Hussein Zangoti is a Ph.D. candidate at the Knight Foundation School of Computing and Information Sciences, Florida International University, under the supervision of Professor Niki Pissinou. Hussein received his B.Sc. in Computer Science from Jazan University, a public research university based in Saudi Arabia, and his M.Sc. in Computer Science from Monmouth University. Since 2010, Hussein is a lecturer on leave and worked as a head in the Computer and Network Engineering Department at Jazan University, one of the largest public, nonprofit institutions of higher education in the Kingdom of Saudi Arabia. Hussein is funded through Jazan University, Saudi Arabia. During his doctoral studies, Hussein served as a graduate research mentor for the NSF-sponsored Research Experience for Undergraduates (REU) and Research Experience for Teachers (RET) programs for three consecutive years and a reviewer for peer-reviewed conferences in the area of computer and networking. Hussein’s research publications and interests include IoT, blockchain, networking, and security.
The rising adoption of blockchain technology in mobile Internet of Things (mIoT) networks necessitates the development of efficient, scalable, and resource-optimized blockchain systems. While numerous studies have attempted to address these issues, comprehensive solutions that adapt to the inherent mobility of mIoT systems remain elusive. In this Ph.D. thesis, we investigate three innovative methods to address these issues in order to advance the current blockchain model for mIoT systems.
The thesis initially introduces a novel k-dimensional spatiotemporal, multidimensional, graphbased blockchain structure to address network partitioning issues arising from the mobility of IoT devices. This unique structure effectively manages blockchain nodes as they move out of cell areas or merge into other cell areas, creating smaller, independent peer-to-peer subnetworks, each with its own blockchain copy. Our experiments revealed improvements in scalability and efficiency, with a logarithmic growth as the blockchain size grows. Additionally, the longest chain length is reduced by more than 99.99% when compared to traditional chain-based structures, making it more efficient to apply all kinds of blockchain operations, such as appending or managing blocks.
Building upon the multidimensional blockchain foundation, the next stage of this research involves developing an efficient merging algorithm for graph-based or multidimensional blockchains in mIoT networks. This algorithm deals with the challenge of merging partitioned blockchains containing similar or identical blocks, where processing these blocks requires substantial time and computational resources during the merging process. Utilizing depth-first search and Merkle tree techniques, the merging algorithm minimizes the time and computational resources spent on identical blocks, resulting in a 72% reduction in merging time compared to algorithms that do not handle block similarity.
Lastly, considering the limited storage capacity of mIoT systems, this thesis introduces a novel Collective Signing-Based Blockchain Storage Optimization (CSBSO) model that aims to minimize storage overhead in resource-constrained mIoT systems. The model utilizes the existing Collective Signing (CoSi) protocol to minimize the storage requirements and leverages a multidimensional blockchain structure for efficient block management and retrieval. Our storage optimization approach identifies and prunes the most irrelevant blocks based on the CoSi protocol. Evaluations using real-world datasets, such as the Ethereum Classic Blockchain dataset and the Facebook users dataset, demonstrate that the CSBSO model outperforms state-of-the-art storage optimization models, saving around 92% of storage space. These results emphasize the potential of CoSi-based storage optimization in effectively reducing blockchain storage overhead in resource-limited applications.