TY - GEN
T1 - Near-optimal location tracking using sensor networks
AU - Sharma, Gokarna
AU - Krishnan, Hari
AU - Busch, Costas
AU - Brandt, Steven R.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/27
Y1 - 2014/11/27
N2 - We consider the problem of tracking mobile objects using a sensor network. We present a distributed tracking algorithm, called Mobile Object Tracking using Sensors MOT, that scales well with the number of sensors and also with the number of mobile objects. MOT maintains a hierarchical structure of detection lists that can efficiently track mobile objects and resolve object queries at any time. MOT guarantees that the cost to update its data structures will be at most O(min{log n, log D}) times the optimal update cost and query cost will be within O(1) of the optimal query cost in the constant-doubling graph model, where n and D, respectively, are the number of nodes and the diameter of the network. Moreover, MOT achieves polylogarithmic approximations for both costs in the general graph model and performs well in practical scenarios. To our best knowledge, MOT is the first algorithm for this problem in a distributed setting that is traffic-oblivious, i.e. agnostic to a priori knowledge of objects movement patterns, mobility and query rate, etc., and is load balanced.
AB - We consider the problem of tracking mobile objects using a sensor network. We present a distributed tracking algorithm, called Mobile Object Tracking using Sensors MOT, that scales well with the number of sensors and also with the number of mobile objects. MOT maintains a hierarchical structure of detection lists that can efficiently track mobile objects and resolve object queries at any time. MOT guarantees that the cost to update its data structures will be at most O(min{log n, log D}) times the optimal update cost and query cost will be within O(1) of the optimal query cost in the constant-doubling graph model, where n and D, respectively, are the number of nodes and the diameter of the network. Moreover, MOT achieves polylogarithmic approximations for both costs in the general graph model and performs well in practical scenarios. To our best knowledge, MOT is the first algorithm for this problem in a distributed setting that is traffic-oblivious, i.e. agnostic to a priori knowledge of objects movement patterns, mobility and query rate, etc., and is load balanced.
KW - Competitive ratio
KW - Hierarchical structure
KW - Location Tracking
KW - Mobile objects
KW - Sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84918833659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84918833659&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2014.85
DO - 10.1109/IPDPSW.2014.85
M3 - Conference contribution
AN - SCOPUS:84918833659
T3 - Proceedings - IEEE 28th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2014
SP - 737
EP - 746
BT - Proceedings - IEEE 28th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2014
PB - IEEE Computer Society
T2 - 28th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2014
Y2 - 19 May 2014 through 23 May 2014
ER -