Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



+44 (0)20 7594 8375j.mccann Website




Miss Teresa Ng +44 (0)20 7594 8300




260ACE ExtensionSouth Kensington Campus






BibTex format

author = {Kolcun, R and Boyle, DE and McCann, JA},
doi = {10.1109/TASE.2016.2530941},
journal = {IEEE Transactions on Automation Science and Engineering},
pages = {1230--1246},
title = {Efficient Distributed Query Processing},
url = {},
volume = {13},
year = {2016}

RIS format (EndNote, RefMan)

AB - A variety of wireless networks, including applications of Wireless Sensor Networks, Internet of Things, and Cyber-physical Systems, increasingly pervade our homes, retail, transportation systems, and manufacturing processes. Traditional approaches communicate data from all sensors to a central system, and users (humans or machines) query this central point for results, typically via the web. As the number of deployed sensors, and thus generated data streams, is increasing exponentially, this traditional approach may no longer be sustainable or desirable in some application contexts. Therefore, new approaches are required to allow users to directly interact with the network, for example, requesting data directly from sensor nodes. This is difficult, as it requires every node to be capable of point-to-point routing, in addition to identifying a subset of nodes that can fulfil a user's query. This paper presents Dragon, a platform that allows any node in the network to identify all nodes that satisfy user queries, i.e., request data from nodes, and relay the result to the user. The Dragon platform achieves this in a fully distributed way. No central orchestration is required, network overheads are low, and latency is improved over existing comparable methods. Dragon is evaluated on networks of various topologies and different network densities. It is compared with the state-of-the-art algorithms based on summary trees, like Innet and SENS-Join. Dragon is shown to outperform these approaches up to 88% in terms of network traffic required, also a proxy for energy efficiency, and 84% in terms of processing delay.
AU - Kolcun,R
AU - Boyle,DE
AU - McCann,JA
DO - 10.1109/TASE.2016.2530941
EP - 1246
PY - 2016///
SN - 1558-3783
SP - 1230
TI - Efficient Distributed Query Processing
T2 - IEEE Transactions on Automation Science and Engineering
UR -
UR -
VL - 13
ER -