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According to the latest news, the 3rd Generation cooperation Project has been taken the inventiveness of creating an interworking architecture involving cellular and WLAN as an additional feature to the cellular network requirements of 3GPP. This article provides an overview of an interworking infrastructure that allows mobile operators to provide the public WLAN dependent entrance service to various 3GPP network subscribers. The enabling features include 3GPP membership reuse, network availability, authentications relying on 3GPP systems, security keys agreement, USIM /SIM card authorization, user data service access and routing, as well as end-user paying. The function of interworking is accomplished without setting any particular 3GPP specifications for the new WLAN entrance networks, but instead by depend on existing features available in the traditional WLAN entrance networks centered on the IEEE 802.11 principles. In this paper, authors have focused on two key aspects of MTC devices, as currently discussed in 3GPP-Release 10. These are named as “the control of subscriptions” and the “congestion/overload control of the network”. A new solution is proposed for the latter one based on the handling of the bulk signals.

Mobile edge computing is a new paradigm in which the computing and storage resources are placed at the edge of the Internet. Data placement and retrieval are fundamental services of mobile edge computing when a network of edge clouds collaboratively provide data services. However existing methods such as distributed hash tables (DHTs) are not enough to achieve efficient data placement and retrieval services for cooperative edge clouds. This paper presents GRED, a novel data placement and retrieval service for mobile edge computing, which is efficient in not only the load balance but also routing path lengths and forwarding table sizes. GRED utilizes the programmable switches to support a virtual-space based DHT with only one overlay hop. Data location can be easily implemented on top of the GRED. We implement GRED in a P4 prototype, which provides a simple and efficient solution. Results from theoretical analysis, simulations, and experiments show that GRED can efficiently balance the load of edge clouds, and can fast answer data queries due to its low routing stretch.

Online diagnosis is one of the data services, which can use the machine learning model placed on the cloud and collected physical data from internet of medical things (IoMT) for better medical services. However, the collected user data, diagnosis results and the deployed machine learning model contain sensitive information of users and the healthcare provider, which may lead to serious privacy leakage. To achieve a secure outsourced diagnosis, both high security and low burden for users should be considered. However, the existing works can not solve these problems simultaneously. In this paper, based on two non-colluding servers, a privacy-preserving cloud-aided diagnosis scheme for IoMT is proposed. Concretely, a hybrid data encryption method based on homomorphic encryption and AES is used to generate user requests in an efficient way. Besides, we propose a class of secure two-party protocols using homomorphic encryption, such as secure kernel function computation, secure multiplication, and secure comparison, and a privacy-preserving diagnosis scheme based on multi-class SVM with these building blocks is constructed, which can keep users offline in the diagnosis process. Finally, the security analysis and evaluation further illustrate that our scheme is superior to the prior works in terms of security and user-friendliness.

Cloud storage is a cost-effective platform to accommodate massive data at low cost. However, advances of cloud services propel data generation, which pushes storage servers to its limit. Deduplication is a popular technique enjoyed by most current cloud servers, which detects and deletes redundant data to save storage and bandwidth. For security concerns, proof-of-ownership (PoW) can be used to guarantee ownership of data such that no malicious user could pass deduplication easily or utilize such mechanism for malicious purposes. Generally, PoW is implemented in static data archive where the data file is supposed to be read-only. However, to satisfy users' needs for dynamical manipulation on data and support real-time data services, it is required to devise efficient PoW for dynamic archive. In this work, we propose the notion of bidirectional and malleable proof-of-ownership (BM-PoW) for the above challenge. Our proposed BM-PoW consists of bidirectional PoW (B-PoW), malleable PoW (M-PoW) and dispute arbitration protocol DAP. We provide the security analysis of our proposal, and performance evaluation that suggests our proposed B-PoW is secure and efficient for large file in static data archive. In addition, our proposed M-PoW achieves acceptable performance under dynamic setting where data is supposed to be outsourced first and updated later in dynamic data archive.

