@inproceedings{aed383770692474a8fe36f4593afc0dc,
title = "A Real-time and Non-cooperative Task Allocation Framework for Social Sensing Applications in Edge Computing Systems",
abstract = "Social sensing has emerged as a new sensing application paradigm where measurements about the physical world are collected from humans or devices on their behalf. A key limitation in the current social sensing solution space is that data processing and analytics are often done in a 'backend' mode (e.g., on dedicated servers or commercial cloud platforms). Such mode ignores the rich processing capability of increasingly powerful edge devices (e.g., mobile phones and nodes in the Internet of Things). Exploiting such edge devices in the social sensing setting introduces new challenges to real-time resource management. In this work, we develop a Bottom-up Game-theoretic Task Allocation (BGTA) framework to solve the critical problem of allocating real-time social sensing tasks to self-aware and non-cooperative edge computing nodes. In particular, we address two important challenges in solving this problem. The first one is 'conflicting interest' where the objectives of applications and edge nodes may be at odds with each other. The second challenge is 'asymmetric and incomplete information' where the application is often unaware of the detailed status (e.g., energy profile, utilization, CPU frequency) and compliance level of the edge nodes. To address these challenges, we first design a non-cooperative task allocation game model to address the conflicting objectives of the applications and edge nodes. We then develop a decentralized Fictitious Play scheme to allow each edge node to make its own decision on which task to execute in a non-cooperative context. Finally, we design a dynamic incentive mechanism to ensure the decisions made by the edge nodes meet objectives of the application. We implement a system prototype deployed on Nvidia Jetson TX1 and Jetson TK1 boards and evaluate our task allocation framework using two real-world social sensing applications. The results show that our scheme can well satisfy Quality of Service (QoS) requirement of the applications while providing optimized payoffs to edge nodes compared to the state-of-the-art baselines.",
keywords = "Edge Computing, Game Theory, Resource Management, Social Sensing",
author = "Zhang, {Daniel Yue} and Yue Ma and Yang Zhang and Suwen Lin and Hu, {X. Sharon} and Dong Wang",
note = "Funding Information: This work is partially supported by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465, IIS-1447795, and CNS-1319904. and Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2018 IEEE.; 24th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2018 ; Conference date: 11-04-2018 Through 13-04-2018",
year = "2018",
month = aug,
day = "8",
doi = "10.1109/RTAS.2018.00039",
language = "English (US)",
series = "Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "316--326",
editor = "Rodolfo Pellizzoni",
booktitle = "Proceedings - 24th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2018",
address = "United States",
}