Scalable and adaptive context delivery mechanism for context-aware computing

— Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for contextaware computing. Smart applications, hosted in myriad devices (e.g PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context-aware computing environment. Therefore we believe, as the system grows larger in near future, scalable delivery of contexts from the middleware to numerous contextaware applications will be inevitable. But, pure unicast or pure broadcast based dissemination can not provide scalability as well as less average latency. Hence, in this paper we present a scalable context delivery mechanism for these middleware to facilitate the development of larger context-aware computing systems. Proposed scheme is based on hybrid data dissemination technique where the most requested data (e.g. HOT contexts) are multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traf c. We dynamically prioritize and classify the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency and we also decrease polling traf c by incorporating leasing mechanism. Extensive simulation proves the proposed scheme to perform better. We also present implementation detail of our prototype that is developed using the available tools such as Jini framework and Java Reliable Multicast Service (JRMS) library. Index Terms— Context delivery, Context aware Middleware, Hybrid data dissemination, Bandwidth Division, Scalable Delivery