ACM EdgeSys 2019  -  Best Paper Award

Edge Chaining Framework for Black Ice Road Fingerprinting

Abstract:

Detecting and reacting efficiently to road condition hazards are 
challenging given practical restrictions such as limited data 
availability and lack of infrastructure support. In this paper, 
we present an edge-cloud chaining solution that bridges the cloud 
and road infrastructures to enhance road safety. We exploit the 
roadside infrastructure (e.g., smart lampposts) to form a processing 
chain at the edge nodes and transmit the essential context to 
approaching vehicles providing what we refer as road fingerprinting. 
We approach the problem from two angles: first we focus on 
semantically defining how an execution pipeline spanning edge and 
cloud is composed, then we design, implement and evaluate a working 
prototype based on our assumptions. In addition, we present 
experimental insights and outline open challenges for next steps.


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ACM Library Access

@inproceedings{Cozzolino:EdgeSys2019,
 author = {Cozzolino, Vittorio and Ding, Aaron Yi and Ott, Joerg},
 title = {Edge Chaining Framework for Black Ice Road Fingerprinting},
 booktitle = {Proceedings of the 2Nd International Workshop on Edge Systems, Analytics and Networking},
 series = {EdgeSys '19},
 year = {2019},
 isbn = {978-1-4503-6275-7},
 location = {Dresden, Germany},
 pages = {42--47},
 numpages = {6},
 url = {http://doi.acm.org/10.1145/3301418.3313944},
 doi = {10.1145/3301418.3313944},
 acmid = {3313944},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Distributed Systems, Edge Computing, IoT},

Vittorio Cozzolino, Aaron Yi Ding, and Joerg Ott. 2019. Edge Chaining Framework for Black Ice Road Fingerprinting. In Proceedings of the 2nd International Workshop on Edge Systems, Analytics and Networking (EdgeSys '19). ACM, New York, NY, USA, 42-47. DOI: https://doi.org/10.1145/3301418.3313944