Wireless Communications and Mobile Computing

With the rapid development of deep learning in recent years, it has shown excellent performance in various image and video processing tasks. In addition, it also has a great role in promoting the spatio-temporal fusion of remote sensing images. The reconstructed image can give people a good visual experience. The invention relates to a remote sensing image fusion method based on a progressive cascade deep residual network and provides an end-to-end progressive cascade deep residual network model for remote sensing image fusion. The use of the MSE loss function may cause oversmoothing of the fused image, so a new joint loss function is defined to capture finer spatial information to improve the spatial resolution of the fused image. Resize-convolution is used to replace the transposed convolution to eliminate the checkerboard effect in the fused image caused by the transposed convolution. Through the experiments on the remote sensing image fusion simulation and real datasets of multiple satellites, the data results of the proposed algorithm are more than 5.25% better than those of the comparative algorithm in the average quantification. The calculation time and system resource occupation are also reduced, which has important theoretical significance and application value in the field of artificial intelligence and image processing. It will play a certain role in promoting the theoretical research and application of remote sensing image fusion.

In recent years, unmanned aerial vehicle (UAV) has gained a lot of attention, mostly due to its low cost, flexile deployment, and broad applications in many fields such as military, agriculture, and environment. In this paper, an UAV-based cognitive radio (CR) network with a wireless powered primary user (PU) is investigated, where the UAVs act as the secondary users (SUs). We assume that one transmission time slot is divided into two phases. In the first phase, UAVs transmit information to base station (BS), while the PU harvests energy from the radio frequency (RF) signals, and the second phase is exclusively occupied by PUs for primary transmission. It is assumed that the PU prices the interference energy incurred from the UAVs for the reason that UAVs need to access PU’s licensed spectrum for their transmission. In this paper, we aim at maximizing utilities of UAVs and utility of PU simultaneously. To analyze the interaction between the UAVs and PU, Stackelberg game was adopted where the UAVs act as followers and the PU acts as a leader. An alternating iterative algorithm is proposed to achieve the Stackelberg equilibrium (SE), i.e., transmission power of PU and UAVs, time allocation, and price that PU charge UAVs. According to the simulation results, the proposed scheme can achieve optimal utility in the view of power saving for UAVs while meeting the requirements of the PU which demonstrate the effectiveness of the proposed scheme.

This paper considers the problem of optimizing the data rate of the cooperative cognitive system subject to the dual constraints of the interference threshold of primary users and power budget of secondary users. In particular, under a single constraint, the rate can reach its peak easily. But under the double restrictions, the peak rate problem becomes complicated and changeable. According to different interference conditions and power supplies, four scenarios are formulated: total interference threshold and total power budget, total interference threshold and separate power budget, separate interference threshold and total power budget, and separate interference threshold and separate power budget. Each scenario needs to be further divided into many situations for discussion due to the sheer particularity. Through careful comparison and classification, we summarize and formulate each situation one by one to achieve the optimal value of the rate. Extensive simulation results demonstrate that the proposed resource allocation policy represents the best compromise between enhancing the rate of the secondary users and satisfying the interference threshold requirements of the primary users.

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.

A large-scale low Earth orbit (LEO) satellite network has the characteristics of a complex link environment, a large number of satellites, and the limited resources of a single satellite. Applying traditional routing algorithms has disadvantages such as high overhead, high end-to-end latency, and low message delivery rate. This paper proposes an improved delay tolerant (DTN) scheme for large-scale LEO satellite networks (LIDTN) to improve transmission efficiency and reduce the resource overhead and end-to-end latency of large-scale satellite networks. This scheme improves the network performance in three aspects: next hop selection, congestion control mechanism, and acknowledgment mechanism. For the next hop selection, we propose an equivalent distance and priori knowledge-based forwarding strategy (EPFS), which has the advantages of low overhead, loop avoidance, and fast convergence. For congestion control, we put forward an emergency function-based bundle drop algorithm (EBDA). For acknowledging, we propose the virtual acknowledgment algorithm (VAA) by combining the characteristics of many path hops and high link disruption rates in large-scale constellations. Finally, we simulate and verify the LIDTN scheme on the OneWeb constellation. The results show that the LIDTN scheme is suitable for large-scale constellations, the EPFS algorithm can reduce the network overhead during data transmission, EBDA can reduce the bundle drop rate, and VAA can reduce the end-to-end latency. LIDTN provides a new solution for large-scale constellation communication.

Internet of Things (IoT) is a promising technology for creating smart environments, smart systems, and smart services. Since security is a fundamental requirement of IoT platforms, solutions that can provide both encryption and authenticity simultaneously have recently attracted much attention from academia and industry. This article analyses in detail state-of-the-art lightweight authenticated encryption (LAE) targeted to IoT systems. This work provides a thorough description of the algorithms, and the study systematically classifies them to facilitate understanding of relevant intricacies of the schemes. Among reviewed algorithms, there is a trade-off to retain design security, resources cost, and efficient performance. ACORN is the effective scheme on various platforms in terms of utilization of resources and power consumption, while MORUS and AES-CLOC are the fastest in hardware platforms. However, they are susceptible to misuse despite their resistance to side channel attacks. In contrast, JOLTICK, PRIMATESs, COLM, DeoxysII, OCB, and AES-JAMBU are provably resistant to nonce misuse. The challenges for possible future research are summarized. Overall, the article provides researchers and developers with practical guidance on various design aspects and limitations as well as open research challenges in the current lightweight authenticated encryption for IoT.