Search Results (17)

The accurate prediction of autonomous vessel CO₂ emissions is critical for achieving IMO 2050 carbon neutrality and optimizing low-carbon maritime operations. Traditional models face limitations in real-time multi-source data analysis and dynamic cross-variable dependency modeling, hindering data-driven decision-making for sustainable autonomous shipping. This study proposes a Multi-scale Channel-aligned Transformer (MCAT) model, integrated with a 5G–satellite–IoT communication architecture, to address these challenges. The MCAT model employs multi-scale token reconstruction and a dual-level attention mechanism, effectively capturing spatiotemporal dependencies in heterogeneous data streams (AIS, sensors, weather) while suppressing high-frequency noise. To enable seamless data collaboration, a hybrid transmission framework combining satellite (Inmarsat/Iridium), 5G URLLC slicing, and industrial Ethernet is designed, achieving ultra-low latency (10 ms) and nanosecond-level synchronization via IEEE 1588v2. Validated on a 22-dimensional real autonomous vessel dataset, MCAT reduces prediction errors by 12.5% MAE and 24% MSE compared to state-of-the-art methods, demonstrating superior robustness under noisy scenarios. Furthermore, the proposed architecture supports smart autonomous shipping solutions by providing demonstrably interpretable emission insights through its dual-level attention mechanism (visualized via attention maps) for route optimization, fuel efficiency enhancement, and compliance with CII regulations. This research bridges AI-driven predictive analytics with green autonomous shipping technologies, offering a scalable framework for digitalized and sustainable maritime operations. Full article

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-assisted device-to-device (D2D)-based hybrid satellite–terrestrial networks (HSTNs). In the considered system, an energy-constrained terrestrial user terminal (UT) harvests energy from the radio frequency (RF) signal of a terrestrial amplify-and-forward (AF) relay and utilizes the harvested energy to cooperate with the shadowed terrestrial Internet of Things (IoT) devices in a D2D communication. Both power splitting (PS)-based and time switching (TS)-based SWIPT-D2D schemes are adopted by the energy-constrained UT to obtain sustainable energy for transmitting information to the shadowed IoT device. Considering shadowed Rician fading for satellite–terrestrial links and Nakagami-m fading for terrestrial links, we analyze the system performance by deriving the closed-form expressions for the outage probability (OP) of both the UT and the IoT device. Our theoretical analyses are validated via Monte Carlo simulations. Full article

This paper presents how the concept of opportunistic integrated sensing and communication (ISAC), focusing on weather sensing, is incorporated into wireless smart cities’ networks. The concept, first introduced in 2006, utilized standard signal level measurements from wireless backhaul cellular networks for rain monitoring. Since then, it has expanded to include technologies like satellite communication and smart cities’ networks. Opportunistic ISAC (OISAC) for weather involves transforming communication networks into virtual sensors by interpreting the signal attenuation caused by environmental factors, such as rain. These virtual sensors form the sensing layer of an IoT system, with built-in connectivity. In this paper, we present the recent advancements in the field, emphasizing the potential of current and future smart cities’ wireless networks for accurate rainfall monitoring. We also demonstrate a test case in the city of Rehovot in Israel, where high spatiotemporal resolution rain maps produced via the OISAC paradigm significantly outperform the spatial resolution achieved by modern weather radars. We also discuss the challenges and opportunities in applying this concept. Full article

