Abstract The Internet of Things (IoT) are standard inter connected devices aimed at join everyday object to the internet. This ecosystem include manufacturing, agriculture, smart cities, industry, as well as healthcare. The capacity of controlling and monitoring the objects of the physical world using IoT generate numerous opportunities. However some extra cost is also added to make the device globally accessible. The aggressive growth of the IoT devices, the multifariousness of IoT network technology, and the diversity of IoT use cases generate a question mark regarding the sustainability of the IoT. The aim of the proposed work to contribute in this regard, for that a dynamic integration of IoT objects that is pre determined for (i) creating an IoT platform dynamically (ii) monitoring the current status of IoT environment (iii) measuring the quality of the overall system (iv) helping to utilize all the interconnected efficiently by adding M2M communication, is introduced. Some property set which is suitable for decentralized IoT platform is also explained. Such types of dynamic IoT platform helps in every IoT application domain including industrial IoT. With the propound research, the aim is to create a dynamic IoT platform to simplify the production of next generation. The primary contribution of the proposed paper is a concept which can help to design IoT device in a faster way, which can be called rapid hardware development approach. Efficiently used human resources, that means any one having common technical knowledge can design the device, and reduce the hardware heterogeneous architecture.

Abstract Following the fourth industrial revolution and subsequent developments in information and communication technology, applying intelligent techniques in microgrid is gaining popularity in academia and business worldwide. A significant amount of data is continuously generated by the widespread use of internet of things (IoT) technologies and sensor networks in microgrids. This data includes essential information to progress the performance of microgrids. This paper includes a comprehensive review of IoT, cloud computing, big data, artificial intelligence, machine learning, blockchain in microgrid and the concepts of digital twin and metaverse and their applications. By aiding in the design, operation management, and maintenance of microgrids, these methods offer a potent tool for managing the massive historical data and real‐time data stream in a proficient and protected way. They also facilitate microgrids operation. Contextual awareness, security, and resilient operation are important for microgrids, therefore their possible improvement in light of these intelligent approaches is comprehensively examined. A theoretical implementation for managing robust operation of microgrids is then described. The discussion of recommendations in microgrids concludes.

Due to the increasing pervasiveness of various wireless machines in Industrial Internet of Things (IIoT) networks, the need for timely updates on information freshness has become more critical. Social IIoT (SIIoT) framework facilitates the development of interconnected networks in smart factories that can establish social relationships between humans and machines. The widespread implementation of device-to-device (D2D) communication in SIIoT networks introduces cross-talk interference, leading to an untimely update on information freshness has become a critical challenge. The Age of Information (AoI) performance metric is used to measure the Information freshness. Dynamic channel behavior plays a crucial role in enhancing the performance of D2D-enabled SIIoT Networks. This article addresses the impact of AoI and user channel behavior on network performances. We propose a mean field game theoretic optimization algorithm (PMOA) that utilizes a Hypergraph model and game theoretic concept. The problem is solved in two stages: first, a hypergraph-based channel selection model is developed to minimize interference under outdated channel state information (CSI). Second, AoI is optimized by adopting the Fokker-Planck equation (FPK). This approach meets constraints on maximum transmission power, data rate variability, and timeliness of information freshness of the devices. The PMOA algorithm is validated through simulation and achieves a significant improvement with a 13.41% increase in network throughput and a reduction in AoI by 39.38% compared to existing benchmark schemes. The implementation of the proposed algorithm may be suitable for updating the information freshness of industrial devices driven by SIIoT-based recommendation systems.

Living Lab, one of the recent emerging smart city concepts, faces long-term sustainability challenges associated with its complexity and breadth of use. To be efficient, it must rely on comprehensive set of information distributed appropriately among all stakeholders to unleash its full innovation potential. This is especially true in the case of positive energy districts, where timely data dissemination is essential for prosumager decisions and their greedy behaviour. This paper interconnects intelligent information exchange, supported by ultra-low latency hybrid access network infrastructure, with the clever use of available fog computing resources to properly disseminate complex energy details to all participating entities. As the optimal distribution of information using proper task offloading is the convergence problem, we recalled higher-order neural units that helped maintain computational and energy efficiency in conjunction with the preservation of the overall system stability. We have achieved a reliable hourly energy consumption prediction with a computationally very lightweight alternative to commonly used deep neural network approaches that can be deployed on available smart appliances with ease. The application and simulation were performed on the dataset provided by one of Europe’s smart city pioneers, where the prosumager positive energy district transition has already started.

