A Scalable Video Multicast Scheme Based on User Demand Perception and D2D Communication.

With the widespread application of 5G technology, there has been a significant surge in wireless video service demand and video traffic due to the proliferation of smart terminal devices and multimedia applications. However, the complexity of terminal devices, heterogeneous transmission channels, and the rapid growth of video traffic present new challenges for wireless network-based video applications. Although scalable video coding technology effectively improves video transmission efficiency in complex networks, traditional cellular base stations may struggle to handle video transmissions for all users simultaneously, particularly in large-scale networks. To tackle this issue, we propose a scalable video multicast scheme based on user demand perception and Device-to-Device (D2D) communication, aiming to enhance the D2D multicast network transmission performance of scalable videos in cellular D2D hybrid networks. Firstly, we analyze user interests by considering their video viewing history and factors like video popularity to determine their willingness for video pushing, thereby increasing the number of users receiving multicast clusters. Secondly, we design a cluster head selection algorithm that considers users' channel quality, social parameters, and video quality requirements. Performance results demonstrate that the proposed scheme effectively attracts potential request users to join multicast clusters, increases the number of users in the clusters, and meets diverse user demands for video quality.

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data.

The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.

Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis.

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight-communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV's flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission's target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV's cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication.

A Microservice and Serverless Architecture for Secure IoT System.

In cross-border transactions, the transmission and processing of logistics information directly affect the trading experience and efficiency. The use of Internet of Things (IoT) technology can make this process more intelligent, efficient, and secure. However, most traditional IoT logistics systems are provided by a single logistics company. These independent systems need to withstand high computing loads and network bandwidth when processing large-scale data. Additionally, due to the complex network environment of cross-border transactions, the platform's information security and system security are difficult to guarantee. To address these challenges, this paper designs and implements an intelligent cross-border logistics system platform that combines serverless architecture and microservice technology. This system can uniformly distribute the services of all logistics companies and divide microservices based on actual business needs. It also studies and designs corresponding Application Programming Interface (API) gateways to solve the interface exposure problem of microservices, thereby ensuring the system's security. Furthermore, asymmetric encryption technology is used in the serverless architecture to ensure the security of cross-border logistics data. The experiments show that this research solution validates the advantages of combining serverless architecture and microservices, which can significantly reduce the operating costs and system complexity of the platform in cross-border logistics scenarios. It allows for resource expansion and billing based on application program requirements at runtime. The platform can effectively improve the security of cross-border logistics service processes and meet cross-border transaction needs in terms of data security, throughput, and latency.

A Financial Management Platform Based on the Integration of Blockchain and Supply Chain.

Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.

Comparative study of electroless copper film on different self-assembled monolayers modified ABS substrate.

Copper films were grown on (3-Mercaptopropyl)trimethoxysilane (MPTMS), (3-Aminopropyl)triethoxysilane (APTES) and 6-(3-(triethoxysilyl)propylamino)-1,3,5- triazine-2,4-dithiol monosodium (TES) self-assembled monolayers (SAMs) modified acrylonitrile-butadiene-styrene (ABS) substrate via electroless copper plating. The copper films were examined using scanning electron microscopy (SEM) and X-ray diffraction (XRD). Their individual deposition rate and contact angle were also investigated to compare the properties of SAMs and electroless copper films. The results indicated that the formation of copper nuclei on the TES-SAMs modified ABS substrate was faster than those on the MPTMS-SAMs and APTES-SAMs modified ABS substrate. SEM images revealed that the copper film on TES-SAM modified ABS substrate was smooth and uniform, and the density of copper nuclei was much higher. Compared with that of TES-SAMs modified resin, the coverage of copper nuclei on MPTMS and APTES modified ABS substrate was very limited and the copper particle size was too big. The adhesion property test demonstrated that all the SAMs enhanced the interfacial interaction between copper plating and ABS substrate. XRD analysis showed that the copper film deposited on SAM-modified ABS substrate had a structure with Cu(111) preferred orientation, and the copper film deposited on TES-SAMs modified ABS substrate is better than that deposited on MPTMS-SAMs or APTES-SAMs modified ABS resins in electromigrtion resistance.