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, can significantly improve disaster response. In particular, community response networks formed by mobile users/devices communicating by using only heterogeneous wireless ad hoc links, called herein CRHWNs, can exploit context awareness, defined as the capability of providing applications with full awareness of execution context. In fact, the correct and timely distribution of the current situation, such as health state and position of injured people, can substantially improve community coordination, thus increasing the possibility of saving human lives. Unfortunately, real-world context-aware services in disaster area scenarios require efficient, reliable, and scalable context data distribution and retrieval, and these properties clash with the limited resources usually supported by mobile devices and wireless communications. Along that direction, this article presents our context data distribution infrastructure for CRHWNs, which achieves data distribution efficiency and reliability by also exploiting useful quality indicators, such as data retrieval time and trustworthiness. We also show how our solution increases context data distribution/ retrieval scalability by dynamically self-adapting (a limited number of) data distribution paths and optimizing context data pushing to interested consumers. Experimental results validate our main assumptions and demonstrate how our solution introduces a limited runtime overhead.