Including Internet Of Things Devices Research Articles

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

A distributed reliable collusion‐free algorithm for selecting multiple coordinators in IOTA using fog computing

SummaryIOTA is a distributed ledger technology with a new structure called Tangle, which offers high scalability, no fees, and near‐instant transfers for the internet‐of‐things (IoT) networks. The most important issue of IOTA is consensus achievement, which is handled by a single node acting as a coordinator. A single and default coordinator node exposes IOTA to the single point of failure and incomplete distribution issues. In this paper, a novel algorithm for selecting multiple coordinators to participate in the consensus process at milestones is proposed (i.e., MCS algorithm) to overcome the problem of a single coordinator in IOTA network. The MCS algorithm is applied in a two‐layered architecture including IoT devices (i.e., Layer 1) and fog nodes (i.e., Layer 2). We define and formulate four different properties as metrics to select multiple coordinators in this architecture. Moreover, a collusion list is defined to decrease the risk of collusion between fog nodes in the network. Experimental results show that using multiple fog nodes as coordinators in IOTA network can improve the average response time and average throughput while does not considerably sacrifice the total cost and system utilization in comparison with the use of a single default coordinator in IOTA network.

IOT-5G-Exploring next-generation media consumption in the area of Journalism

Abstract: The most common buzzwords in the world is “The Internet of things” (IoT) and IOT describes the network of physical objects, so known as, "things" those are rooted with sensors in the devices, application software, technologies that is used for the resolution of connecting one end to another end and exchanging information with other devices and systems over the Internet. The IoT 5G technologies can also be used in journalism and the primary focus is to increase M2M interaction of mass communication devices. One way it is “ubiquitous computing” can occur using any device, in any location, and in any format. The Internet of Things (IoT) is all about small cost sensors grabbing data to communicate with one device to another device using cloud solutions. Coming to the 5th generation mobile network. We have already started using 4G networks and as we know that, start with 1G, 2G, 3G, and 4G networks. The 5th generation network is going to enable an upcoming new network that will associate virtually every person globally connected and everything organized including IoT devices, objects and machines. Central communication and Journalism is the activity of gathering right information, evaluating, generating, and presenting broadcast information. It is high time to start using IOT Technology using 5th generation high-speed network connectivity devices to communicate or data transfer in the area of journalism. Keywords: IOT – Internet Of Things, 5G- Fifth Generation in data network, JMC – Journalism and Mass Communications, M2M – Machine to Machine, Cloud , Artificial intelligence and Machine Learning.

Evaluation and Quality Assurance of Fog Computing-Based IoT for Health Monitoring System

Computation and data sensitivity are the metrics of the current Internet of Things (IoT). In cloud data centers, current analytics are often hosted and reported on suffering from high congestion, limited bandwidth, and security mechanisms. Various platforms are developed in the area of fog computing and thus implemented and assessed to run analytics on multiple devices, including IoT devices, in a distributed way. Fog computing advances the paradigm of cloud computing on the network edge, introducing a number of options and facilities. Fog computing enhances the processing, verdicts, and interventions to occur through IoT devices and spreads only the necessary details. The ideas of fog computing based on IoT in healthcare frameworks are exploited by shaping the disseminated delegate layer of insight between sensor hubs and the cloud. The cloud proposed a system adapted to overcome various challenges in omnipresent medical services frameworks, such as portability, energy efficiency, adaptability, and unwavering quality issues, by accepting the right to take care of certain weights of the sensor network and a distant medical service group. An overview of e-health monitoring system in the context of testing and quality assurance of fog computing is presented in this paper. Relevant papers were analyzed in a comprehensive way for the identification of relevant information. The study has compiled contributions of the existing methodologies, methods, and approaches in fog computing e-healthcare.

