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

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 a low overhead. In this article, we present a feature extraction mechanism for dense networks that takes advantage of dominancebased 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. Research Article Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains Maryam Vahabi, Vikram Gupta, Michele Albano, Raghuraman Rangarajan, and Eduardo Tovar CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA Correspondence should be addressed to Maryam Vahabi; [email protected] Received 12 May 2015; Accepted 1 July 2015 Academic Editor: Davide Brunelli Copyright © 2015 Maryam Vahabi et al.his is an open access article distributed under theCreative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. he vision of the Internet of hings (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. his 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. hus, it becomes imperative to devise distributed information processing mechanisms that identify application-speciic 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) eiciently 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 eicient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.