Lattice networks: capacity limits, optimal routing, and queueing behavior

Barrenetxea, Guillermo; Berefull-Lozano, Baltasar; Vetterli, Martin

doi:10.1109/tnet.2006.876187

Cited by 47 publications

(54 citation statements)

References 18 publications

(24 reference statements)

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“…Specifically-designed routing protocols [21,7,1] can, to some extent, improve the energy efficiency of the WSN. Yet, they can do little for the energy-hole problem: sensor nodes close to the base stations (BSs) 1 forward much more data and drain their batteries much faster than other sensor nodes. The uneven distribution of the energy consumption is a culprit for the poor energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Virtually Moving Base Stations for Energy Efficiency in Wireless Sensor Networks

Zhang

Thiran

Vetterli

2015

Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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Energy efficiency of wireless sensor networks (WSNs) can be improved by moving base stations (BSs), as this scheme evenly distributes the communication load in the network. However, physically moving the BSs is complicated and costly. In this paper, we propose a new scheme: virtually moving the BSs. We deploy an excessive number of BSs and adaptively re-select a subset of active BSs so as to emulate the physical movement. Beyond achieving high energy-efficiency, this scheme obviates the difficulties associated with physically moving the BSs.The challenges are (i) that the energy efficiency of BSs should be considered as well, in addition to that of the sensor nodes and (ii) that the number of candidate subset of active BSs is exponential with the number of BSs. We show that scheduling the virtual movement of BSs is NP-hard. Then, we propose a polynomial-time algorithm that is guaranteed under mild conditions to achieve a lifetime longer than 62% of the optimal one. In practice, as verified through extensive numerical simulations, the lifetime achieved by the proposed algorithm is always very close to the optimum.

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Section: Introductionmentioning

confidence: 99%

Virtually Moving Base Stations for Energy Efficiency in Wireless Sensor Networks

Zhang

Thiran

Vetterli

2015

Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing

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“…Network capacity can be achieved by using an appropriate shortest path routing algorithm Π [8]. Note that the bottleneck of the network is clearly located in the central nodes.…”

Section: A Routing With Infinite Buffersmentioning

confidence: 99%

“…We denote this routing by row-first (column-first) [7]. Indeed, R row-first max (N, ∞) = Cu(N ) [8].…”

Section: A Routing With Infinite Buffersmentioning

confidence: 99%

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Efficient routing with small buffers in dense networks

Barrenetxea¹,

Beferull-Lozano²,

Vetterli³

IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005.

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Abstract-The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's Theorem that give more insight into the effect of routing algorithms on the queue size distributions.Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases.Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.

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“…[2] extends the results in [3] to account for the different traffic models that arise in a sensor network. [4] studies network transport capacity for the case of regular sensor networks. [5] studies the impact of computational constraints on the communication efficiency of sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Sensing capacity for discrete sensor network applications

Rachlin

Negi

Khosla

IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005.

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Abstract-We bound the number of sensors required to achieve a desired level of sensing accuracy in a discrete sensor network application (e.g. distributed detection). We model the state of nature being sensed as a discrete vector, and the sensor network as an encoder. Our model assumes that each sensor observes only a subset of the state of nature, that sensor observations are localized and dependent, and that sensor network output across different states of nature is neither identical nor independently distributed. Using a random coding argument we prove a lower bound on the 'sensing capacity' of a sensor network, which characterizes the ability of a sensor network to distinguish among all states of nature. We compute this lower bound for sensors of varying range, noise models, and sensing functions. We compare this lower bound to the empirical performance of a belief propagation based sensor network decoder for a simple seismic sensor network scenario. The key contribution of this paper is to introduce the idea of a sharp cut-off function in the number of required sensors, to the sensor network community.

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Lattice networks: capacity limits, optimal routing, and queueing behavior

Cited by 47 publications

References 18 publications

Virtually Moving Base Stations for Energy Efficiency in Wireless Sensor Networks

Virtually Moving Base Stations for Energy Efficiency in Wireless Sensor Networks

Efficient routing with small buffers in dense networks

Sensing capacity for discrete sensor network applications

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