Improving the reliability of emergency response networks using revolvernet

RevolverNet, operates over wireless ad-hoc networks where nodes communicate in duty cycles. When not at sleep, nodes beacon their own data and listen for data coming from neighboring nodes. Importantly, this mode of operation is increasingly becoming a prominent feature in a variety of communication setups, including emergency response networks (ERNs). RevolverNet is purposefully designed to take advantage of these beaconing mechanisms to gather network intelligence and achieve data dissemination in a purely distributed and local fashion. We examine two favourable features of RevolverNet that are attractive to ERNs, namely topological mapping and node localization that are highly applicable to ERNs. We show how these two features can be extracted from the underlying adhoc network in an efficient manner and how they can subsequently be used to disseminate information in the network. We present preliminary results on the performance of RevolverNet and discuss future work.