期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2013
卷号：2013
DOI：10.1155/2013/635637
出版社：Hindawi Publishing Corporation
摘要：Biological sensors (biosensors, for short) are tiny wireless devices attached
or implanted into the body of a human or animal to monitor and detect abnormalities
and then relay data to physician or provide therapy on the spot. They are distinguished
from conventional sensors by their biologically derived sensing elements and by being
temperature constrained. Biosensors generate heat when they transmit their measurements
and when they are recharged by electromagnetic energy. The generated heat translates to
a temperature increase in the tissues surrounding the biosensors. If the temperature increase
exceeds a certain threshold, the tissues might be damaged. In this paper, we discuss the problem
of finding an optimal policy for operating a rechargeable biosensor inside a temperature-sensitive
environment characterized by a strict maximum temperature increase constraint. This problem can
be formulated as a Markov Decision Process (MDP) and solved to obtain the optimal policy which
maximizes the average number of samples that can be generated by the biosensor while observing
the constraint on the maximum safe temperature level. In order to handle large-size MDP models,
it is shown how operating policies can be obtained using -learning and heuristics. Numerical and
simulation results demonstrating the performance of the different policies are presented.
