Paper Info
Title
A Cross-Layer Optimization Framework for Distributed Computing in IoT Networks
Abstract
In Internet-of-Thing (IoT) networks, enormous low-power IoT devices execute latency-sensitive yet computation intensive machine learning tasks. However, the energy is usually scarce for IoT devices, especially for some without battery and relying on solar power or other renewables forms. In this paper, we introduce a cross-layer optimization framework for distributed computing among low-power IoT devices. Specifically, a programming layer design for distributed IoT networks is presented by addressing the problems of application partition, task scheduling, and communication overhead mitigation. Furthermore, the associated federated learning and local differential privacy schemes are developed in the communication layer to enable distributed machine learning with privacy preservation. In addition, we illustrate a three-dimensional network architecture with various network components to facilitate efficient and reliable information exchange among IoT devices. Moreover, a model quantization design for IoT devices is illustrated to reduce the cost of information exchange. Finally, a parallel and scalable neuromorphic computing system for IoT devices is established to achieve energy-efficient distributed computing platforms in the hardware layer. Based on the introduced cross-layer optimization framework, IoT devices can execute their machine learning tasks in an energy-efficient way while guaranteeing data privacy and reducing communication costs.
Year
DOI
Venue
2020
10.1109/SEC50012.2020.00067
2020 IEEE/ACM Symposium on Edge Computing (SEC)
Keywords
DocType
ISBN
Distributed computing,machine learning,federated learning,neuromorphic computing.
Conference
978-1-7281-5944-7
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Bodong Shang1548.82
Shiya Liu202.37
Sidi Lu301.35
Yang Yi415926.70
Weisong Shi52323163.09
Lingjia Liu679992.58