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Georgakopoulos, D..  2019.  A Global IoT Device Discovery and Integration Vision. 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). :214–221.
This paper presents the vision of establishing a global service for Global IoT Device Discovery and Integration (GIDDI). The establishment of a GIDDI will: (1) make IoT application development more efficient and cost-effective via enabling sharing and reuse of existing IoT devices owned and maintained by different providers, and (2) promote deployment of new IoT devices supported by a revenue generation scheme for their providers. More specifically, this paper proposes a distributed IoT blockchain ledger that is specifically designed for managing the metadata needed to describe IoT devices and the data they produce. This GIDDI Blockchain is Internet-owned (i.e., it is not controlled by any individual or organization) and is Internet-scaled (i.e., it can support the discovery and reuse billions of IoT devices). The paper also proposes a GIDDI Marketplace that provides the functionality needed for IoT device registration, query, integration, payment and security via the proposed GIDDI Blockchain. We outline the GIDDI Blockchain and Marketplace implementation. We also discuss ongoing research for automatically mining the IoT Device metadata needed for IoT Device query and integration from the data produce. This significantly reduces the need for IoT device providers to supply the metadata descriptions the devices and the data they produce during the registration of IoT Devices in the GIDDI Blockchain.
Yavari, A., Panah, A. S., Georgakopoulos, D., Jayaraman, P. P., Schyndel, R. v.  2017.  Scalable Role-Based Data Disclosure Control for the Internet of Things. 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). :2226–2233.

The Internet of Things (IoT) is the latest Internet evolution that interconnects billions of devices, such as cameras, sensors, RFIDs, smart phones, wearable devices, ODBII dongles, etc. Federations of such IoT devices (or things) provides the information needed to solve many important problems that have been too difficult to harness before. Despite these great benefits, privacy in IoT remains a great concern, in particular when the number of things increases. This presses the need for the development of highly scalable and computationally efficient mechanisms to prevent unauthorised access and disclosure of sensitive information generated by things. In this paper, we address this need by proposing a lightweight, yet highly scalable, data obfuscation technique. For this purpose, a digital watermarking technique is used to control perturbation of sensitive data that enables legitimate users to de-obfuscate perturbed data. To enhance the scalability of our solution, we also introduce a contextualisation service that achieve real-time aggregation and filtering of IoT data for large number of designated users. We, then, assess the effectiveness of the proposed technique by considering a health-care scenario that involves data streamed from various wearable and stationary sensors capturing health data, such as heart-rate and blood pressure. An analysis of the experimental results that illustrate the unconstrained scalability of our technique concludes the paper.