that, with real data sets, none of them are able to sparsify the data while being at the same time incoherent with respect to the routing matrix. The obtained performance is thus not as good as expected and finding a suitable transformation with good sparsification and incoherence properties remains an open problem for data gathering in static WSNs.
Differentiated Data Persistence with Priority Random Linear Codes
, 2006
"... Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challeng ..."
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Cited by 26 (2 self)
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Both peertopeer and sensor networks have the fundamental characteristics of node churn and failures. Peers in P2P networks are highly dynamic, whereas sensors are not dependable. As such, maintaining the persistence of periodically measured data in a scalable fashion has become a critical challenge in such systems, without the use of centralized servers. To better cope with node dynamics and failures, we propose priority random linear codes, as well as their affiliated predistribution protocols, to maintain measurement data in different priorities, such that critical data have a higher opportunity to survive node failures than data of less importance. A salient feature of priority random linear codes is the ability to partially recover more important subsets of the original data with higher priorities, when it is not feasible to recover all of them due to node dynamics. We present extensive analytical and experimental results to show the effectiveness of priority random linear codes. 1
W paths in wireless sensor networks
 Proceedings of MSN 2005
, 2005
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 20 (5 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Opportunistic source coding for data gathering in wireless sensor networks
 IEEE International Conference on Mobile Ad Hoc and Sensor Systems
, 2007
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Bounds on the Benefit of Network Coding: Throughput and Energy Saving in Wireless Networks
"... Abstract—In this paper we establish fundamental limitations to the benefit of network coding in terms of energy and throughput in multihop wireless networks. Thereby we adopt two well accepted scenarios in the field: single multicast session and multiple unicast sessions. Most of our results apply t ..."
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Cited by 12 (0 self)
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Abstract—In this paper we establish fundamental limitations to the benefit of network coding in terms of energy and throughput in multihop wireless networks. Thereby we adopt two well accepted scenarios in the field: single multicast session and multiple unicast sessions. Most of our results apply to arbitrary wireless network and are, in particular, not asymptotic in kind. In terms of throughput and energy saving we prove that the gain of network coding of a single multicast session is at most a constant factor. Also, we present a lower bound on the expected number of transmissions of multiple unicast sessions under an arbitrary network coding. We identify scenarios for which the network coding gain for energy saving becomes surprisingly close to 1, in some cases even exactly 1, corresponding to no benefit at all. Interestingly, we prove that the gain of network coding in terms of transport capacity is bounded by a constant factor π in any arbitrary wireless network and for all traditional channel models. This shows that the traditional bounds on the transport capacity [1]–[4] do not change more than constant factor π if we employ network coding. As a corollary, we find that the gain of network coding on the throughput of large scale homogeneous wireless networks is asymptotically bounded by a constant. Note that our result is more general than the previous work [5] and it is obtained by a different technique. In conclusion, we show that in contrast to wired networks, the network coding gain in wireless networks is constraint by fundamental limitations. I.
On optimum communication cost for joint compression and dispersive information routing
 IEEE Information theory workshop
, 2010
"... Abstract—In this paper, we consider the problem of minimum cost joint compression and routing for networks with multiplesinks and correlated sources. We introduce a routing paradigm, called dispersive information routing, wherein the intermediate nodes are allowed to forward a subset of the received ..."
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Cited by 9 (9 self)
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Abstract—In this paper, we consider the problem of minimum cost joint compression and routing for networks with multiplesinks and correlated sources. We introduce a routing paradigm, called dispersive information routing, wherein the intermediate nodes are allowed to forward a subset of the received bits on subsequent paths. This paradigm opens up a rich class of research problems which focus on the interplay between encoding and routing in a network. What makes it particularly interesting is the challenge in encoding sources such that, exactly the required information is routed to each sink, to reconstruct the sources they are interested in. We demonstrate using simple examples that our approach offers better asymptotic performance than conventional routing techniques. We also introduce a variant of the well known random binning technique, called ‘power binning’, to encode and decode sources that are dispersively transmitted, and which asymptotically achieves the minimum communication cost within this routing paradigm. I.
Towards Optimum Cost in MultiHop Networks with Arbitrary Network Demands
"... This paper considers the problem of minimizing the communication cost for a general multihop network with correlated sources and multiple sinks. For the single sink scenario, it has been shown that this problem can be decoupled, without loss of optimality, into two separate subproblems of distribu ..."
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Cited by 6 (5 self)
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This paper considers the problem of minimizing the communication cost for a general multihop network with correlated sources and multiple sinks. For the single sink scenario, it has been shown that this problem can be decoupled, without loss of optimality, into two separate subproblems of distributed source coding and finding the optimal routing (transmission structure). It has further been established that, under certain assumptions, such decoupling also applies in the general case of multiple sinks and arbitrary network demands. We show that these assumptions are significantly restrictive, and further provide examples to substantiate the loss, including settings where removing the assumptions yields unbounded performance gains. Finally, an approach to solving the unconstrained problem, where routing and coding cannot be decoupled, is derived based on Han and Kobayashi’s achievability region for multiterminal coding.
On Computing Compression Trees for Data Collection in Wireless Sensor Networks
"... Abstract—We address the problem of efficiently gathering correlated data from a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding a ..."
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Abstract—We address the problem of efficiently gathering correlated data from a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding an optimal or a nearoptimal compression tree for a given sensor network: a compression tree is a directed tree over the sensor network nodes such that the value of a node is compressed using the value of its parent. We focus on broadcast communication model in this paper, but our results are more generally applicable to a unicast communication model as well. We draw connections between the data collection problem and a previously studied graph concept called weakly connected dominating sets, and we use this to develop novel approximation algorithms for the problem. We present comparative results on several synthetic and realworld datasets showing that our algorithms construct nearoptimal compression trees that yield a significant reduction in the data collection cost. I.