Towards Building a Secure Blockchain-Based Architecture for Internet of Things (IoT) - Pavithran, Deepa (2020)
IoT (Internet of Things) devices usually generate a large amount of data shared with a centralized cloud to provide various services. Traditional IoT architecture is heavily centralized, where data stored in a cloud environment, is prone to several kinds of threats. Blockchain is a very promising technology that spans many use-cases other than cryptocurrencies. For example, its implementation in the Internet of Things based networks (IoT) is still unclear and demands further research. The traditional adoption of the blockchain protocol for Bitcoin is common but it cannot be used for IoT because Bitcoin is a payment system, whereas the IoT eco-system has a different architecture. Implementing blockchain for IoT may still impose a variety of challenges. In this thesis, we proposed an architecture for the use of blockchain in event-driven IoT. In particular, we identified the key components along with their design considerations and challenges to consider while creating the blockchain architecture for IoT. We also defined gaps that hinder creating a secure blockchain framework for IoT. Various literatures have proposed blockchain architectures for IoT; however, most of them are applicable to use-cases related to smart homes and healthcare. In addition, we identified that the existing architectures have additional overhead of key management. Hence, we proposed a privacy-preserving blockchain architecture for Traffic Speed radars using Hierarchical Identity Based Encryption (HIBE). The proposed architecture uses edge and cloudlet computing paradigm as well as HIBE to preserve privacy. The performance of the proposed architecture is evaluated by conducting extensive experiments. We created the blockchain network using Ethereum and evaluated the system performance. Network performance was evaluated by simulating the network using Contiki OS. Finally, we analyzed the security of the scheme through theoretical analysis and threat-modelling tool that considers the existence of a malicious adversary.