Low earth orbit (LEO) satellites with wide coverage can carry the mobile edge computing (MEC) servers with powerful computing capabilities to form the LEO satellite edge computing system, providing computing services for the global ground users. In this paper, the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program (MINLP) problem. This paper proposes a computation offloading algorithm based on deep deterministic policy gradient (DDPG) to obtain the user offloading decisions and user uplink transmission power. This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme. In addition, the expression of suboptimal user local CPU cycles is derived by relaxation method. Simulation results show that the proposed algorithm can achieve excellent convergence effect, and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.

Reliable Cluster Head (CH) selection-based routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability. In this paper, Hybrid Golden Jackal, and Improved Whale Optimization Algorithm (HGJIWOA) is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink. This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection. The process of CH selection achieved using Golden Jackal Optimization Algorithm (GJOA) completely depends on the factors of maintainability, consistency, trust, delay, and energy. The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance. It further utilized Improved Whale Optimisation Algorithm (IWOA) for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance. It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed. The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%, sustained residual energy of 19.64% with minimized end-to-end delay of 21.82%, better than the competitive CH selection approaches.

To enable simultaneous transmit and receive (STAR) on the same frequency in a densely deployed space with multi-interference sources, this work proposes a digitally-assisted analog self-interference cancellation method, which can acquire reference signals through flexible wired/wireless switching access. Based on this method, the Minimum Mean Square Error algorithm with known channel state information is derived in detail, determining the upper limit of the cancellation performance, and the Adaptive Dithered Linear Search algorithm for real-time engineering cancellation is given. The correctness of theoretical analysis is verified by the practical self-interference channel measured by a vector network analyzer. Furthermore, we have designed and implemented the corresponding multi-interference cancellation prototype with the digitally-assisted structure, capable of handling multiple interferences (up to three) and supporting a large receive bandwidth of 100 MHz as well as a wide frequency coverage from 30 MHz to 3000 MHz. Prototype test results demonstrate that in the presence of three interferences, when the single interference bandwidth is 0.2/2/20 MHz (corresponding to the receive bandwidth of 2/20/100 MHz), the cancellation performance can reach 46/32/22 dB or more.

Artificial intelligence, such as deep learning technology, has advanced the study of facial expression recognition since facial expression carries rich emotional information and is significant for many naturalistic situations. To pursue a high facial expression recognition accuracy, the network model of deep learning is generally designed to be very deep while the model's real-time performance is typically constrained and limited. With MobileNetV3, a lightweight model with a good accuracy, a further study is conducted by adding a basic ResNet module to each of its existing modules and an SSH (Single Stage Headless Face Detector) context module to expand the model's perceptual field. In this article, the enhanced model named Res-MobileNetV3, could alleviate the subpar of real-time performance and compress the size of large network models, which can process information at a rate of up to 33 frames per second. Although the improved model has been verified to be slightly inferior to the current state-of-the-art method in aspect of accuracy rate on the publically available face expression datasets, it can bring a good balance on accuracy, real-time performance, model size and model complexity in practical applications.

Over-the-air computation (AirComp) enables federated learning (FL) to rapidly aggregate local models at the central server using waveform superposition property of wireless channel. In this paper, a robust transmission scheme for an AirComp-based FL system with imperfect channel state information (CSI) is proposed. To model CSI uncertainty, an expectation-based error model is utilized. The main objective is to maximize the number of selected devices that meet mean-squared error (MSE) requirements for model broadcast and model aggregation. The problem is formulated as a combinatorial optimization problem and is solved in two steps. First, the priority order of devices is determined by a sparsity-inducing procedure. Then, a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met. An alternating optimization (AO) scheme is used to transform the resulting nonconvex problem into two convex subproblems. Numerical results illustrate the effectiveness and robustness of the proposed scheme.

Orbital angular momentum (OAM) can achieve multifold increase of spectrum efficiency, but the hollow divergence characteristic and Line-of-Sight (LoS) path requirement impose the crucial challenges for vortex wave communications. For air-to-ground vortex wave communications, where there exists the LoS path, this paper proposes a multi-user cooperative receive (MUCR) scheme to break through the communication distance limitation caused by the characteristic of vortex wave hollow divergence. In particular, we derive the optimal radial position corresponding to the maximum intensity, which is used to adjust the waist radius. Based on the waist radius and energy ring, the cooperative ground users having the minimum angular square difference are selected. Also, the signal compensation scheme is proposed to decompose OAM signals in air-to-ground vortex wave communications. Simulation results are presented to verify the superiority of our proposed MUCR scheme.

