انجمن کامپیوتر ایران

This paper, proposes the use of a reinforcement learning approach for a target tracking sensor network application. Harsh and unpredictable situations of sensor nodes in such an application requires a selftuning mechanism for the nodes to adapt their behavior over time. The method is examined under high dynamic network conditions and compared with a similar method called SORA over different performance measures. The results show a significant improvement over the compared method in the environments with high level of dynamism.

Due to the changing and dynamic environment of web services, Quality of Service (QoS) becomes a key factor to differentiate service providers. Since current web service standards and technologies suffer from the lack of QoS Management, having architectures capable of supporting QoS verification, selection, negotiation and monitoring is inevitable. Software agents have been recognized as a promising technology for managing web services. Using FIPA compliant Multi Agents we were able to propose a Multi Agents based web service QoS Management Architecture. We also introduced a QoS based web service clustering method which helps us to select the best service that suits user quality preferences.