Problems of implementation of integrated satellite-ground NTN-network

This paper presents a consensus dispatch model of the distributed power network based on multi-access edge computing (MEC) and multi-agent system (MAS).MEC decentralizes the processing and storage of data in the distributed power system to the edge node combined with MAS, which reduces the amount of calculation in the dispatch center. The model formulates different objective functions for the edge nodes and the cloud center nodes of the MEC to optimally allocate the output of each unit. Meanwhile, in order to meet the consensus dispatch of distributed units, the operational benefits of flexible load, the operating cost of thermal power units, the penalty cost of abandoning wind and solar power for new energy units, and the operation of energy storage devices are utilized as consensus variables. Based on these consensus variables, an optimal dispatch method of the distributed power network is established. Compared with the traditional dispatch model, this model integrates distributed units and autonomous sub-regions through MAS and MEC, which ensures the system performs consensus dispatch under the balance of supply and demand, and also improves the proportion of new energy consumption.

Mobility as a Service (MaaS) implies the integration of different transport services in a unique platform accessible by commuters on demand. Collection and processing of data concerning the mobility of customers is crucial to calculate trips options satisfying users' needs and preferences. In this paper, we propose to exploit Multi-access Edge Computing (MEC) facilities to more efficiently deploy MaaS solutions. Specifically, we design a MEC-based MaaS framework that is fully compliant with the Open Mobile Alliance (OMA) Lightweight Machine-to-Machine (LwM2M) protocol. The OMA LwM2M server hosted in the MEC platform continuously collects data from the commuters, and uses native MEC applications to provide value-added services. The Observe extension of the Constrained Application Protocol (CoAP) is used to reduce energy consumption during data collection from the OMA LwM2M clients installed in the mobile user devices. Preliminary results are collected that show the performance of the proposed MaaS framework, integrated with a mobility generator tool (i.e., SUMO) that emulates the commuter paths.

Multi-access edge computing (MEC) provides processing and storage capabilities of the cloud into the radio access network. In this paper, we study the deployment of bandwidth management service in MEC environment. The bandwidth management service procedures are mapped onto functionality of the control protocol between radio access network and core network. An extension of the bandwidth management service is proposed that enables detecting of packets generated of specific applications and applying the appropriate enforcement actions. The proposed extension is described by typical use cases, information flows, required information, data model, as well as respective application programming interfaces. Models representing the status of bandwidth allocation as seen by the mobile edge application and network are proposed, formally described and verified. Formal model verification enables mathematical demonstration that the proposed extension is consistently implementable.

The fifth generation (5G) and beyond wireless networks are envisioned to provide an integrated communication and computing platform that will enable multipurpose and intelligent networks driven by a growing demand for both traditional end users and industry verticals. This evolution will be realized by innovations in both core and access capabilities, mainly from virtualization technologies and ultra-dense networks, e.g., software-defined networking (SDN), network slicing, network function virtualization (NFV), multi-access edge computing (MEC), terahertz (THz) communications, etc. However, those technologies require increased complexity of resource management and large configurations of network slices. In this new milieu, with the help of artificial intelligence (AI), network operators will strive to enable AI-empowered network management by automating radio and computing resource management and orchestration processes in a data-driven manner. In this regard, most of the previous AI-empowered network management approaches adopt a traditional centralized training paradigm where diverse training data generated at network functions over distributed base stations associated with MEC servers are transferred to a central training server. On the other hand, to exploit distributed and parallel processing capabilities of distributed network entities in a fast and secure manner, federated learning (FL) has emerged as a distributed AI approach that can enable many AI-empowered network management approaches by allowing for AI training at distributed network entities without the need for data transmission to a centralized server. This article comprehensively surveys the field of FL-empowered mobile network management for 5G and beyond networks from access to the core. Specifically, we begin with an introduction to the state-of-the-art of FL by exploring and analyzing recent advances in FL in general. Then, we provide an extensive survey of AI-empowered network management, including background on 5G network functions, mobile traffic prediction, and core/access network management regarding standardization and research activities. We then present an extensive survey of FL-empowered network management by highlighting how FL is adopted in AI-empowered network management. Important lessons learned from this review of AI and FL-empowered network management are also provided. Finally, we complement this survey by discussing open issues and possible directions for future research in this important emerging area.

