Understanding of any computing environment requires familiarity with its underlying technologies. Internet of Things (IoT), being a new era of computing in the digital world, aims for the development of large number of smart devices that would support a variety of applications and services. These devices are resource‐constrained, and the services they would provide are going to impose specific requirements, among which security is the most prominent one. Therefore, in order to comprehend and conform these requirements, there is a need to illuminate the underlying architecture of IoT and its associated elements. This comprehensive survey focuses on the security architecture of IoT and provides a detailed taxonomy of major challenges associated with the field and the key technologies, including Radio Frequency Identification (RFID) and Wireless Sensor Networks (WSN), that are enabling factors in the development of IoT. The paper also discusses some of the protocols suitable for IoT infrastructure and open source tools and platforms for its development. Finally, a brief outline of major open issues, along with their potential solutions and future research directions, is given.
One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.
Ashkan Yousefpour, Genya Ishigaki, Riti Gour
et al.
With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service for IoT applications through fog computing is becoming an important problem. In this paper, we introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.
The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancelation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.
Valters Skrastins, Vladislavs Medvedevs, Dmitrijs Orlovs
et al.
Narrowband Internet of Things (NB-IoT) is widely used for connecting low-power devices that must operate for years without maintenance. To design reliable systems, it is essential to understand how much energy these devices consume under different conditions and which power sources can support long lifetimes. This study presents a detailed experimental evaluation of NB-IoT power consumption using a commercial System-on-Module (LMT-SoM). We measured various transmissions across different payload sizes, signal strengths, and temperatures. The results show that sending larger packets is far more efficient: a 1280-byte message requires about 7 times less energy per bit than an 80-byte message. However, standby currents varied widely between devices, from 6.7 µA to 23 µA, which has a major impact on battery life. Alongside these experiments, we compared different power sources for a 5-year deployment. Alkaline and lithium-thionyl chloride batteries were the most cost-effective solutions for indoor use, while solar panels combined with supercapacitors provided a sustainable option for outdoor applications. These findings offer practical guidance for engineers and researchers to design NB-IoT devices that balance energy efficiency, cost, and sustainability.
Meshari D. Alanazi, Abdelhady M. A, Ahmed A. Ibrahim
Abstract This paper presents a compact, wideband, high-gain circularly polarized (CP) hemispherical dielectric resonator antenna (HDRA) designed for millimeter-wave 5G applications. The proposed antenna employs a linearly polarized (LP) HDRA excited through an annular slot coupled to a 50-Ω microstrip feed, enabling efficient radiation at millimeter-wave frequencies. Wide impedance bandwidth from 20 to 28 GHz is achieved by overlapping multiple adjacent resonant modes of the HDRA. Circular polarization is realized by introducing a frequency-selective surface (FSS) superstrate positioned at an optimized distance above the antenna. To further enhance the axial-ratio bandwidth and gain, a 2 × 2 HDRA array with a sequential-phase feeding network and an additional dielectric superstrate is implemented. The antenna is fabricated and experimentally validated. Measured results demonstrate an impedance bandwidth of 33.3% (20–28 GHz), a peak realized gain of 11.8 dBi, and a 3-dB axial-ratio bandwidth of 31% (20.5–28 GHz). The proposed design offers a compact and efficient solution for millimeter-wave 5G and IoT applications.
With the rapid development of communication technologies, the Internet of Things (IoT) is getting out of its infancy, into full maturity, and tends to be developed in an explosively rapid way, with more and more data transmitted and processed. As a result, the ability to manage devices deployed worldwide has been given more and advanced requirements in practical application performances. Most existing IoT platforms are highly centralized architectures, which suffer from various technical limitations, such as a cyber-attack and single point of failure. A new solution direction is essential to enhance data accessing, while regulating it with government mandates in privacy and security. In this paper, we propose an integrated IoT platform using blockchain technology to guarantee sensing data integrity. The aim of this platform is to afford the device owner a practical application that provides a comprehensive, immutable log and allows easy access to their devices deployed in different domains. It also provides characteristics of general IoT systems, allows for real-time monitoring, and control between the end user and device. The business logic of the application is defined by the smart contract, which contains rules and conditions. The proposed approach is backed by a proof of concept implementation in realistic IoT scenarios, utilizing Raspberry Pi devices and a permissioned network called Hyperledger Fabric. Lastly, a benchmark study using various performance metrics is made to highlight the significance of the proposed work. The analysis results indicate that the designed platform is suitable for the resource-constrained IoT architecture and is scalable to be extended in various IoT scenarios.
Samuel Marchal, Markus Miettinen, T. D. Nguyen
et al.
IoT devices are being widely deployed. But the huge variance among them in the level of security and requirements for network resources makes it unfeasible to manage IoT networks using a common generic policy. One solution to this challenge is to define policies for classes of devices based on device type. In this paper, we present AuDI, a system for quickly and effectively identifying the type of a device in an IoT network by analyzing their network communications. AuDI models the periodic communication traffic of IoT devices using an unsupervised learning method to perform identification. In contrast to prior work, AuDI operates autonomously after initial setup, learning, without human intervention nor labeled data, to identify previously unseen device types. AuDI can identify the type of a device in any mode of operation or stage of lifecycle of the device. Via systematic experiments using 33 off-the-shelf IoT devices, we show that AuDI is effective (98.2% accuracy).
Deividson Sá Fernandes de Souza, Simone Sehnem, Patricia Guarnieri
et al.
