Non-orthogonal multiple access (NOMA) has been extensively studied to improve the performance of space-terrestrial satellite networks on account of the shortage of frequency band resources. In this paper, terrestrial network and satellite network synergistically provide complete coverage for ground users. A user association scheme on account of the channel gain and distance between the ground users and the BSs is proposed to identify the users to be associated by the BSs, and there is an upper limit for the number of users associated with each BS. Then calculate the channel condition ratio to select the users served by the satellite. The all BSs provide service for those unselected users, and the NOMA technology is applied to terrestrial network. Then, a user pairing scheme which maximize the minimum the ground user channel correlation coefficient is formulated to match the terrestrial users in a NOMA group. On account of multiple antennas equipped by the BSs and satellite, beamforming is performed among groups of BSs and among satellite users so as to reduce multi-user interference. In the power allocation scheme, we introduce the alternative direction method of multipliers (ADMM) algotithm so as to optimize system energy efficiency. In addition, the objective function is a non-convex function, so the Dinkelbach-style scheme is presented to convert non-convex function into the convex-form function. Eventually, the performance of the presented algorithm is simulated and compared with the existing NOMA-FTPA algorithm. The results indicate that the presented algorithm has high superiority in system energy efficiency and it can be applied to this network.

Fog computing makes up for the shortcomings of cloud computing. It brings many advantages, but various peculiarities must be perceived, such as security, resource management, storage, and other features at the same time. This paper investigates the resource contribution model between the fog node and cloud or users when fog computing introduces blockchain. The proposed model practices the reward and punishment mechanism of the blockchain to boost the fog nodes to contribute resources actively. The behavior of the fog node in contributing resources and the completion degree of the task also for contributing resources are packaged into blocks and stored in the blockchain system to form a transparent, open, and tamper-free service evaluation index. The differential game method is employed to model and solve the above process and address the interaction between the optimal resource contribution strategy of the fog node and the optimal benefit under the optimal resource contribution strategy. Indirectly, this service evaluation index also brings long-term economic benefits to fog service providers. Besides, taking advantage of the performance characteristics of the collective maintenance of blockchain and the ability to establish a credible consensus mechanism in an untrusted environment, fog computing nodes, under the proposed architecture, can have specific security protection capabilities.

We propose a novel feature fusion based multi-task convolutional neural network (ConvNet) for simultaneous bit-rate and modulation format identification (BR-MFI) and optical performance monitoring (OPM) in heterogeneous fiber-optic networks. The proposed multi-task ConvNet fuses the intermediate layers through the convolutional operation and then trains multi-task losses on the fused feature. In addition to traditional multi-task ConvNet’s ability of the feature extraction and sharing, our multi-task ConvNet is able to capture both global and local information of phase portraits and has good performance on OPM and BR-MFI tasks in a short processing time ( 51 ms). The simulation results of six signals (consisted of two bit-rates and three modulation formats) demonstrate the root-mean-square (RMS) errors of the optical signal-to-noise ratio (OSNR), chromatic dispersion (CD) and differential group delay (DGD) are 0.81 dB, 1.52 ps/nm and 0.32 ps, respectively. Meanwhile, the 100% classification accuracy can be obtained for BR-MFI. Besides, the effects of the fused feature shape, the location of feature extracted for fusion, the transmitter variations and fiber nonlinearity on the performance of the proposed technique are thoroughly investigated.

With the rapid advancement of internet of vehicles (IoVs), the resource-constrained vehicle terminals cannot satisfy the demands of emerging applications for delay and quality of service (QoS) in remote areas. Low earth orbit (LEO) satellite communication, which is a promising technology for next generation mobile communication, can provide efficient and reliable real-time data transmission and stable service for IoVs. However, the communications between vehicle terminals and satellites cause transmission delay and energy consumption, which have a significant impact on system performance. Mobile edge computing (MEC) can satisfy the latency-sensitive and computation-intensive requirements in vehicular networks. In this paper, we leverage the advantages of LEO satellites to enhance the efficiency of computation offloading in satellite-assisted vehicular edge computing networks. To enhance the effectiveness of computation offloading, we put forward a joint optimization problem to minimize the weighted sum of delay and energy consumption. Despite the mixed- integer nonlinearity of the optimization problem, we formulate the problem as a partially observable markov decision process (POMDP) and present an efficient algorithm to attain the optimal solution. Eventually, we conduct simulations to confirm the validity and efficiency of our proposed algorithm. Simulation results indicate that our proposed algorithm can effectively diminish the average delay and energy consumption of the system, compared to other baseline algorithms.

Modern block ciphers can be used for designing security schemes to meet the security requirements in Underwater Acoustic Sensor Networks. The substitution boxes (S-boxes) used in block ciphers must have good cryptographic properties to resist various attacks. The difficulty in the design of S-boxes is that the cryptographic properties of S-boxes have no obvious distribution. In this paper, we investigate the certain subgraph of the Cayley graph of the symmetric group to get some information about the distribution of the cryptographic properties of S-boxes. Based on the information obtained, an algorithm for designing S-boxes with good cryptographic properties is proposed. Security analysis shows that the preferred S-box constructed by the proposed algorithm has good cryptographic properties.

The BeiDou navigation satellite system (BDS) developed by China can provide users with high precision, as well as all-weather and real-time positioning and navigation. It can be widely used in many applications. However, new challenges emerge with the development of 5G communication system and Internet of Things (IoT) technologies. The BDS needs to be suitable for the large-scaled terminal scenario and provides higher positioning precision. In this paper, a BeiDou differential positioning method based on IoT and edge computing is proposed. The computational pressure on the data center is offloaded to the edge nodes when the massive positioning requests of IoT terminals need to be processed. To ensure the load balancing of the edge nodes, the resource allocation of the terminal positioning requests is performed with the improved genetic algorithm, thereby reducing the service delay of the entire edge network. Moreover, the optimized unscented Kalman filter based on the edge node (EUKF) algorithm is used to improve the positioning precision of IoT terminals. The results demonstrate that the proposed positioning method has better positioning performance which can provide the real-time positioning service for the large-scale IoT terminals.