Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing

By moving service provisioning from the cloud to the edge, edge computing becomes a promising solution in the era of IoT to meet the delay requirements of IoT applications, enhance the scalability and energy efficiency of lightweight IoT devices, provide contextual information processing, and mitigate the traffic burdens of the backbone network. However, as an emerging field of study, edge computing is still in its infancy and faces many challenges in its implementation and standardization. In this article, we study an implementation of edge computing, which exploits transparent computing to build scalable IoT platforms. Specifically, we first propose a transparent computing based IoT architecture, and clearly identify its advantages and associated challenges. Then, we present a case study to clearly show how to build scalable lightweight wearables with the proposed architecture. Some future directions are finally pointed out to foster continued research efforts.

[1]  Albert Y. Zomaya,et al.  A Survey of Mobile Device Virtualization , 2016, ACM Comput. Surv..

[2]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[3]  Hui Guo,et al.  A Survey on Emerging Computing Paradigms for Big Data , 2017 .

[4]  Peter Kilpatrick,et al.  Challenges and Opportunities in Edge Computing , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[5]  Xuemin Shen,et al.  Dynamic Channel Access to Improve Energy Efficiency in Cognitive Radio Sensor Networks , 2016, IEEE Transactions on Wireless Communications.

[6]  Wayes Tushar,et al.  System Design of Internet-of-Things for Residential Smart Grid , 2016, ArXiv.

[7]  Yaoxue Zhang,et al.  4VP: A Novel Meta OS Approach for Streaming Programs in Ubiquitous Computing , 2007, 21st International Conference on Advanced Information Networking and Applications (AINA '07).

[8]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[9]  Nei Kato,et al.  A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues , 2017, IEEE Communications Surveys & Tutorials.

[10]  M. Jones Virtualization for embedded systems The how and why of small-device hypervisors , 2013 .

[11]  Ewa Deelman,et al.  Integration of Workflow Partitioning and Resource Provisioning , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

[12]  Yaoxue Zhang,et al.  Transparent computing: Spatio-temporal extension on von Neumann architecture for cloud services , 2013 .

[13]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[14]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[15]  Ching-Hsien Hsu,et al.  Mobile Edge Computing , 2018, Wirel. Commun. Mob. Comput..

[16]  Claudia Linnhoff-Popien,et al.  Mobile Edge Computing , 2016, Informatik-Spektrum.

[17]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[18]  Nei Kato,et al.  Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control , 2017, IEEE Transactions on Computers.

[19]  Nei Kato,et al.  Toward intelligent machine-to-machine communications in smart grid , 2011, IEEE Communications Magazine.