Coffee is an important economic crop and one of the most popular beverages worldwide. The rise of speciality coffees has changed people's standards regarding coffee quality. However, green coffee beans are often mixed with impurities and unpleasant beans. Therefore, this study aimed to solve the problem of time-consuming and labour-intensive manual selection of coffee beans for speciality coffee products. The second objective of the authors’ study was to develop an automatic coffee bean picking system. They first used image processing and data augmentation technologies to deal with the data. They then used deep learning of the convolutional neural network to analyse the image information. Finally, they applied the training model to connect an IP camera for recognition. They successfully divided good and bad beans. The false-positive rate was 0.1007, and the overall coffee bean recognition rate was 93%.

In this study, a method is proposed for ultra high frequency radio frequency identification (UHF RFID)-based book positioning and counting developed for smart libraries. In the experimental setup created, RFID tags placed in books were automatically detected using three RFID antennas. Using received signal strength indicator information from each antenna for each book, the locations of the books are determined. In addition, classification was made by using machine learning approaches for the study. For this purpose, the best result for sequence determination in the classification study using ensemble trees, K nearest neighbours (KNN), and support vector machine algorithms was obtained with the ensemble subspace KNN algorithm with 94.1%. The best result for cabinet detection was obtained in the study using the ensemble subspace KNN algorithm and a 78.5% accuracy rate was achieved. The best result for rack detection was obtained with the ensemble subspace KNN algorithm with 95.4%. The study is thought to be useful in the automatic determination of the row, cabinet, and rack of books in smart libraries.

The transportation system needs innovative schemes and applications to facilitate mobility in the cities that is user-friendly, easy, enjoyable and convenient according to citizens' constraints. In this study, the authors propose a decentralised architecture-based game-theoretic model for a community-based transportation system. This scheme, which involves multi-transportation forms, allows the user to be an active prosumer who can travel in the city using public and private forms and also make decisions about the trip cost. The authors propose a decentralised game-theoretic transportation algorithm to manage passenger needs, public bus interests, car ride-sharing and bicycle constraints. The simulations prove the effectiveness of the proposed scheme. The effectiveness of the decentralised game-theoretic transportation model appears more clearly when compared with the multi-mode double dynamic approach in [1], as it gives much better optimisation results.

The internet of things (IoT) is a paradigm where the fragmentation of standards, platforms, services, and technologies, often scattered among different vertical domains. Consequently, the smart energy system is one of the vertical domains in which IoT technology is investigated. At the early stages of studying the IoT domains that deal with big data and interoperability, a semantic layer can be served to approach the difficulty of heterogeneity in information and data representation from IoT devices. In 2015, smart appliance reference ontology (SAREF) was introduced to interconnect data of smart devices and facilitate the communication between IoT devices that use different protocols and standards. The modular design of SAREF concedes the definition of any new vertical domain describing functions that the devices perform. In this study, SARGON – SmArt eneRGy dOmain oNtology is offered which extends SAREF to cross-cut domain-specific information representing the smart energy domain and includes building and electrical grid automation together. SARGON ontology is powered by smart energy standards and IoT initiatives, as well as real use cases. It involves classes, properties, and instances explicitly created to cover the building and electrical grid automation domain. This study exhibits the development of SARGON and demonstrates it through a web application.

Electricity demand forecasting is a critical task for efficient, reliable and economical operation of the power grid, which is one of the most essential building blocks of smart cities. Accurate forecasting allows grid operators to properly maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet which can incorporate multiple heterogeneous features such as historical energy consumption data, weather data and calendar information for the demand forecasting task. Using real-world smart meter dataset, we conduct an extensive evaluation to show the advantages of PowerNet over recently-proposed machine learning methods such as Gradient Boosting Tree (GBT), Support Vector Regression (SVR), Random Forest (RF) and Gated Recurrent Unit (GRU). PowerNet demonstrates notable performance in reducing both the median and worst-case prediction errors when forecasting demands of individual residential households. We further provide empirical results concerning the two operational considerations that are crucial when using PowerNet in practice: the time horizon the model can predict with a decent accuracy and the frequency of training the model to retain its modeling capability. Finally, we briefly discuss a multi-layer anomaly/electricity-theft detection approach based on PowerNet demand forecasting.

This case study explores Bristol's goal to create a smart city, anchored by a 30-year city vision and the vital role digital connectivity and smart city technologies play. It describes Bristol's 2050 One City Plan, where city partners are collaboratively shaping the city through place-based leadership, community involvement and co-production, and Bristol City Council's complementary smart city strategy – Connecting Bristol. This digital agenda supports by developing the ‘foundations’ required to become a data-enabled city with world-class connectivity and inclusive public services; elements which enable many One City goals. It examines the importance of a challenge-led, people-focused approach with responsible innovation practices that ensure digital initiatives align with the One City Plan's equitable goals and values. Implementation strategies, including an ‘innovation ambition’ matrix used to manage a portfolio of smart city initiatives, and challenges are described along with the need for inclusive infrastructure and ethical data practices. The emerging role for local government in shaping a trusted smart city is explored. This study ends by discussing Covid-19 and economic recovery, in response to which the Council seeks to become a more agile, streamlined organisation, and concludes by highlighting the need to keep people at the heart of ‘smart’ city development.