Satellite-based Internet Of Things Research Articles

Decision Transformer-Based Efficient Data Offloading in LEO-IoT.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people's attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT

Due to limitations in network coverage and capacity, terrestrial networks cannot meet the growing demand for widespread intelligent applications in recent years. The increasingly popular Satellite-based Internet of Things (S-IoT), with its extensive and boundless coverage, has emerged as the optimal solution. However, the large-scale distributed communication and services have led to significant challenges in scalability and security. Blockchain can help mitigate security and privacy issues in data sharing of S-IoT. To address these challenges, we propose StarCross, a secure and lightweight data sharing framework for S-IoT based on redactable blockchain. Firstly, we develop a space-ground collaborative sharding blockchain network architecture. Low Earth Orbit (LEO) satellite network serves as the communication intermediary between shards. Secondly, StarCross employs an improved Proof of Work (PoW) consensus based on dynamic difficulty to mitigate the challenges of uneven computing power and centralization within each shard. To ensure the atomicity of cross-shard transactions, StarCross introduces a blockchain rewriting mechanism called RBCVC, which combines chameleon hash and voting-based consensus. Thirdly, we conducted theoretical analysis and experimental evaluations of StarCross. The results indicate that compared to existing solutions, StarCross achieves better scalability and security in S-IoT. StarCross’s TPS increased to 260.9% of the baseline, while the transaction confirmation latency decreased by 79.7%.

Provably secure optimal homomorphic signcryption for satellite-based internet of things

Satellites have long been reliable and widely used mediums for communication. With recent advancements in the Internet of Things (IoT), satellite-based IoT has emerged, enhancing global connectivity by enabling real-time communication between remote sensing devices and central hubs through satellite links. However, secure and authenticated communication between Low Earth Orbit (LEO) satellites and resource-constrained ground devices remains underexplored. This paper introduces a novel homomorphic signcryption scheme designed for satellite IoT environments. Our scheme employs hyperelliptic curve cryptography and homomorphic encryption to facilitate the privacy-preserving transmission of aggregated data from multiple IoT devices to LEO satellites. We benchmark our homomorphic signcryption scheme against several established schemes tailored for different network environments, including for satellite networks, Identity-based Signcryption for Smart Grids, 3-Factor Authentication schemes and heterogeneous Vehicular Ad-hoc Networks (VANETs) encryption schemes. Our scheme demonstrates substantial improvements in computational efficiency, communication overhead, and storage capacity. It reduces computational costs by 90% to 99%, communication overhead by 41% to 92%, and storage demands by 62% to 93% compared to existing methods. These quantitative findings demonstrate the practical viability of our approach for satellite IoT deployments with limited resources. Additionally, the robustness of our approach is confirmed through formal security validation using BAN Logic and Scyther, which verifies its strong security, optimal performance, and privacy preservation capabilities, making it ideally suited for real-world satellite IoT systems.

Age-Optimal Downlink NOMA Resource Allocation for Satellite-Based IoT Network

The upcoming satellite-based Internet of Things (S-IoT) has the capability to provide timely status updates to massive terrestrial user equipments (UEs) via non-orthogonal multiple access (NOMA), due to the worldwide coverage inherited from satellite. Considering the constrained power and storage resources while keeping the information freshness in S-IoT, we first propose three constraint conditions including average/peak power constraints, network stability and minimum requirement. Then, we formulate a long-term age of information (AoI) minimization problem under the three constraint conditions. To solve this complex long-term problem, we transform the above mentioned constraints into three queue stability problems via the Lyapunov optimization framework, thus converting our long-term multi-slot stochastic optimization problem into a series of single time slot deterministic optimization problems. Moreover, we leverage the ListNet algorithm to derive the weights of the queue backlog and channel conditions to obtain an optimized power allocation order with linear complexity. Finally, we utilize the particle swarm optimization algorithm to derive the NOMA long-term AoI minimization (AM) power allocation problem within a practical complexity, named NOMA-AM scheme. Simulation results show that the proposed NOMA-AM scheme has the lowest expected weighted sum AoI compared to several benchmark schemes.

