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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

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On the scalability of hierarchical cooperation for dense sensor networks

On the scalability of hierarchical cooperation for dense sen...

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3rd international symposium on information processing in sensor networks

作者： ElBatt, T HRL Labs LLC Informat Sci Lab Malibu CA 90265 USA

ISBN: (纸本)1581138466

In this paper we study the problem of information dissemination in dense multi-hop sensor networks characterized by highly correlated sample measurements. In particular, we investigate the benefits, and trade-offs, of exploiting correlations via cooperatively compressing the data as it hops around the network. First, we study two extreme cooperation strategies, namely no cooperation and network-wide cooperation. We show that network-wide cooperation achieves logarithmic growth rate for the transport traffic with the network size whereas the schedule length growth rate remains linear. Next, we analyze a two-phase cooperation strategy which localizes cooperation within regions of the network in an attempt to assess the performance of strategies bounded by the two aforementioned extremes. Finally, we extend two-phase cooperation to a multi-phase hierarchical cooperation strategy where the number of phases depends on the number of nodes and the size of the cooperation set. The rationale behind this strategy is to achieve logarithmic scaling laws at the expense of more complexity in coordinating nodes' cooperation. In addition, hierarchical cooperation opens room for optimizing the transport traffic and schedule length for a given network size.

关键词： sensor networks data compression spatial correlations transport traffic scheduling latency scaling laws

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Lattice sensor networks: Capacity limits, optimal routing and robustness to failures

Lattice sensor networks: Capacity limits, optimal routing an...

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3rd international symposium on information processing in sensor networks

作者： Barrenechea, G Beferull-Lozano, B Vetterli, M Swiss Fed Inst Technol EPFL LCAV CH-1015 Lausanne Switzerland

ISBN: (纸本)1581138466

We study network capacity limits and optimal routing algorithms for regular sensor networks, namely, square and torus grid sensor networks, in both, the static case (no node failures) and the dynamic case (node failures). For static networks, we derive upper bounds on the network capacity and then we characterize and provide optimal routing algorithms whose rate per node is equal to this upper bound, thus, obtaining the exact analytical expression for the network capacity. For dynamic networks, the unreliability of the network is modeled in two ways: a Markovian node failure and an energy based node failure. Depending on the probability of node failure that is present in the network, we propose to use a particular combination of two routing algorithms, the first one being optimal when there are no node failures at all and the second one being appropriate when the probability of node failure is high. The combination of these two routing algorithms defines a family of randomized routing algorithms, each of them being suitable for a given probability of node failure.

关键词： randomized routing distributed routing cubic grid network torus grid network network capacity failures robustness

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Distributed optimization in sensor networks

Distributed optimization in sensor networks

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3rd international symposium on information processing in sensor networks

作者： Rabbat, M Nowak, R Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)1581138466

Wireless sensor networks are capable of collecting an enormous amount of data over space and time. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. The distributed algorithms are based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data. Applying results from the theory of incremental subgradient optimization, we show that for a broad class of estimation problems the distributed algorithms converge to within an epsilon-ball around the globally optimal value. Furthermore, bounds on the number incremental steps required for a particular level of accuracy provide insight into the trade-off between estimation performance and communication overhead. In many realistic scenarios, the distributed algorithms are much more efficient, in terms of energy and communications, than centralized estimation schemes. The theory is verified through simulated applications in robust estimation, source localization, cluster analysis and density estimation.

关键词： distributed optimization incremental subgradient methods energy-accuracy tradeoff

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Power-efficient sensor placement and transmission structure for data gathering under distortion constraints

Power-efficient sensor placement and transmission structure ...

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3rd international symposium on information processing in sensor networks

作者： Ganesan, D Cristescu, R Beferull-Lozano, B Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

ISBN: (纸本)1581138466

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 joint entropy coding based on explicit communication between sensor nodes, and consider both maximum and average distortion bounds. The optimization is complex 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 one-dimensional 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 1-D analysis to extend our results to the 2-D case, and show that our algorithm for two-dimensional placement and transmission structure provides significant power benefit over a commonly used combination of uniformly random placement and shortest path trees.

关键词： sensor networks data gathering information theory sensor node placement energy efficiency sensing distortion

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Effect of overhearing transmissions on energy efficiency in dense sensor networks

Effect of overhearing transmissions on energy efficiency in ...

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3rd international symposium on information processing in sensor networks

作者： Basu, P Redi, J BBN Technol Cambridge MA 02138 USA

ISBN: (纸本)1581138466

Energy efficiency is an important design criterion for the development of sensor networking protocols involving data dissemination and gathering. In-network processing of sensor data, aggregation, transmission power control in radios, and periodic cycling of node wake-up schedules are some techniques that have been proposed in the sensor networking literature for achieving energy efficiency. Owing to the broadcast nature of the wireless channel many nodes in the vicinity of a sender node may overhear its packet transmissions even if they are not the intended recipients of these transmissions. Reception of these transmissions can result in unnecessary expenditure of battery energy of the recipients. In this paper, we investigate the impact of overhearing transmissions on total energy costs during data gathering and dissemination and attempt to minimize them systematically. We model the minimum energy data gathering problem as a directed minimum energy spanning tree problem where the energy cost of each edge in the wireless connectivity graph is augmented by the overhearing cost of the corresponding transmission. We observe that in dense sensor networks, overhearing costs constitute a significant fraction of the total energy cost and that computing the minimum spanning tree on the augmented cost metric results in energy savings, especially in networks with non-uniform spatial node distribution. We also study the impact of this new metric on the well known energy-efficient dissemination (also called broadcasting) algorithms for multihop wireless networks. We show via simulation that through this augmented cost metric, gains in energy efficiency of 10% or more are possible without additional hardware and minimal additional complexity.

关键词： sensor networks energy efficiency data gathering data dissemination/broadcast overhearing

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Backcasting: Adaptive sampling for sensor networks

Backcasting: Adaptive sampling for sensor networks

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3rd international symposium on information processing in sensor networks

作者： Willett, R Martin, A Nowak, R Rice Univ Houston TX 77005 USA

ISBN: (纸本)1581138466

Wireless sensor networks provide an attractive approach to spatially monitoring environments. Wireless technology makes these systems relatively flexible, but also places heavy demands on energy consumption for communications. This raises a fundamental trade-off: using higher densities of sensors provides more measurements, higher resolution and better accuracy, but requires more communications and processing. This paper proposes a new approach, called "backcasting," which can significantly reduce communications and energy consumption while maintaining high accuracy. Backcasting operates by first having a small subset of the wireless sensors communicate their information to a fusion center. This provides an initial estimate of the environment being sensed, and guides the allocation of additional network resources. Specifically, the fusion center backcasts information based on the initial estimate to the network at large, selectively activating additional sensor nodes in order to achieve a target error level. The key idea is that the initial estimate can detect correlations in the environment, indicating that many sensors may not need to be activated by the fusion center. Thus, adaptive sampling can save energy compared to dense, non-adaptive sampling. This method is theoretically analyzed in the context of field estimation and it is shown that the energy savings can be quite significant compared to conventional approaches. For example, when sensing a piecewise smooth field with an array of 100 x 100 sensors, adaptive sampling can reduce the energy consumption by roughly a factor of 10 while providing the same accuracy achievable if all sensors were activated.

关键词： multiscale analysis field estimation hierarchical communication energy constraints

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Distributed online localization in sensor networks using a moving target

Distributed online localization in sensor networks using a m...

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3rd international symposium on information processing in sensor networks

作者： Galstyan, A Krishnamachari, B Lerman, K Pattem, S Univ So Calif Inst Informat Sci Los Angeles CA 90089 USA

ISBN: (纸本)1581138466

We describe a novel method for node localization in a sensor network where there are a fraction of reference nodes with known locations. For application-specific sensor networks, we argue that it makes sense to treat localization through online distributed learning and integrate it with an application task such as target tracking. We propose distributed online algorithm in which sensor nodes use geometric constraints induced by both radio connectivity and sensing to decrease the uncertainty of their position. The sensing constraints, which are caused by a commonly sensed moving target, are usually tighter than connectivity based constraints and lead to a decrease in average localization error over time. Different sensing models, such as radial binary detection and distance-bound estimation, are considered. First, we demonstrate our approach by studying a simple scenario in which a moving beacon broadcasts its own coordinates to the nodes in its vicinity. We then generalize this to the case when instead of a beacon, there is a moving target with a-priori unknown coordinates. The algorithms presented are fully distributed and assume only local information exchange between neighboring nodes. Our results indicate that the proposed method can be used to significantly enhance the accuracy in position estimation, even when the fraction of reference nodes is small. We compare the efficiency of the distributed algorithms to the case when node positions are estimated using centralized (convex) programming. Finally, simulations using the TinyOS-Nido platform are used to study the performance in more realistic scenarios.

关键词： wireless sensor networks localization

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Set K-cover algorithms for energy efficient monitoring in wireless sensor networks

Set K-cover algorithms for energy efficient monitoring in wi...

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3rd international symposium on information processing in sensor networks

作者： Abrams, Z Goel, A Plotkin, S Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)1581138466

Wireless sensor networks (WSNs) are emerging as an effective means for environment monitoring. This paper investigates a strategy for energy efficient monitoring in WSNs that partitions the sensors into covers, and then activates the covers iteratively in a round-robin fashion. This approach takes advantage of the overlap created when many sensors monitor a single area. Our work builds upon previous work in [13], where the model is first formulated. We have designed three approximation algorithms for a variation of the SET K-COVER problem, where the objective is to partition the sensors into covers such that the number of covers that include an area, summed over all areas, is maximized. The first algorithm is randomized and partitions the sensors, in expectation, within a fraction 1 - 1/e (similar to.63) of the optimum. We present two other deterministic approximation algorithms. One is a distributed greedy algorithm with a 1/2 approximation ratio and the other is a centralized greedy algorithm with a 1 - 1/e approximation ratio. We show that it is NP-Complete to guarantee better than 15/16 of the optimal coverage, indicating that all three algorithms perform well with respect to the best approximation algorithm possible in polynomial time, assuming P not equal NP. Simulations indicate that in practice, the deterministic algorithms perform far above their worst case bounds, consistently covering more than 72% of what is covered by an optimum solution. Simulations also indicate that the increase in longevity is proportional to the amount of overlap amongst the sensors. The algorithms are fast, easy to use, and according to simulations, significantly increase the longevity of sensor networks. The randomized algorithm in particular seems quite practical.

关键词： analysis of algorithms wireless sensor networks energy conservation

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Power-bandwidth-distortion scaling laws for sensor networks

Power-bandwidth-distortion scaling laws for sensor networks

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3rd international symposium on information processing in sensor networks

作者： Gastpar, M Vetterli, M Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)1581138466

The goal of a class of sensor networks is to monitor an underlying physical reality at the highest possible fidelity. sensors acquire noisy measurements and have to communicate them over a power- and possibly bandwidth-constrained interference channel to a set of base stations. The goal of this paper is to analyze, as a function of the number of sensors, the trade-offs between the degrees of freedom of the underlying physical reality, the communication resources (power, temporal and spatial bandwidth), and the resulting distortion at which the physical reality can be estimated by the base stations. The distortion can be expressed as the sum of two fundamentally different terms. The first term reflects the fact that the measurements are noisy. It depends on the number of sensors and on their locations, but it cannot be influenced by the communication resources. The second contribution to the distortion can be controlled by the communication resources, and the key question becomes: What resources are necessary to make it decay at least as fast as the first distortion term, as a function of the number of sensors? This question is answered threefold: First, a lower bound to the power-bandwidth trade-off is derived, showing that at least a constant to linearly increasing total power is required for typical cases (as a function of M). But is this also sufficient? In the second answer, communication strategies are considered where each sensor applies the best possible distributed compression algorithm, followed by capacity-achieving channel codes. For such a separation strategy, it is shown for typical cases that the power must increase exponentially as a function of the number of sensors, suggesting that the lower bound derived in this paper is far too optimistic. However, in the third answer, it is shown that this is not the case: For some example scenarios, the power requirements of the lower bound are indeed achievable, but joint source-channel coding is required. Fina

关键词： sensor networks OPTA (optimum performance theoretically attainable) joint source-channel coding separation theorem

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On distributed sampling of smooth non-bandlimited fields

On distributed sampling of smooth non-bandlimited fields

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3rd international symposium on information processing in sensor networks

作者： Kumar, A Ishwar, P Ramchandran, K Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

ISBN: (纸本)1581138466

Distributed sampling and reconstruction of a physical field using an array of sensors is a problem of considerable interest in environmental monitoring applications of sensor networks. Our recent work has focused on the sampling of bandlimited sensor fields. However, sensor fields are not perfectly bandlimited but typically have rapidly decaying spectra. In a classical sampling set-up it is possible to precede the A/D sampling operation with an appropriate analog anti-alia,sing filter. However, in the case of sensor networks, this is infeasible since sampling must precede filtering. We show that even though the effects of aliasing on the reconstruction cannot be prevented due to the "filter-less" sampling constraint, they can be suitably controlled by oversampling and carefully reconstructing the field from the samples. We show using a dither-based scheme that it is possible to estimate non-bandlimited fields with a precision that depends on how fast the spectral content of the field decays. We develop a framework for analyzing non-bandlimited fields that lead to upper bounds on the maximum pointwise error for a spatial bit rate of R bits/meter. We present results for fields with exponentially decaying spectra as an illustration. In particular, we show that for fields f(t) with exponential tails;i.e., \F(omega)\ < pialphae(-a\omega\), the maximum pointwise error decays as c(2)e(-a1rootR) + c(3)1/rootRe(-2a1rootR) with spatial bit rate R bits/meter. Finally, we show that for fields with spectra that have a finite second moment, the distortion decreases as O((1/N)(2/3)) as the density of sensors, N, scales up to infinity. We show that if D is the targeted non-zero distortion, then the required (finite) rate R scales as O(1/rootDlog1/D).

关键词： distributed sampling sensor networks non-bandlimited aliasing scaling laws dither function A/D conversion oversampling

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