Learning automata: an introduction
Learning automata: an introduction
Scheduling data broadcast in asymmetric communication environments
Wireless Networks
R × W: a scheduling approach for large-scale on-demand data broadcast
IEEE/ACM Transactions on Networking (TON)
High performance data broadcasting systems
Mobile Networks and Applications
Scheduling and caching strategies for correlated data in push-based information systems
ACM SIGAPP Applied Computing Review
A cost-efficient scheduling algorithm of on-demand broadcasts
Wireless Networks
Developing a GIS Using a Mobile Phone Equipped with a Camera and a GPS, and Its Exhibitions
ICDCSW '04 Proceedings of the 24th International Conference on Distributed Computing Systems Workshops - W7: EC (ICDCSW'04) - Volume 7
Preemptive Maximum Stretch Optimization Scheduling for Wireless On-Demand Data Broadcast
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Performance aspects of data broadcast in wireless networks with user retrials
IEEE/ACM Transactions on Networking (TON)
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Where We At? Mobile Phones Bring GPS to the Masses
IEEE Computer Graphics and Applications
Disaster Evacuation Guide: Using a Massively Multiagent Server and GPS Mobile Phones
SAINT '07 Proceedings of the 2007 International Symposium on Applications and the Internet
Learning Minimum Delay Paths in Service Overlay Networks
NCA '08 Proceedings of the 2008 Seventh IEEE International Symposium on Network Computing and Applications
Periodic scheduling with costs revisited
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
WWIC'12 Proceedings of the 10th international conference on Wired/Wireless Internet Communication
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A novel adaptive scheme for wireless push systems is presented in this paper. In this wireless environment two entities play the most important role: the server side and the client side that is connected to the system. The server side is responsible to broadcast an item per transmission in order to satisfy the clients' requests. The performance of the server side depends on item selections. Hence, the server broadcasts an item and the clients are satisfied if the transmitted item was the desired one. In this work, a set of learning automata try to estimate the client demands in a distributed manner. More specifically, an autonomous learning automaton is utilized on each client group, since the clients are gathered into groups based on their location. The output of each automaton is combined in order to produce a well-performed transmission schedule. Concurrently, a round robin phase is adopted, giving the opportunity to the non-popular items to be transmitted. In this manner, the various client demands are treated fairly. The introduced technique is compared with a centralized adaptive scheme and the results indicate that the proposed scheduling framework outperforms the centralized one, in terms of response time and fairness.