Design of oneM2M-Based Fog Computing Architecture

Internet of Things (IoT) technology could provide solutions for many real-world applications, and Big Data analysis of IoT continues the key to realizing smart cities. However, IoT cloud computing cannot yet fully meet the requirements of the low latency and fast response for applications, such as public safety and emergency response services. These deficiencies could be addressed by Fog Computing, which uses front-end devices or serially connected terminal devices to conduct data storage, calculations, or related decentralized control management through decentralized collaborative architecture. According to the original oneM2M IoT architecture, it takes 251.2–537.2 ms of transmission time, for high-resolution image data at front-end edge nodes, but it fails to meet the requirements of low-latency and fast-response applications. To address this, we propose a novel oneM2M-based Fog Computing architecture under which adjacent Fog nodes can coordinate and cooperate with each other to deal with operational requirements. Operations are no longer constrained by a node’s ability to perform tasks or by the requirement of forwarding tasks to the cloud for execution. The proposed computing architecture could transmit high-resolution image data between Fog nodes and then could reduce the transmission time to 16.7–46.2 ms, which is a 94.1% reduction compared with the original oneM2M architecture. In summary, this method could provide low latency and fast response in Fog Computing by maximizing the reduction of the overall end-to-end delay.

[1]  Xiaodong Lin,et al.  Efficient and Secure Service-Oriented Authentication Supporting Network Slicing for 5G-Enabled IoT , 2018, IEEE Journal on Selected Areas in Communications.

[2]  Nan Chen,et al.  Interworking of oneM2M-based IoT systems and legacy systems for consumer products , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[3]  Eui-Nam Huh,et al.  Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm , 2016, IEEE Potentials.

[4]  Xiaodong Lin,et al.  Efficient and Secure Service-oriented Authentication Supporting Network Slicing for 5 G-enabled IoT , 2018 .

[5]  Jianming Zhang,et al.  Energy-efficient and network-aware offloading algorithm for mobile cloud computing , 2014, Comput. Networks.

[6]  Ning Zhang,et al.  Identifying the Most Valuable Workers in Fog-Assisted Spatial Crowdsourcing , 2017, IEEE Internet of Things Journal.

[7]  Charles C. Byers,et al.  Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks , 2017, IEEE Communications Magazine.

[8]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[9]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[10]  Sung Y. Shin,et al.  Smart IoT monitoring framework based on oneM2M for fog computing , 2018, SAC.

[11]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[12]  Aditya Badve,et al.  TrafficIntel: Smart traffic management for smart cities , 2017, 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI).

[13]  Cristina Urdiales,et al.  A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs , 2019, Wirel. Commun. Mob. Comput..

[14]  Fuchun Joseph Lin,et al.  Charging architecture for M2M communications , 2016, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT).

[15]  Xuemin Shen,et al.  Securing Fog Computing for Internet of Things Applications: Challenges and Solutions , 2018, IEEE Communications Surveys & Tutorials.

[16]  Lizhi Wang,et al.  A OneM2M-Compliant Stacked Middleware Promoting IoT Research and Development , 2018, IEEE Access.

[17]  Wei Zhang,et al.  Securing Consumer IoT in the Smart Home: Architecture, Challenges, and Countermeasures , 2018, IEEE Wireless Communications.

[18]  Ismaeel Al Ridhawi,et al.  Minimizing delay in IoT systems through collaborative fog-to-fog (F2F) communication , 2017, 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN).

[19]  Christian Bonnet,et al.  Fog Computing architecture to enable consumer centric Internet of Things services , 2015, 2015 International Symposium on Consumer Electronics (ISCE).

[20]  Fuchun Joseph Lin,et al.  Extending scalability of IoT/M2M platforms with Fog computing , 2018, 2018 IEEE 4th World Forum on Internet of Things (WF-IoT).

[21]  Seung Woo Kum,et al.  Design of fog computing based IoT application architecture , 2017, 2017 IEEE 7th International Conference on Consumer Electronics - Berlin (ICCE-Berlin).

[22]  Kai Chen,et al.  Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities , 2018, IEEE Internet of Things Journal.

[23]  Supeng Leng,et al.  Social-Aware Edge Caching in Fog Radio Access Networks , 2017, IEEE Access.

[24]  Xi Fang,et al.  Managing smart grid information in the cloud: opportunities, model, and applications , 2012, IEEE Network.

[25]  Sung-Ju Lee,et al.  A Fog Operating System for User-Oriented IoT Services: Challenges and Research Directions , 2017, IEEE Communications Magazine.

[26]  Fuchun Joseph Lin,et al.  Distributed Artificial Intelligence Enabled by oneM2M and Fog Networking , 2018, 2018 IEEE Conference on Standards for Communications and Networking (CSCN).

[27]  Alexander Willner,et al.  Towards Programmable Fog Nodes in Smart Factories , 2016, 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W).