Project ExScal

Arora, Anish; Ramnath, Rajiv; Sinha, Prasun; Ertin, Emre; Bapat, Sandip; Naik, Vinayak; Kulathumani, Vinod; Zhang, Hongwei; Sridharan, Karthik; Kumar, Santosh; Cao, Hui; Seddon, Nick; Anderson, Chris; Herman, Ted; Zhang, Chen; Trivedi, Nishank; Gouda, Mohamed G.; Choi, Young-ri; Nesterenko, Mikhail; Shah, Romil; Kulkarni, Sandeep S.; Aramugam, Mahesh; Wang, Limin; Culler, David; Dutta, Prabal; Sharp, Cory; Tolle, Gilman; Grimmer, M. K.; Ferriera, Bill; Parker, Ken R.

doi:10.1007/11502593_33

Cited by 13 publications

(14 citation statements)

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“…nearby sensors within a few hops, or a distance of tens to hundreds of meters, are likely to yield similar readings, and vise versa. For example, in intrusion detection [1], the emergence of an intruder (i.e. human or vehicle) typically only triggers the number of sensors sufficiently close to it.…”

Section: Revisiting Sensor Datamentioning

confidence: 99%

“…Query results are the data items from all sensor motes in the window. 1 An example is shown in Figure 5, where the spatial window W is a rectangular region W = (0.125, 0.5) − (0.375, 0.6875) relative to the network area. Recall from Section 3.1 that geographic locations can be mapped to sensor motes' identifiers using Hilbert curve.…”

Section: Spatial Window Querymentioning

confidence: 99%

“…Sampling in itself does not support recovery of the entire dataset, we thus employ pairwise linear approximation to build approximate datasets from sampled data items. The error metric is L∞-norm, as formulated in Equation 1. Figure 8 studies the relation between approximation error and the percentage of data used for building the approximate version.…”

Section: Study Of Approximation Errormentioning

confidence: 99%

“…Generating events in sensor networks typically requires sophisticated domain knowledge to be embedded in sensor motes, e.g. to infer 'intrusion' events from 'vibration' and 'infrared motion' sensors [1]. On the contrary, aggregations and approximations are meant for general-purpose applications.…”

Section: Related Workmentioning

confidence: 99%

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Enabling ε-approximate querying in sensor networks

Liu

Gao

et al. 2009

Proc. VLDB Endow.

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Data approximation is a popular means to support energy-efficient query processing in sensor networks. Conventional data approximation methods require users to specify fixed error bounds a prior to address the trade-off between result accuracy and energy efficiency of queries. We argue that this can be infeasible and inefficient when, as in many real-world scenarios, users are unable to determine in advance what error bounds can lead to affordable cost in query processing. We envision -approximate querying (EAQ) to bridge the gap. EAQ is a uniform data access scheme underlying various queries in sensor networks. It allows users or query executors to incrementally 'refine' previously obtained approximate data to reach arbitrary accuracy. EAQ not only grants more flexibility to in-network query processing, but also minimizes energy consumption through communicating data upto a just-sufficient level. To enable the EAQ scheme, we propose a novel data shuffling algorithm. The algorithm converts sensed datasets into special representations called multi-version array (MVA). From prefixes of MVA, we can recover approximate versions of the entire dataset, where all individual data items have guaranteed error bounds. The EAQ scheme supports efficient and flexible processing of various queries including spatial window query, value range query, and queries with QoS constraints. The effectiveness and efficiency of the EAQ scheme are evaluated in a real sensor network testbed.

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Section: Revisiting Sensor Datamentioning

confidence: 99%

Section: Spatial Window Querymentioning

confidence: 99%

Section: Study Of Approximation Errormentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Enabling ε-approximate querying in sensor networks

Liu

Gao

et al. 2009

Proc. VLDB Endow.

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show abstract

“…Hence, the existing algorithms are not scalable for extremely large networks, such as those in project ExScal [8]. ExScal consisted of 1,000 extreme scaling motes (XSMs) [9] and 203 XSSs.…”

mentioning

confidence: 99%