Polychannel systems for mass digital communications
Communications of the ACM
Balancing push and pull for data broadcast
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
Reconfigurable Context-Sensitive Middleware for Pervasive Computing
IEEE Pervasive Computing
Adaptive Data Broadcast in Hybrid Networks
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Towards a Better Understanding of Context and Context-Awareness
HUC '99 Proceedings of the 1st international symposium on Handheld and Ubiquitous Computing
FTDCS '04 Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems
Scalable dissemination: what's hot and what's not
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
A survey on context-aware systems
International Journal of Ad Hoc and Ubiquitous Computing
WEA'03 Proceedings of the 2nd international conference on Experimental and efficient algorithms
The computer for the 21st Century
IEEE Pervasive Computing
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Context delivery is an inevitable issue for ubiquitous computing. Context-aware middlewares perform all the functions of context sensing, inferring and delivery to context-aware applications. But one of the major issues for these middlewares is to devise a context delivery scheme that is scalable as well as efficient. Pure unicast or pure broadcast based dissemination can not provide scalability as well as less average latency. In this paper we present a scalable context delivery mechanism for context-aware middlewares based on hybrid data dissemination technique where the most requested data are broadcasted and the rest are delivered through unicast. Our scheme is adaptive in the sense that it dynamically differentiates hot (most requested) and cold (less requested) data according to request rate and waiting time. Inclusion of lease mechanism and bandwidth division further allows us to reduce network traffic and average latency. We validated our claim through extensive simulation