Age of error information and throughput for truncated and layer‐coded HARQ‐based satellite‐IoT systems

SummaryThe work focuses on the simultaneous improvement on the throughput and information freshness of the promising satellite‐based Internet of Things (S‐IoT). To this end, we first propose a method based on the adaptive modulation coding (AMC) and truncated layer‐coded hybrid automatic repeat request (L‐HARQ) strategies by especially considering the difference in the channel state information (CSI) between the transmitter and receiver. On account of the large signal delay of the S‐IoT systems, this paper establishes the channel model based on the correlation of the signal‐to‐noise ratio (SNR) under the outdated channel and the ideal channel. Secondly, considering the fact that the improved backtrack decoding is used in the L‐HARQ, the classical definition of age of information (AoI) is insufficient to quantify the status update freshness, we propose the age of error information (AoEI), which is defined as the elapsed time since the generation of the lastly successfully backtrack‐decoded status update. Thirdly, we characterize the statistical descriptions of the maximum wait time of the successfully recovered status updates and the interdeparture time between the two consecutive status update packets that are lastly successfully received. Then, the closed‐form expression of average AoEI is derived under the assumption of independent and identically distributed fading channels. Finally, the Markov decision process (MDP) framework is used to optimize the packet parameters in each round of L‐HARQ process under the general fading scenario. Thereby, the optimized transmission scheme that minimizes AoI with throughput constraint is solved. The simulations investigate the effect of system parameters on the average AoEI.

Outage Probability Analysis of LR-FHSS in Satellite IoT Networks

Long-range frequency-hopping spread spectrum (LR-FHSS) is a promising solution for long-range and dense deployment of Internet of Things (IoT) networks since it can provide a significant capacity improvement compared to the conventional Aloha-based chirp spread spectrum (CSS). In this letter, we present an analytical approach for deriving the outage probability of LR-FHSS in a satellite-based IoT network taking into account the noise, channel fading impairments, and more importantly, the capture effect. The obtained analytical expressions are validated with computer simulations and show that for a typical target outage probability of 10−2, exploiting LR-FHSS in the considered system model can serve up to 60, 000 and 120, 000 end devices per hour for 48 bytes of information using two specified data rates in the North America region. These numbers present significant capacity increases over the conventional low-power long-range (LoRa) network.

Age-Optimal Network Coding HARQ Scheme for Satellite-Based Internet of Things

Satellite-based Internet of Things (S-IoT) is viewed as an efficient solution to provide timely status updates to the terrestrial user equipment (UE), due to its ubiquitous coverage and broadband access capability inherited from high throughput satellite (HTS). However, the conventional hybrid automatic repeat request (HARQ) cannot guarantee the freshness of status update transmission, because the reliable transmission needs the retransmission of the lost packets, which deteriorates the freshness due to the nontrivial propagation delay and high bit error rate (BER) of the satellite–territory link (STL). In this article, we propose an age-optimal network coding HARQ (NC HARQ) scheme with the metric of information timeliness, i.e., Age of Information (AoI) to realize timely status updates in S-IoT. First, we model the STL as a shadowed Rician (SR) fading channel and derive the closed-form expressions of BER. Then, we propose a fixed interval NC inserted HARQ (f-NC HARQ) scheme, where the NC packets are inserted in the information packets with fixed interval to accelerate the recovery of lost information packets and derive the expressions of Peak AoI (PAoI) and average end-to-end delay. Furthermore, we propose an adaptive NC inserted HARQ (A-NC HARQ) scheme for the drastic variations in the SR fading channel, where the transmission of the status update is modeled as a partially observable Markov decision process (POMDP) problem and solved by a low complexity improved fast informed bound (iFIB) algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the A-NC HARQ scheme can achieve the lowest PAoI and average end-to-end delay.

