NDLI: Resource allocation and scheduling for energy efficient tracking

Content Provider	IEEE Xplore Digital Library
Author	Bommannavar, P. Apostolopoulos, J. Bambos, N.
Copyright Year	2013
Description	Author affiliation: Electr. Eng. & Manage. Sci. & Eng., Stanford Univ., Stanford, CA, USA (Bambos, N.) \|\| Manage. Sci. & Eng., Stanford Univ., Stanford, CA, USA (Bommannavar, P.) \|\| Hewlett-Packard Labs., Palo Alto, CA, USA (Apostolopoulos, J.)
Abstract	We examine the problem of tracking the states of a collection of systems over a finite horizon in a power limited scenario. Specifically, each system has a sensor which can track a property of interest and has a fixed budget with which to make measurements and communicate to a fusion center. The state at each system varies independently according to a Markov model and the transitions between different states occur according known transition matrices. Different systems can have vastly different state evolution statistics. At each time step, the fusion center can request an update from any number of the systems, subject to the constraint that the corresponding sensor has not exhausted its budget to do so. These measurement updates are expensive and hence resource limited. After the fusion center receives all updates, it must estimate the state at each system with minimum error. We give an optimal policy for the fusion center to request updates from each sensor and also provide an optimal policy for the fusion center to allocate the measurement budget to each sensor before deployment, given the transition matrices corresponding to each system.
Starting Page	1532
Ending Page	1537
File Size	259473
Page Count	6
File Format	PDF
ISBN	9781467331227
ISSN	15503607
DOI	10.1109/ICC.2013.6654731
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-09
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Measurement Vectors Resource management Estimation error Dynamic programming Optimization Tracking Markov models Resource allocation
Content Type	Text
Resource Type	Article

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