Energy consideration when integrating Blockchain with IoT for anti-counterfeit

Blockchain technology has been growing in popularity after Bitcoin, the first protocol has demonstrated a strong use case of the technology in Finance. Over the years, as the technology develops more and more, other use cases for the technology which basically relies on a distributed ledger database system have been explored in areas like supply chain and Internet of Things, to help in some of the bottleneck which IoT faces, some of the challenges are security, privacy, scalability, etc. This thesis work will consider energy consumption when integrating IoT with the Blockchain for anti-counterfeit purposes. Because there is little public academic information about the integration of Blockchain with IoT, it is very difficult to ascertain quantitatively, the energy requirement in application areas like anti-counterfeit. This thesis work has to qualitatively, rely on projects whitepapers and application documentation when comparing the energy requirement in the integration of Blockchain and IoT used for counterfeit solutions by different projects. Both private and public (open-sourced) projects were considered and resulted in two broad classifications ‘integration by brands using a unique identifier (RFID and NFC)’ and ‘integration throughout a product lifecycle’. Energy need for each project(s) in a class is considered based on the IoT hardware used and the Blockchain generation and consensus which also seems to have an impact on the implementation cost and complexity of the project

Energy-efficient Clustering Design for M2M Communications

Machine-to-machine (M2M) communications have appeared as an advanced technology for next-generation communications and are undergoing rapid development. In this project, we investigate M2M communications in a wireless cellular network. In M2M communications, clustering is a technology for more efficient data gathering and higher network energy efficiency. We will analyze existing clustering designs in literature and propose two new clustering designs for M2M communications in cellular networks. Performance of the proposed designs will be evaluated thoroughly using both analytical and simulation tools across many aspects, including energy consumption, dead device ratio, residual energy, and network life. The results show that with simple static energy-efficient clustering operations, the network life can be extended by about 50%