Imperial College London
Portrait Picture

ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Vice-Dean (Research) for the Faculty of Engineering
 
 
 
//

Contact

 

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

 
 
//

Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
//

Location

 

260ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Kartakis:2017:10.1145/3064840,
author = {Kartakis, S and Yang, S and Mccann, JA},
doi = {10.1145/3064840},
journal = {ACM TRANSACTIONS ON SENSOR NETWORKS},
title = {Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water Networks},
url = {http://dx.doi.org/10.1145/3064840},
volume = {13},
year = {2017}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - As a typical cyber-physical system (CPS), smart water distribution networks require monitoring of underground water pipes with high sample rates for precise data analysis and water network control. Due to poor underground wireless channel quality and long-range communication requirements, high transmission power is typically adopted to communicate high-speed sensor data streams, posing challenges for long-term sustainable monitoring. In this article, we develop the first sustainable water sensing system, exploiting energy harvesting opportunities from water flows. Our system does this by scheduling the transmission of a subset of the data streams, whereas other correlated streams are estimated using autoregressive models based on the sound-velocity propagation of pressure signals inside water networks. To compute the optimal scheduling policy, we formalize a stochastic optimization problem to maximize the estimation reliability while ensuring the system’s sustainable operation under dynamic conditions. We develop data transmission scheduling (DTS), an asymptotically optimal scheme, and FAST-DTS, a lightweight online algorithm that can adapt to arbitrary energy and correlation dynamics. Using more than 170 days of real data from our smart water system deployment and conducting in vitro experiments to our small-scale testbed, our evaluation demonstrates that Fast-DTS significantly outperforms three alternatives, considering data reliability, energy utilization, and sustainable operation.
AU  - Kartakis,S
AU  - Yang,S
AU  - Mccann,JA
DO  - 10.1145/3064840
PY  - 2017///
SN  - 1550-4859
TI  - Reliability or Sustainability: Optimal Data Stream Estimation and Scheduling in Smart Water Networks
T2  - ACM TRANSACTIONS ON SENSOR NETWORKS
UR  - http://dx.doi.org/10.1145/3064840
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000411778300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/52874
VL  - 13
ER  -
Download