Designing an adaptive learning model for predictive database management in Internet of Things applications

Hub-OS: An interoperable IoT computing platform for resources utilization with real-time support

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

Typical Internet of Things (IoT) applications involve collecting information automatically from diverse geographically-distributed smart sensors and concentrating the information into more powerful computers. The Raspberry Pi platform has become a very interesting choice for IoT applications for several reasons: (1) good computing power/cost ratio; (2) high availability; it has become a de facto hardware standard; and (3) ease of use; it is based on operating systems with a big community of users. In IoT applications, data are frequently carried by means of wireless sensor networks in which energy consumption is a key issue. Energy consumption is especially relevant for smart sensors that are scattered over wide geographical areas and may need to work unattended on batteries for long intervals of time. In this scenario, it is convenient to ease the construction of IoT applications while keeping energy consumption to a minimum at the sensors. This work proposes a possible gateway implementation with specific technologies. It solves the following research question: how to build gateways for IoT applications with Raspberry Pi and low power IQRF communication modules. The following contributions are presented: (1) one architecture for IoT gateways that integrates data from sensor nodes into a higher level application based on low-cost/low-energy technologies; (2) bindings in Java and C that ease the construction of IoT applications; (3) an empirical model that describes the consumption of the communications at the nodes (smart sensors) and allows scaling their batteries; and (4) validation of the proposed energy model at the battery-operated nodes.

The improvement of Internet of Things (IoT) applications has led to a substantial increase in the number of multiple resources of computation, communication, and caching (3C). The fifth generation (5G) and multi-access edge computing (MEC) are promising to enhance the computation offloading of IoT applications with high performance and reliability. According to resource-consuming preferences, IoT applications can be divided into computation-hungry applications and memory-hungry applications. To deal with the computation-hungry applications, Graphics Processing Units (GPUs) are increasingly used to process simple computation tasks. Meanwhile, the running of memory-hungry applications is accompanied by massive data transfers between processing core and memory. These transfers can result in significant energy and performance costs. Processing in memory (PIM) is a computing paradigm that avoids most data movement costs by performing a part of the computations directly in the memory. In this paper, we focus on offloading computation tasks in MEC that require 3C resources with high efficiency and low energy consumption considering latency and resilience constraints in a PIM-assisted multi-core (PAMC) architecture of physical machines (PMs). We formulate an optimization problem to minimize the total weighted resource costs and energy consumption. We also present an algorithm based on the column generation to solve the problem. Simulation results demonstrate that the proposed PAMC architecture can achieve good results in terms of energy consumption and resources utilization in comparison with the traditional PMs’ architecture with the same resources.

The integration of optimization algorithms with IoT (internet of things) applications presents numerous benefits and diverse applications. Optimization algorithms help enhance the efficiency, scalability, and cost-effectiveness of IoT systems. This powerful combination offers advantages such as improved resource allocation, reduced energy consumption, enhanced decision-making, and better resource utilization. It finds applications in smart cities, agriculture, healthcare, manufacturing, and more, optimizing traffic management, precision agriculture, healthcare resource allocation, and supply chain management, among others. In summary, the union of optimization algorithms with IoT unlocks a wide array of opportunities for optimizing processes, conserving resources, and improving the quality of services in various domains. Optimization algorithms are used to find the best solution to a given problem, and when applied to IoT, they can help in various ways, including improving resource allocation, energy efficiency, data analysis, and more. Here the authors discuss some ways in which optimization algorithms can be combined with IoT applications such as resource allocation, energy efficiency, data routing and processing, quality of service (QoS), improvement, etc. The choice of the specific optimization algorithm depends on the nature of the problem and the application. Algorithms like genetic algorithms, particle swarm optimization, simulated annealing, and machine learning techniques (e.g., deep reinforcement learning) can be applied to various IoT optimization problems. Basically, the combination of optimization algorithms with IoT applications can lead to more efficient, cost-effective, and reliable IoT systems across a wide range of domains. It's essential to carefully assess the specific requirements of your IoT application and select the appropriate optimization techniques to achieve the goals.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

The Internet of Things (IoT) has induced many advances in the modern world, thus always arousing great interest from the scientific community. Due to a large number of connected devices, this paradigm has put into circulation an enormous data quantity to be processed and offloaded while respecting latency and energy constraints that Central Cloud alone cannot meet. Hence, Fog Computing has come to fill these gaps by providing computing, management, and storage resources via small‐distributed data centres located at the network edge. The majority of previous research on centralised clustering in an IoT environment did not consider both residual energy and the location of the IoT devices, as well as the signalling communication frequency, to solve an issue of extreme energy consumption during data exchange between IoT devices and Fog entities. Thus, the objective of this study is to find solutions taking into account these criteria to guarantee the Fog system's energy efficiency while coping with the energy consumption increase related to the handling of massive data volume. Indeed, the proposed study presents a novel approach based on Fog‐IoT architecture to ensure intelligent energy management during communication and offloaded task processing in IoT applications. The simulation results show its effectiveness.

