Scalable and mobile context data retrieval and distribution for community response heterogeneous wireless networks

PROJECT TITLE :

Scalable and mobile context data retrieval and distribution for community response heterogeneous wireless networks

ABSTRACT:

Recent studies have indicated that community response networks, locally grouping both professional emergency responders and residents by using mobile and social NetWorking technologies, will considerably improve disaster response. In specific, community response networks shaped by mobile users/devices communicating by using only heterogeneous wireless unintended links, known as herein CRHWNs, can exploit context awareness, defined as the potential of providing applications with full awareness of execution context. Of course, the right and timely distribution of this state of affairs, such as health state and position of injured folks, can substantially improve community coordination, thus increasing the possibility of saving human lives. Unfortunately, real-world context-aware services in disaster area eventualities need economical, reliable, and scalable context knowledge distribution and retrieval, and these properties clash with the restricted resources sometimes supported by mobile devices and wireless Communications. Along that direction, this article presents our context data distribution infrastructure for CRHWNs, that achieves data distribution potency and reliability by conjointly exploiting useful quality indicators, like information retrieval time and trustworthiness. We tend to conjointly show how our resolution will increase context data distribution/ retrieval scalability by dynamically self-adapting (a limited range of) data distribution methods and optimizing context information pushing to interested shoppers. Experimental results validate our main assumptions and demonstrate how our answer introduces a limited runtime overhead.

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