Nowadays, the availability of affordable multi-constellation multi-frequency receivers has broadened access to accurate positioning. The abundance of satellite signals coupled with the implementation of ground- and satellite-based correction services has unlocked the potential for achieving real-time centimetre-level positioning with low-cost instrumentation. Most of the current and future applications cannot exploit well-consolidated satellite positioning techniques such as Network Real Time Kinematic (RTK) and Precise Point Positioning (PPP); the former is inapplicable for large user bases due to the necessity of a two-way communication link between the user and the NRTK service provider, while the latter necessitates long convergence times that are not in keeping with kinematic application. In this context, the hybrid PPP-RTK technique has emerged as a potential solution to meet the demand for real-time, low-cost, accurate, and precise positioning. This paper presents an Internet of Things (IoT) GNSS device developed with low-cost hardware; it leverages a commercial PPP-RTK correction service which delivers corrections via IP. The main target is to obtain both horizontal and vertical decimetre-level accuracies in urban kinematic tests, along with other requisites such as solution availability and the provision of connection ports for interfacing an IoT network. A vehicle-borne kinematic test has been conducted to evaluate the device performance. The results show that (i) the IoT device can deliver horizontal and vertical positioning solutions at decimetre-level accuracy with the targeted solution availability, and (ii) the provided IoT ports are feasible for gathering the position solutions over an internet connection. Full article

In recent years, the space industry has witnessed a resurgence, characterized by a notable proliferation of satellites operating at progressively lower altitudes, promising extensive global coverage and terrestrial-level data transfer speeds, while remaining cost-effective solutions. In particular, Wireless Sensor Networks (WSNs) can benefit from the wide coverage of space infrastructure due to their extensive deployment, disrupted communication nature, and the potential absence of terrestrial support. This study explored the utility of Low-Earth Orbit (LEO) satellite constellations as a communication infrastructure for interconnecting “smart” devices via ground stations in Internet of Things (IoT) scenarios. To this end, we designed and implemented a series of experiments conducted within the OMNeT++ simulator, utilizing an updated iteration of the original Open Source Satellite Simulator (OS3) framework. Our research encompassed an IoT Case Study, incorporating authentic sensor data sourced from the Smart Santander testbed. Throughout our experimentation, we investigated the impact of the constellation design parameters such as the number of satellites and orbital planes, as well as the inter-satellite link configuration on the obtained Round-Trip Time (RTT) and packet loss rates. Full article

As the global population continues to increase, projected to reach an estimated 9.7 billion people by 2050, there will be a growing demand for food production and agricultural resources. Transition toward Agriculture 4.0 is expected to enhance agricultural productivity through the integration of advanced technologies, increase resource efficiency, ensure long-term food security by applying more sustainable farming practices, and enhance resilience and climate change adaptation. By integrating technologies such as ground IoT sensing and remote sensing, via both satellite and Unmanned Aerial Vehicles (UAVs), and exploiting data fusion and data analytics, farming can make the transition to a more efficient, productive, and sustainable paradigm. The present work performs a systematic literature review (SLR), identifying the challenges associated with UAV, Satellite, and Ground Sensing in their application in agriculture, comparing them and discussing their complementary use to facilitate Precision Agriculture (PA) and transition to Agriculture 4.0. Full article

In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations. Full article

Electronic remote identification (ER-ID) is a new radio frequency (RF) technology that is initiated by the Federal Aviation Authorities (FAA). For security reasons, traffic control, and so on, ER-ID has been applied for drones by the FAA to enable them to transmit their unique identification and location so that unauthorized drones can be identified. The current limitation of the existing ER-ID algorithms is that the application is limited to the Wi-Fi and Bluetooth wireless controllers, which results in a maximum range of 10–20 m for Bluetooth and 50–100 m for Wi-Fi. In this study, a mathematical computing technique based on finite state automaton (FSA) is introduced to expand the range of the ER-ID RF system and reduce the energy required by the drone to use the technology. A finite number of states have been designed to include a larger range of wireless network techniques, enabling the drones to be recognized while they are further away and in remote areas. This is achieved by including other means of RF channels, such as 4G/5G, Automatic Dependent Surveillance-Broadcast (ADS-B), long range Internet of things (IoT), and satellite communications, in the suggested ER-ID algorithm of this study. The introduced algorithm is tested via a case study. The results showed the ability to detect drones using all types of available radio frequency communication systems (RF-CS) while also minimizing the consumed energy. Hence, the authorities can control the licensed drones by using available RF-CS devices, such as Bluetooth and Wi-Fi, which are already widely used for mobile phones, as an example. Full article

