Consensus protocols stand behind the success of blockchain technology. This is because parties that distrust each other can make secure transactions without the oversight of a central authority. The first consensus protocol emerged with Bitcoin. Since then, many others have appeared. Some of them have been implemented by official blockchain platforms, whereas others, for the time being, remain as proposals. A blockchain consensus is a trade-off. The new solutions promise to overcome the known drawbacks of blockchain, but they may also bring new vulnerabilities. Moreover, blockchain performance metrics are not clearly defined, as some metrics, such as delay and throughput, which are key factors for the efficiency of standard networks, are purposely constrained by most mainstream blockchain platforms. The main body of this paper consolidates knowledge of blockchains, focusing on the seminal consensus protocols in large-scale market capitalization platforms, and how consensus is achieved for large-scale, decentralized, blockchain architectures. The benefits, limitations, and tradeoffs, as well as the subsequent trend in current consensus development, and its limitations as a general paradigm, are highlighted. The paper also sheds light on overlooked potential performance metrics, and it proposes some novel solutions to some of the identified problems.

This paper introduces a blockchain-based P2P energy trading platform, where prosumers can trade energy autonomously with no central authority interference. Multiple prosumers can collaborate in producing energy to form a single provider. Clients’ power consumption is monitored using a smart meter that interfaces with an IoT node connected to a blockchain private network. The smart contracts, invoked on the blockchain, enable the autonomous trading interactions between parties and govern accounts behavior within the Ethereum state. The decentralized P2P trading platform utilizes autonomous pay-per-use billing and energy routing, monitored by a smart contract. A Gated Recurrent Unit (GRU) deep learning-based model, predicts future consumption based on past data aggregated to the blockchain. Predictions are then used to set Time of Use (ToU) ranges using the K-mean clustering. The data used to train the GRU model are shared between all parties within the network, making the predictions transparent and verifiable. Implementing the K-mean clustering in a smart contract on the blockchain allows the set of ToU to be independent and incontestable. To secure the validity of the data uploaded to the blockchain, a consensus algorithm is suggested to detect fraudulent nodes along with a Proof of Location (PoL), ensuring that the data are uploaded from the expected nodes. The paper explains the proposed platform architecture, functioning as well as implementation in vivid details. Results are presented in terms of smart contract gas consumption and transaction latency under different loads.

With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.

Recent advances in control, communication, and management systems, as well as the widespread use of renewable energy sources in homes, have led to the evolution of traditional power grids into smart grids, where passive consumers have become so-called prosumers that feed energy into the grid. On the other hand, the integration of blockchain into the smart grid has enabled the emergence of decentralized peer-to-peer (P2P) energy trading, where prosumers trade their energy as tokenized assets. Even though this new paradigm benefits both distribution grid operators and end users in many ways. Nevertheless, there is a conflict of interest between the two parties, as on the one hand, prosumers want to maximize their profit, while on the other hand, distribution system operators (DSOs) seek an optimal power flow (OPF) operating point. Due to the complexity of formulating and solving OPF problems in the presence of renewable energy sources, researchers have focused on mathematical modeling and effective solution algorithms for such optimization problems. However, the control of power generation according to a defined OPF solution is still based on centralized control and management units owned by the DSO. In this paper, we propose a novel, fully decentralized architecture for an OPF-based demand response management system that uses smart contracts to force generators to comply without the need for a central authority or hardware.

<p>Object detection and tracking is one of the most relevant computer technologies related to computer vision and image processing. It may mean the detection of an object within a frame and classify it (human, animal, vehicle, building, etc) by the use of some algorithms. It may also be the detection of a reference object within different frames (under different angles, different scales, etc.). The applications of the object detection and tracking are numerous; most of them are in the security field. It is also used in our daily life applications, especially in developing and enhancing business management. Inventory or stock management is one of these applications. It is considered to be an important process in warehousing and storage business because it allows for stock in and stock out products control. The stock-out situation, however, is a very serious issue that can be detrimental to the bottom line of any business. It causes an increased risk of lost sales as well as it leads to reduced customer satisfaction and lowered loyalty levels. On this note, a smart solution for stock-out detection in warehouses is proposed in this paper, to automate the process using inventory management software. The proposed method is a machine learning based real-time notification system using the exciting Scale Invariant Feature Transform feature detector (SIFT) and Random Sample Consensus (RANSAC) algorithms. Consequently, the comparative study shows the overall good performance of the system achieving 100% detection accuracy with features’ rich model and 90% detection accuracy with features’ poor model, indicating the viability of the proposed solution.</p>