End-user Services Research Articles

Decentralized collaborative machine learning for protecting electricity data

In recent years, there has been a noticeable surge in electric power load due to economic development and improved living standards. The growing need for smart power solutions, such as leveraging user electricity data to forecast power peaks and utilizing power data statistics to enhance end-user services, has been on the rise. However, the misuse and unauthorized access of data have prompted stringent regulations to safeguard data integrity. This paper presents a novel decentralized collaborative machine learning framework aimed at predicting peak power loads while protecting the privacy of users’ power data. In this scheme, multiple users engage in collaborative machine learning training within a peer-to-peer network free from a centralized server, with the objective of predicting peak power loads without compromising users’ local data privacy. The proposed approach leverages blockchain technology and advanced cryptographic techniques, including multi-key homomorphic encryption and consistent hashing. Key contributions of this framework include the development of a secure dual-aggregate node aggregation algorithm and the establishment of a verifiable process within a decentralized architecture. Experimental validation has been conducted to assess the feasibility and effectiveness of the proposed scheme, demonstrating its potential to address the challenges associated with predicting peak power loads securely and preserving user data privacy.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

A Novel Method of Using Machine Learning Techniques to Protect Clouds Against Distributed Denial of Service (DDoS) Attacks.

The term "cloud computing" describes a method of providing hardware- and software-based services over the internet. This allows users to access their data and apps from any device. The benefits of cloud computing include scalability, virtualization, access to user assets, lower infrastructure costs, and flexibility. However, one drawback is that it is susceptible to distributed denial of service attacks, which occur when multiple computer systems collaborate to target a particular resource, website, or server. Distributed denial of service (DDoS) attacks present a serious risk to computer networks and constitute a major cyber security challenge. This results in a denial of service for end users, as a result of false connection requests, a flood of messages, and twisted packets causing the system to slow down or even crash. Real people and services cannot access cloud computing. The issue of machine learning algorithms for identifying distributed denial of service (DDoS) attacks is explored in this article. In order to identify and defend cloud systems from harmful assaults, we developed a new machine learning approach in this work that is based on transfer learning. This study offers NSL-KDD datasets and two methodologies. There are two types of filters available: the Learning Vector Quantization (LVQ) Filters and the Principal Component Analysis (PCA) method, which reduces dimensionality. The features selected from each method were pooled using Decision Tree (DT), Naïve Bayes (NB), and Support Vector Machine (SVM) to detect distributed denial of service attacks (DDoS). We contrasted the results of several classifications. In terms of attack detection, LVQ-based DT performed better results as compared to other methods.

Exploiting stream scheduling in QUIC: Performance assessment over wireless connectivity scenarios

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions.

The challenges of installing Picture Archiving and Communication Systems (PACS) in a low- or poor-income resource nation: Our experience in a teaching hospital in Sub-Saharan Africa

The practice of radiology as a subspecialty of medicine has undergone remarkable advancement as a result of picture archiving and communication Systems(PACS) technology. This technology allows for storage and retrieval of radiographic images and reports at any time, and possible transfer to other locations for secondary viewing and opinions. PACS enables healthcare professionals to access and review medical images quickly, allowing for faster and more accurate diagnosis and treatment planning. However, there are challenges involved in its installation and operations in our health facility, a teaching hospital in Sub-Saharan Africa. These challenges range from lack of huge financial resources required to acquire PACS equipment and accessories to lack of steady and regular electric power supply. Other challenges include inadequate dedicated space for its installation, inadequate functional cooling systems for its sensitive components, inadequate internet services for end users and paucity of qualified and trained personnel. There is, therefore, need for increased government and private sector funding in order to surmount these challenges, thereby enhancing health promotion in Sub-Saharan Africa.

Quantum-compliant users scheduling optimization in joint transmission mobile access networks

Joint Transmission (JT) is the dynamic coordination of transmission and/or reception at multiple geographically separated sites to improve end-user service quality. When user equipment receives signals from multiple sites, downstream performance improves. An optimization problem arises in selecting the best user subset for JT within a multiple-input–multiple-output (MIMO) system. Unfortunately, a pure brute-force approach is not feasible due to exponential time growth with user combinations, unsuitable for real-time selection in mobile networks with users continuously changing in time. This article proposes quantum-compliant heuristics using quadratic unconstrained binary optimization (QUBO) for JT user scheduling. QUBO handles initial user selection, followed by brute-force exploration for the solution. Numerical results indicate that quantum-compliant methods decrease solution time without substantial accuracy loss compared to brute-force methods.

