Investigate, develop and validate an adaptive middleware-based management system that can provide a reliable, scalable and cost-efficient linkage to leverage IoT with Big Data cloud.
Theme 2: Adaptive Middleware for Reliable Cyber-Physical Systems
Supporting reliability and scalability in Cyber-Physical Systems (CPS) for the Internet of Things (IoT) requires a large amount of additional resource usage for encoding/decoding additional data and signals; and additional wireless network communication to monitor and manage the processes, which results in a high cost in terms of both lifetime of the front line battery-powered machines (e.g. sensors, robots) and the bandwidth cost of wireless network usage that is required to enable the communication between the machines and the backend controller systems. Although there exist a fair number of software middleware frameworks for IoT, they did not fully address the challenges in reliability, scalability and cost-efficiency together.
The goal of this project is to develop a reliable, scalable, cost-efficient middleware that will hasten the development of IoT applications for both academia and industry. Domain-specific scientists can also benefit from the middleware platform for performing real-world testing in the IoT environments without spending significant time on setting up the experimental environment.
The proposed middleware consists of context-aware ubiquitous computing schemes, and platform-independent process management schemes. The system can clearly identify the reliability and efficiency of each IoT task execution towards providing self-adaptation and self-optimisation. The software components of the middleware will be designed based on the plug-and-play concept in which the system can dynamically modify and adjust its processes at runtime. A prototype will be implemented and tested on a real-world IoT established by wireless network-connected portable machines and cloud services. The machines are able to collaborate autonomously (e.g. for data acquisition) under the governance of the cloud systems.
More details at https://www.excite.it.ee/
Distributed Business Process Management System for IoT
Big Data in IoT represents a vast volume of data generated from the IoT networks and organisations can make use of them, which is not available before. Ideally, organisations will gain benefit from the Big Data to improve and enhance their business process more efficiently and more intelligently. In order to realise the vision, Business Process Management System for IoT (BPMS4IoT) needs to address the challenge of varying forms of data. The data of IoT comes in various formats from different co-existing objects in IoT networks. The information system can utilise machine-learning mechanism to identify the correlation between the data from different objects and generate the meaningful information. However, processing the various formats of data may not be a swift task. For example, in order to identify a suspicious activity in an outdoor environment, the system may integrate the video data and temperature data from different sensors and then either utilise an external third party cloud service for the analysis processes or invoke the external database service to retrieve the related data and analyse them in the intra-organisational information system. The challenge is if such a need is on-demand, how does the system generate the result in time because it involves the data transmission time in different networks and the large volume of data processing. One promising solution is to apply the distributed BPMS model in which the processes can be handled at the self-organised edge network of IoT.
- Chii Chang, Satish Narayana Srirama, and Rajkumar Buyya. ACM DL Author-ize serviceMobile Cloud Business Process Management System for the Internet of Things: A Survey. ACM Computing Surveys. Volume 49, Issue 4, December 2016, Article No. 70. ACM Press, New York, USA. ISSN 0360-0300, DOI: 10.1145/3012000.
- Jakob Mass, Chii Chang and Satish N. Srirama. WiseWare: A Device-to-Device-based Business Process Management System for Industrial Internet of Things. In Proceedings of the 9th IEEE International Conference on Internet of Things (iThings 2016), Dec. 16-19, Chengdu, China, 2016, pp. 269-275.
Fog and Edge Computing
Big data services rely on the Internet of Things (IoT) systems to integrate heterogeneous devices and to deliver data to the central server for analytics. However, the increasing number of connected devices and the various networking issues (e.g. network latency, unstable bandwidth, unstable connectivity etc.) will influence the overall performance of the system. Especially when the big data service needs to provide the timely responses. Therefore, the system needs fog/edge computing (F/EC) architecture, which distributes certain computational tasks to the devices at the edge network of IoT, where the data source devices located at, towards enhancing the overall performance.
- How to effectively deploy and manage F/EC architecture in IoT for big data services?
- How does F/EC architecture handle mobile objects/devices (e.g. vehicles, human, animals) in IoT systems?
In order to answer the first question, we have studied the most recent works in F/EC domain. The outcome of this study includes one IEEE Computer magazine article (published)  and two book chapters (submitted) [5, 6].
To answer the second question, we have studied mobile fog computing domain and have developed an adaptive framework together with a corresponding algorithm to handle mobile objects/devices in F/EC. The result has been published (or to be published) in the peer-reviewed international conference , international journal  and book  (please refer the publication list).
- Chii Chang, Mohan Liyanage, Sander Soo, Satish Narayana Srirama. Fog Computing as a Resource-Aware Enhancement for Vicinal Mobile Mesh Social Networking. In Proceedings of the 31st IEEE International Conference on Advanced Information Networking and Applications (AINA-2017). 27-29 March 2017, Tamkang University, Taipei, Taiwan.
- Chii Chang, Satish Narayana Srirama, and Rajkumar Buyya, Indie Fog: An Efficient Fog-Computing Infrastructure for the Internet of Things, IEEE Computer, Volume 50, Issue 9, Pages: 92-98, ISSN: 0018-9162, IEEE COMPUTER SOCIETY, USA, September 2017.
- Sander Soo, Chii Chang, Seng W. Loke, Satish Srirama, (2017), Proactive Mobile Fog Computing using Work Stealing: Data Processing at the Edge, International Journal of Mobile Computing and Multimedia Communications (IJMCMC), Vol. 8, No. 4.
- Sander Soo, Chii Chang, Seng W. Loke, Satish Srirama. Dynamic Fog Computing: Practical Processing at Mobile Edge Devices. In Algorithms, Methods, and Applications in Mobile Computing and Communications, edited by Agustinus Borgy Waluyo, IGI Global, 2018. (Accepted to be published).
- Chii Chang, Amnir Hadachi, Satish Narayana Srirama and Mart Min. Mobile Big Data: Foundations, State-of-the-Art, and Future Directions. In Encyclopedia of Big Data Technologies, edited by Sherif Sakr and Albert Y. Zomaya. (Submitted).
- Chii Chang, Satish Narayana Srirama and Rajkumar Buyya. Introduction to Fog and Edge Computing. In Fog and Edge Computing: Principles and Paradigms, edited by Rajkumar Buyya and Satish Narayana Srirama.