Intelligent Sensing in Dynamic Environments Using Markov Decision Process

Intelligent Sensing in Dynamic Environments Using Markov Decision Process

In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically...

Dynamic Decision Model using Partially Observable Markov Decision Process

Optimizing Red Blood Cells Consumption Using Markov Decision Process

In healthcare systems, one of the important actions is related to perishable products such as red blood cells (RBCs) units that its consumption management in different periods can contribute greatly to the optimality of the system. In this paper, main goal is to enhance the ability of medical community to organize the RBCs units’ consumption in way to deliver the unit order timely with a focus ...

Intelligent decision making in dynamic environments: methods, architectures and applications

This Special Issue presents recent research results in the fast growing area of intelligent decision technologies (IDT) in dynamic environments. Intelligent decision making in dynamic environments can be distinguished by a number of aspects from a conventional decision making. First, due to situational changes in an operational field, time is an inherent dimension of the decision making process...

Partially Observable Markov Decision Process Approximations for Adaptive Sensing

Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification, and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the problem. Suc...