Edge Of Network Research Articles

A Survey on Securing Image-Centric Edge Intelligence

Facing enormous data generated at the network edge, Edge Intelligence (EI) emerges as the fusion of Edge Computing and Artificial Intelligence, revolutionizing edge data processing and intelligent decision-making. Nonetheless, this emergent mode presents a complex array of security challenges, particularly prominent in image-centric applications due to the sheer volume of visual data and its direct connection to user privacy. These challenges include safeguarding model/image privacy and ensuring model integrity against various security threats, such as model poisoning. Essentially, those threats originate from data attacks, suggesting data protection as a promising solution. Although data protection measures are well-established in other domains, image-centric EI necessitates focused research. This survey examines the security issues inherent to image-centric EI and outlines the protection efforts, providing a comprehensive overview of the landscape. We begin by introducing EI, detailing its operational mechanics and associated security issues. We then explore the technologies facilitating security enhancement (e.g., differential privacy) and edge intelligence (e.g., compact networks and distributed learning frameworks). Next, we categorize security strategies by their application in data preparation, training, and inference, with a focus on image-based contexts. Despite these efforts on security, our investigation identifies research gaps. We also outline promising research directions to bridge these gaps, bolstering security frameworks in image-centric EI applications.

Research on Distributed Secure Storage Framework of Industrial Internet of Things Data Based on Blockchain

The conventional centralized Industrial Internet of Things (IIoT) framework is plagued by issues like subpar security performance and challenges related to storage expansion. This paper proposes a two-tier distributed secure storage framework based on blockchain for IIoT data. The authors first introduce the two-layer framework, which includes the edge network layer and the blockchain storage layer. The nodes in the edge network layer are classified into administrator nodes and ordinary nodes. It provides a lower latency network environment compared to cloud computing to preprocess raw industrial data. The blockchain storage layer provides storage space to keep data secure and traceable. Secondly, the authors propose a differentiated storage solution. Based on the timestamps of industrial data and the specific media access control (MAC) address, the Universally Unique Identifier (UUID) of the raw data is generated and uploaded to the blockchain for secure storage. Encrypt the corresponding raw data using the elliptic curve cryptography algorithm, and then upload it to InterPlanetary File System (IPFS) to expand the storage capacity of the blockchain. Deploy a smart contract on the blockchain to compare UUIDs for consistency in an automated, lightweight method to determine data integrity. Finally, we analyze the advantages brought by the integration of blockchain and IIoT. Additionally, the authors design comparative tests on different storage methods. The results prove that the security of this paper’s scheme is improved, and the storage performance is extended. Noteworthy enhancements include heightened throughput of data uploaded to the blockchain and minimized delay overhead.

Two-Layer Optimization With Utility Game and Resource Control for Federated Learning in Edge Networks

Two-Layer Optimization With Utility Game and Resource Control for Federated Learning in Edge Networks

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

A new approach based on genetic algorithm for computation off-loading optimization in multi-access edge computing networks

The proliferation of smart devices and the increasing demand for resource- intensive applications present significant challenges in terms of computational efficiency, leading to surge in data traffic. While cloud computing offers partial solutions, its centralized architecture raises concerns about latency. Multi-access edge computing (MEC) emerges as promising alternative by deploying servers at the network edge to bring computations closer to user devices. However, op- timizing computation offloading in the dynamic MEC environment remains a complex challenge. This paper introduces novel genetic algorithm-based ap- proach for efficient computation offloading in MEC, considering processing and transmission delays, user preferences, and system constraints. The pro- posed approach integrates computation offloading and resource allocation al- gorithm based on evolutionary principles, combined with a greedy strategy to maximize overall system performance. By utilizing genetic algorithms, the pro- posed method enables dynamic adaptation to changing conditions, eliminating the need for intricate mathematical models and providing an appealing solution to the complexities inherent in MEC. The urgency of this research arises from the critical need to enhance mobile application performance. Simulation re- sults demonstrate the robustness and efficacy of our approach in achieving near- optimal solutions while efficiently balancing computation offloading, minimiz- ing latency, and maximizing resource utilization. Our approach offers flexibility and adaptability, contributing to advancement of MEC networks and addressing the requirements of latency-sensitive applications.

