Although the blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing electronic medical records, is an attractive mission for recent research to balance the needs for increasing data privacy and the regular interaction among patients and health providers. Various systems that adopts the blockchain in managing medical records have been proposed. However, there is a need for more work to better characterize, understand and evaluate the employment of blockchain technology in the healthcare industry. In this paper, a design of blockchain based system, namely MedChain, for managing medical records is proposed. MedChain is designed to improve the current systems as it provides interoperable, secure, and effective access for medical records by patients, health care providers, and other third parties, while keeping the patients' privacy. MedChain employs timed-based smart contracts for governing transactions and controlling accesses to electronic medical records. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of health providers regarding their efforts on maintaining medical records and creating new blocks. Extensive experiments are conducted to evaluate the MedChain performance, and results indicate the efficiency of our proposal in handling a large dataset at low latency.
Although blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing academic records, is a new mission for recent research to balance the needs for increasing data privacy and the regular interaction among students and universities. In this paper, a design for a blockchain-based system, namely UniChain, for managing Electronic Academic Records (EARs) is proposed. UniChain is designed to improve the current management systems as it provides interoperable, secure, and effective access to EARs by students, universities, and other third parties, while keeping the students’ privacy. UniChain employs timed-based smart contracts for governing transactions and controlling access to EARs. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of universities regarding their efforts on maintaining academic records and creating new blocks. Extensive experiments were conducted to evaluate the UniChain performance, and the results indicate the efficiency of the proposal in handling a large dataset at low latency.
Accurate and efficient model predictive control (MPC) is essential for Internet of energy (IoE) to enable active real-time control, decentralized demand-supply balance, and dynamic energy management. The MPC consists of short-term electric load forecasting, whose accuracy is affected by the load characteristics, such as overdispersion, autocorrelation, and seasonal patterns. The forecasting efficiency depends on the computational time that is required to produce accurate results and is affected by the IoE data volume. Although several fundamental short-term forecasting models have been proposed, more accurate and efficient models are needed for IoE. Therefore, we propose a novel forecasting temporal negative binomial linear model (NBLM) that handles overdispersion and captures nonlinearity of electric load. We also classify the load into low, moderate, and high intraday seasons to increase the forecast accuracy by modeling the autocorrelation in each season, separately. The temporal NBLM was evaluated using real-world data from Jericho city, and its results were compared to other forecasting models. The temporal NBLM is found more accurate than the other models as the mean absolute percentage error (MAPE) is reduced by 29% compared to the ARMA model. In addition, the proposed model is more efficient as its running time is reduced by 63% in the training phase and by 87% in the forecast phase compared to the Holt-Winter model. This increase in accuracy and efficiency makes the proposed model applicable for load forecasting in IoE contexts where data volume is large and load is highly fluctuated, is overdispersed, is autocorrelated, and follows seasonal patterns.
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