Abstract
Biological sensors are a very promising technology that will take healthcare 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 biological sensors implanted into a human body might damage the tissues around them. Dynamic sensor scheduling is one way to manage and evenly distribute the generated heat. In this paper, the dynamic sensor scheduling problem is formulated as a Markov decision process (MDP). Unlike previous works, the temperature increase in the tissues caused by the generated heat is incorporated into the model. The solution of the model gives an optimal policy that when executed will result in the maximum possible network lifetime under a constraint on the maximum temperature level tolerable by the patient's body. In order to obtain the optimal policy in a lesser amount of time, two specific types of states are aggregated to produce a considerably smaller MDP model equivalent to the original one. Numerical and simulation results are presented to show the validity of the model and superiority of the optimal policy produced by it when compared with two policies one of which is specifically designed for biological wireless sensor networks.
Highlights
Biological wireless sensor networks (BWSNs) are networks made up of biological sensors which are tiny wireless devices attached or implanted into the body of a human or animal to monitor and control biological processes
A famous application of BWSNs is the geodesic sensor network developed by EGI corporation [1]
The large state space of the Markov decision process (MDP) model makes the computation of the optimal policy a highly intensive process and only feasible for small-scale networks
Summary
Biological wireless sensor networks (BWSNs) are networks made up of biological sensors (biosensors, for short) which are tiny wireless devices attached or implanted into the body of a human or animal to monitor and control biological processes. The sensor network collects electroencephalographical (EEG) measurements of the brain and delivers them to a controller which processes them and displays the results Another example is glucose biosensors which monitor the blood glucose level in a diabetic patient. The thermal management problem in BWSNs is studied It is shown how it can be modeled as a stochastic control problem. A considerable reduction in the size of the MDP model is achieved when the states in these two classes are aggregated.
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