Intelligent Terminals Research Articles

Attack and Protection Technology of Intelligent Terminals for New Energy Internet Transmission, Transformation, and Distribution

In order to improve the ability of the new energy Internet to defend against external attacks, the author proposes a dual Markov chain-based FDIA detection method suitable for the new energy Internet. Taking into account the FDIA principles and characteristics of the new energy Internet, and the fact that the new energy Internet contains a large amount of measurement data and changing operating states, the data to be detected is mapped to two different state spaces, and two different Markovs are generated. The detector of FDIA is generated according to the accuracy of energy Internet operation state estimated by the model. The correctness and effectiveness of the proposed detection method are verified by experimental cases. Experimental results show: The proposed FDIA detection method has excellent detection probability and less detection calculation, the detection probability can reach 98.60%, and the false alarm probability is only 1.35%, compared with the support vector machine method, the calculation amount is reduced by an order of magnitude. It is proven that the method can meet the application requirements of the new energy Internet.

Certificateless Batch Authentication Scheme and Intrusion Detection Model Based on the Mobile Edge Computing Technology NDN-IoT Environment

With the rapid development of mobile communication technology, the data transmission rate of mobile communication has been significantly improved and emerging Internet of things applications need a lot of computing resources to meet their own computing needs. However, the existing intelligent terminals have limited computing power and cannot meet the low-latency computing requirements with limited energy consumption. Mobile edge computing technology is considered to be one of the technical solutions that can efficiently solve this problem. Through mobile edge computing technology, intelligent terminals can actively divert some computing tasks to the computing servers deployed in edge network nodes and return the computing results to intelligent terminals after the edge computing servers have finished computing, thus greatly reducing the computing delay of users. In this paper, aiming at multiple edge server networks, combined with dual-connection technology, based on the research of a single edge computing server network, it is further extended to two edge computing servers and a joint optimization problem of computing task allocation and security performance requirements of intelligent terminals and edge computing servers is proposed, so as to minimize the global time delay for completing the computing tasks of intelligent terminals. By layering the problem, this paper proposes a corresponding algorithm, which can efficiently obtain the optimal solution of the initial problem. Compared with the linear search algorithm, the algorithm proposed in this paper can significantly reduce the computation time.

Construction and Management of Intelligent Campus Based on Student Privacy Protection under the Background of Artificial Intelligence and Internet of Things

With the coming of the era of artificial intelligence development and the mobile Internet of things, privacy security will become more and more prominent. As the contemporary college students have gradually developed into one of the main forces of the next generation of network information technology and intelligent terminals of Internet mobile communication, the problem of privacy and protection of college students in the era of artificial intelligence and the Internet of Things has naturally become a practical problem urgently solved by colleges and universities. This paper studies the application of “artificial intelligence + Internet of Things” (AIoT) in the construction and management of intelligent campus for student privacy protection. Firstly, some basic concepts and theories related to artificial intelligence and the Internet of Things are summarized, and the basic technical framework of “Artificial intelligence + Internet of Things” (AIoT) is preliminarily explained and analyzed. Secondly, relevant simulation experiments are carried out, and data support and help are provided for subsequent solutions and management methods by analyzing the simulation results. Through the investigation and analysis of the intelligent campus construction and management based on student privacy protection under the background of artificial intelligence and the Internet of Things, the final research results show that the background of artificial intelligence and the Internet of Things has a great role in promoting the function of student privacy protection, so this research has an important guiding role for student privacy protection in the context of artificial intelligence and the Internet of Things.

University Teaching Management and Education Reform Based on Multisource Data and Edge Architecture of IoT

With the rapid development of wireless sensor and network technology, learners can learn anytime and anywhere through various intelligent terminals. For this reason, this paper is research on education and teaching reform in universities that use cloud computing and big data. Teaching practice has proved that the teaching reform based on popular technology not only combines theory with practice, can improve students’ interest, and achieve good teaching effect but also is a beneficial exploration for students’ comprehensive training. Aiming at the existing problems of distributed teaching system, this paper proposes to teach the theoretical knowledge points of distributed system under the framework of cloud computing and at the same time cooperate with related teaching reform of comprehensive design.