The healthcare industry faces challenges regarding the security and efficiency of data management for patient health records (PHRs). We propose SafePHR: a secure and efficient medical data service with application aware deduplication in fog-to-multicloud encrypted storage. It introduces a fog-to-multicloud cooperative storage model, which combines low-latency and high-safety local fog with unlimited capacity and built-in disaster recovery in remote multi-cloud to enhance eHealth data management. It also provides an application aware secure deduplication scheme to improve the data traffic and space efficiency of cloud storage for encrypted PHRs using variants of convergent encryption. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a fault-tolerant coding scheme. Compared with the state-of-the-art of cloud-assisted eHealth systems, our experiments demonstrate that SafePHR can ensure data confidentiality of deduplication, achieve a competitive low cloud storage space overhead, and improve system performance with high fault-tolerance capability.

Low-latency data service is an increasingly critical challenge for data center applications. In the modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of Q-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

Background The COVID-19 pandemic has presented unique challenges for rapidly designing, initiating, and delivering therapeutic clinical trials. PRINCIPLE (Platform Randomised Trial of Treatments in the Community for Epidemic and Pandemic Illnesses) is the UK national platform investigating repurposed therapies for COVID-19 treatment of older people in the community at high risk of complications. Standard methods of patient recruitment were failing to meet the required pace and scale of enrolment. This paper describes the development and appraisal of a near real-time, data-driven, ethical approach for enhancing recruitment in community care by contacting people with a recent COVID-19 positive test result from the central NHS Test and Trace service within approximately 24–48 h of their test result. Methods A multi-disciplinary team was formed to solve the technical, ethical, public perception, logistical and information governance issues required to provide a near-real time (approximately within 24–48 h of receiving a positive test) feed of potential trial participants from test result data to the research team. PRINCIPLE was also given unique access to the Summary Care Record (SCR) to ensure safe prescribing, and to enable the trial team to quickly and safely bring consented patients into the trial. A survey of the public was used to understand public perceptions of the use of test data for this proposed methodology. Results Prior to establishing the data service, PRINCIPLE registered on average 87 participants per week. This increased by up to 87 additional people registered per week from the test data, contributing to an increase from 1013 recruits to PRINCIPLE at the start of October 2020 to 2802 recruits by 20 December 2020. Whilst procedural caveats were identified by the public consultation, out of 2639 people contacted by PRINCIPLE following a positive test result, no one raised a concern about being approached. Conclusions This paper describes a novel approach to using near-real time NHS operational data to recruit community-based patients within a few days of presentation with acute illness. This approach increased recruitment and reduced time between positive test and randomisation, allowing more rapid evaluation of treatments and increased safety for participants. End-to-end public and patient involvement in the design of the approach provided evidence to inform information governance decisions. Trial registration PRINCIPLE is funded by UK Research and Innovation and the Department of Health and Social Care through the National Institute for Health Research. EudraCT number: 2020-001209-22. 26/03/2020 ISRCTN registry: ISRCTN86534580. 20/03/2020 REC number: 20/SC/058 IRAS number: 281958

Digital twin (DT), machine learning, and industrial Internet of things (IIoT) provide great potential for the transformation of the container terminal from automation to intelligence. The production control in the loading and unloading process of automated container terminals (ACTs) involves complex situations, which puts forward high requirements for efficiency and safety. To realize the real-time optimization and security of the ACT, a framework integrating DT with the AdaBoost algorithm is proposed in this study. The framework is mainly composed of physical space, a data service platform, and virtual space, in which the twin space and service system constitute virtual space. In the proposed framework, a multidimensional and multiscale DT model in twin space is first built through a 3D MAX and U3D technology. Second, we introduce a random forest and XGBoost to compare with AdaBoost to select the best algorithm to train and optimize the DT mechanism model. Third, the experimental results show that the AdaBoost algorithm is better than others by comparing the performance indexes of model accuracy, root mean square error, interpretable variance, and fitting error. In addition, we implement empirical experiments by different scales to further evaluate the proposed framework. The experimental results show that the mode of the DT-based terminal operation has higher loading and unloading efficiency than that of the conventional terminal operation, increasing by 23.34% and 31.46% in small-scale and large-scale problems, respectively. Moreover, the visualization service provided by the DT system can monitor the status of automation equipment in real time to ensure the safety of operation.

