TY - JOUR
T1 - Efficient detection of motion-trend predicates in wireless sensor networks
AU - Avci, Besim
AU - Trajcevski, Goce
AU - Tamassia, Roberto
AU - Scheuermann, Peter
AU - Zhou, Fan
PY - 2017/3/15
Y1 - 2017/3/15
N2 - This work addresses the problem of efficient distributed detection of predicates capturing the motion trends of mobile objects evaluated with respect to a (boundary of a) polygonal region, in the settings in which the (location, time) data is obtained via tracking in Wireless Sensor Networks (WSN). Specifically, we discuss in-network distributed algorithms for detecting two motion-trend predicates: Continuously Moving Towards and Persistently Moving Towards: first for a single object, and then the corresponding variants for multiple objects. We also present methodologies which consider the energy vs. latency trade-offs when multiple tracked objects are being considered for validating the monitored predicates. Our experiments demonstrate that our proposed technique yield substantial energy savings when compared to the naïve centralized and cluster-based approaches in which the raw (location, time) data is transmitted to a dedicated sink where the predicates are being evaluated.
AB - This work addresses the problem of efficient distributed detection of predicates capturing the motion trends of mobile objects evaluated with respect to a (boundary of a) polygonal region, in the settings in which the (location, time) data is obtained via tracking in Wireless Sensor Networks (WSN). Specifically, we discuss in-network distributed algorithms for detecting two motion-trend predicates: Continuously Moving Towards and Persistently Moving Towards: first for a single object, and then the corresponding variants for multiple objects. We also present methodologies which consider the energy vs. latency trade-offs when multiple tracked objects are being considered for validating the monitored predicates. Our experiments demonstrate that our proposed technique yield substantial energy savings when compared to the naïve centralized and cluster-based approaches in which the raw (location, time) data is transmitted to a dedicated sink where the predicates are being evaluated.
KW - Data aggregation
KW - Distributed algorithms
KW - Motion trends
KW - Spatial data
KW - WSN
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84995794105&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84995794105&partnerID=8YFLogxK
U2 - 10.1016/j.comcom.2016.08.012
DO - 10.1016/j.comcom.2016.08.012
M3 - Article
AN - SCOPUS:84995794105
VL - 101
SP - 26
EP - 43
JO - Computer Communications
JF - Computer Communications
SN - 0140-3664
ER -