Autonomous farm vehicles (AFVs) encounter significant challenges in large-scale networking and massive data transmission. The rapid development of global low Earth orbit (LEO) satellite networks provides reliable support for AFVs. However, the time-varying characteristics of the satellite-terrestrial channel and large-scale collaborative scheduling among AFVs pose challenges for joint computation offloading between satellites and AFVs. This paper proposes a satellite and autonomous farm vehicle integrated network (SAFVIN) architecture. We formulate the joint satellite and AFVs computation offloading problem as a Markov decision process (MDP). We propose a deep rein forcement computation offloading (DRCO) method that adapts to satellite networks. Unlike traditional computation offloading methods, the proposed DRCO takes into account the time varying satellite network channel states. The DRCO can rapidly converge to high-quality decisions in satellite network with strong randomness, thereby adapting to dynamic environments more quickly and achieving superior performance. We compare the proposed DRCO with the heuristic coordinate descent (CD), and with deep Q-network (DQN) and deep deterministic policy gradient (DDPG) algorithms. The DRCO achieves a 2% lower latency loss while only incurring 21% of the time overhead required by the CD. Furthermore, unlike DQN and DDPG algorithms, which rely on continuous time frame input and output for network updates, the proposed DRCO can directly leverage past experience to adapt to dynamic satellite network. Compared with other deep reinforcement learning algorithms including DQN and DDPG, the DRCO achieves an average energy consumption reduction of approximately 10%.

The communication among nodes in the space–air–sea integrated network (SASIN) relies on collaborative multihop transmission. Hence, effective routing techniques should be designed to optimize multiple indicators. Routing optimization for multihop is usually focused on optimizing a single metric. Moreover, designing effective routing strategies for multihop networks with SASIN is challenging as balancing multiple performance metrics can lead to conflicts. In this article, we propose near-Pareto multiobjective routing optimization for SASIN, which adopts multiobjective combinatorial optimization (MOCOP) to strike a tradeoff among multiple objectives. We establish the SASIN system model, including channel models of communication links between satellites, aircraft, and ships. Furthermore, we use multiobjective optimization methods to formulate objective functions of spectral efficiency, energy efficiency, and delay. We employ the multiobjective evolutionary algorithms (MOEAs) for approximating the set of the Pareto optimal solutions. An improved nondominated sorting genetic algorithm II (INSGA II) and an improved strength Pareto evolutionary algorithm II (ISPEA II) are proposed to generate approximations of the Pareto optimal set. We evaluated the MOCOP formulation, and the SASIN network topology was built based on real data and simulated data. The simulation results indicate that a set of beneficial tradeoff solutions can be obtained for providing flexible selection of communication connections by addressing the multiobjective routing problem formulated. The results demonstrate that the MOEAs utilized have the potential to find Pareto-optimal solutions for SASIN.

Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.

The development of the Internet of Things promotes the increasing demand for edge computing, resulting in a large amount of temporal data. Protecting data from tampering has become key to industrial intelligent management. Blockchain technology has become an ideal choice for ensuring data trustworthiness due to its immutability and other characteristics. However, existing technologies do not provide sufficient support for temporal data. There are still issues such as chaotic data organization, low query efficiency, and insufficient lightweight validation. To address these challenges, we combine the Secure Hash Algorithm and Merkle tree to serialize temporal data. We design a temporal Merkle prefix forest on the blockchain cloud main chain and construct an index for intra-block localization. Verification can be completed by monitoring the latest tree, significantly improving query efficiency. For edge-side devices with limited computing resources, we design a temporal Bloom Merkle tree, where lightweight nodes only need to pass the Merkle proof of the root node to verify data integrity. Experimental results demonstrate that our method significantly improves query efficiency and reduces storage requirements, meeting the reliability and lightweight requirements of temporal data management in the Internet of Things.

Sparse code multiple access (SCMA) has excellent application prospects in satellite-terrestrial links because of its high spectral efficiency and access capacity. In the end-to-end SCMA systems, channel modeling is a fundamental task for the communication algorithm design and performance optimization, which however is very challenging as it requires in-depth domain knowledge and technical expertise in radio signal propagations, especially for modeling satellite-terrestrial fading channels. In this article, a convolutional autoencoder-aided SCMA paradigm based on the stochastic channel modeling and autoencoder structure is developed. We are the first to exploit generative adversarial network to represent the satellite-terrestrial fading channel effects for the convolutional autoencoder-aided SCMA. Specifically, convolutional neural networks (CNNs) are employed to jointly construct the encoder and decoder for SCMA to alleviate the curse of dimensionality. Furthermore, we propose a conditional Wasserstein generative adversarial network with the gradient penalty (CWGAN-GP)-based channel modeling approach to achieve approximately accurate conditional channel distribution. Particularly, the received signal corresponding to the pilot symbol is used as a part of the condition information, and the Wasserstein distance is used as a measure of the distance between the distributions. Gradient penalty is adopted to solve the problem of weight pruning forcing Lipschitz constraints, which leads to some data being unable to converge. The numerical results demonstrate the effectiveness of the proposed approach in terms of the bit error rate (BER), block error rate (BLER), and complexity in satellite-terrestrial fading channels.

