The Internet of Things (IoT) is expected to bring new opportunities for improving several services for the Society, from transportation to agriculture, from smart cities to fleet management. In this framework, massive connectivity represents one of the key issues. This is especially relevant when IoT systems are expected to cover a large geographical area or a region not reached by terrestrial network connections. In such scenarios, the usage of satellites might represent a viable solution for providing wide area coverage and connectivity in a flexible and affordable manner. Our paper presents a survey on current solutions for the deployment of IoT services in remote/rural areas by exploiting satellites. Several architectures and technical solutions are analyzed, underlining their features and limitations, and real test cases are presented. It has been highlighted that low-orbit satellites offer an efficient solution to support long-range IoT services, with a good trade-off in terms of coverage and latency. Moreover, open issues, new challenges, and innovative technologies have been focused, carefully considering the perimeter that current IoT standardization framework will impose to the practical implementation of future satellite based IoT systems.
Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.
Sarhad Arisdakessian, O. A. Wahab, A. Mourad
et al.
In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals, such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices, such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multiparty architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing, and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: 1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog, and cloud computing layers; 2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of subtechniques; and 3) propose a comprehensive cybersecurity framework that combines the concepts of explainable artificial intelligence (XAI), federated learning, game theory, and social psychology to offer future IoT systems a strong protection against cyberattacks.
Business is dynamic and rapidly changing. Global markets were previously the playing field of multinational corporations (MNCs), while small and medium enterprises (SMEs) were local; however, the removal of imposed barriers and recent technological advances in manufacturing, transportation, and communications have indorsed SMEs and international entrepreneurs (IEs) global access. SMEs and IEs are increasingly fueling economic growth and innovation, and these trends are presenting both opportunities and challenges to both MNCs and SMEs in the global arena. This review systematically examines comparative SME and IE research, analyzing (after fine-tuning) 762 articles published in leading journals from 1992 to September 2018. Our bibliometric and systematic review classifies SME and IE research findings into three echelons: (i) subjects, (ii) theories, and (iii) methods.
Connected automated vehicles (CAVs) serve as a promising enabler for future intelligent transportation systems because of their capabilities in improving traffic efficiency and driving safety, and reducing fuel consumption and vehicle emissions. A fundamental issue in CAVs is platooning control that empowers a convoy of CAVs to be cooperatively maneuvered with desired longitudinal spacings and identical velocities on roads. This paper addresses the issue of resilient and safe platooning control of CAVs subject to intermittent denial-of-service (DoS) attacks that disrupt vehicle-to-vehicle communications. First, a heterogeneous and uncertain vehicle longitudinal dynamic model is presented to accommodate a variety of uncertainties, including diverse vehicle masses and engine inertial delays, unknown and nonlinear resistance forces, and a dynamic platoon leader. Then, a resilient and safe distributed longitudinal platooning control law is constructed with an aim to preserve simultaneous individual vehicle stability, attack resilience, platoon safety and scalability. Furthermore, a numerically efficient offline design algorithm for determining the desired platoon control law is developed, under which the platoon resilience against DoS attacks can be maximized but the anticipated stability, safety and scalability requirements remain preserved. Finally, extensive numerical experiments are provided to substantiate the efficacy of the proposed platooning method.
Internet of Things (IoT) is considered as the enabling platform for a variety of promising applications, such as smart transportation and smart city, where massive devices are interconnected for data collection and processing. These IoT applications pose a high demand on storage and computing capacity, while the IoT devices are usually resource constrained. As a potential solution, mobile edge computing (MEC) deploys cloud resources in the proximity of IoT devices so that their requests can be better served locally. In this work, we investigate computation offloading in a dynamic MEC system with multiple edge servers, where computational tasks with various requirements are dynamically generated by IoT devices and offloaded to MEC servers in a time-varying operating environment (e.g., channel condition changes over time). The objective of this work is to maximize the completed tasks before their respective deadlines and minimize energy consumption. To this end, we propose an end-to-end Deep Reinforcement Learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. The simulation results are provided to demonstrate that the proposed approach outperforms the existing methods.