In the service dimension, the construction of fitness science data supervision service mode is discussed. Based on the stakeholder theory, through the statistical analysis of the stakeholders of fitness science data supervision, three core stakeholders of the government, users and data service personnel are identified. Based on these three dimensions, we find out the core concepts of government policy model, user demand model and service model. At the same time, each dimension is deeply analyzed. Through the relationship analysis between these three dimensions, the user-oriented collaborative supervision service model of fitness scientific data is expected to guide the specific service practice of fitness scientific data supervision through the establishment of this model. In addition, an unsupervised learning method in machine learning, the isolation forest algorithm, is introduced to detect abnormal data; at the same time, using real fitness data sets, through comparative experiments with local anomaly factor algorithms, it is verified that the isolation forest algorithm has a good effect of anomaly detection; this article also uses redis cache to optimize the performance of the fitness data monitoring system, which solves the access pressure of the main database in a multi-user high-concurrency environment; Finally, the usability and stability of the system are verified by functional tests and stress tests.

Effectively monitoring urban air quality, and analyzing the source terms of the main atmospheric pollutants is important for public authorities to take air quality management actions. Previous works, such as long-term observations by monitoring stations, cannot provide customized data services and in-time emergency response under urgent situations (gas leakage incidents). Therefore, we first review the up-to-date approaches (often machine learning and optimization methods) with respect to urban air quality monitoring and hazardous gas source analysis. To bridge the gap between present solutions and practical requirements, we design a conceptual framework, namely MAsmed (Multi-Agents for sensing, monitoring, estimating and determining), to provide fine-grained concentration maps, customized data services, and on-demand emergency management. In this framework, we leverage the hybrid design of wireless sensor networks (WSNs) and mobile crowdsensing (MCS) to sense urban air quality and relevant data (e.g. traffic data, meteorological data, etc.); Using the sensed data, we can create a fine-grained air quality map for the authorities and relevant stakeholders, and provide on-demand source term estimation and source searching methods to estimate, seek, and determine the sources, thereby aiding decision-makers in emergency response (e.g. for evacuation). In this paper, we also identify several potential opportunities for future research.

With the development of high-speed railways (HSRs), people's lives have become more convenient. At the same time, people are demanding more data services on trains in terms of wireless communications and mobile computation. Recently, Multi-access Edge Computing (MEC), a new computing paradigm, has the great potential to improve the user experience of computing services by placing the cloud infrastructures closer to end-users so that users can offload their computation-intensive and delay-sensitive tasks to the edge servers. In this paper, we concentrate on finding proper data routing paths for each offloaded task and each process result, with the objective of maximizing the network throughput by serving as many tasks as possible while meeting the MEC network resource constraints and the user demand constraints within a finite time horizon of equal time slots. It is challenging because of the unspecific data volume of an offloaded task and handovers of task offloading procedures that may frequently occur. Thus, we first investigate the impact of handovers in uplinks and downlinks. Then we propose a graph-based model to portray the resource availability in the network. Furthermore, a heuristic algorithm is devised, which finds a set of single-source-single-destination delay-constrained shortest paths in a series of auxiliary graphs. Finally, we perform an empirical evaluation of the proposed algorithm by simulations. The simulation results show that the proposed algorithm could achieve a higher network throughput than the other two benchmark heuristics.

