Coded Storage-and-Computation: A New Paradigm to Enhancing Intelligent Services in Space-Air-Ground Integrated Networks

Space-air-ground integrated networks (SAGINs) provide global information sharing, large-scale coverage, and ubiquitous collaboration architecture, and alleviate the pressure of rapid traffic growth on terrestrial wireless networks. On the other hand, the emergence of artificial intelligence (AI) technology in all walks of life has attracted significant attention on intelligent services. However, frequent link errors and dynamic connections in SAGINs aggravate the issues of data failure and computation slowdown, and constrain the improvement of AI service efficiency. In this article, we propose a novel coded stor-age-and-computation (CSC) architecture, which can offer reliable storage and flexible computation offloading to accelerate distributed machine learning. Through several case studies, we demonstrate that the designed CSC-AI system can realize reliable massive data retrieval and fast computation offloading in SAGINs. Finally, we identify future research issues for CSC-AI system optimization in SAGINs.

[1]  Ju Ren,et al.  A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms , 2019, ACM Comput. Surv..

[2]  Yunpeng Wang,et al.  Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory , 2015, PloS one.

[3]  Kannan Ramchandran,et al.  Speeding Up Distributed Machine Learning Using Codes , 2015, IEEE Transactions on Information Theory.

[4]  Balaji Srinivasan Babu,et al.  Erasure coding for distributed storage: an overview , 2018, Science China Information Sciences.

[5]  Weihua Zhuang,et al.  Software Defined Space-Air-Ground Integrated Vehicular Networks: Challenges and Solutions , 2017, IEEE Communications Magazine.

[6]  Muriel Médard,et al.  The Storage Versus Repair-Bandwidth Trade-off for Clustered Storage Systems , 2018, IEEE Transactions on Information Theory.

[7]  Ursula Challita,et al.  Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial , 2017, IEEE Communications Surveys & Tutorials.

[8]  Jun Li,et al.  On the Incentive Mechanisms for Commercial Edge Caching in 5G Wireless Networks , 2018, IEEE Wireless Communications.

[9]  Nei Kato,et al.  Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence , 2018, IEEE Wireless Communications.

[10]  Nan Zhao,et al.  Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach , 2018, IEEE Transactions on Vehicular Technology.

[11]  Matti Latva-aho,et al.  Key drivers and research challenges for 6G ubiquitous wireless intelligence , 2019 .

[12]  Ying Liu,et al.  Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning , 2016 .

[13]  Tao Huang,et al.  Machine learning based optimization for vehicle-to-infrastructure communications , 2019, Future Gener. Comput. Syst..

[14]  Julius Hannink,et al.  Activity recognition in beach volleyball using a Deep Convolutional Neural Network , 2017, Data Mining and Knowledge Discovery.

[15]  Nei Kato,et al.  The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective , 2017, IEEE Wireless Communications.