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Despite the huge deployment of Base Stations (BS), Mobile Network Operators (MNOs) are still faced with the daunting challenge of providing adequate coverage and capacity in indoor environments. Furthermore, the trust-less environment in which MNOs operate makes it even more difficult to achieve interoperability across carriers. Recently the concept of Micro-operators (uO) has emerged as a promising solution towards addressing this problem, through the deployment of small cells. However, their success has been severely hampered by the absence of a framework for creating and managing business agreements between key stakeholders-MNOs & uOs. This paper proposes a blockchain-enabled Software Defined Networking (SDN) approach for managing agreements and radio resources between MNOs over small cell networks. Specifically, our solution uses a smart contract to validate transactions between MNOs. Simulation results shows that our solution guarantees seamless handoff and high availability between different operators in contrast to a break in connectivity in the absence of an agreement.

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Industry 4.0 leverages on cyber-physical systems (CPSs) that enable different physical sensors, actuators, and controllers to be interconnected via switches and cloud computing servers, forming complex online systems. Protecting these against advanced cyber threats is a primary concern for future application. Cyberattackers can impair such systems by producing different types of cyber threats, ranging from network attacks to CPS controller attacks, which could impose catastrophic damage to CPS infrastructure, companies, governments, and even the general public. This paper proposes a learned monitor, analyze, plan, execute, and knowledge (MAPE-K) base model as a method for supporting self-adaptation for the CPSs, ensuring reliability, flexibility, and protection against cyber threats. The model aims to gauge normal behavior in an industry environment and generate alarms to alert users to any abnormalities or threats. In turn, our evaluation shows 99.55% accuracy in detecting cyber threats.

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.