Imperial College London
Alternate

ProfessorKinLeung

Faculty of Engineering, Department of Electrical and Electronic Engineering

Tanaka Chair in Internet Technology
 
 
 
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Contact

 

+44 (0)20 7594 6238kin.leung Website

 
 
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Assistant

 

Miss Vanessa Rodriguez-Gonzalez +44 (0)20 7594 6267

 
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Location

 

810aElectrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Leung:2019:10.1109/TNET.2019.2953581,
author = {Leung, K and Nazemi, S and Swami, A},
doi = {10.1109/TNET.2019.2953581},
journal = {IEEE ACM Transactions on Networking},
pages = {2432--2443},
title = {Distributed optimization framework for in-network data processing},
url = {http://dx.doi.org/10.1109/TNET.2019.2953581},
volume = {27},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In-Network Processing (INP) is an effective way to aggregate and process data from different sources and forward the aggregated data to other nodes for further processing until it reaches the end user. There is a trade-off between energy consumption for processing data and communication energy spent on transferring the data. Specifically, aggressive data aggregation consumes much energy for processing, but results in less data for transmission, thus using less energy for communications, andvice versa. An essential requirement in the INP process is to ensure that the user expectation of quality of information (QoI) is delivered during the process. Using wireless sensor networks for illustration and with the aim of minimising the total energy consumption of the system, we study and formulate the trade-off problem as a nonlinear optimisation problem where the goal is to determine the optimal data reduction rate, while satisfying the QoI required by the user. The formulated problem is a Signomial Programming (SP) problem, which is a non-convex optimisationproblem and very hard to be solved directly. We propose two solution frameworks. First, we introduce an equivalent problem which is still SP and non-convex as the original one, but we prove that the strong duality property holds, and propose an efficient distributed algorithm to obtain the optimal data reduction rates, while delivering the required QoI. The second framework applies to the system with identical nodes and parameter settings. In such cases, we prove that the complexity of the problem can be reduced logarithmically. We evaluate our proposed frameworks under different parameter settings and illustrate the validity and performance of the proposed techniques through extensive simulation.
AU  - Leung,K
AU  - Nazemi,S
AU  - Swami,A
DO  - 10.1109/TNET.2019.2953581
EP  - 2443
PY  - 2019///
SN  - 1063-6692
SP  - 2432
TI  - Distributed optimization framework for in-network data processing
T2  - IEEE ACM Transactions on Networking
UR  - http://dx.doi.org/10.1109/TNET.2019.2953581
UR  - https://ieeexplore.ieee.org/document/8924930
UR  - http://hdl.handle.net/10044/1/74634
VL  - 27
ER  -
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