Public Communication for Effective Service Delivery to Refugees in Uganda. The Case of Kiryandongo Refugee Settlement

This study argues that Public Communication in Refugee Settlements, when managed professionally and structurally with a deliberate open feedback mechanism for the end-users of services provided by the government and its development partners, could be a great tool for the evaluation and enhancement of public services provided in Refugee Settlements. The study analyses the education and health sectors as parameters for the government’s service delivery in the case of the Kiryandongo Refugee Settlement. Taking a qualitative research methodology approach with the case of Kiryandongo Refugee Settlement, the study found that the Uganda Refugees Act (2006), which authorises service provision by the government to the refugees, has no specific public communication strategy or policy clearly spelled as a communication framework to support service provision feedback. Therefore, the study recommends a direct public engagement communication strategy involving refugees and relevant stakeholders in the public service provision with the ultimate goal of improving service delivery at Kiryandongo Refugee Settlement

Mobility as a Service: an Italian MaaS pilot at regional level with public governance

Mobility as a Service (MaaS) is the new mobility challenge. In Italy, the Piedmont Region has been implementing the BIPforMaaS, the first Italian regional MaaS project with public governance, aimed at creating a framework of rules for the full deployment of MaaS in the urban and metropolitan area of Turin and in the whole Piedmont Region. Public governance guarantees fair and non-discriminatory competition, while providing a more effective service for end users. This paper presents the results of the BIPforMaaS pilot project, which involved 67 active users, with the goal of field-testing the use of a MaaS app that allows users to book various mobility services. The results showed that the proposed MaaS system can effectively integrate multiple mobility services while ensuring flexibility and efficiency. An environmental analysis is also presented in order to show how MaaS can contribute to a more sustainable mobility system by reducing CO2 emissions.

STUDY OF MULTI-STAGE SOFTWARE DEPLOYMENT STRATEGYWITH THE EXCLUSION OF SIDE EFFECTS

The article addresses a problem and its solution that arose during onboarding of new subscribers to the existing data import and distribution service. The analysis of all available solutions demonstrates that none fully meet the requirements. The process of searching for the values of the required indicators and the process of creating reports necessarily uses indexes. Messages of different types are placed in their respective MongoDB collections. Over the long period of operation of the repository, up to 64 indexes per collection have accumulated. According to the policies of connecting to the storage (Connect timeout) and waiting for a response (Socket timeout), the need to set such long-time expectations for consumers is unacceptable and usually, by default, is equal to 30 seconds. A multi-step strategy for delivering the solution that allows the service to meet availability requirements was proposed. The novelty of the article is in the description and investigation of a new method of delivering solutions to the software deployment environment—production. The implementation of the described strategy opens a new approach for solving similar classes of problems related to the type of interaction with users, when custom settings are a mandatory part of the service and are guaranteed to carry the threat of service unavailability. This approach opens up new opportunities for achieving high availability of systems, reducing risks and increasing user satisfaction. Based on the analysis of the proposed approaches, the latest strategy of multi-step delivery of the service to users was developed, the result of which was the acceptable response waiting time of up to 5 seconds. The implementation of this strategy contributed to the achievement of the availability of software products - 99.999%, ensuring the reliability and stability of services for end users.

Efficient Slice Creation in Network Slicing using K-Prototype Clustering and Context-Aware Slice Selection for Service Provisioning

The advent of 5G technology has ushered in a new era of communication where the customization of network services is crucial to meet diverse user demands. Network slicing has emerged as a pivotal technology to achieve this customization. In this research, we present an innovative approach to optimize network slicing in 5G by employing K-Prototype Clustering for slice creation and Context-Aware Slice Selection for efficient resource allocation. In slice creation, we delve into the innovative application of the K-Prototype clustering algorithm. Recognizing that 5G networks encompass numerical and categorical attributes, the K-Prototype algorithm enables the creation of network slices that cater to diverse service requirements. By harnessing this clustering technique, our proposed method optimizes the creation of network slices, resulting in improved resource utilization and reduced network congestion. Furthermore, we introduce the concept of Context-Aware Slice Selection, which considers the dynamic and evolving nature of network demands. Context-awareness ensures that network slices are selected based on real-time contextual information, enabling a more adaptive and responsive network. This approach leads to the efficient allocation of resources and a higher quality of service for end-users. To evaluate the performance of our proposed methodology, we employ key performance metrics, including slice selection accuracy, slice selection delay, and radio link failure. Through comprehensive testing and analysis, our research demonstrates that our approach consistently outperforms existing methods in terms of these metrics.

Recent Developments in Materials for Physical Hydrogen Storage: A Review.

The depletion of reliable energy sources and the environmental and climatic repercussions of polluting energy sources have become global challenges. Hence, many countries have adopted various renewable energy sources including hydrogen. Hydrogen is a future energy carrier in the global energy system and has the potential to produce zero carbon emissions. For the non-fossil energy sources, hydrogen and electricity are considered the dominant energy carriers for providing end-user services, because they can satisfy most of the consumer requirements. Hence, the development of both hydrogen production and storage is necessary to meet the standards of a "hydrogen economy". The physical and chemical absorption of hydrogen in solid storage materials is a promising hydrogen storage method because of the high storage and transportation performance. In this paper, physical hydrogen storage materials such as hollow spheres, carbon-based materials, zeolites, and metal-organic frameworks are reviewed. We summarize and discuss the properties, hydrogen storage densities at different temperatures and pressures, and the fabrication and modification methods of these materials. The challenges associated with these physical hydrogen storage materials are also discussed.

DDoS attack detection in smart grid network using reconstructive machine learning models.

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.

ОГЛЯД МЕХАНІЗМІВ ROOT CAUSE АНАЛІЗУ ТЕЛЕКОМУНІКАЦІЙНИХ МЕРЕЖ

Telecommunication networks are an integral part of modern information society, providing data transmission and communication among millions of users worldwide. The complexity and scale of these networks are constantly growing, which increases the demands on their reliability and stability. One of the key tasks in ensuring the efficient operation of telecommunication networks is the identification and elimination of the root causes of failures and anomalies that can significantly affect the quality of service for end users. Root cause analysis (RCA) is a powerful tool for identifying the primary causes of problems, preventing their recurrence, and enhancing the overall reliability of networks. The article provides a detailed review of contemporary methods and tools of RCA used in telecommunication networks. In particular, methods such as fishbone diagram analysis, the «five whys» method, fault tree analysis, and the application of machine learning for big data analysis are considered. Each of these approaches has its advantages and disadvantages, and their comprehensive use allows for higher accuracy and efficiency in identifying the root causes of problems. Practical examples demonstrate the effectiveness of applying various RCA methods to solve specific problems in telecommunication networks. For instance, the use of fishbone diagram analysis and the «five whys» method allows for the identification of the primary causes of network delays and equipment issues at base stations. The implementation of failure prediction systems based on machine learning significantly enhances network reliability by allowing for the early detection of potential problems and the necessary technical measures. The prospects for the development of RCA in telecommunication networks are outlined. The primary focus is on the development of adaptive and self-learning systems capable of dynamically responding to changing network conditions. The use of more complex artificial intelligence algorithms and tools for predicting potential problems will significantly improve the effectiveness of RCA and ensure high-quality telecommunication services.

Digitization of Accounting in the Innovative Management of Autonomous Robotic Transport

The digitization of economic processes is advancing across all sectors, contributing to the development of Industry 5.0. A key element of this fifth industrial revolution is the activation of robotic economic activity. Recently, advancements in autonomous robotic transport have been implemented in practice. However, both the practical application of unmanned vehicles and scientific developments in this field have shown low efficiency in the implementation of projects for the autonomous transportation of goods and passengers. This inefficiency stems from insufficient attention to the accounting and management aspects of autonomous robotic transport operations. The scientific and practical novelty of this study lies in improving accounting and management practices in the context of digitalization, specifically by addressing the fundamental transformations in economic processes caused by the use of autonomous vehicles. The key organizational factors influencing accounting for robotic transport operations include the type of transported objects, fuel and energy resource consumption, human involvement, the capacity and number of goods (or passengers) transported at one time, continuous operation, maintainability, software update capabilities, autonomous interaction with other transport means, and communication and information sharing with customers of transport services. A method for digitizing the accounting of fuel and energy costs, personnel wages, social activity deductions, depreciation, operational costs, and other costs related to the functioning of autonomous robotic transport has been developed. This method leverages IoT data and considers the organizational prerequisites mentioned. The use of two-dimensional calculation units, such as "kilogramkilometre" and "passenger-kilometre" units, for the digitalization of cost calculations for passenger and cargo transportation via autonomous robotic transport has been proposed. Additionally, the procedure for determining the cost of transport services for end users and the formation of information arrays for the innovative management of transport enterprises has been refined. The elimination of organizational restrictions in managing autonomous transport operations, alongside the need for information synchronization between transport enterprises and other business entities within the information ecosystem of a smart city, highlights future research prospects in this area.