Ecological response mechanism of alkaline wastewater periphytic biofilms to pH alteration

The environmental impact of indiscriminate alkaline wastewater discharge is increasingly concerning. Studying the ecological response mechanisms of periphytic biofilms to such wastewater is crucial for deepening our understanding of its broader environmental implications. In this study, periphytic biofilms collected from the drainage outlet of a tunnel construction project were developed for 56 days at initial pH values of 10 (pH0 10) and 12 (pH0 12). These stabilized biofilms were then transferred to a neutral environment (pH 7) for another 35 days. High-throughput 16S rRNA sequencing revealed changes in the growth characteristics, microbial interactions, and microbial community composition, function, and assembly patterns of the biofilms. Specifically, results revealed decreased microbial activity, live/dead bacteria ratios, and polysaccharide content in extracellular polymeric substances when the pH was reduced from alkaline to neutral, with incomplete recovery observed after 35 days of incubation in a neutral environment. During neutral pH incubation, proteobacterial abundance increased by 58.39 % and 33.08 % in biofilms cultured at pH0 10 and pH0 12, respectively, becoming the dominant flora, with chemoheterotrophic functions increasing by 8.11 % and 38.23 %, respectively. Network analysis indicated that biofilms in the pH0 12 culture group had a more advanced organizational co-occurrence network, with more complex and stable interactions, than those in the pH0 10 culture group. Microbial community α diversity, key differential species count, the proportion of positively correlated network edges, and null model data showed that microbial communities in biofilms cultured at pH0 10 were more resilient, whereas those cultured at pH0 12 were more resistant. Overall, this study reveals that alkaline wastewater periphytic biofilms adapt and develop high tolerance and metabolic activity but struggle to recover their original state in a short-term neutral environment, with biofilms cultured at pH0 12 exhibiting more complex interactions and higher resistance and those cultured at pH0 10 showing greater resilience.

REC-Fed: A Robust and Efficient Clustered Federated System for Dynamic Edge Networks

REC-Fed: A Robust and Efficient Clustered Federated System for Dynamic Edge Networks

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

Bridge the Present and Future: A Cross-Layer Matching Game in Dynamic Cloud-Aided Mobile Edge Networks

Adaptive Clustering-Based Straggler-Aware Federated Learning in Wireless Edge Networks

Adaptive Clustering-Based Straggler-Aware Federated Learning in Wireless Edge Networks

Ultra-Low Power Circuit Design Techniques for Audio-Based Environmental Sensing in IoT Networks

Abstract: ULP circuit design consideration is critical in enhancing AS-based ESL in IoT networks essential in designing IoT devices to work with minimal power consumption. These designs enable the sensors to operate continuously interesting environments while at the same time utilizing methods like duty cycling, adaptive sampling, and wake-up circuitries. AFE parts help to minimize pre-ADC signal manipulation within the hardware and true and near-threshold processing, as well as subthreshold processing, decreases the power dissipation even more without affecting utility. Energy harvesting including, solar and piezoelectric handling ensures kinetic energy is accumulated for efficient functioning without batteries. Furthermore, incorporating lightweight machine learning (ML) models allows for audio classification and anomalous event detection on the network edge and with low data transfer. In combination, these developments bring more efficient and resource-saving IoT networks providing essential data for applications in environmental and climate change sensing, smart cities, public safety, and industrial security. This approach is essential when building IoT systems that are to last, to meet the increasing need for intelligent, self-sustaining, energy-efficient systems