To guarantee secure communication against eavesdropping and malicious attack, an artificial noise (AN)-aided frequency-hopping (FH) architecture is adopted in this article. But the inevitable time misalignment between the received signal and locally reconstructed AN will deteriorate the AN cancellation performance, yielding significant secrecy degradation at the FH receiver. In view of this, first, the AN cancellation performance under time misalignment is evaluated via signal to AN-plus-noise ratio, and the system secrecy is analyzed via secrecy rate. Then, to mitigate the performance degradation raised by time misalignment, the transmitting power allocation scheme for AN and confidential signal (CS) is optimized, and the optimal hopping period is designed. Notably, the obtained conclusions in both the performance evaluation and transmitter optimization sections hold no matter whether the eavesdropper can realize FH synchronization or not. Simulations verify that time misalignment will raise non-negligible performance degradation. Besides, the power ratio of AN to CS should decrease as time misalignment increases, and for perfect time synchronization, the transmitting power of AN and CS should be equivalent. In addition, a longer hopping period is preferred for secrecy enhancement when time misalignment gets exacerbated.

Delay tolerant wireless sensor networks (DTWSN) is a class of wireless network that finds its deployment in those application scenarios which demand for high packet delivery ratio while maintaining minimal overhead in order to prolong network lifetime; owing to resource-constrained nature of sensors. The fundamental requirement of any network is routing a packet from its source to destination. Performance of a routing algorithm depends on the number of network parameters utilized by that routing protocol. In the recent years, various routing protocol has been developed for the delay tolerant networks (DTN). A routing protocol known as spray and wait (SnW) is one of the most widely used routing algorithms for DTN. In this paper, we study the SnW routing protocol and propose a modified version of it referred to as Pentago SnW which is based on pentagonal number series. Comparison to binary SnW shows promising results through simulation using real-life scenarios of cars and pedestrians randomly moving on a map.

In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network (WSN) caused by uneven energy consumption among nodes, a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm (FF-PIA) is proposed to optimise the data transmission path. After having obtained the optimal number of cluster head node (CH), its result might be taken as the basis of producing the initial population of FF-PIA algorithm. The Lévy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search. Moreover, a Gaussian perturbation strategy is applied to update the optimal solution, ensuring the algorithm can jump out of the local optimal solution. And, in the WSN data gathering, a one-dimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks (DCRNN). We conducted experiments on the National Oceanic and Atmospheric Administration (NOAA) dataset. It shows that the DCRNN model-driven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance. FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.

In this work, we propose a comprehensive theoretical framework for the multilevel NAND (NOT AND logic) flash memory, built upon the modified Student's t distribution where the distortion of the threshold voltage caused by the random telegraph noise, cell-to-cell interference and data retention noise are jointly considered. Based on the superposition modulation, we build a non-orthogonal multi-user communication model where a linear mapping is conducted between the verify voltages and binary antipodal symbols. Aimed at improving the storage efficiency, we propose an unequal amplitude mapping (UAM) solution by optimizing the weighting coefficients of verify voltages to intelligently adjust the width of each state. Moreover, the uniform storage efficiency region and sum storage efficiency of different labelings with various decoding schemes are discussed. Simulation results validate the effectiveness of our proposed UAM solution where an up to 20.9% storage efficiency gain can be achieved compared to the current used benchmark scheme. In addition, analytical and simulation results also demonstrate that the successive cancellation decoding outperforms other decoding schemes for all labelings.

Satellite Internet (SI) provides broadband access as a critical information infrastructure in 6G. However, with the integration of the terrestrial Internet, the influx of massive terrestrial traffic will bring significant threats to SI, among which DDoS attack will intensify the erosion of limited bandwidth resources. Therefore, this paper proposes a DDoS attack tracking scheme using a multi-round iterative Viterbi algorithm to achieve high-accuracy attack path reconstruction and fast internal source locking, protecting SI from the source. Firstly, to reduce communication overhead, the logarithmic representation of the traffic volume is added to the digests after modeling SI, generating the lightweight deviation degree to construct the observation probability matrix for the Viterbi algorithm. Secondly, the path node matrix is expanded to multi-index matrices in the Viterbi algorithm to store index information for all probability values, deriving the path with non-repeatability and maximum probability. Finally, multiple rounds of iterative Viterbi tracking are performed locally to track DDoS attack based on trimming tracking results. Simulation and experimental results show that the scheme can achieve 96.8% tracking accuracy of external and internal DDoS attack at 2.5 seconds, with the communication overhead at 268KB/s, effectively protecting the limited bandwidth resources of SI.

