No abstract

No abstract

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.

No abstract

No abstract

No abstract

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.

The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network's health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network's true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.