Anxiety and depression in Chinese cataract patients: a network analysis

BackgroundPrior research has demonstrated that people with cataracts are more likely to experience anxiety and depression co-occurring when the condition advances to a degree that impairs vision beyond its physiological effects. According to network theory, there is a connection between the interplay of anxiety and depression and the genesis of comorbidity. Using a network viewpoint, our study examined the network properties of anxiety-depression in cataract patients to pinpoint central and bridge symptoms as well as possible intervention targets for more focused and successful therapies.MethodA total of 1,254 cataract patients were enrolled in our study. The Nine-item Patient Health Questionnaire (PHQ-9) and the Seven-item Generalized Anxiety Disorder scale (GAD-7) were used to measure depression and anxiety symptoms, respectively. The R 4.3.3 software was utilized for network model building and descriptive statistics. Furthermore, we displayed a “Thoughts of death” flow network.ResultIn the network, A5 “Restlessness”- D7 “Concentration difficulties” showed the strongest edge. A2 “Uncontrollable worry” and D2 “Depressed or sad mood” could be identified as the central symptoms. A7 “Afraid something will happen” and D7 “Concentration difficulties” could be identified as bridge symptoms. The strongest edge in the flow network was D9 “Thoughts of death”-D6 “Feeling of worthlessness”.Conclusion“Uncontrollable worry”, “Depressed or sad mood”, “Afraid something will happen” and “Concentration difficulties” could be potential targets for the prevention of anxiety and depression in cataract patients. Furthermore, this study emphasizes how important it is to prevent suicide in cataract patients, and the symptom “Feeling of worthlessness” can be used as an effective target.

The Role of Internet of Things and Fog Computing in Smart Cities

Smart cities represent the next evolution of urban centers, integrating advanced technologies to create better living environments. These cities will feature billions of connected devices, such as sensors and actuators, generating vast amounts of data. This data typically flows to cloud servers for processing. However, traditional Big Data analytics and cloud computing have proven inefficient in some scenarios, as not all data is pertinent enough for cloud-level processing. Transmitting all data from IoT and edge devices to the cloud consumes excessive storage and network resources. Fog computing emerges as a solution by decentralizing some data processing, analytics, and storage to the network's edge, closer to the data source. This approach alleviates the load on central servers and reduces latency, ensuring timely responses. Fog computing leverages edge computing to handle immediate data processing needs, making the overall system more efficient. This paper will cover the fundamental concepts of smart cities, IoT, and fog computing. It will present the important technologies and architectural frameworks that underpin these systems. Furthermore, several case studies will illustrate the practical applications and benefits of integrating fog computing with cloud computing and IoT in smart cities. These examples will demonstrate how such integrations lead to more efficient, responsive, and sustainable urban environments.

Enhanced In-Network Caching for Deep Learning in Edge Networks

Enhanced In-Network Caching for Deep Learning in Edge Networks

Workload-based adaptive decision-making for edge server layout with deep reinforcement learning

Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers’ workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on a real dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.

Replicating the performance of a portfolio of stocks using minimum dominating set

Analyzing a portfolio with many assets (stocks) is mathematically challenging. This article considers a large portfolio within a graph theory framework to obtain a tracking portfolio of the actual network. Each asset forms a vertex (node), and the correlation between assets forms the weight of the edges in the graphical network. The large graphical network is efficiently managed using Minimum Dominating Sets (MDS). Finding the MDS of a given portfolio is a well-known NP-hard problem in graph theory. An integer linear programming formulation of MDS is used, and the optimal solution is found using a Gurobi solver. Additionally, greedy and local search algorithms are developed to find the MDS, reducing computation time for extensive portfolios without significantly compromising solution quality. The MDS obtained by the solver and the algorithms are directly compared with an alternative portfolio selection strategy of randomly sub-sampling a certain percentage of the actual portfolio based on size. The expected return of the tracking portfolio is compared to the actual portfolio’s expected return graphically, and a statistical significance t-test is performed to confirm the validity of the MDS, . Further, a sensitivity analysis of the expected return of the tracking portfolio obtained from the algorithms is conducted for three different threshold values of the pairwise correlation between assets. Computational results are performed on eight independent instances, with the universe of stocks varying throughout the computation.

