In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, called BEoT that has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.

Industry 5.0 has become a reality now and it is a paradigm that integrates contemporary innovations and concepts. Artificial Intelligence (AI) is a key component and asset of the industrial transformation which allows intelligent devices to perform functionalities such as self-examination, assessment, and evaluation autonomously. AI-based methodologies using ML and deep learning assist manufacturers and industrialists in forecasting service requirements and minimizing downtime. Recent research has discovered a remarkable change in the processes, systems, applications, and products in industries. Also, there is a significant challenge with the explainability of the decisions provided by the models using deep learning algorithms and their inadequate ability to be coupled with each other. Therefore, Explainable artificial intelligence (XAI) is required without compromising the efficiency of the models developed using deep learning algorithms. XAI investigates and develops algorithms, techniques, and models that produce human-comprehensible explanations of AI-based systems and can increase transparency and performance. The explainability nature of XAI will help humans understand the model and the reason behind the predictions, thus improving the model’s transparency and the reliability of the outcomes. Furthermore, an Industry 5.0-enabled environment has a variety of data from varied sources, and this multi-source information must be fused to derive meaningful and optimal decisions. Therefore, all AI-integrated applications must derive actionable insights through information fusion. Hence, the adoption of XAI methodologies in Industry 5.0 can help humans make trustworthy decisions for critical applications requiring information fusion. In this paper, we present a state-of-the-art survey on adopting XAI in Industry 5.0. We discuss the adoption of XAI in various applications such as smart factories, smart Healthcare, E-Governance, smart transportation, Education 5.0, Agriculture 5.0, and Energy 5.0. Finally, some research issues and future directions of integrating XAI with Industry 5.0 are also discussed and highlighted to promote more study in the potential field.