Age-Critical and Secure Blockchain Sharding Scheme for Satellite-Based Internet of Things

It is witnessed that blockchain technology has been widely studied in Internet of Things (IoT) applications due to its decentralized tamper-resistance. Meanwhile, satellite-based IoT (S-IoT) becomes popular and has been regarded as a potential solution of the scalability due to its ubiquitous coverage inherited from satellites. Nevertheless, the large-scale blockchain network enabled S-IoT (BNS-IoT) would be limited by timely performing consensus. In this paper, we propose an age-critical blockchain sharding (ABS) scheme with the metric of information timeliness, i.e., age of information (AoI) to realize timely consensus in BNS-IoT. Specifically, we propose a forking-waiting-retransmission (FR) mechanism for the ABS scheme to deal with forking events, and realize a secure consensus. Then, we derive the closed-form expressions of average AoI (AAoI), throughput and security performance of the FR mechanism in ABS scheme, respectively, and compare with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -block confirmation and select the longest-chain ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -LC) mechanism. Simulation results show that our ABS scheme can realize the linear expansion of throughput with the increasing number of shards, and our FR mechanism can greatly improve the security by sacrificing minor AAoI compared with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -LC mechanism. Furthermore, our ABS scheme can outperform the conventional random sharding (RS) scheme in terms of AAoI and throughout.

Satellite IoT Based Road Extraction from VHR Images Through Superpixel-CNN Architecture

In the past few decades, technology has progressively become ineluctable in human lives, primarily due to the growth of certain fields like space technology, Big Data, the Internet of Things (IoT), and machine learning. Space technology has revolutionized communication mechanisms while creating opportunities for various research areas, including remote sensing (RS)-inspired applications. On the other hand, IoT presents a platform to use the power of the internet over a whole range of devices through a phenomenon known as social IoT. These devices generate a humongous amount of data that requires handling and managing by big data technology incorporated with deep learning techniques to reduce the manual workload of an operator. Moreover, deep learning architectures like convolutional neural networks (CNNs) have presented a scope to extract the underlying features from the large-scale input images in providing better solutions for tasks such as automatic road detection that come at the cost of time and memory overhead. In this context, we have proposed a three-layer edge-fog-cloud-based intelligent satellite IoT architecture that uses the superpixel-based CNN approach. At the fog layer, the superpixel-based simple linear iterative cluster (SLIC) algorithm uses the images captured by the satellites of the edge level to produce the smaller-sized superpixel images that can be transferred even in a low bandwidth link. The CNN module at the cloud level is then trained with these superpixel images to predict the road networks from these RS images. Two popular road datasets: the DeepGlobe Road dataset and the Massachusetts Road dataset, have been considered to prove the usefulness of the proposed SLIC-CNN architecture in satellite-based IoT platforms to address the problems like RS image-based road extraction. The proposed architecture achieves better performance accuracy than the classical CNN while reducing the incurred overhead by a noticeable limit. • Presents a superpixel-inspired deep learning-based architecture that optimizes the time and memory overhead. • Presents the three-layer edge-fog-cloud-based satellite IoT architecture to deploy the proposed SLIC-CNN framework. • Discusses the efficiency of the proposed deep architecture to work in a low bandwidth environment for road segmentation. • Highlights the application areas such as emergency alarm systems that can use the architecture to operate in real-time.

Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat

Many regions in developing countries do not have any access to communication networks even though the number of devices connected through the Internet of Things (IoT) is increasing significantly. A small satellite platform could provide global network coverage in low Earth orbit to these remote locations at a low cost. This paper describes the overall mission architecture and the implementation of remote IoT using a 1U volume in 6U CubeSat platform named KITSUNE. In KITSUNE, one of the missions is to leverage IoT for building a network of remote ground sensor terminals (GST) in 11 mostly developing countries. This paper evaluates the capacity and coverage of a satellite-based IoT network for providing remote data-collection services to these countries. The amount of data that could be collected from the GSTs and forwarded accurately to the users determines the actual capacity of the Store and Forward (S&amp;F) mission. Therefore, there are several proposed parameters to estimate this capacity in this study. In addition, these parameters are retrieved from the simulations, ground test results, and on-orbit observations with the KITSUNE satellite. The proposed IoT system, which is composed of the GSTs and IoT subsystem onboard KITSUNE satellite, is determined to be capable of providing valuable information from remote locations. In addition, the collected data are achieved and analyzed to monitor sensory data specific to each country, and it could help to generate prediction profiles as well.

Grant Free Age-Optimal Random Access Protocol for Satellite-Based Internet of Things

In satellite-based Internet of Things (S-IoT) system, the timely status updating of terrestrial sensing user equipments (UEs) to satellite could be hampered by the long propagation delay, especially in massive machine type communications (mMTC). To guarantee the information freshness in S-IoT, a new performance indicator called age of information (AoI) is exploited to analyze the average AoI (AAoI) in the overload case of mMTC, and a grant free age-optimal (GFAO) random access protocol is proposed to lower the AAoI. Specifically, the closed-form expression of AAoI is derived by tracing the instantaneous AoI evolution of each UE through Markov analysis. Then, the proposed GFAO random access protocol is proved to achieve a minimum AAoI and a maximum throughput in S-IoT, by adjusting the number of access time slots in each transmission frame in the overload case of mMTC. Extensive simulations are conducted to validate the theoretical analysis, and show that there exists different optimal value of access time slots in system load region from 0.2 to 3, which can minimize AAoI and maximize throughput in the proposed GFAO random access protocol.

Potential Game-Based Radio Resource Allocation in Uplink Multibeam Satellite IoT Networks

In this article, we introduce a potential game-based approach to implement collaborative user scheduling and power allocation in the uplink multibeam satellite-based Internet of things (S-IoT) networks. First of all, a framework of the multibeam uplink S-IoT system is proposed, where full frequency reuse and co-channel interference among different spot beams are considered. To provide broadband S-IoT service effectively, we formulate an initial optimization problem of maximizing uplink sum transmission capacity and then transform it into an interference avoidance problem to address the mathematical intractability. Specifically, a game-theoretic model is implemented to solve the transformed optimization problem, which is proved to be a potential game and existence of Nash equilibriums (NEs). Moreover, an iterative algorithm with low computational complexity, motivated by the finite improvement property, is designed to implement collaborative user scheduling and power allocation to NE point. Finally, the simulation results prove the convergence and effectiveness of the proposed potential game-based approach.

Help from space: grant-free massive access for satellite-based IoT in the 6G era

The Sixth-Generation (6G) standard for wireless communications is expected to realize ubiquitous coverage for massive Internet of Things (IoT) networks by 2030. Satellite-based communications are recognized as a highly promising technical enabler to satisfy IoT service requirements in the 6G era. This study analyzes multiple access technologies, which are essential for the effective deployment of satellite-based IoT. First, we thoroughly investigate the existing research related to massive access, including information-theory considerations as well as Non-Orthogonal Multiple Access (NOMA) and Random Access (RA) technologies.Then, we explore the influence of the satellite transmission environment on multiple access technologies. Based on this study, a Non-orthogonal Massive Grant-Free Access (NoMaGFA) scheme, which reaps the joint benefits of RA and NOMA, is proposed for asynchronous transmissions in satellite-based IoT to achieve improved system throughput and enhance the system robustness under varying traffics. Finally, we identify some important and interesting future developments for satellite-based IoT, including waveform design, transceiver design, resource allocation, and artificial intelligence-enhanced design.

