Data-Driven Innovations in Networked Systems and Applications: Recent Developments and Emerging Trends

Dear Colleagues,

Networked systems and applications are at the heart of the digital revolution, powering the connected world we live in. The relentless march of innovation in this domain is redefining the boundaries of what is possible. At the core of this transformation lies data—the lifeblood of our connected ecosystems.  From 5G's connectivity revolution to IoT's intelligent interconnectivity and edge computing's real-time processing, the latest trends are shaping a future in which our networks optimize efficiency and redefine security and privacy. If you're at the forefront of this digital evolution—leveraging data analytics, AI, and ML, and pioneering novel applications—we invite you to share your insights. Join us on a journey into the heart of data-driven innovation, where networks and applications shape a smarter, connected world.

The scope of the Special Issue includes, but is not limited to, the following topics:

• Emerging Technologies: Exploration of emerging technologies, such as 5G, IoT, edge computing, and beyond, and their impact on networked systems and applications.
• Network Design and Architecture: Innovations in network design, architecture, and protocols to enhance performance, scalability, and efficiency.
• Wireless and Mobile Communications: Advancements in wireless and mobile networking, including mobile ad-hoc networks, vehicular networks, and mobile app development.
• Cloud Computing and Virtualization: Strategies for efficient cloud utilization, virtualization technologies, and cloud-based applications.
• Data Management and Analytics: Techniques for data management, analytics, and big data processing in networked systems.
• Security and Privacy: Innovations in network security, privacy preservation, and threat detection.
• Applications and Use Cases: Case studies and novel applications of networked systems across various domains, including healthcare, transportation, smart cities, and more.
• Scalability and Performance Optimization: Methods to enhance the scalability and performance of networked systems to meet the growing demands of the digital age.

Prof. Dr. Ming Liu
Guest Editor

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• networked systems and applications
• Internet of Things (IoT)
• artificial intelligence (AI)
• big data

Title: Network Intrusion Detection based on PreNorm-Transformer
Authors: Shuquan Feng
Affiliation: Tianjin Normal University
Abstract: Network intrusion detection has always been a critical component of safeguarding the information security ecosystem against malicious attacks. Early network intrusion detection methods primarily relied on predefined rules and patterns, making them less effective against emerging threats. Traditional machine learning-based intrusion detection methods struggled to capture complex sequence information and temporal features, resulting in low detection accuracy and high false alarm rates when dealing with large volumes of network traffic. Existing research in deep learning has shown the ability to identify temporal dependencies within network data streams, but their performance on long sequences was suboptimal. In this study, we constructed a PreNorm-Transformer network model based on a multi-head self-attention mechanism. This model not only extracts key features but also thoroughly explores the temporal characteristics among attack data streams. Through pre-processing of features to reduce resource overhead and enhance model fitting capabilities, we selected the optimal loss function for parallel training, enabling intrusion behavior detection on network flow data. Experimental results on the UNSW-NB15 dataset demonstrate that the intrusion detection method based on the PreNorm-Transformer network model achieves an accuracy rate of 94.28%, surpassing SVM's 85.99%, LSTM's 88.67%, and the Combined Multiple Convolutional Model (CAL)'s 90.37%.

Title: Smartphone as Assist Device for Task Scheduling in UAV Networks for Disaster Relief
Authors: Lin Li
Affiliation: Tianjin Normal University
Abstract: Earthquake disasters are usually very destructive and greatly threaten human life and property. Based on the relatively mature Unmanned Aerial Vehicles (UAVs) technology and their high flexibility, UAVs are widely used for information collection and processing to carry out post-disaster relief tasks. However, drones are limited by their battery capacity, which makes them hard to perform both large-scale information-gathering and data-processing work at the same time. Nowadays, as people's daily portable devices, smartphones (SPs) have rich computing power. Hence, we choose to develop smartphones (SPs) to assist UAVs in compute-incentive task execution in this paper. In task scheduling process, we try to consider the quality of service (QoS) of diversity of tasks in actual scenarios. Meanwhile, to balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem is built and the Hybrid Immune and Bat Scheduling Algorithm (HIBSA) is proposed to optimize our optimization problem. Through a large number of comparative simulation experiments, we show that HIBSA can achieve the best performance compared to the comparative algorithms.

Title: PDPHE: Personal Data Protection for Trans-Border Transmission based on Homomorphic Encryption
Authors: Yan Liu; Changshui Yang; Qiang Liu; Mudi Xu; Chi Zhang; Lihong Cheng; Wenyong Wang
Affiliation: Dalian Jiaotong University
Abstract: In digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, Personal Data Protection based on Homomorphic Encryption (PDPHE), to creatively apply the privacy computing technology Homomorphic Encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical Full Homomorphic Encryption (FHE) algorithm DGHV by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE can not only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing.