RT Journal Article
JF IEEE Transactions on Computers
YR 2017
VO 66
IS 2
SP 183
TI ADDSEN: Adaptive Data Processing and Dissemination for Drone Swarms in Urban Sensing
A1 Di Wu,
A1 Dmitri I. Arkhipov,
A1 Minyoung Kim,
A1 Carolyn L. Talcott,
A1 Amelia C. Regan,
A1 Julie A. McCann,
A1 Nalini Venkatasubramanian,
A1 undefined,
A1 undefined,
A1 undefined,
A1 undefined,
K1 Sensors
K1 Data processing
K1 Vehicles
K1 Mobile communication
K1 Middleware
K1 Learning (artificial intelligence)
K1 urban sensing
K1 Drone swarms
K1 cyber-physical systems
K1 data processing
K1 data dissemination
K1 online Q-learning
AB We present $ADDSEN$ middleware as a holistic solution for Adaptive Data processing and dissemination for Drone swarms in urban SENsing. To efficiently process sensed data in the middleware, we have proposed a cyber-physical sensing framework using partially ordered knowledge sharing for distributed knowledge management in drone swarms. A reinforcement learning dissemination strategy is implemented in the framework. $ADDSEN$ uses online learning techniques to adaptively balance the broadcast rate and knowledge loss rate periodically. The learned broadcast rate is adapted by executing state transitions during the process of online learning. A strategy function guides state transitions, incorporating a set of variables to reflect changes in link status. In addition, we design a cooperative dissemination method for the task of balancing storage and energy allocation in drone swarms. We implemented $ADDSEN$ in our cyber-physical sensing framework, and evaluation results show that it can achieve both maximal adaptive data processing and dissemination performance, presenting better results than other commonly used dissemination protocols such as periodic, uniform and neighbor protocols in both single-swarm and multi-swarm cases.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN 0018-9340
LA English
DO 10.1109/TC.2016.2584061
LK http://doi.ieeecomputersociety.org/10.1109/TC.2016.2584061