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.

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Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid

With rapid development of enabling technologies such as Internet of Things, robotics, and big data, the fourth industrial revolution known as “Industry 4.0” (I4.0) has become a great opportunity for productivity innovation around the world. Industrial Internet of Things (IIoT), as a typical implication of Cyber-Physical Systems (CPS) in I4.0 era, has attracted significant attention. It aims at optimizing resources and improving production efficiency for manufacturing industry through deep interconnection of physical equipment, wide collection of multi-dimensional data, intelligent awareness of environment and automatic control. Blockchain is a kind of distributed ledger technology and has features including decentralization, anti-tamper, transparency, anonymity, and contract-autonomy. These features are helpful to improve the services and promote the development of IIoT. In this paper, we provide a comprehensive survey on the literatures applying blockchain technology into IIoT. We firstly introduce the related works and background knowledge. And then, the motivations and benefits for applying blockchain into IIoT are discussed in detail. Furthermore, based on the layered architecture of blockchain we summarized in the previous work, we outline the research framework of blockchain in IIoT. Meanwhile, the related application research is presented from the perspectives of devices security, data collection and sharing, and industrial application. Moreover, we explore the technical requirements of blockchain platforms in IIoT applications. Finally, some challenges and future directions on blockchain in IIoT, as well as broader perspectives, are concluded for further study.

A Comprehensive Survey on Blockchain in Industrial Internet of Things: Motivations, Research Progresses, and Future Challenges

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.

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A Novel Blockchain-Based Integrity and Reliable Veterinary Clinic Information Management System Using Predictive Analytics for Provisioning of Quality Health Services

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.

Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities

Internet of Things is advancing, and the augmented role of smart navigation in automating processes is at its vanguard. Smart navigation and location tracking systems are finding increasing use in the area of the mission-critical indoor scenario, logistics, medicine, and security. A demanding emerging area is an Indoor Localization due to the increased fascination towards location-based services. Numerous inertial assessments unit-based indoor localization mechanisms have been suggested in this regard. However, these methods have many shortcomings pertaining to accuracy and consistency. In this study, we propose a novel position estimation system based on learning to the prediction model to address the above challenges. The designed system consists of two modules; learning to prediction module and position estimation using sensor fusion in an indoor environment. The prediction algorithm is attached to the learning module. Moreover, the learning module continuously controls, observes, and enhances the efficiency of the prediction algorithm by evaluating the output and taking into account the exogenous factors that may have an impact on its outcome. On top of that, we reckon a situation where the prediction algorithm can be applied to anticipate the accurate gyroscope and accelerometer reading from the noisy sensor readings. In the designed system, we consider a scenario where the learning module, based on Artificial Neural Network, and Kalman filter are used as a prediction algorithm to predict the actual accelerometer and gyroscope reading from the noisy sensor reading. Moreover, to acquire data, we use the next-generation inertial measurement unit, which contains a 3-axis accelerometer and gyroscope data. Finally, for the performance and accuracy of the proposed system, we carried out numbers of experiments, and we observed that the proposed Kalman filter with learning module performed better than the traditional Kalman filter algorithm in terms of root mean square error metric.

https://www.mdpi.com/1424-8220/20/16/4410/pdf

Toward Accurate Position Estimation Using Learning to Prediction Algorithm in Indoor Navigation.

Waste has a direct impact on human health and the surrounding environment. Apart from the health aspect, many industries' growth is effected by waste material such as the food industry. Waste management authorities are interested in reducing the cost of waste management operations and searching for sustainable waste management solutions. For effective planning of waste management, reliable data analysis is required to produce results that can facilitate the planning process. Data mining and machine learning-based data analysis over the waste data can produce a more detailed, and in-time waste information generation, which can lead to effectively manage the waste amount of specific area. In this paper, a descriptive data analysis approach, along with predictive analysis, is used to produce in-time waste information. The performance of the proposed approach is evaluated using a real waste dataset of Jeju Island, South Korea. Waste bins are virtualized on its actual location on the Jeju map in Quantum Geographic Information Systems(QGIS) software. The performance results of the predictive analysis models are evaluated in terms of Mean Absolute Error(MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). Performance results indicate that predictive analysis models are reliable for the effective planning and optimization of waste management operations.

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Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

https://www.mdpi.com/2079-9292/10/8/880/pdf

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

This research work proposed a hybrid model to maximize energy consumption and maximize user comfort in residential buildings. The proposed model consists of two widely used optimization algorithms named the firefly algorithm (FA) and genetic algorithm (GA). The hybridization of two optimization approaches results in a better optimization process, leading to better performance of the process in terms of minimum power consumption and maximum occupant’s comfort. The inputs of the optimization model are illumination, temperature, and air quality from the user, in addition with the external environment. The outputs of the proposed model are the optimized values of illumination, temperature, and air quality, which are, in turn, used in computing the values of user comfort. After the computation of the comfort index, these values enter the fuzzy controllers, which are used to adjust the cooling/heating system, illumination system, and ventilation system according to the occupant’s requirement. A user-friendly environment for power consumption minimization and user comfort maximization using data from different sensors, user, processes, power control systems, and various actuators is proposed in this work. The results obtained from the hybrid model have been compared with many state-of-the-art optimization algorithms. The final results revealed that the proposed approach performed better as compared to the standard optimization techniques.

Imran

Papers

Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid

Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities

Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges

Energy Consumption Optimization and User Comfort Maximization in Smart Buildings Using a Hybrid of the Firefly and Genetic Algorithms