With the continuous miniaturization of sensors and processors and ubiquitous wireless connectivity, unmanned aerial vehicles (UAVs), also referred to as drones, are finding many new uses in enhancing our life and paving the way to the realization of Internet of Drones (IoD). In the IoD, a myriad of multisized and heterogeneous drones seamlessly interact with Zone Service Providers (ZSPs) to achieve the goal of assisting drones in accessing controlled airspace and providing navigation services. However, due to the high mobility of drones and the limited communication bandwidth between drones and ZSP, service scheduling becomes a critical issue when a set of drones wants to upload/download data to/from ZSP. In this article, we propose a priority-based service scheduling scheme, also named $Psched$ , to provide efficient data upload/download service at ZSP in the IoD. The basic idea is that the $Psched$ objectively and equitably assigns a weight to multiple service scheduling parameters based on multiattribute decision making theory, calculates the serving priority of each service request group, and then serves the service request groups based on the calculated serving priority accordingly. In addition, the $Psched$ takes into account of bandwidth competition between upload and download service requests, and provides a service request balancing to achieve the maximum benefits of service scheduling scheme. In the experimental study, we choose request deadline, data size, and data popularity as service scheduling parameters, and conduct extensive simulation experiments using OMNeT++ for performance evaluation and comparison. The simulation results show that the proposed priority-based service scheduling scheme can not only increase service ratio but also can improve fresh data service ratio and average request serving latency, indicating a viable and efficient approach for satisfying service requests at ZSP in the IoD.

Geographic information technologies and data are widely used to identify a variety of spatial relationships. The paper is devoted to the study of the mutual location between the road network (as exemplified by the public roads network) and cellular coverage of the Russian telecom operators on the territory of Russia. Transport researches are characterized with an emphasis of GIS projects. Then, an overview of modern web services, Open Data and Open Source developments in transport projects is made. The data, geodata services and web projects of cellular coverage for the territory of Russia are described by the examples of Russian-language sources. The practical part of the study shows the global transport accessibility raster datasets for the test region of Russia (Irkutsk Region, Siberian Federal District). An analysis of spatial relationships between roads and cellular coverages provided by VimpelCom/ Beeline, Megafon and MTS is based on the Open Data in the Open Source GIS application QGIS. Coverage varies between 63% (VimpelCom for the buffer zone of 2.5 km) and 94% (Megafon for the zone of 10 km), where 100% is the total number of cells (polygons) within the network of a respective operator in the region. We have noted that Open Data on the public road network and cellular communication for the territory of Russia differ by region, the studied communication datasets and data services do not contain significant attributive information, they consist of polygon geometry only. Frequency of their updates depends on the operating companies. The operators then provide the data for the publication to the Russian ministries and regional administrations.

Digitization—the continuing convergence of the real and the virtual worlds will be the main driver of innovation and change in all sectors of our economy. The exponentially growing amount of data and the convergence of different affordable technologies that came along with the definite establishment of Information and Communication Technology are transforming all areas of the economy. The Internet of Things, Data, and Services plays a vital role in mastering the energy transformation, in developing a sustainable mobility and logistics sector, in providing enhanced health care, and in securing a competitive position for the leading manufacturing industry. This article discusses the impact, challenges, and opportunities of digitization and concludes with examples of recommended policy action.

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies “big data” from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

This chapter introduces some underlying core technologies of Industry 4.0 and Intelligent Manufacturing. It also introduces cloud computing, including its essentials, service models, and deployment models, as well as the concept of cloud manufacturing. A smart factory could adopt Internet of Things as its data collection and communication platform and use cloud computing which has abundant computing resources or edge computing which is near the data sources to perform big data analytics on a large amount of production‐related data. As cloud computing services became mature, several service models were introduced, including Infrastructure as a Service, Platform as a Service, and Software as a Service. The chapter presents big data infrastructure, including the application demands, and core software stack components. It also present two big data infrastructural services, that is, Hadoop data service and distributed R language computing service, based on Hadoop for a semiconductor manufacturing foundry in Taiwan, ROC.

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

The proliferation of telco networks and mobile terminals brings the accumulation of tremendous amounts of measure report(MR) data at a rapid pace. The MR data is generated by mobile objects while connecting to data services and is stored in backend data centers. To geo-tag or localize such MR data is believed to have a profound effect on the analytics and optimizations of telco and traffic networks. However, MR records are of noisy and partial observations regarding to mobile objects’ geo-locations and hence pose challenges to accurate telco data localization. There have been quite a few attempts. Single-point localization methods map a MR record to a location, but come out with limited accuracies due to the ignorance of spatiotemporal coherence of successive MR records. Recent efforts on sequential localization techniques alleviate this by mapping a sequence of MR records to a trajectory. However, existing solutions are often with assumptions on specific models, e.g., mobility and signal strength distributions, or priori knowledge on topology space, e.g., road networks, limiting the deployment in practice. To this end, we propose a data-driven framework to tackle the challenges in sequential telco localization. We solely use raw MR records and a public third-party GPS dataset for the learning of the correlations between mobile objects’ locations and MR records, requiring no model assumptions and priori knowledge. To handle the data-intensive workloads during the learning process, we use materialized views for efficient online localization and light-weighted indexing techniques for periodical parameters tuning, in order to improve the efficiency and scalability. Results on real data show that our solution achieves 58.8 percent improvement in median localization errors compared with state-of-art sequential localization techniques that require hypothesis models and priori knowledge, making our solution superior in terms of effectiveness, efficiency, and employability.

