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![Fig. 1. Growth of Connected Devices from 1950 to 2050 [8]](profile/Mohamed-Firdhous/publication/330245871/figure/fig1/AS:899149893468162@1591385448348/Growth-of-Connected-Devices-from-1950-to-2050-8_Q320.jpg)
![Fig. 2. IoT Layered Architectures Proposed in Literature [11], [12]](profile/Mohamed-Firdhous/publication/330245871/figure/fig2/AS:899149897670657@1591385449149/oT-Layered-Architectures-Proposed-in-Literature-11-12_Q320.jpg)
![Fig. 3. IoT Layered Architecture Proposed by Tan and Wang [13]](profile/Mohamed-Firdhous/publication/330245871/figure/fig3/AS:899149897691136@1591385449181/oT-Layered-Architecture-Proposed-by-Tan-and-Wang-13_Q320.jpg)
Internet of Things has gained the attention of almost everybody due to its capability of monitoring and controlling the environment. IoT helps making decisions supported by real data collected using large number of ordinary day-to-day devices that have been augmented with intelligence through the installation of sensing, processing and communicatio...
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... was forecasted that the number of devices connected to the Internet would reach 25 billion in 2020 from 10 billion in 2014 and surpass 100 billion by 2050 [9]. Figure 1 shows the growth of connected devices on the Internet starting from 1950s to 2050 by forecaseted IBM in 2015. ...
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![Fig. 2. Reference Architecture Model for Industry 4.0 (RAMI 4.0) [21].](profile/Pavel-Vrba-2/publication/334651564/figure/fig2/AS:784164785750018@1563970862317/Reference-Architecture-Model-for-Industry-40-RAMI-40-21_Q320.jpg)
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Advances in the domain of electronics and information and communication technology provide a tremendous set of new possibilities and services. The development of the Internet of Things concept where different sensors, actuators, and control devices are connected together and where functions are provided to the Internet, is on the way to be used als...
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... IoT Expansion: The rapidly increasing number of IoT- connected devices is fundamental to this proposal. Projections suggest that the global count of IoT devices could exceed 100 billion by 2050 [11]. This vast network of devices, each capable of sending and receiving signals, presents a unique opportunity to create a distributed sensor network acting collectively as a large-scale radio telescope. ...
... The system's operational bandwidth is projected to be 300 MHz at a center frequency of 1.4 GHz. With an estimated N IoT = 1 × 10 12 global IoT devices contributing to the beamforming process [11]. The individual device antenna gain at the sky direction is calculated as G IoT = D IoT L path η rad . ...
This paper introduces an innovative approach to radio astronomy by utilizing the global network of Internet of Things (IoT) devices to form a distributed radio telescope. Leveraging existing IoT infrastructure with minimal modifications, the proposed system employs widely dispersed devices to simultaneously capture both astronomical and communication signals. Digital beamforming techniques are applied to align the astronomical signals, effectively minimizing interference from communication sources. Calibration is achieved using multiple distributed satellites transmitting known signals, enabling precise channel estimation and phase correction via GPS localization. We analyze two calibration methods, Phase Alignment Calibration (PAC) and Eigenvalue-Based Calibration (EVC), and demonstrate that EVC outperforms PAC in environments with significant variation in node performance. Compared to the Green Bank Telescope (GBT), the IoT-based telescope enhances antenna gain by three orders of magnitude and increases survey speed by eight orders of magnitude, owing to the vast number of nodes and expansive field of view (FoV). These findings demonstrate the feasibility and significant advantages of the IoT-enabled telescope, paving the way for cost-effective, high-speed, and widely accessible astronomical observations.
... Wireless networking technologies are crucial for smooth communication in IoT deployment. Robust communication solutions are essential for sustaining uninterrupted connections, hence ensuring the reliable operation of IoT systems [36]. Wireless communication enables data transmission to the cloud, where it is processed to aid decision-making processes. ...
The use of Internet of Things (IoT) technology is crucial for improving energy efficiency in smart buildings, which could minimize global energy consumption and greenhouse gas emissions. IoT applications use numerous sensors to integrate diverse building systems, facilitating intelligent operations, real-time monitoring, and data-informed decision-making. This critical analysis of the features and adoption frameworks of IoT in smart buildings carefully investigates various applications that enhance energy management, operational efficiency, and occupant comfort. Research indicates that IoT technology may decrease energy consumption by as much as 30% and operating expenses by 20%. This paper provides a comprehensive review of significant obstacles to the use of IoT in smart buildings, including substantial initial expenditures (averaging 15% of project budgets), data security issues, and the complexity of system integration. Recommendations are offered to tackle these difficulties, emphasizing the need for established processes and improved coordination across stakeholders. The insights provided seek to influence future research initiatives and direct the academic community in construction engineering and management about the appropriate use of IoT technology in smart buildings. This study is a significant resource for academics and practitioners aiming to enhance the development and implementation of IoT solutions in the construction sector.
