Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
On detection and tracking of variant phenomena clouds. / Thai, M.T.; Tiwari, R.; Bose, R. et al.
In: ACM Transactions on Sensor Networks, Vol. 10, No. 2, 34, 01.01.2014.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - On detection and tracking of variant phenomena clouds
AU - Thai, M.T.
AU - Tiwari, R.
AU - Bose, R.
AU - Helal, Sumi
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes.We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches. © 2014 ACM 1550-4859/2014/01-ART32 15.00.
AB - Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes.We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches. © 2014 ACM 1550-4859/2014/01-ART32 15.00.
KW - Distributed event processing
KW - Optimization
KW - Phenomena detection and tracking
KW - Sensor networks
KW - Detection and tracking
KW - Dynamic variations
KW - In-situ detections
KW - In-situ techniques
KW - Localized algorithm
KW - Resource utilizations
KW - Speed of motion
KW - Algorithms
KW - Automation
KW - Energy utilization
KW - Intelligent buildings
KW - Mathematical models
KW - Oil spills
KW - Tracking (position)
U2 - 10.1145/2530525
DO - 10.1145/2530525
M3 - Journal article
VL - 10
JO - ACM Transactions on Sensor Networks
JF - ACM Transactions on Sensor Networks
SN - 1550-4859
IS - 2
M1 - 34
ER -