Browsing by Author "Yacine, Merrad"

This research aims to solve some problems related to blockchain peer-to-peer energy trading, which are purchased energy under consumption and transaction authentication and demand response control, which still rely on centralized schemes. The purpose of this thesis is to control and authenticate a fully Decentralized pay-per-use energy trading platform. It presents a blockchain-based peer-to-peer (P2P) energy trading platform where prosumers can trade energy autonomously and without interference from a central authority. Multiple prosumers can collaborate on energy generation to form a single supplier. Customers' electricity consumption is monitored via a smart meter connected to an IoT node connected to a private blockchain network. Smart contracts invoked on the blockchain enable autonomous trading interactions between parties and govern the behaviour of accounts within the Ethereum state. The decentralized P2P trading platform uses autonomous usage-based billing and energy routing monitored by a smart contract. A Deep Learning-based Gated Recurrent Unit (GRU) model predicts future consumption based on past data collected on the blockchain. The predictions are then used to set Time of Use (TOU) ranges using the K-means cluster. The data used to train the GRU model is shared among all parties within the network, making the predictions transparent and verifiable. By implementing K-Mean clustering in a smart contract on the blockchain, the set of TOU is independent and unchallengeable, To ensure the validity of the data uploaded to the blockchain, a consensus algorithm is proposed to detect fraudulent nodes, along with a Proof of Location (POL) to ensure that the data is uploaded by the expected nodes. To address the conflict of interest between prosumers and distribution system operators (DSOs) in decentralized P2P energy trading platforms, where prosumers seek to maximize their profit on the one hand, while DSOs seek optimal power flow (OPF) on the other hand, a novel fully decentralized architecture is proposed for an OPF-based demand response management system that uses smart contracts to force generators into compliance without the need for a central authority or hardware. The study details the proposed platform architecture, operation, and implementation. The results are presented mainly in terms of gas consumption of smart contracts and transaction latency for different loads. The work presented in the thesis is relevant in the sense that we have attempted to address a popular and important contemporary research issue related to blockchain technology. The research work holds great potential for industrial use. In the future era of renewable energy, decentralization of energy trading is a necessity for the future society.

With the enormous growth of IOT sensing devices that is tending to the Internet of everything, the data aggregated by these devices are the biggest data streams generated in the history of the IT. For this reason, aggregating those data to the cloud for leveraging the powerful cloud computing processing and storage is essential. This led to the emergence of the sensor cloud concept which is not only a mere aggregation of the sensors’ data to the cloud for further processing, storage and visualization. But also, a virtualization of the sensors, making them accessible to different end users’ applications, which are requesting their data. This without the end user application developer being aware of the sensor localization and hardware. So, the revolution brought by the sensor cloud paradigm is that any end user can develop an application that requests any sensor deployed anywhere, by any organization, without knowing the sensor API, localization or hardware, as long as the sensor is registered to the sensor cloud infrastructure. From the above mentioned it is obvious that the scheduling of the applications requesting the sensors is of main concern, and that optimizing the scheduling policy would definitely lead to the optimizing of the quality of the sensing as a service to the end users. However, most of the time, the scheduling policies implanted by the sensor cloud platform provider are hidden from the users and leveraging the platform is not free. This makes experimenting scheduling policies on a real platform either not possible or really cost inefficient. Here is where simulation plays a big role in research. Having a simulation platform where sensor cloud infrastructures agents and components can be modeled, scheduling policies configured, and execution time assessed would be crucial to researcher trying to develop the infrastructure performance and quality of service. In this work we will be discussing the development of such a platform, by extending cloudsim, a famous cloud computing simulation platform, and we will try to come up with an optimum quantum time in a time-sharing scheduling policy by experimenting on the simulator.