... Comparison of key characteristics: ZigBee, Wi-Fi, Bluetooth, and LoRa in wireless communication technologies[26]. ...
This paper conducts an in-depth study of a wireless, hierarchical structure-based active balancing system for power batteries, aimed at addressing the rapid advancements in battery technology within the electric vehicle industry. The system is designed to enhance energy density and the reliability of the battery system, developing a balancing system capable of managing cells with significant disparities in characteristics, which is crucial for extending the lifespan of lithium-ion battery packs. The proposed system integrates wireless self-networking technology into the battery management system and adopts a more efficient active balancing approach, replacing traditional passive energy-consuming methods. In its design, inter-group balancing at the upper layer is achieved through a soft-switching LLC resonant converter, while intra-group balancing among individual cells at the lower layer is managed by an active balancing control IC and a bidirectional buck–boost converter. This configuration not only ensures precise control but also significantly enhances the speed and efficiency of balancing, effectively addressing the heat issues caused by energy dissipation. Key technologies involved include lithium-ion batteries, battery management systems, battery balancing systems, LLC resonant converters, and wireless self-networking technology. Tests have shown that this system not only reduces energy consumption but also significantly improves energy transfer efficiency and the overall balance of the battery pack, thereby extending battery life and optimizing vehicle performance, ensuring a safer and more reliable operation of electric vehicle battery systems.
... Scalability for IoT is the ability to support more systems/components, such as connected devices/users, and new application features/capabilities, without impacting performance [91]. By 2050, the number of connected devices on the Internet will exceed 100 billion [92], which highlights scalability as a critical issue for the IoT [93]. The management of IoT scalability could be described in two key points [94]: The first point is that IoT devices are growing, as a result existing management protocols are not sufficiently adaptable to accommodate the demands of IoT devices. ...
Smart cities rely mainly on the Internet of Things (IoT) to make an urban area smart to offer its citizens a high quality of life with optimal use of resources and preservation of the environment. IoT is the key component that collects raw data on the surrounding environment to be analyzed to extract information that supports decision-making. The widespread use of IoT results in the emergence of smart homes, smart energy, smart transportation, and smart healthcare, which build a smart city. On the other hand, challenges such as heterogeneity, scalability, security, and privacy hinder the efficient functioning of the IoT in the construction of smart cities. This article presents a comprehensive overview on the concept of IoT moving forward to the concept of smart city, highlighting key elements and characteristics, studying and reviewing state-of-the-art research on this theme. Future directions are discussed to guide researchers, who focus on interoperability between IoT platforms in smart cities and on IoT architectures based on micro-services. Case studies of successful smart cities are presented for gaining learned lessons. The impact of integrating wireless networks (5G and 6G) in the IoT is also clarified in the future direction. The significance of this research is found in its comprehensive examination of various aspects of the smart city instead of concentrating on a singular facet.
... IoT platforms deliver a wide variety of communication technologies and tools. [28,29]. The fourth component is the data storage of the IoT platform. ...
The increasing prevalence and ubiquity of autonomous cars has prompted researchers and industry to make substantial advancements and improvements in the development of an intelligent transportation system. The primary objective of the intelligent transportation system (ITS) is to enhance traffic efficiency through the reduction of traffic congestion. The platform provides clients with additional data, including historical traffic information, facilities in the surrounding area, real-time updates on operating status, and availability of seats. Currently, a multitude of technologies are being incorporated into ITS, such as artificial intelligence (AI) and the Internet of Things (IoT). IoT encompasses a collection of interconnected physical items, sometimes referred to as “things,” which are equipped with integrated sensors, software, and other technological components enabling them to communicate and share data with other objects, devices, and systems over the Internet. This chapter will examine the function of IoT in ITS. The initial segment will consist of an overview of the IoT platform. The introduction of ITS will follow thereafter. Subsequently, the role of IoT in ITS will be examined. This chapter will discuss three essential components of intelligent transportation systems: smart management systems, smart parking, and smart roadways. Furthermore, a depiction of the Internet of Things’ role in accident detection is delineated. This chapter demonstrates the efficacy of incorporating cloud computing into IoT in the context of intelligent transportation systems.
... The popularity of IoMT-based monitoring of physiological signals has increased in recent years due to its advantages, including enhanced patient mobility, continuous patient observation, and reduced healthcare expenses [55,56]. Wearable devices and edge computing-as the main carriers of IoMT-have opened doors to new possibilities in continual learning [57]. These devices, particularly wearables, generate a constant stream of time series data, which can be harnessed for real-time learning applications. ...
Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems.
... Cisco's forecasts from over 10 years ago [1] predicted that the number of connected devices would reach 50 billion by 2020. Further predictions suggest that the number of connected devices will surpass 100 billion by 2050 [2]. ...