In industrial production, mass data transmission and processing are required with an increase in production data, which causes large-scale and complicated wiring in the manufacturing industry. However, it is difficult to achieve improved performance through traditional wireless communication technologies. 5G, a leading communication technology, with its Ultra-Reliable Low Latency Communication (uRLLC) combined with Multi-access Edge Computing (MEC), provides huge potential for the service with requirements for higher bandwidth, lower latency, and more computation. The Local Area Network type (LAN-type) Service supported by the 5G system reduces the net distance, which further decreases latency and provides data security. Based on this, in this paper, a 5G MEC gateway system is proposed to realize local wireless communication in a factory. The gateway system includes an intranet Address Resolution Mechanism, which is designed to resolve User Equipment IP addresses, a MEC server, which acts as a switch for packets broadcasting, and network tunnels, which are established between gateways to transmit data in the 5G LAN. In addition, the implemented testbed includes Raspberry Pi, which acts as the gateway, and the OpenVPN server, which runs in the MEC. Finally, two cases are shown to transfer Profinet and OPC UA data with this system.

Multi-access edge computing (MEC) can enhance the computing capability of mobile devices, while non-orthogonal multiple access (NOMA) can provide high data rates. Combining these two strategies can effectively benefit the network with spectrum and energy efficiency. In this paper, we investigate the task delay minimization in multi-user NOMA-MEC networks, where multiple users can offload their tasks simultaneously through the same frequency band. We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts. We aim to minimize the task delay among users by optimizing their tasks partition ratios and offloading transmit power. The delay minimization problem is first formulated, and it is shown that it is a nonconvex one. By carefully investigating its structure, we transform the original problem into an equivalent quasi-convex. In this way, a bisection search iterative algorithm is proposed in order to achieve the minimum task delay. To reduce the complexity of the proposed algorithm and evaluate its optimality, we further derive closed-form expressions for the optimal task partition ratio and offloading power for the case of two-user NOMA-MEC networks. Simulations demonstrate the convergence and optimality of the proposed algorithm and the effectiveness of the closed-form analysis.

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

In multi-access edge computing (MEC) systems, there are several local cache servers caching contents to satisfy the users’ requests, instead of letting the users download via the remote cloud server. In this paper, a content scheduling problem (CSP) in MEC systems is considered. Taking into account jointly the freshness of the cached contents and the traffic data costs, we study how to schedule content updates along time in a multi-cell setting. Different from single-cell scenarios, a user may have multiple candidate cache servers, and thus all cells and their caching decisions must be jointly taken. We first prove that CSP is $\mathcal{N}\mathcal{P}$-hard, then we formulate CSP using integer linear programming. For problem solving, via a mathematical reformulation, we derive a column generation algorithm embedded into a rounding scheme. Our performance evaluation demonstrates that the solutions obtained are within 0.8% from global optimality.

Content caching in mobile edge networks has stirred up tremendous research attention. However, most existing studies focus on predicting content popularity in mobile edge servers (MESs). In addition, they overlook how the content is cached, especially how to cache the content with user devices. In this paper, we propose CMU, a three-layer (Cloud-MES-Users) content caching framework and investigate the performance of different caching strategies under this framework. A user device who has cached the content can offer the content sharing service to other user devices through device-to-device communication. In addition, we prove that optimizing the transmission performance of CMU is an NP-hard problem. We provide a solution to solve this problem and describe how to calculate the number of distributed caching nodes under different parameters, including time, energy and storage. Finally, we evaluate CMU through a numerical analysis. Experiment results show that content caching with user devices could reduce the requests to Cloud and MESs, and decrease the content delivery time as well.

Network attack and data breach statistics are abundant; from the 2020 Cisco Annual Internet Report citing an anticipated increase in Distributed Denial-of-Service (DDoS) attacks from 7.9 million in 2018 to 15.4 million by 2023, to almost daily reports of data breaches, hackers targeting network device vulnerabilities, attacks on network services etc. This is, of course, unsurprising. Our lives are increasingly reliant on communication networks. In 2020, because of the COVID-19 pandemic, we have seen the accelerated provision of health services in the home and an increased prevalence of home schooling and working. This has placed a significant burden on our home networks, one which cyber-criminals have been only too eager to exploit. The challenge to protect network users extends from there.

