At present, in pharmacology one of the most serious problems is counterfeit drugs. The Health Research Funding organization reported that in developing countries, nearly 10–30% of the drugs are fake. Counterfeiting is not the main issue itself, but, rather, the fact that, as compared to traditional drugs, these counterfeit drugs produce different side effects to human health. According to WHO, around 30% of the total medicine sold in Africa, Asia, and Latin America is counterfeit. This is the major worldwide problem, and the situation is worse in developing countries, where one out of every 10 medicines are either fake or do not follow drug regulations. The rise of Internet pharmacies has made it more difficult to standardize drug safety. It is difficult to detect counterfeits because these drugs pass through different complex distributed networks, thus forming opportunities for counterfeits to enter the authentic supply chain. The safety of the pharmaceutical supply chain has become a major concern for public health, which is a collective process. In this paper, we propose a novel drug supply chain management using Hyperledger Fabric based on blockchain technology to handle secure drug supply chain records. The proposed system solves this problem by conducting drug record transactions on a blockchain to create a smart healthcare ecosystem with a drug supply chain. A smart contract is launched to give time-limited access to electronic drug records and also patient electronic health records. We also carried out a number of experiments in order to demonstrate the usability and efficiency of the designed platform. Finally, we used Hyperledger Caliper as a benchmarking tool to conduct the performance of the designed system in terms of transactions per second, transaction latency, and resource utilization.

Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data.

It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network’s performance has improved because training and testing time do not increase when we include more nodes in the network.

The recent advances in information management systems coupled with machine learning algorithms paved the way for a significant revolution in animal healthcare industries. However, the data in such systems suffer from various challenges such as security, reliability, and convenience, to name a few. Traditional systems are not useful to meet these critical issues because these systems have not a consistent structure for data security and reliability policies. Therefore, a new solution is required to enhance data accessibility and should regulate government security policies to ensure the accountability of the usage of the medical records system. Moreover, it is also required to analyze historical data of veterinary clinic using data mining and machine learning techniques to predict the future appointments scheduling requests, which is essential for veterinary management to drive better future decisions, for instance, future demands of medical supplies and to plan veterinary medical staff, etc. This paper aims to fill the gap by proposing a novel blockchain-based reliable and intelligent veterinary information management system (RIVIMS) using smart contract and machine learning techniques. The proposed RIVIMS consists of two main modules; blockchain-based secured veterinary information management, data and predictive analytics modules. First, a blockchain-based secure and reliable veterinary clinic information management system is developed using Hyperledger Fabric. Second, a smart contract enabled data, and predictive analytics modules are developed using permissioned blockchain framework. The data and predictive modules aim to analyze veterinary clinic patients appointments data in order to discover underlying patterns and build a robust prediction model using machine learning algorithms. The data and predictive helps veterinary management to drive better future business decisions to provide better healthcare services to veterinary patients. Hyperledger Caliper is used as a benchmark tool to evaluate the performance of the developed blockchain-based system in terms of transaction per second, transaction success rate, transaction throughput, and transaction latency. Furthermore, machine learning performance measures have utilized, such as MAE, RMSE, and R2 score to evaluate the overall performance of the prediction model. The experimental results demonstrate the effectiveness and robustness of the proposed RIVIMS.

The exponentially growing population, urbanization, and economic development have led to the rising generation of municipal solid waste. Municipal solid waste management is thus a significant hurdle for urban societies as it consumes a large chunk of public funds, and, when mishandled, it can lead to environmental and social hazards. Some of the prerequisites required for effective waste management are the monitoring of bins, timely collection of bins, and prioritization of those areas which produce more solid waste. In this paper, we propose an optimal route recommendation system for waste carriers vehicles to effectively collect solid waste based on the profile of a particular area. This article contributes with a multi-objective optimization approach to generate a route by minimizing the route distance and maximizing the amount of waste. Then, a family of evolutionary methods is employed to solve the proposed objective function and find the optimal route for waste carrier vehicles. The experiment is carried out on the real-world solid waste data of Jeju Island, South Korea. The data is processed to predict the behavior of people of a specified grid location in terms of waste disposal. Therefore, the recommendation system caters to the predicted waste across a set of bins inside the area, and considering the constraints such as total allowed distance and time, proposes a route that is best in terms of distance (fuel consumption) and waste collection. Different use cases are illustrated to signify the proposed system, and results indicate that it can be a step forward for the implementation of smart cities, which is the goal of Jeju Island.