Design of Enterprise Economic Management Intelligent Terminal for Big Data Application

With the rapid development of world economic informatization, the research and application of intelligent terminals are more and more extensive. Through the comprehensive application of big data in modern enterprise economic management, enterprises can more accurately grasp all kinds of information and mine the deep value of data information. In the era of big data, to realize the stable growth of the enterprise economy, relevant personnel need to gradually explore the enterprise economic management mode, take the enterprise financial management as the core development project, speed up the exchange of internal and external data, improve the brand awareness of the enterprise, and let the enterprise occupy a leading position in the market. Traditional enterprise economic management methods cannot run through big data combined with intelligent terminals. Therefore, this article solves the problem of enterprise economic management under the background of big data through the design of intelligent terminals. The design of an intelligent terminal for enterprise economic management under the background of big data can provide complete information for management positions. It is not only a new attempt for enterprises to explore economic management mode but also a major innovation based on an enterprise scale.

Distributed User Association With Grouping in Satellite–Terrestrial Integrated Networks

The satellite–terrestrial integrated network (STIN) has been envisioned as an emerging architecture to provide global anytime anywhere network access, and satisfy transmission requirements of high-capacity backhaul data. However, the integration of satellite and terrestrial networks will aggravate the diversity of base stations (BSs) in backhaul delay and capacity, as well as coverage area, which makes it difficult for users with diverse requirements to access the most suitable service BS. As an effort to address the above problems, a distributed user association with grouping (DUAG) mechanism is proposed via the interaction between BSs and users to maximize the sum rate and balance the load of STIN while meeting the user’s demand by user grouping. First, the transmission characteristics of terrestrial and backhaul links are analyzed after constructing a STIN model, which consists of satellite, three types of BSs, and the variety of intelligent terminals. Then, the user association problems are formulated to maximize the sum rate and balance the load of STIN via jointly considering the backhaul capacity of BSs and mobility and delay of users. Meanwhile, the DUAG mechanism is proposed to associate users with the most suitable service BSs. In DUAG, a greedy-based user association algorithm with user grouping is developed for maximizing the sum rate via giving priority to users with high data rate, and a matching algorithm with user grouping is designed for balancing the load by means of performing multiple iterations between users and BSs. Simulation results demonstrate that the proposed DUAG can maximize the sum rate and balance the load of STIN while guaranteeing the delay demand of user with the increase of user density.

Spatial Grid-Based Position Calculation Method for Satellite-Ground Communication Links

With the rapid development of global satellite constellation clusters and the popularity of ground-based intelligent terminals, the interconnection between large-scale low Earth orbit satellites and ground base stations has become more frequent. The calculation of satellite position requires a large number of floating point square calculations and coordinate system conversions, with high computational complexity and long computation time. Based on the grid encoding method of GeoSOT, we propose a GeoSOT-based grid calculation for a satellite-ground position (GCSGP) model. We use binary bit operations instead of complex topological operations and floating point calculations, and use grid distance calculations instead of existing angle calculations. Thus, the relative positions of satellites to the ground can be quickly calculated and thus connected to more suitable satellites quickly. The results of the grid calculation were verified by simulation experiments. The calculation speed was improved by about 82% compared with the traditional method. The test accuracy error of the 10-level grid at a terminal 45° inclination was less than 1% at all heights, and the maximum error of the 10-level grid within a 10–45° inclination was 7.2% at 10,000 km altitude, which provides theoretical support for the subsequent grid space calculation.

Accurate Depth Recovery Method Based on the Fusion of Time-of-Flight and Dot-Coded Structured Light

3D vision technology has been gradually applied to intelligent terminals ever since Apple Inc. introduced structured light on iPhoneX. At present, time-of-flight (TOF) and laser speckle-based structured light (SL) are two mainstream technologies applied to intelligent terminals, both of which are widely regarded as efficient dynamic technologies, but with low accuracy. This paper explores a new approach to achieve accurate depth recovery by fusing TOF and our previous work—dot-coded SL (DCSL). TOF can obtain high-density depth information, but its results may be deformed due to multi-path interference (MPI) and reflectivity-related deviations. In contrast, DCSL can provide high-accuracy and noise-clean results, yet only a limited number of encoded points can be reconstructed. This inspired our idea to fuse them to obtain better results. In this method, the sparse result provided by DCSL can work as accurate “anchor points” to keep the correctness of the target scene’s structure, meanwhile, the dense result from TOF can guarantee full-range measurement. Experimental results show that by fusion, the MPI errors of TOF can be eliminated effectively. Dense and accurate results can be obtained successfully, which has great potential for application in the 3D vision task of intelligent terminals in the future.

FGL_Droid: An Efficient Android Malware Detection Method Based on Hybrid Analysis

With the popularity of Android intelligent terminals, malicious applications targeting Android platform are growing rapidly. Therefore, efficient and accurate detection of Android malicious software becomes particularly important. Dynamic API call sequences are widely used in Android malware detection because they can reflect the behaviours of applications accurately. However, the raw dynamic API call sequences are very usually too long to be directly used, and most existing works just use a truncated segment of the sequence or statistical features of the sequence to perform malware detection, which loses the execution order information of applications and consequently results in high false alarm rate. In this work, we propose a method that transforms the dynamic API call sequence into a function call graph, which retains most of the application execution order information with significantly reduced sequence size. To compensate for the missed behaviour information during the transformation, the advanced features of permission requests extracted from the application are utilized. We then propose FGL_Droid, which fusions the transformed function call graph feature and the extracted permission request feature to perform accurate malware detection. Experiments on benchmark dataset show that FGL_Droid achieves a high detection accuracy of 0.975 and a high F-score of 0.978, which are better than the existing methods.

Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks

The Internet of Things (IoT) is currently in a stage of rapid development. Hundreds of millions of sensing nodes and intelligent terminals undertake the tasks of sensing and transmitting data. Data collection is the key to realizing data analysis and intelligent application of IoT. The life cycle of IoT is limited by the energy of the IoT nodes in the network. A complex computing model will bring serious or even unbearable burdens to IoT nodes. In this study, we use the data prediction method to explore time correlation data and adjust the appropriate spatial sampling rate on the basis of the spatial correlation of sensory data to further reduce data. Specifically, the improved and optimized DNA-binding protein (DBP) data prediction method can increase the time interval of sensing data to further reduce energy consumption. Based on the spatial characteristics of the sensing data, substituting the data of similar nodes can reduce the sampling rate. The probabilistic wake-up strategy is also adopted to adjust the spatial correlation of the sensing data. On the basis of node priority, an optimized greedy algorithm is proposed to select the appropriate dominating node for eliminating redundant nodes and improving network energy utilization. Experiments have proven that our scheme reduces network energy consumption under the premise of ensuring data reliability.

An Intelligent Teaching Strategy of Cross-Cultural Business Communication Course Based on Big Data

The Internet's growth has opened up new avenues for educational reform, and educational informatization has shifted the traditional classroom-teaching model. Teachers' teaching techniques have changed dramatically because of the introduction of a variety of new mobile intelligent terminals to the campus and classroom. This paper examines the design content of intelligent teaching methods against the backdrop of big data, using a cross-cultural business communication course as an example to reform the classroom-teaching mode, with the goal of resolving the current classroom-teaching dilemma and improving teaching effectiveness through the new intelligent teaching mode.