Anthracycline chemotherapeutics increase risk for cardiovascular disease. The early molecular events that link inherited risk to cardiac dysfunction remain poorly defined. We therefore exposed iPSC-derived cardiomyocytes from six individuals to three anthracyclines and an anthracenedione, which act as topoisomerase inhibitors (TOP2i) to induce DNA damage, and generated proteomics data following three and 24 hours of TOP2i treatment. We constructed a 19 module co-expression network and identified four protein modules that associate with response to all TOP2i. Integration with a co-expression network generated from paired transcriptomic data revealed that the two most drug-responsive protein modules are preserved in the RNA network. The most preserved RNA module is enriched for p53 motifs in associated active regulatory regions and p53 target genes, which propagates to enrichment of p53 targets in the most drug-responsive preserved protein module. Integration of the protein network with genome-wide association studies across hundreds of cardiovascular traits identified a preserved protein module enriched for genetic risk of atrial fibrillation, PR interval, and longitudinal strain, suggesting that the molecular response to TOP2i is linked to genetic variation influencing cardiac electrophysiology and contractile performance. Differential protein abundance in this module associates with impaired calcium handling in cardiomyocytes thereby linking molecular effects to cellular decline. Together, these results define a TOP2i-induced gene regulatory response propagated to the proteome that underlies early cardiotoxicity, and demonstrate how a multi-level network architecture can provide insight into genetically mediated susceptibility to drug-induced stress.

Seaport logistics information sharing platform construction is the main component of regional logistics informatization, and has become a trend. In the review of on the related researches, this paper puts forward a logistics information sharing platform framework facing e-commerce for An international Seaport of China (abbr.QDIP). This platform with multi-level network architecture, using the module and components design, has good expansibility and maintainability. We can see the completion of public information platform for improving the level of modern logistics management, seaport and related logistics enterprise to enhance the overall competitive strength, is of great significance for the construction of information platform to provide basis for decision.

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What’s more, these models often overlook the exploitation of PAN images’ inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network’s feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance.

Noise introduces a fundamental limit on the performance of any communication system. Satellite communication systems are particularly susceptible to noise because of their inherent low received power. During the link budget calculations, noises of the equipment and line loss have to be considered. An alternative, but equivalent way of expressing the noisiness of the equipment and line loss is through noise temperature. The noise temperature which represents noise of all components in satellite receiving system chain is called composite noise temperature. Thus, on this paper the methodology applied for composite noise temperature caculations for satellite links is presented. In order to illustrate these calculations, data of Vienna ground station within MOST project are applied. The project MOST (Microvariability and Oscillations of Stars) is a Canadian micro satellite space telescope mission. The project MOST consists of a Low Earth Orbiting (LEO) Satellite and three ground stations, one of them in Vienna. The Vienna ground station system was set up at the Institute for Astronomy of the University of Vienna in cooperation with the Institute of Communications and Radio-Frequency Engineering of the Vienna University of Technology. The satellite link operates on 2GHz band [1].

LTE-based mobile satellite communication system is a combination of terrestrial LTE system and satellite communication techniques, which aims at improving the current LTE system's technical specifications for better service quality. AMC (Adaptive Modulation and Coding) technique is applied in current LTE system to improve system capacity and data transmission efficiency. CQI (Channel Quality Indicator), which is measured by UE, is an essential indicator for AMC process. However in satellite system, the said data received by eNodeB are all outdated due to the long delay caused by satellite link. So there exists need for predicting CQI data in advance. ARIMA (Autoregressive Integrated Moving Average Model) is a good choice for predicting CQI in terrestrial system, but its prediction ability is limited when prediction length is too long in satellite communication system. To solve this problem, a prediction scheme is proposed in this paper. This method predicts CQI states, instead of predicting the specific CQI values, and provides the reference CQI for AMC. A modified ARIMA model is applied for reducing the prediction complexity and delay. Simulation shows that the proposed method can realize CQI prediction with acceptable RMSE performance.