Early Collision Detection for Massive Random Access in Satellite-Based Internet of Things

As a complementary solution for seamless and ubiquitous coverage, satellite communications will play crucial roles in future global Internet of Things (IoT). Focusing on enhancing access efficiency and resource utilization of massive machine-type devices (MTDs), we propose an efficient collision detection scheme at the first step of random access (RA) procedure for satellite-based IoT. By leveraging a single root Zadoff-Chu (ZC) sequence with an elaborate set of cyclic shift offsets to generate all the available preamble sequences, the proposed scheme can achieve rapid collision detection and load estimation in one-shot correlation operation, while having the robustness to the non-orthogonal interference. The preamble detection probability, collision detection probability, and load monitoring accuracy, are mathematically analyzed, and an optimal set of preamble selection probabilities is given to maximize the overall load monitoring accuracy. Simulation results exhibit the remarkable performance improvement of our scheme in typical satellite line-of-sight (LOS) scenario, by compared to the state-of-the-art collision detection schemes.

Outage-Constrained Robust Multigroup Multicast Beamforming for Satellite-Based Internet of Things Coexisting With Terrestrial Networks

Satellite-based Internet of Things (IoT) is recognized as a cost-effective approach for global access. In this article, we aim at improving the spectrum efficiency of satellite systems to serve a huge number of IoT devices. To this end, we present a cognitive satellite-terrestrial framework, where a multibeam satellite system with full frequency reuse shares the spectrum with terrestrial networks based on the underlay paradigm. Considering DVB-S2X recommendations, geometric configurations, and channel characteristics, we investigate a robust multigroup multicast beamforming design for the satellite-based IoT coexisting with terrestrial networks in the presence of a phase error on channel state information, and characterize the achievable rate region under the outage probability constraint for the terminal and the power consumption constraint for the satellite. Based on the concept of rate profile, an associated optimization problem is formulated to design robust beamformers and determine the Pareto boundary of the region. To solve the intractable problem, we propose a two-level iterative algorithm on the basis of joint bisection search and penalty function enabled nonsmooth optimization. In particular, we develop a Bernstein-type inequality aided method and a large deviation inequality aided method to obtain a tractable and conservative approximation for the probabilistic constraint, respectively. Numerical results are provided to confirm the validity and superiority of our proposed scheme over the existing approaches and reveal the impact of key parameters on the achievable system performance.

Auction-Based Multichannel Cooperative Spectrum Sharing in Hybrid Satellite-Terrestrial IoT Networks

In this article, we investigate the multichannel cooperative spectrum sharing in hybrid satellite-terrestrial Internet of Things (IoT) networks with the auction mechanism, which is designed to reduce the operational expenditure of the satellite-based IoT (S-IoT) network while alleviating the spectrum scarcity issues of terrestrial-based IoT (T-IoT) network. The cluster heads of selected T-IoT networks assist the primary satellite users transmission through cooperative relaying techniques in exchange for spectrum access. We propose an auction-based optimization problem to maximize the sum transmission rate of all primary S-IoT receivers with the appropriate secondary network selection and corresponding radio resource allocation profile by the distributed implementation while meeting the minimum transmission rate of secondary receivers of each T-IoT network. Specifically, the one-shot Vickrey-Clarke-Groves (VCG) auction is introduced to obtain the maximum social welfare, where the winner determination problem is transformed into an assignment problem and solved by the Hungarian algorithm. To further reduce the primary satellite network decision complexity, the sequential Vickrey auction is implemented by sequential fashion until all channels are auctioned. Due to incentive compatibility with those two auction mechanisms, the secondary T-IoT cluster yields the true bids of each channel, where both the nonorthogonal multiple access (NOMA) and time division multiple access (TDMA) schemes are implemented in cooperative communication. Finally, simulation results validate the effectiveness and fairness of the proposed auction-based approach as well as the superiority of the NOMA scheme in secondary relays selection. Moreover, the influence of key factors on the performance of the proposed scheme is analyzed in detail.

