TreeCast: a stateless addressing and routing architecture for sensor networks

Chaudhuri, Santashil Pal; Du, Shu; Saha, Amit; Johnson, David B.

doi:10.1109/ipdps.2004.1303258

Cited by 27 publications

(34 citation statements)

References 21 publications

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“…This could enable a significant reduction in power. Such aggregation can also be implemented hierarchically in frameworks such as TreeCast [27] or DIMENSIONS [13].…”

Section: Advantages Of Dcs For Sensor Networkmentioning

confidence: 99%

Universal distributed sensing via random projections

Duarte

Wakin

Baron

et al. 2006

Proceedings of the Fifth International Conference on Information Processing in Sensor Networks - IPSN '06

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This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction. DCS is well-suited for sensor network applications, thanks to its simplicity, universality, computational asymmetry, tolerance to quantization and noise, robustness to measurement loss, and scalability. It also requires absolutely no intersensor collaboration. We apply our framework to several real world datasets to validate the framework.

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“…This could enable a significant reduction in power. Such aggregation can also be implemented hierarchically in frameworks such as TreeCast [27] or DIMENSIONS [13].…”

Section: Advantages Of Dcs For Sensor Networkmentioning

confidence: 99%

Universal distributed sensing via random projections

Duarte

Wakin

Baron

et al. 2006

Proceedings of the Fifth International Conference on Information Processing in Sensor Networks - IPSN '06

View full text Add to dashboard Cite

show abstract

“…Since the measurements can arrive out of order, they can be sent individually over the network or grouped into packets if desired. Many different options exist for routing the measurements, including TreeCast [27] and DIMENSIONS [13].…”

Section: Communication To the Data Sinkmentioning

confidence: 99%

Universal distributed sensing via random projections

Duarte

Wakin

Baron

et al. 2006

2006 5th International Conference on Information Processing in Sensor Networks

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“…Our algorithm FORM QT (shown in Figure 9) that creates binary trees of a given depth builds on TREECAST [9], a stateless routing scheme. Each root runs the modified algorithm to create attribute-based trees.…”

Section: Proposed Tree Formation Algorithmmentioning

confidence: 99%

“…Section 6 discusses the construction of QT in details. We first propose an algorithm which determines how to select the root of QT followed by discussion about the formation of each QT similar to Reference [9]. Expressions for optimal depth of QT are derived in this section.…”

Section: Introductionmentioning

confidence: 99%

Using polynomial regression for data representation in wireless sensor networks

Banerjee¹,

Chowdhury²,

Agrawal³

2006

Int J Communication

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SUMMARYUnlike conventional sensor networks, wireless sensors are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this paper, we exploit the spatiotemporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighbouring sensors and itself. We propose a tree based polynomial regression algorithm (TREG), that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, only the coefficients computed by the regression function, TREG are passed to achieve the following goals: (i) the sink can get attribute values in the regions devoid of sensor nodes, and (ii) readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area. Since physical attributes exhibit a gradual change over time, we propose an iterative scheme, UPDATE COEFF, which obviates the need to perform the regression function repeatedly and uses approximations based on previous readings. Extensive simulations are performed on real world data to demonstrate the effectiveness of the aggregation algorithm, TREG. Results reveal that for a network density of 0.0025, a complete binary tree of depth 4 could provide the absolute error to be less than 6%. A data compression ratio of about 0.02 is achieved using our proposed algorithm, which is almost independent of the tree depth. In addition, our proposed updating scheme makes the aggregation process faster while maintaining the desired error bounds.

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TreeCast: a stateless addressing and routing architecture for sensor networks

Cited by 27 publications

References 21 publications

Universal distributed sensing via random projections

Universal distributed sensing via random projections

Universal distributed sensing via random projections

Using polynomial regression for data representation in wireless sensor networks

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