Radar altimetry is a remote sensing technique with a wide range of practical and scientific applications for observing the global ocean, land surface waters, and the Earth’s ice sheets. Understanding complex and technical satellite radar altimetry systems and using the data and information products derived from them can be immensely challenging. Nonexpert users, such as the general public and students in particular, may find that this complexity prevents them from reaping the benefits of the considerable information that satellites can provide. Over the past 28 years and more, experts and amateurs in altimetry outreach have developed programs, activities and information products using a variety of media, targeting a broad spectrum of users in order to demonstrate the significant value of satellite altimetry missions to society. The goal of outreach professionals and nonprofessionals is to convey complex scientific and technical concepts in an understandable and engaging way to a variety of audiences. Outreach people themselves also come from a wide variety of backgrounds: from employees of international space agencies building and launching satellites, through to Mission and Science Team members validating and using the data for research purposes, and science communicators —all with a wide range of motivations. This article reviews the challenges and achievements of altimetry outreach and is largely based on the records of the Outreach and Data Services splinter session of the Ocean Surface Topography Science Team meetings. It includes minutes of splinter sessions, agendas, posters, and collected showcase presentations.

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

A fractional calculus fluid model can be used to better explain the bursty data service traffic, which is long-range dependence and has a fractal like the feature of network data flow. The heavy-tailed delay distributions, the hyperbolic decay of the packet delay auto-covariance function and fractional differential equations are shown to be formally related. Effective capacity is a useful model to describe wireless networks with QoS constraints. This paper builds a fluid model to describe the traffic of multi-hop wireless networks under QoS constraints. The proposed method could analyze the relationship between latency and a complicated traffic model, which is more similar to the real scenario.

We present context-aware benchmarking and performance engineering of a mature TByte-scale air quality database system which was created by the Tropospheric Ozone Assessment Report (TOAR) and contains one of the world’s largest collections of near-surface air quality measurements. A special feature of our data servicehttps://join.fz-juelich.deis on-demand processing of several air quality metrics directly from the TOAR database. As a service that is used by more than 350 users of the international air quality research community, our web service must be easily accessible and functionally flexible, while delivering good performance. The current on-demand calculations of air quality metrics outside the database together with the necessary transfer of large volume raw data are identified as the major performance bottleneck. In this study, we therefore explore and benchmark in-database approaches for the statistical processing, which results in performance enhancements of up to 32%.

The traditional small and medium enterprise management system has a low efficiency of operation and management. A small and medium enterprise management system based on an edge algorithm is designed in this paper to address this problem. In the proposed algorithm, the management system is used for efficient information transmission and sharing. The system consists of a collaborative function layer, data service layer, basic environment layer, and application layer. The application layer realizes the storage and integration of enterprise information data through the regulation of the collaborative management center and realizes the privacy encryption protection of information based on the protection mechanism of edge computing. The information is transmitted to the data layer for classified storage through the network communication of the basic environment layer. The collaborative function layer takes the process management control engine combined with each service interface to complete the information retrieval of the data layer. It uses the algorithm based on the number of fusion common scoring users and information interest relationship to query and push information. The test results show that the system has good information query performance, pushing query information in one minute. The running function can meet the user needs and effectively achieve information privacy protection.

