Dragon: Data discovery and collection architecture for distributed IoT

Wireless Low-powered Sensing Systems (WLSS) are becoming more prevalent, taking the form of Wireless Sensor/Actuator Networks, Internet of Things, Phones etc. As node and network capabilities of such systems improve, there is more motivation to push computation into the network as it saves energy, prolongs system lifetime, and enables timely responses to events or control activities. Another advantage of such edge-processing is that these networks can become autonomous in the sense that users can directly query the network via any node in the network and are not required to connect to gateways or retrieve data via long range communications. Dragon is a scheme that efficiently identifies nodes that can reply to user requests based on static criteria that either describes that node or its data and provides the ability to near-optimally route queries or actuation control messages to those nodes. Dragon is scalable and agile as it does not require any central point orchestrating the search. In this paper we demonstrate significant performance improvements compared with state-of-the-art approaches in terms of numbers of messages required (up to 93% less) and its ability to scale to 100s of nodes.