Currently, wireless communication systems that use radio frequency are commonly deployed, for example, mobile communication systems, satellite systems, and the Internet of Things (IoT) systems. Based on their easy installation, wireless communication systems have benefits over other wired communication systems. However, using high frequencies to transfer data via wireless communication can hold significant risks for human health. Several researchers have studied this topic using visible light instead of Radio Frequency (RF) waveforms in communication systems. Many potential approaches are relevant in this regard, i.e., visible light communication, light fidelity, free-space optical, and optical camera communication. Artificial intelligence is also influencing the future of industry and people and is used to solve complex problems, create intelligent solutions, and replace human intelligence as the driving force behind emerging technologies such as big data, smart factories, and the IoT. In this paper, we proposed the architecture of the MIMO C-OOK (Multiple-Input Multiple-Output Camera On–Off Keying) scheme, which uses a convolutional neural network for light-emitting diode detection and a deep learning neural network for threshold predictions considering long-distance communication and mobility support. Our suggested method aimed to improve the performance of the traditional camera on–off keying scheme by increasing data rate, communication distance, and low bit error rate. Our suggested technique may achieve a communication distance of up to 22 m with a low error rate when considering the mobility impact (2 m/s, i.e., walking velocity) by controlling the exposure time, focal length, and employing Forward Error Correction code. Full article

The blooming of internet of things (IoT) services calls for a paradigm shift in the design of communications systems. Short data packets sporadically transmitted by a multitude of low-cost low-power terminals require a radical change in relevant aspects of the protocol stack. For example, scheduling-based approaches may become inefficient at the medium access (MAC) layer, and alternatives such as uncoordinated access policies may be preferred. In this context random access (RA) in its simplest form, i.e., additive links on-line Hawaii area (ALOHA), may again become attractive as also proved by a number of technologies adopting it. The use of forward error correction (FEC) can improve its performance, yet a comprehensive analytical model including this aspect is still missing. In this paper, we provide a first attempt by deriving exact expressions for the packet loss rate and spectral efficiency of ALOHA with FEC, and extend the result also to time- and frequency-asynchronous ALOHA aided by FEC. We complement our study with extensive evaluations of the expressions for relevant cases of study, including an IoT system served by low-Earth orbit (LEO) satellites. Non-trivial outcomes show how time- and frequency-asynchronous ALOHA particularly benefit from the presence of FEC and become competitive with ALOHA. Full article

The flourishing of Internet of Things (IoT) applications, characterized by vast transmitter populations and the sporadic transmission of small data units, demands innovative solutions for the sharing of the wireless medium. In this context, satellite connectivity is an important enabler for all scenarios in which terminals are under-served by terrestrial communications and are thus fundamental for providing worldwide coverage. In turn, the design of medium access policies that attain efficient use of the scarce spectrum and can cope with flexible yet unpredictable IoT traffic is of the utmost importance. Starting from these remarks, we investigate in this work the coexistence of a quality of service (QoS)-constrained service with IoT traffic in a shared spectrum as alternative to a more traditional orthogonal allocation among the two services, with an eye on satellite applications. Leaning on analytical tools, we provide achievable rate regions, assuming a slotted ALOHA access method for IoT terminals and accounting for practical aspects, such as the transmission of short packets. Interesting trends emerge, showcasing the benefit of an overlay allocation with respect to segregating the resources for the two services. Full article

As one of the information industry’s future development directions, the Internet of Things (IoT) has been widely used. In order to reduce the pressure on the network caused by the long distance between the processing platform and the terminal, edge computing provides a new paradigm for IoT applications. In many scenarios, the IoT devices are distributed in remote areas or extreme terrain and cannot be accessed directly through the terrestrial network, and data transmission can only be achieved via satellite. However, traditional satellites are highly customized, and on-board resources are designed for specific applications rather than universal computing. Therefore, we propose to transform the traditional satellite into a space edge computing node. It can dynamically load software in orbit, flexibly share on-board resources, and provide services coordinated with the cloud. The corresponding hardware structure and software architecture of the satellite is presented. Through the modeling analysis and simulation experiments of the application scenarios, the results show that the space edge computing system takes less time and consumes less energy than the traditional satellite constellation. The quality of service is mainly related to the number of satellites, satellite performance, and task offloading strategy. Full article