... The centralized design approach for IoT applications leads to high network traffic as data flows from sensors to edge nodes and servers through gateways. The number of IoT devices has seen a massive surge in recent years and is expected to surpass 100 billion by 2050 [1], [2]. Due to this growth, edge computing has emerged to reduce network traffic in IoT networks [3]. ...
... Compression can significantly reduce the amount of data that needs to be transferred to the gateway [1]. Researchers have proposed several compression algorithms, categorized into entropy [35], dictionary [36], and sliding window [37]. ...
data compression at the Internet of Things (IoT) edge node aims to minimize data traffic in smart cities. The traditional Huffman Coding Algorithm (HCA) is shown as the most effective compression algorithm for sensor data. However, implementing the algorithm at IoT edge nodes is hindered due to memory limitations; HCA requires a large amount of memory to construct a Huffman tree to compress data. To address this issue, this paper proposes a new lossless Huffman Deep Compression (HDC) algorithm that incorporates the sliding window technique to fit in memory, reduces the complexity of the Huffman tree using deep learning pruning and pooling techniques, and uses pattern matching with pattern weights instead of using symbol matching and symbol frequencies in HCA. This paper contributes by introducing a sliding window approach to minimize memory usage, leveraging pattern matching and weights for higher compression, and employing deep learning techniques to reduce the Huffman tree size through pruning and pooling. Experiments were performed using the Esp8266 MCU IoT node on eight numerical attributes from sensors of six of Malaysia’s air pollution station datasets. The findings demonstrate that the HDC algorithm has substantially reduced data size (p-value<0.0005), achieving a higher compression ratio (CR) by 1.4x while reducing data size by up to 59%. Furthermore, this achievement is attained while utilizing less than 80 KB of IoT memory and consuming at most 44 mAmps per slide compression. Furthermore, the compression performance correlated linearly with the number of patterns in each sliding window. With such excellent performance, using HDC at IoT edge is a considerable solution to reduce the smart-cities network traffic.
... In the beginning, IoT relied on traditional technologies such as Bluetooth, Zigbee, Wi-Fi, and so on, which succeeded in providing high-speed and reliable transmissions. On the other hand, they demand high deployment costs and energy consumption, as in Wi-Fi and Bluetooth, or they provide short-range coverage of connected devices with lower energy consumption, as in Zigbee and Bluetooth Low Energy (BLE) [5]. Hence, they do not offer efficient solutions to long-range communication that require the devices to operate at low power in order to live for years [2,4]. ...
The internet of things (IoT) revolutionized human life, whereby a large number of interrelated devices are connected to exchange data in order to accomplish many tasks, leading to the rapid growth of connected devices, reaching the tens of billions. The Low Power Wide Area (LPWA) protocols paradigm has emerged to satisfy the IoT application requirements, especially in terms of long-range communication and low power consumption. However, LPWA technologies still do not completely meet the scalability requirement of IoT applications. The main critical issues are the restrictive duty cycle regulations of the sub-GHz band in which most LPWA technologies operate, as well as the random access to the medium. Ingenu Random Phase Multiple Access (RPMA) is an LPWA technology that uses the 2.4 GHz band that is not subject to the duty cycle constraint. Furthermore, RPMA uses Direct-Sequence Spread Spectrum (DSSS) as a modulation technique; hence, it is an excellent candidate technology for handling scalable LPWA networks. In this paper, we perform mathematical and simulation analysis to assess RPMA scalability and the factors that affect it, especially when all the available channels are used. The results indicate that RPMA has impressive scalability. Indeed, by taking advantage of the multichannel feature in RPMA, the network capacity can be increased by up to 38 times. Aditionally, randomly selecting the Spreading Factors (SF) degrades the network scalability, as working on higher SFs will increase the probability of collision. Thus, we proposed an SF distribution algorithm that ensures effective packet delivery with minimum collision.
... As humanity moves toward the Society 5.0 [1] paradigm and industries move toward the Industry 4.0 [2,3] model, smart devices will become increasingly ubiquitous; it is expected that more than 100 billion such devices will exist by 2050 [4]. With more computing power and smaller (or no) screens, the user interface design paradigm is shifting from graphical user interfaces (GUI) to conversational user interfaces (CUI) [5]. ...
The number of smart devices is expected to exceed 100 billion by 2050, and many will feature conversational user interfaces. Thus, methods for generating appropriate prosody for the responses of embodied conversational agents will be very important. This paper presents the results of the “Talk to Kotaro” experiment, which was conducted to better understand how people from different cultural backgrounds react when listening to prosody and phone choices for the IPA symbol-based gibberish speech of the virtual embodied conversational agent Kotaro. It also presents an analysis of the responses to a post-experiment Likert scale questionnaire and the emotions estimated from the participants’ facial expressions, which allowed one to obtain a phone embedding matrix and to conclude that there is no common cross-cultural baseline impression regarding different prosody parameters and that similarly sounding phones are not close in the embedding space. Finally, it also provides the obtained data in a fully anonymous data set.
