Imperial College London
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ProfessorJulieMcCann

Faculty of Engineering, Department of Computing

Professor of Computer Systems
 
 
 
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Contact

 

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

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

260ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Yang:2016,
author = {Yang, S and Tahir, Y and Chen, P and Alan, M and McCann, J},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
title = {Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing},
url = {http://hdl.handle.net/10044/1/29044},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs.
AU  - Yang,S
AU  - Tahir,Y
AU  - Chen,P
AU  - Alan,M
AU  - McCann,J
PB  - Institute of Electrical and Electronics Engineers (IEEE)
PY  - 2016///
SN  - 0743-166X
TI  - Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
UR  - http://hdl.handle.net/10044/1/29044
ER  -
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