research-article

Acting selfish for the good of all: contextual bandits for resource-efficient transmission of vehicular sensor data

Authors:

Benjamin Sliwa,

Christian WietfeldAuthors Info & Claims

Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

Pages 345 - 350

https://doi.org/10.1145/3397166.3413466

Published: 11 October 2020 Publication History

Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

Acting selfish for the good of all: contextual bandits for resource-efficient transmission of vehicular sensor data

Pages 345 - 350

Abstract
References

Abstract

In this work, we present Black Spot-aware Contextual Bandit (BS-CB) as a novel client-based method for resource-efficient opportunistic transmission of delay-tolerant vehicular sensor data. BS-CB applies a hybrid approach which brings together all major machine learning disciplines - supervised, unsupervised, and reinforcement learning - in order to autonomously schedule vehicular sensor data transmissions with respect to the expected resource efficiency. Within a comprehensive real world performance evaluation in the public cellular networks of three Mobile Network Operators (MNOs), it is found that 1) The average uplink data rate is improved by 125%-195% 2) The apparently selfish goal of data rate optimization reduces the amount of occupied cell resources by 84%-89% 3) The average transmission-related power consumption can be reduced by 53%-75% 4) The price to pay is an additional buffering delay due to the opportunistic medium access strategy.

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Cited By

Sliwa BAdam RWietfeld C(2021)Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.306045970:6(5332-5346)Online publication date: Jun-2021
https://doi.org/10.1109/TVT.2021.3060459

Index Terms

Acting selfish for the good of all: contextual bandits for resource-efficient transmission of vehicular sensor data
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Mobile agents
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning
    2. Machine learning approaches
      1. Classification and regression trees
2. Networks

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Published In

cover image ACM Conferences

Mobihoc '20: Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

October 2020

384 pages

ISBN:9781450380157

DOI:10.1145/3397166

General Chairs:
Tommaso Melodia
Northeastern University
,
Eylem Ekici
The Ohio State University
,
Program Chairs:
Alhussein Abouzeid
Rensselaer Polytechnic Institute
,
Minghua Chen
City University of Hong Kong

Copyright © 2020 ACM.

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New York, NY, United States

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Published: 11 October 2020

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Mobihoc '20: The Twenty-first ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing

October 11 - 14, 2020

Virtual Event, USA

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Cited By

Sliwa BAdam RWietfeld C(2021)Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2021.306045970:6(5332-5346)Online publication date: Jun-2021
https://doi.org/10.1109/TVT.2021.3060459

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