The convergence of Vehicular Ad Hoc Networks (VANETs) and the Internet of Things (IoT) is giving rise to the Internet of Vehicles (IoV), a key enabler of next-generation intelligent transportation systems. This survey provides a comprehensive analysis of the architectural, communication, and computing foundations that support VANET–IoT integration. We examine the roles of cloud, edge, and in-vehicle computing, and compare major V2X and IoT communication technologies, including DSRC, C-V2X, MQTT, and CoAP. The survey highlights how sensing, communication, and distributed intelligence interact to support applications such as collision avoidance, cooperative perception, and smart traffic management. We identify four central challenges—security, scalability, interoperability, and energy constraints—and discuss how these issues shape system design across the network stack. In addition, we review emerging directions including 6G-enabled joint communication and sensing, reconfigurable surfaces, digital twins, and quantum-assisted optimization. The survey concludes by outlining open research questions and providing guidance for the development of reliable, efficient, and secure VANET–IoT systems capable of supporting future transportation networks.
With the widespread adoption of highways in the mountainous regions of southwestern China, the electricity load of numerous tunnels and service areas has increased rapidly. Constructing photovoltaic (PV) microgrids in service areas has become an important means of energy conservation, consumption reduction, and carbon emission mitigation. However, constrained by mountainous terrain, the PV power generation conditions in highway service areas exhibit significant micro-terrain variations, making it difficult to effectively evaluate PV utilization efficiency. This paper proposes a dynamic block optimization model for PV microgrids that considers regional layout constraints. The model utilizes an intelligent adjustment mechanism to plan PV panel layouts in highway service areas, optimizing energy utilization efficiency and economic benefits. Additionally, long short-term memory (LSTM) networks are employed for short-term PV output prediction to address the challenges posed by varying weather and seasonal changes. This approach comprehensively considers the intermittency and instability of PV power generation, enabling dynamic block optimization to autonomously adjust the PV power output in response to load fluctuations. Through simulation case studies, the model is validated to effectively improve the utilization rate and economic performance of PV microgrids under various environmental conditions and demonstrates superior performance compared with traditional static block methods.
Paulina Wiączek, Iryna Sitak, Alena Novak Sedlackova
et al.
The purpose of this research was, among other things, to show that the carpooling model can be regarded as a manifestation of collective intelligence and a response to the crisis of sustainability in today's ICT-based economy, which makes carpooling services a cheaper alternative to transportation services provided by other modes of transport: train and bus. The main research problem is contained in the question: How do collective intelligence and the tenets of sustainability influence the popularity of carpooling and the lower cost of carpooling services, compared to those provided by other forms of transportation? The critical literature analysis method, the comparison method and the statistical method, among others: the Mann Whitney U test and the Kruskal Wallis ANOVA test, were used in implementation of the study.
Juliane Anke, Madlen Ringhand, David Schackmann
et al.
The popularity of e-scooter riding and the massive climb of related crashes has brought up new challenges for road safety, contradicting the potential benefits of more sustainable transport. While the current literature already draws a picture of e-scooter-related road safety issues, the underlying perceptions and motives of the riders have been hardly considered. An online survey and focus group interviews with regular e-scooter riders (owners of private e-scooters, frequent users of rental vehicles) were set out to obtain insights into the perceptions of hazards and safety-critical events, as well as immediate reactions and long-term protective strategies thereof. Results show that road safety campaigning should consider infrastructure hazards and hazards related to interactions with other road users. Furthermore, the results reveal an overlap regarding road safety problems between e-scooter riding and cycling, implying a potential for joint efforts. Individual behavioral strategies that the riders reported, like immediate reactions and long-term protective strategies, present input for education and training. Moreover, courses of action for traffic planning or maintenance, vehicle manufacturers, and driver education were identified, raising awareness of road infrastructure deficiencies, needed improvements in vehicle design, and the promotion of being considerate of each other in traffic. The findings highlight the benefits of drawing on the road users themselves to get a more complete picture and understanding of the underlying motives to enhance the safety of e-scooter riding.
Mahnoush Minuyee, Abebe Dress Beza, Laleh Behjat
et al.