This paper provides a novel intelligent scheduling strategy for a real-world transportation dynamic scheduling case from an engine workshop of general motor company (GMEW), which is a key production line throughout the manufacturing process. In order to reduce the carbon emission in the scheduling process and make up for ignoring the energy consumption of each part in the scheduling when optimizing the carbon emission of the workshop and the factory. This paper first formulates a fuzzy random chance-constrained programming model of inverse scheduling problem (ISP) with energy consumption. A multi-strategy parallel genetic algorithm based on machine learning (RL-MSPGA) is proposed, which uses machine learning to improve the genetic algorithm. First, the parallel idea is developed to accelerate the process of evolution of genetic algorithm, and the initial population is divided into clusters by$k$-means clustering algorithm. Second, similar individuals are evenly distributed to different sub-populations to ensure the diversity and uniformity of sub-populations. Third, in the process of evolution, the sub-populations communicate with each other, and extend the excellent individuals to replace the poor ones in other populations, so as to improve the overall quality of the population. Fourth, the self-learning of the crossover probability is realized by the self-learning of the self-sensing environment, which makes the crossover probability adapt to the evolutionary process according to experience. Finally, the real instance is used to validate the different algorithms. It can effectively adjust the completion time and the proportion of energy consumption, thus providing the possibility for the production of energy-saving enterprises. This implies that the suggested model is reasonable and the provided algorithm can effectively solve the inverse shop scheduling problem.

Abstract Parkinson's disease is a degenerative disease that leads to brain disorder and nonfunctioning of different body parts. Deep learning tools like artificial neural network (ANN), convolution neural network (CNN), regression Analysis (RA), and so on, has been considered to a great extent in recent days. Several data sets based on the motor and nonmotor symptoms are applied to different classifier for correct identification of Parkinson's patient from healthy people. In this paper, hybridization of two deep learning tools such as, RA and ANN are done for effective diagnosis of the disease by probability estimation. The communal merits of individual approaches of the existing approaches are realized in this context for accurate probability estimation. Data preprocessing and probability estimation of preprocessed data is done in RA. The second existing approach is used to identify the PD patient by comparing with a predefined threshold value of a neuron. The estimation is performed on the data set of speech recognition, iron content, and pulse rate among a group of people. The proposed approach is compared with the existing approaches like, SVM and k‐NN classifier. The computed result reveals the superiority of the proposed algorithm with 93.46% accuracy.

A smart city refers to a city equipped with the basic infrastructure to provide a good quality of life and a clean and sustainable environment to its citizens using smart technology-based solutions. It is a smart way to provide the best services to its residents and develop the infrastructure. The smart city focuses on controlling available resources safely, sustainably, and efficiently to improve the economy and societal outcomes. People, systems, and things in the cities generate data. However, their heterogeneity makes it difficult to publish, organize, discover, interpret, combine, analyse, and consume them. The next generation of these technologies, including the 6G and intelligent internet of things (6G/IIoT), have been recently proposed aiming to provide endless networking capabilities to the city users. General estimates revealed that the number of smart IoT devices would approach over 50 billion by 2022. With the proliferation of smart IoT devices, smart applications are expected to lead to further innovation in 6G/IIoT-oriented cities. This special issue aims to stimulate discussion on the IoT and Big Data analytics for future smart cities with smart applications including smart health, smart governance, smart homes, and smart buildings, smart mobility and transportation, smart factories, and smart data-driven decision-making. In this special issue, each paper was reviewed by three or more experts during the assessment process. After evaluating the overall scores, six papers were selected for inclusion in this special issue. The selected papers present in-depth studies of practical issues and challenging problems in Big Data Analytics with IoT-oriented Infrastructures for Future Smart Cities. This paper (Haque et al., 2022) focuses on the overview and conceptual development of the smart city. Initially, the work discusses the smart city idea and fundamentals explored in various pieces of literature. Further various smart city applications along with notable implementations are put forth to understand the quality of living standards. This article (Zhang & Liu, 2022) conducts APP page management through technical analysis of mobile devices and user experience-oriented design. The framework design of the browser and the construction of the interface MVC design mode, and then through the questionnaire survey method to the user experience needs and expectations of the medical APP to design the main key of the page function. In this paper (Galyan et al., 2022), simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range-free methods. The proposed technique is useful for mapping several instances like fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities, and so forth. In this work (Jain et al., 2022), the authors have applied logistic regression, decision tree, support vector machine, linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, random forest, and k-nearest neighbour algorithms to predict the stability of the grid. The authors have used the smart grid stability data set freely available on Kaggle to train and test the models. This work (Mandloi & Arya, 2022) proposed a Machine Programming based approach for the deployment of 5 G-enabled UmBSs. The centroid-based clustering algorithms such as; K-means, K-medoid, and FCM were proposed to find the centroid of the cluster. Then, the UmBSs were deployed at the centroid location of each cluster. This paper (Dixit et al., 2022) presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in existing surveys, and the evaluation metrics for AI-based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique.