Microservices as an emerging architectural approach have been widely applied in the development of online applications. However, in large-scale service systems, frequent data communications, complex invocation dependencies, and strict latency requirements pose significant challenges to efficient microservice orchestration. In addition, microservices need to frequently access the database to achieve data persistence, creating a mutual dependency between the two, and this symmetry further increases the complexity of service orchestration and coordinated deployment. In this context, the strong coupling of service deployment, database layout, and request routing makes effective local optimization difficult. However, existing research often overlooks the impact of databases, fails to achieve joint optimization among databases, microservice deployments, and routing, or lacks fine-grained orchestration strategies for multi-instance models. To address the above limitations, this paper proposes a joint optimization framework based on the Database-as-a-Service (DaaS) paradigm. It performs fine-grained multi-instance queue modeling based on queuing theory to account for delays in data interaction, request queuing, and processing. Furthermore this paper proposes a proximal policy optimization algorithm based on multi-stage joint decision-making to address the orchestration problem of microservices and database instances. In this algorithm, the action space is symmetrical between microservices and database deployment, enabling the agent to leverage this characteristic and improve representation learning efficiency through shared feature extraction layers. The algorithm incorporates a two-layer agent policy stability control to accelerate convergence and a three-level experience replay mechanism to achieve efficient training on high-dimensional decision spaces. Experimental results demonstrate that the proposed algorithm effectively reduces service request latency under diverse workloads and network conditions, while maintaining global resource load balancing.

Blockchain technology is leveraged in the Internet of Things (IoT) systems to enhance data reliability and management efficiency, ensuring integrity, security, and auditability through a decentralized ledger architecture. However, resource-constrained IoT devices are unable to store the complete blockchain due to prohibitive resource consumption and performance degradation. While collaborative storage strategies have been proposed to mitigate these constraints, existing approaches often prioritize storage scalability without sufficiently addressing the selection of cooperative nodes for distributed ledger maintenance. This can lead to significant communication delays during block retrieval, undermining the real-time performance and overall efficiency of the blockchain-enabled IoT system. To address this challenge, this paper introduces a clustering-based collaborative storage scheme and proposes a novel joint optimization algorithm that iteratively refines both node clustering and block allocation strategies within the blockchain network. By structuring IoT devices into clustered peers, the algorithm reduces block query latency and facilitates efficient blockchain synchronization and update processes. Experimental evaluations confirm that the proposed method effectively alleviates storage limitations and lowers access costs in static blockchain-based IoT environments.

The rapid advancement of unmanned aerial vehicle (UAV) technology has led to its widespread adoption in military reconnaissance, disaster monitoring, environmental inspection, and related fields. However, a single UAV often faces limitations when executing large-scale and complex missions. UAV swarm technology, which employs multi-agent collaboration, can significantly improve task execution efficiency and overall system performance, representing an area of considerable research importance. Current studies on task allocation and path planning for UAV swarms exhibit certain shortcomings, particularly the high computational complexity and insufficient real-time performance of existing path planning methods when applied to highly dynamic, multi-objective, and large-scale complex scenarios. To address the above challenge, this paper proposes a Gale-Shapley-based Genetic Algorithm (GSGA) for UAV swarm task allocation and path planning. First, a multi-UAV data inspection system model is formulated based on an energy consumption model, analyzing the influence of factors including geographical fairness, data utility, and energy consumption. The proposed GSGA integrates the Gale-Shapley stable matching algorithm for one-to-one task assignment between UAVs and sub-regions with a genetic algorithm optimized for intra-region path planning. Dynamic programming is further employed to refine the flight paths. The results show that the GSGA strategy can effectively improve the balance of task allocation, optimize path length and inspection quality. The proposed method demonstrated robust performance in complex scenarios characterized by numerous task targets and intricate regional partitions, consistently enabling UAVs to complete inspection tasks with high collaborative efficiency.

As blockchain technology becomes widely adopted in Mobile Internet of Things (MIoT) networks, the growing volume of blockchain data significantly increases storage pressure on peer nodes. Collaborative storage, which distributes blockchain data across nodes in cluster, offers a promising solution. However, the frequent movement of mobile nodes disrupts cluster structures, and existing static solutions fail to address this dynamic nature, rendering them ineffective. To address this issue, we propose a Dynamic Cluster-based Mobile Node Migration Scheme (DCMM), comprising two key components: new cluster selection and block redistribution. The Dynamic Node Synchronization Algorithm (DNSA) optimizes cluster selection, and the Dynamic Block Allocation Algorithm (DBAA) manages efficient block redistribution. Comparative analysis with five baseline approaches shows that DCMM improves performance by over 16.69% in the weighted optimization objective, which considers access costs, migration costs, and dwell times. These results demonstrate that our approach significantly optimizes network costs compared to baseline algorithm.

With the widespread adoption of the Internet of Things (IoT), UAV–vehicle collaborative inspection systems are crucial for large-scale, IoT-enabled monitoring. Empowered by the IoT, these systems optimize resource allocation and boost the efficiency of IoT-based applications. Nevertheless, variable vehicle and UAV speeds due to wind and precipitation complicate path planning and task scheduling in the IoT-integrated setup. To solve this, this study offers an adaptive solution for dynamic, complex-weather scenarios within the IoT framework. A dynamic task-processing model was developed first, using real-time IoT sensor data for better decisions. Then, the KGTSA optimization algorithm was designed. It combines K-means clustering, HGA, and TS, considering UAV and vehicle speed variations in complex weather and making full use of IoT-device data. K-means generates an initial solution, HGA refines it, and TS fine-tunes UAV routes and task assignments. The simulation results show that KGTSA significantly cuts data collection time while maintaining flexibility. It efficiently manages speed and path uncertainties in complex weather, optimizing task efficiency without weather forecasts. Compared to traditional algorithms, KGTSA shortens data collection time and adapts better to dynamic IoT environments for real-world efficiency.

As the problem of surface garbage pollution becomes more serious, it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods. Due to lightness, unmanned aerial vehicles (UAVs) can traverse the entire water surface in a short time through their flight field of view. In addition, unmanned surface vessels (USVs) can provide battery replacement and pick up garbage. In this paper, we innovatively establish a system framework for the collaboration between UAV and USVs, and develop an automatic water cleaning strategy. First, on the basis of the partition principle, we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection. Second, we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm. Finally, based on the swarm intelligence algorithm, we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning. The system can simultaneously perform inspection and clearance tasks under certain constraints. The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.

The service modules of the traditional Mobile Edge Computing (MEC) are difficult to deploy, extend, and maintain in real networks because of the highly sophisticated systems. To promote the generalization, openness, and flexibility of the network edge environment, an increasing number of studies are exploring the integration of microservices with MEC. However, the existing work usually treats microservice deployment and request routing as two separate issues, ignoring the interaction between them. Therefore, this paper focuses on the joint optimization of microservice deployment and request routing in the multi-edge cloud scenarios. We establish a problem model for minimizing the average response latency, considering the transmission of requests across edge clouds. Then, in view of the complexity of the scene, this paper proposes a joint training strategy of microservice deployment and request routing based on deep reinforcement learning and Best Fit Decreasing algorithm. The algorithm takes the change of microservice deployment scheme as the action of the agent, introduces the Best Fit Decreasing algorithm to construct request routing based on the deployment scheme, and calculates rewards using the complete joint microservice deployment and request routing scheme for subsequent network training. Finally, experimental results show that the proposed algorithm can effectively reduce the response time delay and system running power compared with other algorithms.

The emerging low earth orbit (LEO) broadband satellite networks are creating new opportunities to enable superior video distribution. With numerous satellites deployed, broadband constellations are capable of distributing videos across the globe by efficient multicasting techniques. However, existing work only studied IP multicast for broadband constellations, which suffer from limited scalability and tree performance. With recent breakthroughs in software defined networking, novel software defined multicasting (SDM) techniques manage to achieve efficient data transfer through intelligent and granular management, outperforming traditional IP Multicast. This paper leverages software defined multicasting in the promising broadband constellations to empower satellite-based Internet video distribution. Based on rectilinear Steiner trees, this paper proposes a novel software defined multicasting framework for broadband satellite networks. In addition, this paper designs simple, agile, and scalable multicast segment routing protocols implementing source routing and equal cost multipath routing. The proposed protocols also adapt to frequent member updates and network failures with efficient tree recovery and local rerouting mechanisms. Comprehensive experiments demonstrate the effectiveness and efficiency of our approach when compared with traditional algorithms.

As one of the promising self-powered sensors on Internet of Things (IoT) platforms, unmanned aerial vehicles (UAVs) have attracted much attention for parcel delivery. Their high flexibility and low cost facilitate last-one-mile delivery. However, the limitations of battery capacity and payloads prevent drones from delivering independently over large scales. In this case, it is available to employ vehicles to assist the drones. The vehicles can be private-own trucks and vehicles in public transportation systems (PTSs). Compared to trucks, PTSs such as buses and trains do not require extra operating and fuel costs. Given these advantages, this paper adopts PTSs to assist UAVs in parcel delivery. Nevertheless, the fixed routes and schedules of public vehicles pose new challenges to the Routing and Scheduling problem for PTS-assisted Multi-drone parcel Delivery (RSPMD). To tackle the problem, we propose a novel routing and scheduling algorithm, referred to as the PTS-assisted multi-Drone parcel Delivery (PDD) algorithm. Considering the schedules of the public vehicles, the algorithm jointly optimizes the distance and time cost of drones by iteratively combining parts of existing routes. To the best of our knowledge, we are the first to address RSPMD in which UAVs ride public vehicles to deliver parcels in a wide area. Simulation results are finally presented to demonstrate that PDD outperforms existing solutions in terms of effectiveness and efficiency.

Recently, blockchain is introduced to ensure the security of the device data in Internet of Things (IoT) systems. However, storing the entire blockchain ledger in resource-constrained IoT devices is impractical. A few existing papers attempt to mitigate the storage issues of the blockchain ledgers through the collaborative storage. Nonetheless, these studies solely consider collaborative storing of the entire blockchain ledger in a single consensus unit, treating all the nodes as an individual peer but ignoring which nodes should be assigned to form a consensus unit together. This may lead to significant latency in block invocations among the nodes. To this end, this article innovatively explores the grouping of all the devices in the IoT network into multipeers at a global level which significantly reduces the access latency. In this article, we propose a clustering-based collaborative storage scheme for the blockchain in storage-limited IoT systems. The proposed algorithm takes storage and communication latency into consideration and clusters various IoT nodes into multiple peers, ensuring that the entire blockchain stays updated within these clusters. Furthermore, we propose a series of effective block allocation and replacement strategies in both the static and dynamic scenarios. The experimental results verify that our algorithm effectively solves the problem of insufficient storage in blockchain systems.

Edge computing technologies with container-based microservice architectures promise to provide stable and low-latency services for large-scale and complex edge applications. However, due to the limited CPU and storage resources in edge computing scenarios, the coarse-grained service deployment on edge nodes causes performance bottlenecks. In addition, the effective deployment of microservices is tightly correlated with request routing, but the current research ignores the joint optimization of multi-instance deployment and routing. In this paper, we first model the problem of jointly optimizing service deployment and routing in a dynamically changing environment with multi-edge network collaboration based on a queuing network analysis. Secondly, we design heuristic algorithms to scale microservice instances horizontally in dynamic user request states. In addition, we propose a reinforcement learning algorithm based on reward shaping (RSPPO) to minimize user waiting delay and edge network resource consumption. We also solve the microservice deployment and request routing problem for multi-edge collaboration to achieve load balancing among edge nodes. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm.

In the era of big data, thanks to the development of digital information technology (e.g., data mining), the concept of the smart campus has emerged and changed the traditional education model. However, the smart education products, mainly the teachermate platform, cannot provide algorithm-based personalized education services for teachers and students by targeting and analyzing student data. Based on this background, this paper takes some students as the sample, and uses the data of various learning behavior characteristics on the teachermate platform and the final exam paper results of the student grade management system on the teacher's side as the data source, and analyzes the data through data mining algorithms. The final results of the analysis were obtained, and suggestions for optimization of the platform based on academic alerts were obtained for faculty, teachers, and the teachermate platform.

教材是学校教育、知识传授和人才培养最基本的教育资源.选取2010年以来新出版或再版的8本国际知名电子技术基础教材,重点介绍其编写特色、知识体系和内容更新情况,可以为我国相关教材的编写提供一些参考.同时,结合我国电子技术教学的历史和现状,提出了几点课程改革的建议.

对电子信息类专业中开展课程思政的必要性进行了分析,从学院顶层提出了"一体两翼三融合四统一"的课程思政育人体系架构,在电子信息类专业进行了探索和实践,并调查了学生对于课程思政的理解认识情况.结果表明,84.5％学生认为课程思政能促进个人发展.将思政元素和专业知识的有机巧妙融合,能够实现教书育人相统一.

At present, with the rapid development of Internet technology and the arrival of the big data, more and more smart labs are put into use. The analysis of multi-source data in the labs has a wider application scenario. Experimental teaching based on big data platform has become more and more popular. Learning and using the multi-source massive data generated by the experimental teaching platform has become a hot issue in the field of educational research and application. Based on the surveillance video of the circuit laboratory of the smart labs, this paper uses the open-source posture recognition framework OpenPose to process the human posture in the video, and then obtain the human bone point data. It trains the appropriate classifier to classify and recognize the behavior based on machine learning, and analyzes the correlation between the behavior data and the performance data to establish a learning prediction model, which on the one hand provides scientific guidance for student management, On the other hand, it provides valuable decision-making information for experimental teaching reform.

The emerging low earth orbit (LEO) broadband constellations are capable of distributing videos via advanced multicasting techniques to multiplex bandwidth in both satellite access and backbone networks, thereby reducing huge amounts of traffics. Previous work on LEO-based multicast routing relied on IP multicast, which fails to employ global information to achieve the optimal bandwidth saving. In this article, we leverage the novel software-defined multicasting paradigm on LEO constellations to empower advanced video distribution with optimized multicast trees, thereby significantly outperforming conventional multicasting techniques. In the presence of quality-of-service (QoS) constraints, we propose weighted rectilinear Steiner minimal trees (WRSTs), which balance bandwidth saving and QoS requirements. Our multicast tree algorithm adopts the Voronoi diagram and Delaunay triangulation to obtain suitable candidate Steiner points, which are exploited by subsequent iterative edge substitutions for WRST construction. We also design QoS-aware multicast management schemes to deal with frequent member updates and potential failures in LEO constellations. We carry out thorough experiments to demonstrate the effectiveness and efficiency of the proposed routing tree construction algorithm and multicasting protocols when compared with traditional algorithms.

The emerging low earth orbit (LEO) satellite networks are expected to provide the world's most advanced Internet services. Besides, terrestrial networks are in constant evolution and already moving to embrace the relatively new paradigm of software defined networking (SDN). In this paper, we take the advantages of SDN features and leverage traffic engineering (TE) to enhance the ISL performance in broadband LEO satellite networks. We investigate unicast and multicast TE for SDN-enhanced LEO constellations to empower satellite-based Internet services. In LEO satellite networks, unicast supports ubiquitous network access and provides basic network services, while multicast features superior satellite-based video distribu-tion. For unicast TE in grid ISL networks, we present a simple yet efficient k-segment routing based strategy with segment rout-ing (SR) techniques, which can achieve near optimal max link utilization when compared with the multi-commodity flow solu-tion. In the meanwhile, our solution eliminates routing tables and only imposes little routing information stored in packet headers. For multicast TE, we employ rectilinear Steiner trees (RSTs) to maximize bandwidth saving and exploit obstacle-avoiding rec-tilinear Steiner trees (OARSTs) to address the contention of multiple multicast groups. Based on RSTs and OARSTs, we pro-pose an effective per-flow management scheme to balance traffic among multiple multicast flows in the presence of limited link capacities. Simulation results demonstrate the effectiveness and efficiency of our approaches on reducing routing information and accommodating more multicast groups.

The emerging large-scale low earth orbit (LEO) broadband satellite networks manifest great potentials in distributing videos across the globe via efficient multicast techniques. However, existing work only studied IP multicast (IPMC) for LEO constellations, which suffers from limited scalability and tree performance. In this paper, we employ the promising software defined multicast (SDM) techniques in large-scale LEO constellations to empower satellite-based Internet video distribution. We present a multi-layer rectilinear Steiner tree (ML-RST) construction algorithm for multicast routing in large-scale LEO constellations. We extend the spanning graph and edge substitution to three-dimensional (3D) scenes. Based on multi-layer spanning graphs and multi-layer edge substitution approaches, we manage to efficiently construct ML-RSTs with ${O}$ ( ${n}$ log ${n}$ ) complexity. Experimental results show that our approach can achieve an average 10% improvement in bandwidth saving compared with existing algorithms.

Federated Learning (FL) is a new computing paradigm in privacy-preserving Machine Learning (ML), where the ML model is trained in a decentralized manner by the clients, preventing the server from directly accessing privacy-sensitive data from the clients. Unfortunately, recent advances have shown potential risks for user-level privacy breaches under the cross-silo FL framework. In this paper, we propose addressing the issue by using a three-plane framework to secure the cross-silo FL, taking advantage of the Local Differential Privacy (LDP) mechanism. The key insight here is that LDP can provide strong data privacy protection while still retaining user data statistics to preserve its high utility. Experimental results on three real-world datasets demonstrate the effectiveness of our framework.

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.