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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/457537 |
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| _version_ | 1832555309901545472 |
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| author | Maryam Vahabi Vikram Gupta Michele Albano Raghuraman Rangarajan Eduardo Tovar |
| author_facet | Maryam Vahabi Vikram Gupta Michele Albano Raghuraman Rangarajan Eduardo Tovar |
| author_sort | Maryam Vahabi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5d1469249d384878ad01d0869f2ae391 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5d1469249d384878ad01d0869f2ae3912025-02-03T05:48:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/457537457537Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast DomainsMaryam Vahabi0Vikram Gupta1Michele Albano2Raghuraman Rangarajan3Eduardo Tovar4 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 CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, Portugal CISTER/INESC-TEC, ISEP, Polytechnic Institute of Porto, 4249-015 Porto, PortugalThe 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.https://doi.org/10.1155/2015/457537 |
| spellingShingle | Maryam Vahabi Vikram Gupta Michele Albano Raghuraman Rangarajan Eduardo Tovar Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains International Journal of Distributed Sensor Networks |
| title | Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains |
| title_full | Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains |
| title_fullStr | Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains |
| title_full_unstemmed | Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains |
| title_short | Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains |
| title_sort | feature extraction in densely sensed environments extensions to multiple broadcast domains |
| url | https://doi.org/10.1155/2015/457537 |
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