Scene classification based on multi-source remote sensing image is important for image interpretation, and has many applications, such as change detection, visual navigation and image retrieval. Deep learning has become a research hotspot in the field of remote sensing scene classification, and dataset is an important driving force to promote its development. Most of the remote sensing scene classification datasets are optical images, and multimodal datasets are relatively rare. Existing datasets that contain both optical and SAR data, such as SARptical and WHU-SEN-City, which mainly focused on urban area without wide variety of scene categories. This largely limits the development of domain adaptive algorithms in remote sensing scene classification. In this paper, we proposed a multi-modal remote sensing scene classification dataset (MRSSC) based on Tiangong-2, a Chinese manned spacecraft which can acquire optical and SAR images at the same time. The dataset contains 12167 images (optical 6155 and 6012 for optical and SAR, resp.) of seven typical scenes, namely city, farmland, mountain, desert, coast, lake and river. Our dataset is evaluated by state-of-theart domain adaptation methods to establish a baseline with average classification accuracy of 79.2%. The MRSSC dataset will be released freely for the educational purpose and can be found at China Manned Space Engineering data service website (http://www.msadc.cn). This dataset will fill the gap between remote sensing scene classification between different image sources, and paves the way for a generalized image classification model for multi-modal earth observation data.

Cloud storage and cloud services provide a stronger computing power and distributed computing capability for IoT users with a minimal cost. However, the security issues of cloud always limit the development of cloud computing and storage. In the meanwhile, the channel instability and exposure of the public network make the security of data in transmission challenged (HTTPS protocol cannot guarantee the security of data after receiving by servers). Even if homomorphic encryption can protect IoTs’ sensitive data, attackers still can infer sensitive behaviors about users by listening to the frequency of cloud services usage. To solve the above problems, in this paper, we propose a novel data transmission structure named HBRSS for high-security data transmission and data processing in insecure cloud environments and channels. HBRSS harnesses proposed data splitting principle to divide the data into blocks, packages the block data and forms a block ring based on the concept of blockchain to ensure the non-tamperability and non-destructibility of data. In addition, we propose an improved partial homomorphic encryption algorithm, which adds fuzzy processing for the data service functions to improve function-privacy. We also build a virtual mistrusted cloud service scene by using Docker and Kubernetes to evaluate our method’s performance, which can also be utilized as a standard attack drill platform for all researchers to test their own security algorithms. Based on our best knowledge, this platform is the first open-source automatic cloud attack exploitation system that contains attacks against browsers, channels, and servers. The experimental results indicate that our new encryption algorithm brings larger key-space and lower power consumption compared with some encryption algorithms.

Ultra-reliable and low-latency communication (URLLC) is one of the main service categories in the fifth-generation communication systems, which lays the foundation for emerging data-exploring video applications with strict end-to-end latency requirement. Unmanned aerial vehicle (UAV) enabled radio access network (UAV-RAN) is an effective architecture to improve the wireless coverage. Combining wireless caching with UAV-RAN is one of the key driving forces to reduce the burden of wireless traffic, provide resilient coverage and reduce the delay in heterogeneous URLLC networks. In this paper, a collaborative caching strategy is proposed for UAV-RANs. First, a scalable video coding (SVC)-based video library is presented to provide users with videos of different perceptual qualities, and the popularity of different definition videos is also analyzed. Then, we propose a heterogeneous network where UAVs within a distance from based station (BS) can be provided with error-free data and UAVs at several stop points can provide data service to users while BS can offer all users data service. Next, an SVC-based two-tier cooperative caching model is proposed, where BS and UAVs store different video encoded files on the basis of caching matrix. Finally, the energy consumption of UAV and the caching hit probability (CHPro) are analyzed, and the optimal caching matrix is obtained via the maximization of CHPro. The maximization problem is proved to be NP hard, and we propose a two-step solution based on the simulated annealing algorithm. Considering URLLC, the average service delay is also analyzed and proved that the proposed strategy is efficient in reducing delay. Simulation results demonstrate that the collaborative caching strategy is more suitable for the UAV-RANs with a skew video popularity library and an ultra-dense user distribution. Compared with other contrasting schemes, the proposed collaborative caching strategy can achieve quasi-optimal performance.