Equitable access to healthcare facilities is essential to quality of life. However, many vulnerable communities encounter barriers because transportation systems are not designed to serve all residents equally. These disparities are particularly significant for childhood asthma, a public health concern where timely care is essential to prevent adverse outcomes. This study addresses the gaps in understanding how various transportation modes, including public transit, private vehicles, and taxis, influence healthcare accessibility for children with asthma. Using data from 18,393 hospital visits in Calgary, Canada (2010–2021), we evaluate spatiotemporal accessibility across three travel modes, considering emergency and non-emergency healthcare visit scenarios with varying travel cost thresholds through a two-step floating catchment area (2SFCA) method. Horizontal equity is quantified using the Gini coefficient, while vertical equity incorporates socioeconomic factors and asthma prevalence. Our findings reveal that personal vehicles provide the highest and most reliable accessibility, especially during emergencies, whereas public transit frequently fails to meet emergency accessibility demands, particularly at night. Taxis tend to be unaffordable for low-income users but offer comparable accessibility for higher-income travelers in non-emergency contexts. The vertical equity analysis identifies areas characterized by high socioeconomic vulnerability, elevated asthma prevalence, and limited access to healthcare, highlighting zones that warrant targeted interventions to enhance equity in healthcare accessibility.
Jorge Torres Gómez, Pit Hofmann, Lisa Y. Debus
et al.
Recent developments in the Internet of Bio-Nano Things (IoBNT) are laying the groundwork for innovative applications across the healthcare sector. Nanodevices designed to operate within the body, managed remotely via the internet, are envisioned to promptly detect and actuate on potential diseases. In this vision, an inherent challenge arises due to the limited capabilities of individual nanosensors; specifically, nanosensors must communicate with one another to collaborate as a cluster. Aiming to research the boundaries of the clustering capabilities, this survey emphasizes data-driven communication strategies in molecular communication (MC) channels as a means of linking nanosensors. Relying on the flexibility and robustness of machine learning (ML) methods to tackle the dynamic nature of MC channels, the MC research community frequently refers to neural network (NN) architectures. This interdisciplinary research field encompasses various aspects, including the use of NNs to facilitate communication in MC environments, their implementation at the nanoscale, explainable approaches for NNs, and dataset generation for training. Within this survey, we provide a comprehensive analysis of fundamental perspectives on recent trends in NN architectures for MC, the feasibility of their implementation at the nanoscale, applied explainable artificial intelligence (XAI) techniques, and the accessibility of datasets along with best practices for their generation. Additionally, we offer open-source code repositories that illustrate NN-based methods to support reproducible research for key MC scenarios. Finally, we identify emerging research challenges, such as robust NN architectures, biologically integrated NN modules, and scalable training strategies.
This paper explores the near field (NF) covert communication with the aid of rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS). In particular, the RIS operates in the NF of both the legitimate user and the passive adversary, enhancing the legitimate users received signal while suppressing the adversarys detection capability. Whereas, the base station (BS) applies RSMA to increase the covert communication rate composed of a private and a shared rate component. To characterize system covertness, we derive closed form expressions for the detection error probability (DEP), outage probability (OP), and optimal detection threshold for the adversary. We formulate a non-convex joint beamforming optimization problem at the BS and RIS under unit-modulus constraints to maximize the covert rate. To tackle this, we propose an alternating optimization (AO) algorithm, where the BS beamformer is designed using a two-stage iterative method based on successive convex approximation (SCA). Additionally, two low-complexity techniques are introduced to further reduce the adversarys received power. Simulation results demonstrate that the proposed algorithm effectively improves the covert communication rate, highlighting the potential of near field RSMA-RIS integration in covert communication.
Integrated sensing and communication (ISAC) and ubiquitous connectivity are two usage scenarios of sixth generation (6G) networks. In this context, low earth orbit (LEO) satellite constellations, as an important component of 6G networks, is expected to provide ISAC services across the globe. In this paper, we propose a novel dual-function LEO satellite constellation framework that realizes information communication for multiple user equipments (UEs) and location sensing for interested target simultaneously with the same hardware and spectrum. In order to improve both information transmission rate and location sensing accuracy within limited wireless resources under dynamic environment, we design a multiple-satellite cooperative information communication and location sensing algorithm by jointly optimizing communication beamforming and sensing waveform according to the characteristics of LEO satellite constellation. Finally, extensive simulation results are presented to demonstrate the competitive performance of the proposed algorithms.