A decentralized network made up of mobile nodes is termed the Mobile Ad-hoc Network (MANET). Mobility and a finite battery lifespan are the two main problems with MANETs. Advanced methods are essential for enhancing MANET security, network longevity, and energy efficiency. Hence, selecting an appropriate cluster. The cluster's head further boosts the network's energy effectiveness. As a result, a Hybrid Swallow Swarm Optimisation-Memetic Algorithm (SSO-MA) is suggested to develop the energy efficiency & of the MANET network. Then, to secure the network Abnormality Detection System (ADS) is proposed. The MATLAB-2021a platform is used to implement the suggested technique and conduct the analysis. In terms of network performance, the suggested model outperforms the current Genetic Algorithm, Optimised Link State Routing protocol, and Particle Swarm Optimisation techniques. The performance of the model has a minimum delay in the range of 0.82 seconds and a Packet Delivery Ratio (PDR) of 99.82%. Hence, the validation shows that the Hybrid SSO-MA strategy is superior to the other approaches in terms of efficiency.

The security of information transmission and processing due to unknown vulnerabilities and backdoors in cyberspace is becoming increasingly problematic. However, there is a lack of effective theory to mathematically demonstrate the security of information transmission and processing under non-random noise (or vulnerability backdoor attack) conditions in cyberspace. This paper first proposes a security model for cyberspace information transmission and processing channels based on error correction coding theory. First, we analyze the fault tolerance and non-randomness problem of Dynamic Heterogeneous Redundancy (DHR) structured information transmission and processing channel under the condition of non-random noise or attacks. Secondly, we use a mathematical statistical method to demonstrate that for non-random noise (or attacks) on discrete memory channels, there exists a DHR-structured channel and coding scheme that enables the average system error probability to be arbitrarily small. Finally, to construct suitable coding and heterogeneous channels, we take Turbo code as an example and simulate the effects of different heterogeneity, redundancy, output vector length, verdict algorithm and dynamism on the system, which is an important guidance for theory and engineering practice.

Applying non-orthogonal multiple access (NOMA) to the mobile edge computing (MEC) network supported by unmanned aerial vehicles (UAVs) can improve spectral efficiency and achieve massive user access on the basis of solving computing resource constraints and coverage problems. However, the UAV-enabled network has a serious risk of information leakage on account of the openness of wireless channel. This paper considers a UAV-MEC secure network based on NOMA technology, which aims to minimize the UAV energy consumption. To achieve the purpose while meeting the security and users' latency requirements, we formulate an optimization problem that jointly optimizes the UAV trajectory and the allocation of network resources. Given that the original problem is non-convex and multivariate coupled, we proposed an effective algorithm to decouple the non-convex problem into independent user relation coefficients and subproblems based on successive convex approximation (SCA) and block coordinate descent (BCD). The simulation results showcase the performance of our optimization scheme across various parameter settings and confirm its superiority over other benchmarks with respect to energy consumption.

In this paper, we investigate a multi-UAV aided NOMA communication system, where multiple UAV-mounted aerial base stations are employed to serve ground users in the downlink NOMA communication, and each UAV serves its associated users on its own bandwidth. We aim at maximizing the overall common throughput in a finite time period. Such a problem is a typical mixed integer nonlinear problem, which involves both continuous-variable and combinatorial optimizations. To efficiently solve this problem, we propose a two-layer algorithm, which separately tackles continuous-variable and combinatorial optimization. Specifically, in the inner layer given one user association scheme, subproblems of bandwidth allocation, power allocation and trajectory design are solved based on alternating optimization. In the outer layer, a small number of candidate user association schemes are generated from an initial scheme and the best solution can be determined by comparing all the candidate schemes. In particular, a clustering algorithm based on K-means is applied to produce all candidate user association schemes, the successive convex optimization technique is adopted in the power allocation subproblem and a logistic function approximation approach is employed in the trajectory design subproblem. Simulation results show that the proposed NOMA scheme outperforms three baseline schemes in downlink common throughput, including one solution proposed in an existing literature.

