• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3B
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• pp.175-182
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• 2006

In this paper, we propose a Localized Adaptive QoS Routing Scheme using POMDP and Exploration Bonus Techniques. Also, this paper shows that CEA technique using expectation values can be simply POMDP problem, because performing dynamic programming to solve a POMDP is highly computationally expensive. And we use Exploration Bonus to search detour path better than current path. For this, we proposed the algorithm(SEMA) to search multiple path. Expecially, we evaluate performances of service success rate and average hop count with $\phi$ and k performance parameters, which is defined as exploration count and intervals. As result, we knew that the larger $\phi$, the better detour path search. And increasing n increased the amount of exploration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1036-1057
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• 2013

Wireless body area network (WBAN) is a promising candidate for future health monitoring system. Nevertheless, the path to mature solutions is still facing a lot of challenges that need to be overcome. Energy efficient scheduling is one of these challenges given the scarcity of available energy of biosensors and the lack of portability. Therefore, researchers from academia, industry and health sectors are working together to realize practical solutions for these challenges. The main difficulty in WBAN is the uncertainty in the state of the monitored system. Intelligent learning approaches such as a Markov Decision Process (MDP) were proposed to tackle this issue. A Markov Decision Process (MDP) is a form of Markov Chain in which the transition matrix depends on the action taken by the decision maker (agent) at each time step. The agent receives a reward, which depends on the action and the state. The goal is to find a function, called a policy, which specifies which action to take in each state, so as to maximize some utility functions (e.g., the mean or expected discounted sum) of the sequence of rewards. A partially Observable Markov Decision Processes (POMDP) is a generalization of Markov decision processes that allows for the incomplete information regarding the state of the system. In this case, the state is not visible to the agent. This has many applications in operations research and artificial intelligence. Due to incomplete knowledge of the system, this uncertainty makes formulating and solving POMDP models mathematically complex and computationally expensive. Limited progress has been made in terms of applying POMPD to real applications. In this paper, we surveyed the existing methods and algorithms for solving POMDP in the general domain and in particular in Wireless body area network (WBAN). In addition, the papers discussed recent real implementation of POMDP on practical problems of WBAN. We believe that this work will provide valuable insights for the newcomers who would like to pursue related research in the domain of WBAN.

• Journal of Korean Institute of Industrial Engineers
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• v.16 no.2
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• pp.1-9
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• 1990

The control of deterioration processes for which only incomplete state information is available is examined in this study. When the deterioration is governed by a Markov process, such processes are known as Partially Observable Markov Decision Processes (POMDP) which eliminate the assumption that the state or level of deterioration of the system is known exactly. This research investigates a two state partially observable Markov chain in which only deterioration can occur and for which the only actions possible are to replace or to leave alone. The goal of this research is to develop a new jump algorithm which has the potential for solving system problems dealing with continuous state space Markov chains.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.93-99
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• 2011

In this paper, we propose a policy-gradient routing scheme under Reinforcement Learning that can be used adaptive QoS routing. A policy-gradient RL routing can provide fast learning of network environments as using optimal policy adapted average estimate rewards gradient values. This technique shows that fast of learning network environments results in high success rate of routing. For prove it, we simulate and compare with three different schemes.