Research hotspots and tendency of intelligent manufacturing

<p indent="0mm">During the past few years, many countries have put forward their national manufacturing strategies. Although these strategies are proposed and developed under different backgrounds, their common objective is to achieve intelligent manufacturing, which has the potential to revolutionise manufacturing industry and is being nurtured through national projects such as “Industry 4.0” in Germany, “Made Smarter” in the UK, “Industrial Internet” in the US, and “Intelligent Manufacturing” in China, etc. These projects promote a new manufacturing philosophy to guide the development of industrial manufacturing in a digital, intelligent and networked global environment, as well as upgrade the traditional manufacturing to intelligent manufacturing. With taking advantages of artificial intelligence and advanced manufacturing technologies, intelligent manufacturing connects all manufacturing facilities and enables physical processes and information flows to be available when and where they are needed across holistic manufacturing supply chains, this will benefit optimizing manufacturing resources, improving product quality, system productivity and sustainability, and reducing costs. Intelligent manufacturing has attracted intense attention due to the broad application of artificial intelligence and information science technology in manufacturing industry. In this study, 1370 papers in intelligent manufacturing are taken as data sources, which are retrieved from the Web of Science Core Collection Database between 1987 to 2017. The present paper aims to reveal the development status, the number of documents by leading countries, hotspots and trends of intelligent manufacturing by VOSviewer tool. Based on annual outputs and growth rate of literatures, the initial development stage (1987−1997), the stable development stage (1998−2012) and the rapid development stage (2013−2017) of intelligent manufacturing are proposed. On the whole, to date, China takes a leading position out of more than 100 countries or regions, followed by America and England. With more than <sc>30 years</sc> of evolution, the connotation of intelligent manufacturing mainly goes through the process from digitalization to networking and then to intellectualization. In the recent research (since 2013), new enabling technologies for intelligent manufacturing have been developed, including Internet of Things (IoT), Big Data, Digital Twin (DT), Cloud Manufacturing, and Cyber-Physical System (CPS), which endow the manufacturing process with intelligence. For instance, IoT devices can communicate and interact with each other, and thus enable the real-time monitoring and dynamic control. Big data analytics together with AI can help to mine out potential rules, knowledge and patterns, in order to smart prediction, evaluation, optimization and decision-making. DT aims at constructing interactive virtual mirrors for physical assets and fusing both real and simulated data to give more insights. Cloud Manufacturing transforms manufacturing resources and manufacturing capabilities into manufacturing services, which can be managed and operated in an intelligent and unified way to enable the full sharing and circulating of manufacturing resources and manufacturing capabilities. These technologies can be combined with each other and greatly promote the innovation of manufacturing in design, production, operation and maintenance, etc. The findings presented in this study reveal the technical developing tendency and current hot topics in intelligent manufacturing and will facilitate scholars’ investigation in the related fields, a comprehensive taxonomy of Intelligent Manufacturing can also be developed through analyzing the results of this review.

Mobility Management for 5G IoT Devices: Improving Power Consumption With Lightweight Signaling Overhead

In mobile wireless networks, mobility management (MM) is an important process to track and locate user equipments (UEs), including Internet-of-Things (IoT) devices, while moving throughout the network. In long term evolution (LTE) and fifth generation (5G) wireless networks, the two MM procedures are known as tracking area update (TAU) and Paging , which are burdensome for both mobile IoT/UEs and network—the IoT/UEs and network always initiate the TAU and Paging , respectively. Because of potentially very high-volume traffic and increasing density of high-mobility IoT/UEs, the TAU/Paging procedure increases the accompanied signaling overhead and the power consumption in the battery-limited IoT/UEs. Hence, this problem will become even worse in 5G because the latter is expected to accommodate exceptional services (e.g., longer IoT/UE battery lifetime). We propose a new solution to solve this problem, named gNB-based UE mobility tracking (gNB-based UeMT). This solution has four features achieving 5G goals. First, the mobile IoT/UEs will no longer trigger the TAU to report their location changes, giving much higher power savings with no signaling overhead. Instead, second, the network elements, gNBs, take over the responsibility of Tracking and Locating these IoT/UEs, giving always-known IoT/UE locations. Third, our Paging procedure is markedly improved over the conventional one, providing very fast IoT/UE reachability with no Paging messages being sent simultaneously. Fourth, this solution guarantees lightweight signaling overhead with very low Paging delay; it achieves about 92% reduction in the corresponding signaling overhead. To this end, our solution adds no implementation complexity; instead, it exploits the already existing LTE/5G communication protocols, functions, and measurement reports.

Web of Things Platforms for Distance Learning Scenarios in Computer Science Disciplines: A Practical Approach

Problem-based learning is a widely used learning methodology in the field of technological disciplines, especially in distance education environments. In these environments, the most used tools, which provide learning scenarios, are remote and virtual laboratories. Internet of Things (IoT) devices can be used as remote or virtual laboratories. In addition to this, they can be organized/orchestrated to build remote maker spaces through the web. These types of spaces are called the Web of Things (WoT). This paper proposes the use of these types of spaces and their integration as practical activities into the curricula of technological subjects. This approach will allow us to achieve two fundamental objectives: (1) To improve the academic results (grades) of students; and (2) to increase engagement and interest of students in the studied technologies, including IoT devices. These platforms are modeled using archetypes based on different typologies and usage scenarios. In particular, these usage scenarios will implement a learning strategy for each problem to be solved. The current work shows the evolution of these archetypes and their application in the teaching of disciplines/subjects defined in computer science, such as distributed computing and cybersecurity.