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20
Cooperative Techniques Supporting Sensor-based People-centric Inferencing
- In Proc. of 6th Int’l Conf. on Pervasive Computing
, 2008
"... Abstract. People-centric sensor-based applications targeting mobile device users offer enormous potential. However, learning inference models in this setting is hampered by the lack of labeled training data and appropriate feature inputs. Data features that lead to better classification models are n ..."
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Cited by 19 (11 self)
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Abstract. People-centric sensor-based applications targeting mobile device users offer enormous potential. However, learning inference models in this setting is hampered by the lack of labeled training data and appropriate feature inputs. Data features that lead to better classification models are not available at all devices due to device heterogeneity. Even for devices that provide superior data features, models require sufficient training data, perhaps manually labeled by users, before they work well. We propose opportunistic feature vector merging, and the socialnetwork-driven sharing of training data and models between users. Model and training data sharing within social circles combine to reduce the user effort and time involved in collecting training data to attain the maximum classification accuracy possible for a given model, while feature vector merging can enable a higher maximum classification accuracy by enabling better performing models even for more resource-constrained devices. We evaluate our proposed techniques with a significant places classifier that infers and tags locations of importance to a user based on data gathered from cell phones. 1
A Survey of Distributed Data Aggregation Algorithms
, 2011
"... Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val-ues result from the distributed computation of functions like count, sum and average. So ..."
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Cited by 10 (1 self)
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Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting val-ues result from the distributed computation of functions like count, sum and average. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal char-acteristics.
Data Gathering with Tunable Compression in Sensor Networks
- IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
, 2008
"... We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computation-intensive applications, where traditi ..."
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Cited by 7 (0 self)
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We study the problem of constructing a data gathering tree over a wireless sensor network in order to minimize the total energy for compressing and transporting information from a set of source nodes to the sink. This problem is crucial for advanced computation-intensive applications, where traditional “maximum ” in-network compression may result in significant computation energy. We investigate a tunable data compression technique that enables effective tradeoffs between the computation and communication costs. We derive the optimal compression strategy for a given data gathering tree and then investigate the performance of different tree structures for networks deployed on a grid topology as well as general graphs. Our analytical results pertaining to the grid topology and simulation results pertaining to the general graphs indicate that the performance of a simple greedy approximation to the Minimal Steiner Tree (MST) provides a constantfactor approximation for the grid topology and good average performance on the general graphs. Although theoretically, a more complicated randomized algorithm offers a poly-logarithmic performance bound, the simple greedy approximation of MST is attractive for practical implementation.
Analysis of quality of surveillance in fusion-based sensor networks
"... Abstract—Recent years have witnessed the deployments of wireless sensor networks for mission-critical applications such as battlefield monitoring and security surveillance. These appli-cations often impose stringent Quality of Surveillance (QoSv) requirements including low false alarm rate and short ..."
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Cited by 6 (0 self)
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Abstract—Recent years have witnessed the deployments of wireless sensor networks for mission-critical applications such as battlefield monitoring and security surveillance. These appli-cations often impose stringent Quality of Surveillance (QoSv) requirements including low false alarm rate and short detection delay. In practice, collaborative data fusion techniques that can deal with sensing uncertainty and enable sensor collaboration have been widely employed in sensor systems to achieve stringent QoSv requirements. However, most previous analytical studies on the surveillance performance of wireless sensor networks are based on simplistic models (such as the disc model) that cannot capture the stochastic and collaborative nature of sensing. In this paper, we systematically analyze the fundamental relationship between QoSv, network density, sensing parameters, and target properties. The results show that data fusion is effective in achieving stringent QoSv requirements, especially in the senarios with low signal-to-noise ratios (SNRs). In contrast, the disc model is only suitable when the SNR is sufficiently high. Our results help understand the limitations of disc model and provide insights into improving QoSv of sensor networks using data fusion. I.
Modeling and Throughput Analysis for SMAC with a Finite Queue Capacity
"... Abstract — SMAC is a popular duty-cycled MAC protocol, designed for wireless sensor networks to save energy and prolong the network lifetime. However, existing work evaluates the performance of SMAC solely through simulations or field measurements. To the best of our knowledge, there are no analytic ..."