Exploring Ground Reflection Effects on Received Signal Strength Indicator and Path Loss in Far-Field Air-to-Air for Unmanned Aerial Vehicle-Enabled Wireless Communication

Unmanned aerial vehicle (UAV)-enabled wireless communications are becoming increasingly important in applications such as maritime and forest rescue operations. UAV systems often depend on wireless networking and mobile edge computing (MEC) devices for effective deployment, particularly in swarm UAV-enabled MEC configurations focusing on channel modeling and path loss characteristics for air-to-air (A2A) communications. This paper examines path loss characteristics in far-field (FF) ground reflection scenarios, specifically comparing two environments: FF1 (forest floor) and FF2 (seawater floor). LoRa modules operating at 868 MHz were deployed for communication between a transmitting UAV (Tx-UAV) and a receiving UAV (Rx-UAV) to conduct this study. We investigated the received signal strength indicator (RSSI) and path loss characteristics across channel bandwidths of 125 kHz and 250 kHz and spread factors (SF) of 7, 9, and 12. Experimental results show that ground reflection has minimal impact in the FF1 scenario, whereas, in the FF2 scenario, ground reflection significantly influences communication. Therefore, in the seawater environment, a UAV-enabled LoRa MEC configuration using a 250 kHz bandwidth and an SF of 7 is recommended to minimize the effects of ground reflection.

An innovative framework to securely transfer data through the Internet of Things using advanced generative adversarial networks

The Internet of Things (IoT) is developing so quickly that cloud-centric computing finds it challenging to keep up with the demands of usability and low latency. Edge computing unifies computing, networking, storage and applications into a distributed open system. At the edge of the IoT, it provides intelligent services. The edge network is made up of several wired and wireless networks, and edge nodes have constrained amounts of memory and processing power. The edge network is vulnerable to several types of cyberattacks because of these factors. Large-scale network data gathering and detection for IoT security is also challenging for an IoT edge node to provide. Data analytics for intrusion detection guarantees high accuracy of intrusion detection systems (IDSs), but the implementation of such algorithms on IoT might be a challenge due to the limited resources on edge nodes. Inspired in part by these challenges, we suggest a sophisticated IDS based on a generative adversarial network (GAN). This article suggests a novel method for detecting intrusions in the IoTs networks that make use of Colony Predator Algorithm (CPA) for optimization of the detection process. Through the use of GANs to create real-looking data samples, the suggested technique helps to have reliable anomaly and intrusion detection. The integration improves the training process, making it more efficient and enabling the CPA system to better differentiate between benign and malicious activities, which exponentially raises the system's efficiency.

The role of mobile edge computing in advancing federated learning algorithms and techniques: A systematic review of applications, challenges, and future directions

Mobile Edge Computing (MEC) and Federated Learning (FL) have recently attracted considerable interest for their potential applications across diverse domains. MEC is an architecture for distributed computing that utilizes computational capabilities near the network edge, enabling quicker data processing and minimizing latency. In contrast, FL is a method in the field of Machine learning (ML) that allows for the simultaneous involvement of multiple participants to collectively train models without revealing their raw data, effectively tackling concerns related to security and privacy. This systematic review explores the core principles, architectures, and applications of FL within MEC and vice versa, providing a comprehensive analysis of these technologies. The study emphasizes FL and MEC's unique characteristics, advantages, and drawbacks, highlighting their attributes and limitations. The study explores the complex architectures of both technologies, showcasing the cutting-edge methods and tools employed for their implementation. Aside from examining the foundational principles, the review explores the depths of the internal mechanisms of FL and MEC, offering a valuable in-depth of their architecture understanding and the fundamental principles and processes that facilitate their operation. At last, the concluding remarks and future research directions are provided.

A High-Concurrency Tasks Emergency Offloading Framework for Industrial Edge Networks

In the envisioned scenario of smart factories, large-scale distributed intelligent industrial devices generate a substantial number of concurrent heterogeneous industrial tasks. A single device is inadequate to meet the demanding requirements of complex tasks, which necessitate high computational capabilities and low latency in the system. Typically, multi-access edge computing (MEC) is employed to address this challenge. However, industrial edge networks face threats such as network attacks, natural disasters, and hardware failures, posing security risks to the industrial edge network. Therefore, we have designed an emergency offloading framework to enhance its resilience. Based on a task path analysis method with multiple priority levels, we developed an emergency task scheduling pre-classification (ETSPC) algorithm. Experimental results demonstrate that the proposed algorithm significantly reduces the delay of emergency offloading for high-concurrency tasks, with a maximum reduction of up to 19.2%. In extreme scenarios, the task completion rate of the ETSPC algorithm increases by 45.3% compared to the control algorithm.