Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Transmission Scheduling for Optimizing Sensor Network Lifetime: A Stochastic Shortest Path Approach
IEEE Transactions on Signal Processing
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Biosensors are a very promlsmg technology that will take health care to the next level. However, there are obstacles that must be overcome before the full potential of this technology can be realized. One such obstacle is that the heat generated by implanted biosensors may damage the tissues around them. Dynamic sensor scheduling is one way to manage the heat generated by implanted biosensors. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process. Not like previous works, the temperature increase in the tissues caused by heat is incorporated into the model. The solution of the model gives an optimal policy that when executed, it will result in the maximum possible network lifetime under a constraint on the maximum temperature tolerable by the patient's body. The optimal policy is compared with two policies one of which is specifically designed for biosensor networks. Numerical and simulation results show the validity of the model and superiority of the optimal policy produced by the model in terms of both network lifetime and temperature increase.