Fog computing for next-generation Internet of Things: Fundamental, state-of-the-art and research challenges

The concept of Internet-of-Things (IoT) emerged around two decades ago but has gained momentum during the last few years. The modern technology has infused this innovative paradigm on several scales but still lacks shadowing many aspects of our daily life. Several businesses have attempted its interjection in their processes, however, some of the features of IoT have been avoided by most of them due to its cryptic nature and requiring technical skills prior to its replete use. The development of IoT is well ahead of its need to be and linked to an inability of adopting its fast-paced changes along with the lack of strong business operational models. This paper contributes to the developing of a smart home where electrical appliances would react according to the environment while users would be able to control them through smart gadgets. The conventional setup of arranging and organising the electrical appliances is compared to the newly-developed, several patterns have been observed through which carbon footprints and electricity consumption could be reduced. The collected data has been analysed and it proves that IoT does not only helps save energy consumption but also to save the environment. This paper also highlights the challenges faced by consumers who are affected by the penetration of IoT into their lives along with benefits of infusing IoT applications. The overall theme of this paper is, therefore, to make the consumer understand the benefits of IoT applications into their lives using Arduino setup to control electrical home appliances as a benchmark.

This timely and comprehensive volume explores the evolving concept of Society 5.0 - a forward-looking, human-centered society where cutting-edge technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), robotics, and augmented reality seamlessly integrate into daily life, healthcare, industry, and public services to enhance quality of life and social well-being. Industry 5.0, as a vital enabler of this vision, emphasizes collaborative interactions between humans and smart systems, driving innovations in personalized healthcare, sustainable industries, and connected communities. This first part of a multi-volume set provides readers with foundational insights into the principles, enabling technologies, and strategic opportunities shaping Industry 5.0 and Society 5.0. Edited contributions from subject matter experts offer a roadmap for harnessing Medical IoT (Internet of Medical Things) applications within this new societal framework. The book covers essential topics such as high-performance computing in healthcare decision support, AI-driven diagnostic solutions, cybersecurity in digital health systems, and ethical data practices in patient care. It also highlights emerging use cases, including IoT-enabled wearable devices for women's security and smart diagnostic tools for early disease detection. Key features: Presents a detailed overview of Industry 5.0 and Society 5.0, their fundamentals, enabling technologies, and future prospects. Examines the role of Industry 5.0 in transforming medical and healthcare services through the integration of IoT and AI-driven solutions. Explores innovative applications such as AI-based diabetic retinopathy detection, IoT applications in digital healthcare, and patient-centric data ethics frameworks. Discusses advances in smart farming and sustainable industrial practices within the Industry 5.0 paradigm. Highlights opportunities for bridging research gaps in high-performance computing for holistic healthcare decision support. Provides use case studies on IoT-enabled healthcare security solutions and digital health systems.

Recent years, the rapid development of IoT (Internet of Things) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications, i.e., device heterogeneity, long-range communication and cloud/edge-device integration. Difficulties are raised by the above features towards IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4,000 devices within 11.2 physical-minutes for 10 simulation-minutes, which is about 3× faster than the state-of-art approach.

The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data are processed for machine learning analysis and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

The Internet of Things (IoT) undergoes a rapid transformation this last decade, thanks to the appearance of low-power wide area network technologies, such as LoRa/LoRaWAN, SigFox, and narrowband IoT, which allow reducing the deployment cost of sensors and other IoT devices. Many emerging services such as smart city, Industry 4.0, and autonomous driving are based on IoT devices and applications to collect and analyze data and control end devices (i.e., actuators). Among these services, several IoT applications, such as data analytics, need to be deployed at the edge to either reduce the latency to access data or treat the high amount of generated data locally. However, in the context of LoRa/LoRaWAN, most of the current IoT service deployments run the applications at a central cloud to ease the integration with existing software as a service (SaaS) platforms, without exploiting the benefits of edge computing. In this article, we propose a new framework that leverages the ETSI multi-access edge computing (MEC) model to deploy LoRabased IoT applications at the edge. In particular, the proposed model takes advantage of the ETSI MEC features, such as dynamic deployment of an IoT application at the edge and application life cycle management. In addition, the proposed framework allows running an IoT application as a 5G network slice at the edge.

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. that are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need 98to be addressed, in one side the programmability and the communication of multiple heterogeneous mobile/autonomous/robotic things for cooperating, their coordination, configuration, exchange of information, security, safety and protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrency require new ideas for integrating the intelligent “devices”, collaborative robots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing, self-repair of resources, changing resource state, (re-) configuration and context based IoT systems for service implementation and integration with IoT network service composition are of paramount importance when new “cognitive devices” are becoming active participants in IoT applications. This chapter aims to be an overview of the IoRT concept, technologies, architectures and applications and to provide a comprehensive coverage of future challenges, developments and applications.

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.