A robust packet‐dropping covert channel for mobile intelligent terminals

Covert communication in this regard has been widely used for protecting the secrecy of communication. Voice over Long Term Evolution (VoLTE) is a packet-switched core network solution for high-speed and high-quality end-to-end services which usually applied to the communication between intelligent systems. However, covert channels using inter-packet delays and packet order in VoLTE services are limited by specific rules. Since minor modifications to overt traffic can be detected, existing covert channel solutions cannot be directly applied to VoLTE. Therefore, this study presents a robust packet loss covert timing channel by cascade hash coding with intelligent system. To ensure robustness and undetectability, we design hash-based inter-codeword verification, codeword self-verification based on cyclic redundancy check, and adaptive mapping matrix. The sender modulates the covert messages according to the sequence number of the actively dropped packets, and the receiver can retrieve the covert messages using a specialized verification method. To evaluate undetectability, robustness, throughput, and construction costs, a large number of experiments in mobile intelligent terminals have been conducted. The experimental results prove that the proposed scheme is feasible for VoLTE communication, as the covert message is shown to be transmitted secretly, and the bit error and throughput are within acceptable ranges.

Construction of A Mobile Learning Environment Based on Complex Network

Fragmentation Learning based on intelligent mobile terminals can generate a large amount of mobile learning trajectory data, and the network formed by these mobile terminals is a very complex network. This paper researched the construction of a mobile learning environment based on complex network, in the hopes of providing a new idea for solving problems in the learning information dissemination of mobile terminals and the integration of mobile learning resources. In the beginning, this paper introduced the concept and features of complex network for mobile learning in the context of an English fragmentation learning environment, built a learning information dissemination model, and proposed an immune strategy under the conditions that terminal nodes won’t perform similar repeated learning after completing mobile learning. Then, the paper elaborated on the construction process of a mobile learning environment based on keyword co-occurrence, and established the keyword co-occurrence adjacency matrix and weighted matrix. In the end, experimental results proved the accuracy and usability of the proposed strategy.

An Intelligent Vehicle Alarm User Terminal System Based on Emotional Identification Technology

Negative emotions could increase the risks of traffic accidents. However, the driver’s emotional identification is rarely considered in the current design of intelligent vehicle alarm user terminals (IVAUTs). To solve the problem, this paper tries to design an IVAUT system based on emotional identification technology. Firstly, the transformer network was combined with a convolutional neural network (CNN) into a voice emotional identification system for intelligent vehicle alarm, and an emotional labeling approach was provided. Next, a bimodal fusion model was developed based on decompose-CNNs, which includes an appearance module, an optical flow module, and a bimodal fusion module. The proposed emotional identification approach was proved effective through experiments.

Privacy preserving dynamic data release against synonymous linkage based on microaggregation

The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

Trust-based federated learning for network anomaly detection

With the rapid development of social networks and the massive popularity of intelligent mobile terminals, network anomaly detection is becoming increasingly important. In daily work and life, edge nodes store a large number of network local connection data and audit data, which can be used to analyze network abnormal behavior. With the increasingly close network communication, the amount of network connection and other related data collected by each network terminal is increasing. Machine learning has become a classification method to analyze the features of big data in the network. Face to the problems of excessive data and long response time for network anomaly detection, we propose a trust-based Federated learning anomaly detection algorithm. We use the edge nodes to train the local data model, and upload the machine learning parameters to the central node. Meanwhile, according to the performance of edge nodes training, we set different weights to match the processing capacity of each terminal which will obtain faster convergence speed and better attack classification accuracy. The user’s private information will only be processed locally and will not be uploaded to the central server, which can reduce the risk of information disclosure. Finally, we compare the basic federated learning model and TFCNN algorithm on KDD Cup 99 dataset and MNIST dataset. The experimental results show that the TFCNN algorithm can improve accuracy and communication efficiency.

