Recent developments in the Internet of Vehicles (IoV) enable interconnected vehicles to support ubiquitous services. Various emerging service applications are promising to increase the Quality of Experience (QoE) of users. On-board computation tasks generated by these applications have heavily overloaded the resource-constrained vehicles, forcing it to offload on-board tasks to other edge intelligence (EI)-assisted servers. However, excessive task offloading can lead to severe competition for communication and computation resources among vehicles, thereby increasing the processing latency, energy consumption, and system cost. To address these problems, we investigate the transmission-awareness and computing-sense uplink resource management problem and formulate it as a time-varying Markov decision process. Considering the total delay, energy consumption, and cost, quantum-inspired reinforcement learning (QRL) is proposed to develop an intelligence-oriented edge offloading strategy. Specifically, the vehicle can flexibly choose the network access mode and offloading strategy through two different radio interfaces to offload tasks to multiaccess edge computing (MEC) servers through WiFi and cloud servers through 5G. The objective of this joint optimization is to maintain a self-adaptive balance between these two aspects. Simulation results show that the proposed algorithm can significantly reduce the transmission latency and computation delay.
Index Terms: Cloud computing | edge intelligence (EI) | Internet of Vehicles (IoV) | multiaccess edge computing (MEC) | quantum-inspired reinforcement learning (QRL)

This study examines how the application of the IoT in smart cities is discussed in the current academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to build the keyword co-occurrence network and to cluster the pertinent literature. Results show the significant growth of IoT research in recent years. The most productive authors, journals, and countries were also identified. The main findings from the keyword co-occurrence clustering and an in-depth qualitative analysis indicate that the IoT is used alongside other technologies including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless telecommunication networks. The major applications of the IoT for smart cities include smart buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the first attempts to map global IoT research in a smart city context and uses a comprehensive set of articles and bibliometric techniques to provide scholars and practitioners with an overview of what has been studied so far and to identify research gaps at the intersection of the IoT and the smart city.
keywords: اینترنت اشیا | شهر هوشمند | مرور | کتاب سنجی | Internet of things | Smart city | Review | Bibliometrics

Internet of Things (IoT) systems in different areas, such as manufacturing, transportation, and healthcare, are the convergence of several technologies. There are many concerns about security and privacy drawbacks in IoT systems. Apart from confidentiality supported by encryption primitives, authenticity and non-repudiation are of utmost importance. IoT entities generally use conventional digital signature schemes to achieve imperative goals. However, there are some state-of-the-art digital signatures with more functionalities, IoT-friendly properties, and privacy-preserving features.
This survey paper aims to accelerate the adoption of advanced digital signatures. We bridge the gap between the advanced theoretical digital signatures recently designed in cryptographic oriented papers and the applied IoT systems. It aids researchers in achieving more security, privacy as well as some unique functionality aspects. First, we illustrate the benefits of the hierarchical and heterogeneous IoT architecture supporting the end-edge-fog-cloud continuum accompanying blockchain technology. Second, our survey delves into five state-of-the-art digital signatures, including randomizable, keyless, double-authentication-prevention, sanitizable, and redactable schemes, that are aligned with entities in IoT systems. We provide an outline, taxonomy, comparison table, and diverse IoT-based use cases for each of them. Then, the integration of primitives and the relationship diagrams give guidelines to help select the appropriate advanced digital signatures and highlights how researchers can use them with different IoT entities for preserving privacy and management of trust.
keywords: امضای دیجیتالی | حفظ حریم خصوصی اینترنت اشیا | بلاک چین | محاسبات ابری | Digital signature | IoT Privacy-preserving | Blockchain | Cloud computing

In the context of the global health crisis of 2020, the tendency of many people to self-diagnose at home virtually, prior to any physical interaction with medical professionals, has been increased. Existing self-diagnosis systems include those accessible via the Internet, which involve entering one’s symptoms. Several other methods do exist, for example, people read medical blogs or notes, which are often wrongly interpreted by them and they arrive at a completely different assumption regarding the cause of their symptoms. In this paper, a system called HealthCloud is proposed, for monitoring health status of heart patients using machine learning and cloud computing. This study aims to offer the ‘best of both worlds’, by combining the information required for the person to understand the disease in sufficient detail, with an accurate prediction as to whether they may have (in this case) heart disease or not. The presence of heart disease is predicted using machine learning algorithms such as Support Vector Machine, K-Nearest Neighbours, Neural Networks, Logistic Regression and Gradient Boosting Trees. This paper evaluates these machine learning algorithms to obtain the most accurate model, in compliance with Quality of Service (QoS) parameters. The performance of these machine learning models is measured and compared using the metrics such as Accuracy, Sensitivity (Recall), Specificity, AUC scores, Execution Time, Latency, and Memory Usage. For better establishment of the results, these machine learning algorithms have been cross validated with 5-fold cross validation technique. With an accuracy rate of 85.96%, it has been found that Logistic Regression is the most responsive and accurate model amongst those models assessed. The Precision, Recall, Cross Validation mean and AUC Score for this model were 95.83%, 76.67%, 81.68% and 96% respectively. The algorithm and the mobile application were tested on Google Cloud Firebase with existing user inputs from the dataset, as well as with unseen new data. The use of this system can assist patients, both in reaching self-diagnosis decisions and in monitoring their health.
keywords: Machine learning | Smart healthcare | Heart disease prediction | Cloud computing

This study determines the potential barriers to achieving circularity in dairy supply chains; it proposes a framework which covers big data driven solutions to deal with the suggested barriers. The main contribution of the study is to propose a framework by making ideal matching and ranking of big data solutions to barriers to circularity in dairy supply chains. This framework further offers a specific roadmap as a practical contribution while investigating companies with restricted resources. In this study the main barriers are classified as ‘eco- nomic’, ‘environmental’, ‘social and legal’, ‘technological’, ‘supply chain management’ and ‘strategic’ with twenty-seven sub-barriers. Various big data solutions such as machine learning, optimization, data mining, cloud computing, artificial neural network, statistical techniques and social network analysis have been suggested. Big data solutions are matched with circularity focused barriers to show which solutions succeed in overcoming barriers. A hybrid decision framework based on the fuzzy ANP and the fuzzy VIKOR is developed to find the weights of the barriers and to rank the big data driven solutions. The results indicate that among the main barriers, ‘economic’ was of the highest importance, followed by ‘technological’, ‘environmental’, ‘strategic’, ‘supply chain management’ then ‘social and legal barrier’ in dairy supply chains. In order to overcome circularity focused barriers, ‘optimization’ is determined to be the most important big data solution. The other solutions to overcoming proposed challenges are ‘data mining’, ‘machine learning’, ‘statistical techniques’ and ‘artificial neural network’ respectively. The suggested big data solutions will be useful for policy makers and managers to deal with potential barriers in implementing circularity in the context of dairy supply chains.
Keywords: Dairy supply chain | Barriers | Circular economy | Big data solution | Fuzzy ANP - VIKOR | Group decision making system