TY - GEN
T1 - Optimal management of rechargeable biosensors in temperature-sensitive environments
AU - Osais, Yahya
AU - Yu, F. Richard
AU - St-Hilaire, Marc
PY - 2010
Y1 - 2010
N2 - Biosensors are tiny wireless medical devices which are attached or implanted into the body of a human being or animal to monitor and control biological processes. They are distinguished from conventional sensors by their biologically derived sensing elements. Biosensors generate heat when they transmit their measurements and their temperature rises when recharged by electromagnetic energy. These phenomena translate 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 operating policy for a rechargable biosensor under a strict maximum temperature increase constraint. This problem can be formulated as a Markov decision process with an average reward criterion. The solution is an optimal policy that maximizes the average number of samples which can be generated by the biosensor while observing the constraint on the maximum safe temperature level. Due to the exponential nature of the problem, a heuristic policy is proposed. The performance of the policies is studied through simulation. A greedy policy is used as a baseline for comparison.
AB - Biosensors are tiny wireless medical devices which are attached or implanted into the body of a human being or animal to monitor and control biological processes. They are distinguished from conventional sensors by their biologically derived sensing elements. Biosensors generate heat when they transmit their measurements and their temperature rises when recharged by electromagnetic energy. These phenomena translate 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 operating policy for a rechargable biosensor under a strict maximum temperature increase constraint. This problem can be formulated as a Markov decision process with an average reward criterion. The solution is an optimal policy that maximizes the average number of samples which can be generated by the biosensor while observing the constraint on the maximum safe temperature level. Due to the exponential nature of the problem, a heuristic policy is proposed. The performance of the policies is studied through simulation. A greedy policy is used as a baseline for comparison.
UR - https://www.scopus.com/pages/publications/78649407615
U2 - 10.1109/VETECF.2010.5594518
DO - 10.1109/VETECF.2010.5594518
M3 - Conference contribution
AN - SCOPUS:78649407615
SN - 9781424435746
T3 - IEEE Vehicular Technology Conference
BT - 2010 IEEE 72nd Vehicular Technology Conference Fall, VTC2010-Fall - Proceedings
ER -