Superimposed Pilot Code-Domain NOMA Scheme for Satellite-Based Internet of Things

Satellite-based Internet of Things (S-IoT) is regarded as an effective solution to enable ubiquitous connectivity in a global coverage and cost-effective manner for massive machine type communications (mMTC) in the future Internet of Things. In this article, we propose a superimposed pilots code domain nonorthogonal multiple access over a shadowed-Rician fading and path loss satellite-ground channel model in uplink mMTC S-IoT system. First, we analyze the interference due to the collision in the random access scenario, and propose an iteration channel estimation based on ridge regression algorithm to attain a more precise channel state information. Moreover, we utilize successive interference cancellation (SIC) and successive joint decoding (SJD) to recover the singleton user equipments. Then, we derive the expressions of the outage probability and maximum system throughput of SP scheme, and compared with the regular orthogonal pilot (RP) scheme under SIC and SJD, respectively. Simulations validate our analytical results and show that the achievable system throughput of the SP scheme under SJD can outperform that of the RP scheme in a S-IoT system for short packet transmission.

Energy Efficient Network Coding HARQ Transmission Scheme for S-IoT

Satellite-based Internet of Things (S-IoT) is suggested to meet the emergency applications and services of ubiquitous terrestrial IoT user equipments (UEs) in the future IoT. Since the limited power constraints of the satellite and the IoT UEs, it is a vital challenging to design an energy efficient transmission scheme, which can reduce the number of retransmission rounds while offer high throughput. In this article, we combine the network coding and hybrid automatic repeat request (HARQ) to propose a network coding HARQ (NC HARQ) transmission scheme for typical S-IoT scenarios, i.e., the multiple unicast downlink transmission and network coding bidirectional relaying scenarios. We derive the theoretical expressions for the metrics of system performance, including the average number of transmission rounds, the system throughput and the energy efficiency (EE). Specifically, our derivations are completed in two methods, i.e., the mutual information cumulation method and the matrix exponential (ME) distribution method. Based on the above analytical results, we formulate and solve the maximum EE optimization problem to obtain the optimal maximum transmission rounds in two typical communication scenarios. Simulations are provided to validate our analytical results, and confirm the improvement of our proposed NC HARQ scheme.

Network Utility Maximization Resource Allocation for NOMA in Satellite-Based Internet of Things

High-throughput satellite (HTS) is viewed as a promising solution for the next generation of satellite-based Internet of Things (S-IoT). Considering that the onboard communication resources, such as power and storage, are limited, we formulate a joint network stability and resource allocation optimization problem to maximize the long-term network utility of a nonorthogonal multiple access (NOMA) S-IoT downlink system. First, we establish two virtual queues for both the data queueing and power expenditure. Then, a joint optimal problem can be formulated as a problem that optimizes the time average of network utility, which perfectly matches the Lyapunov optimization framework. Therefore, by taking into account the condition of successive interference cancellation decoding, we propose a practical solution under the Karush–Kuhn–Tucker (KKT) conditions, and further introduce an optimal solution by using the particle swarm optimization (PSO) algorithm for the joint resource allocation problem. The simulation results demonstrate that our joint optimization allocation schemes outperform the existing benchmark schemes.

Prediction of Satellite Shadowing in Smart Cities with Application to IoT.

The combination of satellite direct reception and terrestrial 5G infrastructure is essential to guarantee coverage in satellite based-Internet of Things, mainly in smart cities where buildings can cause high power losses. In this paper, we propose an accurate and fast graphical method for predicting the satellite coverage in urban areas and SatCom on-the-move scenarios. The aim is to provide information that could be useful in the IoT network planning process, e.g., in the decision of how many terrestrial repeaters are really needed and where they should be placed. Experiments show that the shadowed areas predicted by the method correspond almost perfectly with experimental data measured from an Eutelsat satellite in the urban area of Barcelona.