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted non-orthogonal multiple access (NOMA) communication systems are investigated in its vicinity, where a STAR-RIS is deployed within a predefined region for establishing communication links for users. Both beamformer-based NOMA and cluster-based NOMA schemes are employed at the multi-antenna base station (BS). For each scheme, the STAR-RIS deployment location, the passive transmitting and reflecting beamforming (BF) of the STAR-RIS, and the active BF at the BS are jointly optimized for maximizing the weighted sum-rate (WSR) of users. To solve the resultant non-convex problems, an alternating optimization (AO) algorithm is proposed, where successive convex approximation (SCA) and semi-definite programming (SDP) methods are invoked for iteratively addressing the non-convexity of each sub-problem. Numerical results reveal that 1) the WSR performance can be significantly enhanced by optimizing the specific deployment location of the STAR-RIS; 2) both beamformer-based and cluster-based NOMA prefer asymmetric STAR-RIS deployment.

Sparse code multiple access (SCMA) has excellent application prospects due to its high spectral efficiency and accsess capacity. However, due to the nonorthogonal characteristic of SCMA in code domain, the codebook needs to be manually designed for all communication scenarios, and the receiver has high computational complexity. To address this issue, deep neural network-aided SCMA (DNN-SCMA) is proposed, but it is difficult to capture channel state information (CSI) in the dynamic and time-varying communication scenarios, which hinders the overall learning and optimization fot end-to-end communications. This paper proposes a stochastic channel model with conditional generative adversarial network (CGAN) for DNN-aided SCMA in a data-driven way. Particularly, a model-free learning method is adopted to accurately learn different types of random channel models, which realizes effective acquisition of dynamic channel information. Finally, the end-to-end training is achieved through the use of back propagation (BP), and then by an iterative training of the composed networks, the end-to-end loss can be optimized in a supervised manner. Results show the feasibility of CGAN-based channel modeling in end-to-end DNN-SCMA.

Internet of Things as an essential integrated part of the future wireless communication system provides ubiquitous connectivity and information exchange to enable a range of applications and services, which has triggered spectrum resource pressure, multiple access, bandwidth efficiency, and security issues. Focusing on these issues, a high spectral efficiency secure access (HSESA) scheme based on dual nonorthogonal is proposed first in this paper. The scheme which can be recognized as a dual nonorthogonal scheme is designed by the nonorthogonal multiplexing and nonorthogonal multiple access. Particularly, HSESA scheme is equipped with secure multiplexing by using security matrix to improve physical layer security. Moreover, spectral efficiency analysis is given and the throughput of HSESA has been derived. Moreover, iterative detection (ID) and maximum likelihood (ML) are, respectively, combined with message passing algorithm (MPA) as detection schemes, and their respective performance advantages are analyzed. Simulation results show that the detection scheme using ID combined with MPA has lower complexity, while ML combined with MPA has better bit error rate performance, and the spectral efficiency is also enhanced by the proposed HSESA.

Cloud computing provides powerful computing ability of mobile devices in the Internet of Things (IoT) networks. However, the large amounts of data interaction with cloud suffers bandwidth limit and energy efficiency for data processing and transmission, and the energy consumption of data processing is far less than data transmission. In this paper, offloading is considered in transmission to improve the battery lifetime by employing a spectral-energy efficient transmission scheme with efficient computing in IoT-Cloud. The offloaded resource can be saved to serve more services if the physical air interface is designed efficiently. In addition, many personal things are unloaded to the IoT-Cloud which creates a risk of privacy and security. The improved offloading efficiency of data transmission scheme secure truncating orthogonal frequency division multiplexing (STOFDM) is generated by deliberately truncating the orthogonal frequency division multiplexing signal in time domain. Particularly, the truncations are selected by a dynamic random private matrix based on the proposed offloading power amplifier theorem. The corresponding legitimate receiver is designed with private mapping using the efficient fast Fourier transformation (FFT) for offloading computation. Moreover, the closed-form expression for the FFT-based STOFDM system is analyzed and be verified by simulation results. In light of the analysis, the STOFDM performs intercarrier interference as an orthogonal sequence is partially transmitted, which degrades the reliability of transmission link. Further, two enhanced detectors with low computing-complexity is also given to improve the performance of Bob while restrict eavesdropper's reception and further provides offloading computation.

Conventional binary descriptor only uses limited spatial information from original image patch, such as BRIEF, which will result in limited discriminative power. We settle this problem through further excavating feature information and propose a binary descriptor encoding not only intensity comparison information but also intensity order information. Results based on experiments of performance evaluation have shown that the proposed binary descriptor outperforms other binary descriptors under rotation and scale changes.