Imperial College London

ProfessorJulieMcCann

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems
 
 
 
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Contact

 

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

 
 
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Location

 

258ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kolcun:10.1145/2991561.2991568,
author = {Kolcun, R and Boyle, D and McCann, J},
doi = {10.1145/2991561.2991568},
publisher = {IEEE},
title = {Efficient In-Network Processing for a Hardware-Heterogeneous IoT},
url = {http://dx.doi.org/10.1145/2991561.2991568},
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - As the number of small, battery-operated, wireless-enabled devices deployed in various applications of Internet of Things (IoT), Wireless Sensor Networks (WSN), and Cyber-physical Systems (CPS) is rapidly increasing, so is the number of data streams that must be processed. In cases where data do not need to be archived, centrally processed, or federated, in-network data processing is becoming more common. For this purpose, various platforms like D RAGON , Innet, and CJF were proposed. However, these platforms assume that all nodes in the network are the same, i.e. the network is homogeneous. As Moore’s law still applies, nodes are becoming smaller, more powerful, and more energy efficient each year; which will continue for the foreseeable future. Therefore, we can expect that as sensor networks are extended and updated, hardwareheterogeneity will soon be common in networks - the same trend as can be seen in cloud computing infrastructures. This heterogeneity introduces new challenges in terms of choosing an in-network data processing node, as not only its location, but also its capabilities, must be considered. This paper introduces a new methodology to tackle this challenge, comprising three new algorithms - Request, Traverse, and Mixed - for efficiently locating an in-network data processing node, while taking into account not only position within the network but also hardware capabilities. The roposed algorithms are evaluated against a naïve approach and achieve up to 90% reduction in network traffic during long-term data processing, while spending a similar amount time in the discovery phase.
AU - Kolcun,R
AU - Boyle,D
AU - McCann,J
DO - 10.1145/2991561.2991568
PB - IEEE
TI - Efficient In-Network Processing for a Hardware-Heterogeneous IoT
UR - http://dx.doi.org/10.1145/2991561.2991568
UR - http://hdl.handle.net/10044/1/40989
ER -