Purpose – This paper aims to provide a comprehensive literature review on the practices and challenges in managing waste electrical and electronic equipment (WEEE) in smart cities. Design/methodology/approach – A systematic literature review was conducted using the Methodi Ordinatio. Articles published between 2012 and 2022 were analyzed, totaling 149 references, of which 30 were included in the final review. Findings – Emerging technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI) are frequently highlighted as promising solutions for efficient e-waste management. Governance models and public policies are widely recognized as crucial for the successful implementation of WEEE management practices in smart cities. Originality/value – This study underscores the role of advanced technologies, such as IoT and AI, in enhancing urban mobility and WEEE management. Key challenges include information security, privacy, interoperability, costs and sustainability. The findings reveal a convergence between smart cities and WEEE management, fostering the circular economy and the recovery of valuable materials.
Urban groups. The city. Urban sociology, Cities. Urban geography
Angelo Martone, Alessia D’Ambrosio, Michele Ferrucci
et al.
We present a detailed dataset collected via a wireless IoT sensor network monitoring three industrial centrifugal pumps (units A, B, and C) at the Italian Aerospace Research Centre (CIRA), along with the methods for data collection and structuring. <b>Background</b>: Centrifugal pumps are critical in industrial plants, and monitoring their condition is essential to ensure reliability, safety, and efficiency. High-quality operational data under normal operating conditions are fundamental for developing effective maintenance strategies and diagnostic models. <b>Methods</b>: Data were gathered by means of smart sensors measuring motor and pump vibrations, temperatures, outlet fluid pressures, and environmental conditions. Data were transmitted over a WirelessHART mesh network and acquired through an IoT architecture. <b>Results</b>: The dataset consists of eight CSV files, each representing a specific pump during a distinct operational day. Each file includes timestamped measurements of displacement, peak vibration values, sensor temperatures, fluid pressure, ambient temperature, and atmospheric pressure. <b>Conclusions</b>: This dataset supports advanced methodologies in feature extraction, multivariate signal analysis, unsupervised pattern discovery, vibration analysis, and the development of digital twins and soft sensing models for predictive maintenance optimization.
Bibliography. Library science. Information resources
Higinio Gonzalez-Jorge, Eduardo Ríos-Otero, Enrique Aldao
et al.
Railway infrastructures necessitate the inspection of various elements to ensure operational safety. This study concentrates on five key components: rail, sleepers and ballast, track geometry, and catenary. The operational principles of the primary defect measurement sensors are elaborated, emphasizing the use of ultrasound, eddy currents, active and passive optical elements, accelerometers, and ground penetrating radar. Each sensor type is evaluated in terms of its advantages and limitations. Examples of mobile inspection platforms are provided, ranging from laboratory trains to draisines and track trolleys. The authors foresee future trends in railway inspection, including the implementation of IoT sensors, autonomous robots, and geospatial intelligence technologies. It is anticipated that the integration of sensors within both infrastructure and rolling stock will enhance maintenance and safety, with an increased utilization of autonomous robotic systems for hazardous and hard-to-reach areas.
Real-time safety monitoring on construction sites is essential for ensuring worker safety, but traditional detection methods face challenges in dynamic environments with moving objects, occlusions, and complex conditions. To address these limitations, we propose GS-LinYOLOv10, an improved model based on YOLOv10, specifically designed for drone-based safety monitoring. The GSConv module introduces a lightweight feature extraction mechanism, reducing computational complexity without compromising detection accuracy. The Linformer-based attention mechanism efficiently captures global context, addressing challenges in dynamic and complex environments. The model integrates IoT sensor data for real-time feedback, incorporates the GSConv module for lightweight feature extraction, and utilizes a Linformer-based attention mechanism to efficiently capture global context. These innovations reduce computational complexity while significantly improving detection accuracy. Experimental results show that GS-LinYOLOv10 achieves a precision of 91.2% and a mean average precision (mAP) of 89.4%, outperforming existing models. The integration of IoT sensors allows the drone system to dynamically adjust its monitoring focus, improving adaptability to changing environments and enhancing hazard detection. This research provides an advanced, drone-based IoT-enhanced solution for real-time construction site safety monitoring, offering a more effective and efficient approach to safety management.
This paper investigates the problem of computation offloading and resource allocation in an integrated space–air–sea network based on unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellites supporting Maritime Internet of Things (M-IoT) devices. Considering the complex, dynamic environment comprising M-IoT devices, UAVs and LEO satellites, traditional optimization methods encounter significant limitations due to non-convexity and the combinatorial explosion in possible solutions. A multi-agent deep deterministic policy gradient (MADDPG)-based optimization algorithm is proposed to address these challenges. This algorithm is designed to minimize the total system costs, balancing energy consumption and latency through partial task offloading within a cloud–edge-device collaborative mobile edge computing (MEC) system. A comprehensive system model is proposed, with the problem formulated as a partially observable Markov decision process (POMDP) that integrates association control, power control, computing resource allocation, and task distribution. Each M-IoT device and UAV acts as an intelligent agent, collaboratively learning the optimal offloading strategies through a centralized training and decentralized execution framework inherent in the MADDPG. The numerical simulations validate the effectiveness of the proposed MADDPG-based approach, which demonstrates rapid convergence and significantly outperforms baseline methods, and indicate that the proposed MADDPG-based algorithm reduces the total system cost by 15–60% specifically.