An Enhanced Reliable Access Scheme for Massive IoT Applications in Ubiquitous IoT Systems

As a key infrastructure for intelligent manufacturing, the Industrial Internet of Things (IIoT) can connect people, massive intelligent terminals, automated control equipment, and convergence nodes to achieve a high integration of industrial systems with advanced computing, sensor technology, fifth-generation technology (5G), and the Internet. How to realize the robust and reliable connection of massive IoT applications has become the research focus of industry and academia. However, the massive IoT industrial equipment, diverse business types, complex networking mode, and broadcast characteristics of wireless media have posed serious challenges to providing stable and reliable connections in industrial communication scenarios. In order to overcome this problem, the paper proposes an enhanced reliable access scheme (ERAS) for massive IoT applications in software-defined industrial heterogeneous networks. Based on the idea of software-defined networking (SDN), the ERAS is designed from two perspectives of the control plane and the data plane. Based on the idea of greedy algorithms, ERAS uses priority, interference set, deadline, and other information to find the local optimal solution and then obtain the global optimum. First, low-priority periodic data is accessed to the channel based on the earliest deadline. Then, high-priority data (such as control and interactive services) judges the channel occupancy status according to the access flow table, and if there is an idle channel in the current time slot, it is normally accessed. Otherwise, the subslots reserved with high priority in the control channel are used to preempt the low-priority channel. This preemption mechanism can ensure reliable and real-time connections for massive IIoT. Simulation results show that the ERAS performs better than traditional methods in terms of delay, deadline guarantee rate, and packet loss rate.

Recent research on emerging organic electrode materials for energy storage

Due to the growth of the demand for rechargeable batteries in intelligent terminals, electric vehicles, energy storage, and other markets, electrode materials, as the essential of batteries, have attracted tremendous attention. The research of emerging organic electrode materials in batteries has been boosted recently to their advantages of low cost, environmental friendliness, biodegradability, and designability. This manuscript highlights and classifies several recent studies on organic electrode materials and lists their potential applications in various battery systems. Finally, the challenge and perspective of organic electrode materials are also summarized.

E-Learning in Geography: new perspectives in post-pandemic

<abstract> <p>Recent developments in low-cost information technology, fast internet, intelligent terminals, apps that can manage the most varied activities in a professional but simple way, have allowed their diffusion in sectors traditionally reluctant to rapid change. The pandemic crisis caused by Covid-19 and the consequent provisions of social distancing to curb its spread, have also forced the world of education to deal with this modality. Terms such as Distance Learning and Smart Working have forcefully entered the vocabulary and daily life of millions of people. The persistence of the pandemic due to the variants of the virus is convincing even the most reluctant to change that the new "normal" will have to rely on information technology to a greater extent than in the past. In much of the Western world, where culture has long been a thriving economic sector, much has been invested for decades to strengthen and disseminate distance learning activities recognized as economically and socially beneficial. The European institutions have moved in this direction more recently. The author's experience in the last two years in emergency remote education in geography at university level has not always been positive. He therefore felt the urgency to consider adopting existing standards and best practices in order to improve outcomes and achieve effective online geography learning. The evident advantages of adopting good quality e-learning for an extended community would facilitate understanding and acceptance even in the Italian school and academic environment, usually conservative and conformist. In this environment, up to now, many have in fact feared that it could lead to the endorsement of distance learning, viewed with contempt if not really with aversion as it is considered to be of poor quality.</p> </abstract>

A New Task Scheduling Scheme Based on Genetic Algorithm for Edge Computing

With the continuous evolution of smart grid and global energy interconnection technology, amount of intelligent terminals have been connected to power grid, which can be used for providing resource services as edge nodes. Traditional cloud computing can be used to provide storage services and task computing services in the power grid, but it faces challenges such as resource bottlenecks, time delays, and limited network bandwidth resources. Edge computing is an effective supplement for cloud computing, because it can provide users with local computing services with lower latency. However, because the resources in a single edge node are limited, resource-intensive tasks need to be divided into many subtasks and then assigned to different edge nodes by resource cooperation. Making task scheduling more efficient is an important issue. In this paper, a two-layer resource management scheme is proposed based on the concept of edge computing. In addition, a new task scheduling algorithm named GA-EC(Genetic Algorithm for Edge Computing) is put forth, based on a genetic algorithm, that can dynamically schedule tasks according to different scheduling goals. The simulation shows that the proposed algorithm has a beneficial effect on energy consumption and load balancing, and reduces time delay.