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Optical wireless communication (OWC) is a promising technology that can provide high data rates while supporting multiple users. The optical wireless (OW) physical layer has been researched extensively, however, less work was devoted to multiple access and how the OW front end is connected to the network. In this paper, an OWC system which employs a wavelength division multiple access (WDMA) scheme is studied, for the purpose of supporting multiple users. In addition, a cloud/fog architecture is proposed for the first time for OWC to provide processing capabilities. The cloud/fog-integrated architecture uses visible indoor light to create high data rate connections with potential mobile nodes. These OW nodes are further clustered and used as fog mini servers to provide processing services through the OW channel for other users. Additional fog-processing units are located in the room, the building, the campus and at the metro level. Further processing capabilities are provided by remote cloud sites. Two mixed-integer linear programming (MILP) models were proposed to numerically study networking and processing in OW systems. The first MILP model was developed and used to optimize resource allocation in the indoor OWC systems, in particular, the allocation of access points (APs) and wavelengths to users, while the second MILP model was developed to optimize the placement of processing tasks in the different fog and cloud nodes available. The optimization of tasks placement in the cloud/fog-integrated architecture was analysed using the MILP models. Multiple scenarios were considered where the mobile node locations were varied in the room and the amount of processing and data rate requested by each OW node was varied. The results help to identify the optimum colour and AP to use for communication for a given mobile node location and OWC system configuration, the optimum location to place processing and the impact of the network architecture. This article is part of the theme issue ‘Optical wireless communication’.

Integrating the Industrial Internet of Things (IIoT) into supply chain management enables flexible and efficient on-demand exchange of goods between merchants and suppliers. However, realizing a fair and transparent supply chain system remains a very challenging issue due to the lack of mutual trust among the suppliers and merchants. Furthermore, the current system often lacks the ability to transmit trade information to all participants in a timely manner, which is the most important element in supply chain management for the effective supply of goods between suppliers and the merchants. This thesis presents a blockchain-based supply chain management system in the IIoT. The proposed system takes advantage of blockchain technology in terms of its transparency and tamper-proof nature to support fair goods exchange between merchants and suppliers. Additionally, the decentralization and pseudonymity property will play a significant role in preserving the privacy of participants in the blockchain. In particular, fairness in the IIoT is first defined. Then, a design for a smart contract for fair goods exchange is presented to prevent malicious behavior through imposing penalties. The proposed system was prototyped on Ethereum and experiments were conducted to demonstrate its feasibility. This is for you, Dad. Even from across the sea, I can feel your love Arriving at this stage and writing this acknowledgement was always a dream I hoped to achieve one day. In my journey towards this degree, I met many people who I have to credit for helping me to arrive at this stage, otherwise this dream would not have been achieved. I will start with an inspiration, a teacher and brother, Dr. Xiaodong Lin, who was a pillar of support and a role model for me throughout my journey. He always believed in me and gave me this opportunity. Furthermore, he has always been there at all times providing his heartfelt help and support in addition to giving me invaluable suggestions and guidance in my quest for knowledge. I shall be eternally grateful to Dr. Xiaodong Lin for his assistance. I take great pleasure extending my appreciation to my colleagues in the BBCR Group at the University of Waterloo, for all their help and support. Special thanks to Li Ming , Dongxiao Liu , Yuan Zhang and Anjia Yang whose precious friendship I will always cherish. Special thanks also go to Dr. Ilias Kotsireas and my examining committee members for the time and effort they have given, and to all my friends at Durham College, especially prof. Karl Alexander for his support. Last but not least, I would like to thank all my family who provided me with unconditional love during my academic journey here in Canada.

Vehicular Clouds processing is a new field of research that aims to exploit the vehicles' onboard computational resources as a part of a cooperative distributed cloud computing environment. In this paper, we propose a vehicular cloud network architecture where a group of vehicles near a traffic light cluster and form a temporal vehicular cloud by aggregating their computational resources in that cluster. The goal of the proposed architecture is to minimize the processing and network power consumed in the data center of a cloud operator. To this end, arriving processing tasks are optimally assigned to the centralized cloud and/or the formed vehicular clouds to reduce the total power consumption of the centralized cloud by reducing its average processing workload and network traffic. Furthermore, task assignment among vehicular clouds is constrained by tasks completion time. Our proposed system is analyzed using a mixed integer linear programming (MILP) model where two task assignment approaches were considered: single task assignment and distributed task assignment. In the first approach, each task is not split among multiple clouds, while splitting is allowed in the second approach. It was found that the power consumption of the centralized cloud is reduced by 45% (in the first approach) and 60% (in the second approach) compared to the case where all tasks are assigned to the centralized cloud only. The higher power saving of the centralized cloud in the second approach comes from the ability of vehicular clouds to host more processing workload, an average of 37% more workload, compared to the single task assignment approach.