How to ensure the security of device access is a common concern in the Internet of Things (IoT) scenario with extremely high device connection density. To achieve efficient and secure network access for IoT devices with constrained resources, this paper proposes a lightweight physical-layer authentication protocol based on Physical Unclonable Function (PUF) and channel pre-equalization. PUF is employed as a secret carrier to provide authentication credentials for devices due to its hardware-based uniqueness and unclonable property. Meanwhile, the short-term reciprocity and spatio-temporal uniqueness of wireless channels are utilized to attach an authentication factor related to the spatio-temporal position of devices and to secure the transmission of authentication messages. The proposed protocol is analyzed formally and informally to prove its correctness and security against typical attacks. Simulation results show its robustness in various radio environments. Moreover, we illustrate the advantages of our protocol in terms of security features and complexity through performance comparison with existing authentication schemes.

Intelligent reflecting surface (IRS) assisted with the wireless powered communication network (WPCN) can enhance the desired signal energy and carry out the power-sustaining problem in ocean monitoring systems. In this paper, we investigate a reliable communication structure where multiple buoys transmit data to a base station (BS) with the help of the unmanned aerial vehicle (UAV)-mounted IRS and harvest energy from the base station simultaneously. To organically combine WPCN with maritime data collection scenario, a scheduling protocol that employs the time division multiple access (TDMA) is proposed to serve multiple buoys for uplink data transmission. Furthermore, we compare the full-duplex (FD) and half-duplex (HD) mechanisms in the maritime data collection system to illustrate different performances under these two modes. To maximize the fair energy efficiency under the energy harvesting constraints, a joint optimization problem on user association, BS transmit power, UAV's trajectory and IRS's phase shift is formulated. To solve the non-convex problem, the original problem is decoupled into several subproblems, and successive convex optimization and block coordinate descent (BCD) methods are employed obtain the near-optimal solutions alternatively. Simulation results demonstrate that the UAV-mounted IRS can significantly improve energy efficiency in our considered system.

5G technology has endowed mobile communication terminals with features such as ultra-wideband access, low latency, and high reliability transmission, which can complete the network access and interconnection of a large number of devices, thus realizing richer application scenarios and constructing 5G-enabled vehicular networks. However, due to the vulnerability of wireless communication, vehicle privacy and communication security have become the key problems to be solved in vehicular networks. Moreover, the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable requirement. In order to achieve efficient and secure communication while protecting vehicle privacy, this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on blockchain. Firstly, the key agreement is accomplished using certificateless public key cryptography, and based on the aggregate signature and the cooperation between the vehicle and the trusted authority, an efficient key updating method is proposed, which reduces the overhead and protects the privacy of the vehicle while ensuring the communication security. Secondly, by introducing blockchain and using smart contracts to load the vehicle public key table for key management, this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving vehicles. Finally, the formal security proof under the eck security model and the informal security analysis is conducted, it turns out that our scheme is more secure than other authentication schemes in the vehicular networks. Performance analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.

The emergence of multi-access edge computing (MEC) aims at extending cloud computing capabilities to the edge of the radio access network. As the large-scale internet of things (IoT) services are rapidly growing, a single edge infrastructure provider (EIP) may not be sufficient to handle the data traffic generated by these services. Most of the existing work addressed the computing resource shortage problem by optimizing tasks schedule, whereas others overcome such issue by placing computing resources on demand. However, when considering a multiple EIPs scenario, an urgent challenge is how to generate a coalition structure to maximize each EIP’s gain with a suitable price for computing resource block corresponding to a container. To this end, we design a scheme of EIPs collaboration with a market price for containers under a scenario that considers a collection of service providers (SPs) with different budgets and several EIPs distributed in geographical locations. First, we bring in the net profit market price model to generate a more reasonable equilibrium price and select the optimal EIPs for each SP by a convex program. Then we use a mathematical model to maximize EIP’s profits and form stable coalitions between EIPs by a distributed coalition formation algorithm. Numerical results demonstrate that our proposed collaborative scheme among EIPs enhances EIPs’ gain and increases users’ surplus.