Internet of Things (IoT) service systems are aimed at monitoring and controlling the behavior of the physical world using a vast interlinked network of devices such as sensors, gateways, switches, routers, computing resources, applications/services, and also humans in order to link the digital world with the physical. IoT service systems drive the vision of a smart interconnected digital-physical world where interactions among different components can be handled in a proper way. The challenges of IoT service systems are also significant, such as fast growth of the scale, deep complexity of data sensing and processing, intense system monitoring in real time, and efficient and effective management for IoT-based service systems (smart grid, smart healthcare, industry4.0, fog/edge). To address the above challenges, novel technologies including high performance control methods, efficient detection and protection for IoT security and cross layer technologies for IoT service systems, have to be investigated.
This special issue aims to gather high quality research papers in the area of scalable techniques and services for managing IoT service systems. The main focus of this SI is to address new mechanisms in IoT service systems, advanced development for large scale IoT service systems, scalability-aware solutions for heterogeneous IoT service systems, and new efficient lower power strategy for IoT service systems.
Topics of interest include, but are not limited to:
- Optimizations for heterogeneous IoT service computing systems, energy aware IoT service computing systems.
- Scalability: Scalable algorithms for monitoring performance, security, privacy, and data quality across digital and physical worlds.
- Machine learning: Models and techniques for automatically predicting root causes of performance degradation end-to-end.
- Service management: Real time operations of distributed IoT service computing systems; novel middleware for monitoring end-to-end IoT service computing systems; innovative IoT performance benchmarking and performance profiling use cases.
Submitted articles must not have been previously published or currently submitted for journal publication elsewhere. As an author, you are responsible for understanding and adhering to our submission guidelines. You can access them at http://ieeeauthorcenter.ieee.org. Please submit your paper to Manuscript Central at https://mc.manuscriptcentral.com/tsc-cs.
All papers will be peer-reviewed and selected based on their “originality” and merit, such as relevance to the TSC themes, as per requirement of TSC.
Submission Due: CLOSED
1st Decision: 2019-01-15; 2019-03-31
Revised Submission Due: 2019-03-15; 2019-05-31
2nd Decision: 2019-04-15; 2019-06-30
Camera-ready Due: 2019-05-15; 2019-07-31
Guest Editorial Team
Prof. Rajiv Ranjan
School of Computing Science
Newcastle University, UK
Prof. Ching-Hsien Hsu
Department of Comp. Sci. and Info. Engineering
Chung Hua University, Taiwan
Assoc. Prof. Lydia Y. Chen
TU Delft, The Netherlands
Prof. Dimitrios Georgakopoulos
Swinburne University of Technology