Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

Maryam Vahabi*, Vikram Gupta, Michele Albano, Raghuraman Rangarajan, Eduardo Tovar

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time-consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with low overhead. In this paper, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.

Original languageEnglish
Article number457537
JournalInternational Journal of Distributed Sensor Networks
Volume2015
Number of pages21
ISSN1550-1329
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

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