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Cited by 6 (3 self)
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Abstract — SMAC is a popular duty-cycled MAC protocol, designed for wireless sensor networks to save energy and prolong the network lifetime. However, existing work evaluates the performance of SMAC solely through simulations or field measurements. To the best of our knowledge, there are no analytical models for evaluating the performance of SMAC. In this paper, we propose a Markov model to describe the behavior of SMAC with a finite queue capacity. This model enables us to find the expected throughput of SMAC under variable number of nodes, queue capacities, contention window sizes, and data arrival rates. We validate the model through extensive simulations, which provide throughput values within 5 % of the throughput values obtained through our model. Our proposed Markov model can be used to estimate the throughput of SMAC under many different network and node conditions, and more importantly, it provides us with a better understanding of the way that different parameters affect the performance of SMAC. I.
Sleeping Multipath Routing: A Trade-off Between Reliability and Lifetime
, 2011
"... Abstract—In wireless sensor networks, multipath routing is used to alleviate reliability degradation due to multihop transmissions over error-prone wireless channels. However, multipath routing is not energy-efficient, as it requires all the nodes in the network to always be awake. To prolong the ne ..."
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Cited by 4 (1 self)
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Abstract—In wireless sensor networks, multipath routing is used to alleviate reliability degradation due to multihop transmissions over error-prone wireless channels. However, multipath routing is not energy-efficient, as it requires all the nodes in the network to always be awake. To prolong the network lifetime while maintaining a certain reliability performance, we propose Sleeping Multipath Routing, which selects the minimum number of disjoint paths to achieve a given reliability requirement and puts the rest of the network to sleep. Simulation results show that Sleeping Multipath Routing can significantly extend the network lifetime, and it can trade off reliability for lifetime. I.
A Better Choice for Sensor Sleeping
"... Abstract. Sensor sleeping is a widely-used and cost-effective technique to save energy in wireless sensor networks. Protocols at different stack levels can, either individually or simultaneously, make the sensor sleep so as to extend the application lifetime. To determine the best choice for sensor ..."
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Cited by 3 (2 self)
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Abstract. Sensor sleeping is a widely-used and cost-effective technique to save energy in wireless sensor networks. Protocols at different stack levels can, either individually or simultaneously, make the sensor sleep so as to extend the application lifetime. To determine the best choice for sensor sleeping under different network conditions and application requirements, we investigate single layer and multi-layer sleeping schemes at the routing and MAC layers. Our results show that routing layer sleeping performs better when there is high network redundancy or high contention, while MAC layer sleeping performs better when there is low contention or in small networks. Moreover, multi-layer sleeping requires cross-layer coordination to outperform single layer sleeping under low contention. Therefore, our conclusions can not only guide the implementation of practical sensor networks, but they also provide hints to the design of cross-layer power management to dynamically choose the best sleeping scheme under different network and application scenarios.
A mobile-agent-based adaptive data fusion algorithm for multiple signal ensembles in wireless sensor networks
"... Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correla-tion structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sen-sor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble ..."
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Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correla-tion structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sen-sor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theo-retically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF en-joys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruc-tion performance and network energy efficiency.
Article Reliable Adaptive Data Aggregation Route Strategy for a Trade-off between Energy and Lifetime in WSNs
, 2014
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EUCLIDEAN STEINER SHALLOW-LIGHT TREES
- JOURNAL OF COMPUTATIONAL GEOMETRY
, 2015
"... A spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree is called a shallow-light tree (shortly, SLT). More specifically, an (α, β)-SLT of a weighted undirected graph G = (V,E,w) with respect to a designated vertex rt ∈ V is a spanning tree of G with: (1) r ..."
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A spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree is called a shallow-light tree (shortly, SLT). More specifically, an (α, β)-SLT of a weighted undirected graph G = (V,E,w) with respect to a designated vertex rt ∈ V is a spanning tree of G with: (1) root-stretch α – it preserves all distances between rt and the other vertices up to a factor of α, and (2) lightness β – it has weight at most β times the weight of a minimum spanning tree MST (G) of G. Tight tradeoffs between the parameters of SLTs were established by Awerbuch et al. in PODC’90 and by Khuller et al. in SODA’93. They showed that for any > 0, any graph admits a (1 + , O(1 ))-SLT with respect to any root vertex, and complemented this result with a matching lower bound. Khuller et al. asked if the upper bound β = O(1 ) on the lightness of SLTs can be improved in Euclidean spaces. In FOCS’11 Elkin and this author gave a negative answer to this question, showing a lower bound of β = Ω(1 ) that applies to 2-dimensional Euclidean spaces. In this paper we show that Steiner points lead to a quadratic improvement in Eu-clidean SLTs, by presenting a construction of Euclidean Steiner (1 + , O( 1