Satellites can provide strong value-add and complementarity with the new cellular system of the fifth generation (5G) in cost-effective solutions for a massive number of users/devices/things. Due to the inherent broadcast nature of satellite communications, which assures access to remote areas and the support to a very large number of devices, satellite systems will gain a major role in the development of the Internet of Things (IoT) sector. In this vision, reliable multicast services via satellite can be provided to deliver the same content efficiently to multiple devices on the Earth, or for software updating to groups of cars in the Machine-to-Machine (M2M) context or for sending control messages to actuators/IoT embedded devices. The paper focuses on the Network coding (NC) techniques applied to a hybrid satellite/terrestrial network to support reliable multicast services. An energy optimization method is proposed based on joint adaptation of: (i) the repetition factor of data symbols on multiple subcarries of the transmitted orthogonal frequency division multiplexing (OFDM) signal; and (ii) the mean number of needed coded packets according to the requirements of each group and to the physical satellite links conditions. Full article

Sparse code multiple access (SCMA) is a promising code domain non-orthogonal multiple-access scheme which is able to support massive connectivity and grant-free transmission in future satellite-based Internet of Things (IoT) communication networks. Traditional grant-free SCMA is based on time synchronization, which is no longer favorable in such satellite communication networks since the amount of signaling generated to keep all transmitters’ time synchronized is impractical for large networks. Moreover, without centralized codebook assignment, grant-free SCMA suffers from codebook collisions which mean more than one terminal selecting the same codebook being interfered. Motivated by these issues, a novel uplink grant-free asynchronous flipped SCMA scheme named AF-SCMA is proposed in this paper. With the concept of flipped diversity, a specific SCMA-encoded packet is transmitted with its flipped replica together. Successive interference cancellation technique combined with a sliding window is adopted to resolve the packet collisions including codebook collisions at the gateway station. The performance of AF-SCMA is investigated via both mathematical analysis and simulations. Simulation results show that the proposed AF-SCMA provides remarkable performance in terms of throughput and packet loss ratio (PLR), and can benefit from the received signal power unbalance. Full article

Computational Intelligence (CI) has been addressed as a great challenge in recent years, particularly the aspects of routing, task scheduling, and other high-complexity issues. Especially for the Contact Plan Design (CPD) that schedules contacts in dynamic and resource-constrained networks, a suitable CI algorithm can be exchanged for a high-quality Contact Plan (CP) with the appropriate computational overhead. Previous works on CPD mainly focused on the optimization of satellite network connectivity, but most of them ignored network topology characteristics. In this paper, we study the CPD issue in the spatial node based Internet of Things (IoT), which enables the spatial nodes to deliver data cooperatively via intelligent networking. Specifically, we first introduce a Multi-Layer Space Communication Network (MLSCN) model consisting of satellites, High Altitude Platforms (HAPs), Unmanned Aerial Vehicles (UAVs), and ground stations, on which a Time-Evolving Graph (TEG) is used to illustrate the CPD process. Then, according to the characteristics of each layer in the MLSCN, we design the corresponding CPs for the inter-layer contacts and intra-layer contacts. After that, a CI algorithm named as Multidirectional Particle Swarm Optimization (MPSO) is proposed for inter-layer CPD, which utilizes a grid-based initialization strategy to expand the diversity of individuals, in which a quaternary search method and quaternary optimization are introduced to improve efficiency of particle swarms in iterations and to ensure the continuous search ability, respectively. Furthermore, an optimized scheme is implemented for the intra-layer CPD to reduce congestion and improve transmission efficiency. Simulation results show that the proposed CPD scheme can realize massive data transmission with high efficiency in the multi-layer spatial node-based IoT. Full article