Integration of cloud, fog, and edge technologies for the optimization of high-load systems

The study is dedicated to analyzing methods and tools for optimizing the performance of high-load systems using cloud, fog, and edge technologies. The focus is on understanding the concept of high-load systems, identifying the main reasons for increased load on such systems, and studying the dependency of the load on the system's scalability, number of users, and volume of processed data. The introduction of these technologies implies the creation of a multi-level topological structure that facilitates the efficient operation of distributed corporate systems and computing networks. Modern approaches to load management are considered, the main factors affecting performance are investigated, and an optimization model is proposed that ensures a high level of system efficiency and resilience to peak loads while ensuring continuity and quality of service for end-users. The methodology is based on a comprehensive approach, including the analysis of existing problems and the proposal of innovative solutions for optimization, the application of architectural solutions based on IoT, cloud, fog, and edge computing to improve performance and reduce delays in high-load systems. The scientific novelty of this work lies in the development of a unique multi-level topological structure capable of integrating cloud, fog, and edge computing to optimize high-load systems. This structure allows for improved performance, reduced delays, and effective system scaling while addressing the challenges of managing large data volumes and servicing multiple requests simultaneously. The conclusions of the study highlight the significant potential of IoT technology in improving production processes, demonstrating how the integration of modern technological solutions can contribute to increased productivity, product quality, and risk management.

Digitalisation in the provision of social services: Rural counties perspective

Digitisation affects all sectors of the economy, including social work. For social service providers (broadly speaking), it is both an opportunity and a challenge. The extent to which professionals manage the use of the digital space for social services has an impact on the accessibility and quality of services for end-users. Technology and innovative digital solutions create opportunities to tackle territorial inequalities and improve the quality of life of people living in rural areas. The provision of social services and assistance is an autonomous function of each municipality. Digital literacy is a component of human capital that includes skills in information flow, communication, safety, content creation, and problem solving in a digital environment. The article presents the results of a study on the digital literacy of social service providers working in rural areas of Latvia. The aim of the study is to find out the self-assessment of digital literacy, understanding, and attitudes towards digitalisation of social services among social service providers. The research instrument is based on the “Citizens’ Digital Competence Framework” (Digi Comp 2.1). The results of the study provide a basis for discussion not only on routine skills in the use of technical tools, but also on a deeper understanding of digitisation in the context of the emergence of a modern information society.

The impact of IMS mobility management with google mobile phone on quality of experience

The widespread of a Wireless Local Area Network (WLAN) access network in residential areas provides low-cost and high-speed access to the Third-Generation Partnership Project (3GPP) services for end users. This has virtually made WLAN to be a complementary access network to Universal Mobile Telecommunications System (UMTS). The interoperability of WLAN and UMTS is made possible with the use of a mobility management mechanism in IP Multimedia Subsystems (IMS) architecture. Current problems in mobility management mechanisms are found in device mobility such as mobile phones. Such problems affect several layers and disrupt services. In this paper, IMS mobility management mechanisms with G1 mobile phone as an IMS client testbed is implemented, and its impact on the Quality of Experience (QoE) during end-to-end VoIP call session and its conformity to the 3GPP requirements of seamless service continuity is evaluated. The QoE driven mobility management mechanism is then proposed and evaluated to improve the QoE and to meet the 3GPP requirements of seamless service continuity during the handover between UMTS and WLAN access networks. Preliminary results show that there is an improvement in QoE and the requirements of 3GPP service continuity were met. The proposed Quality of Service (QoS) driven mobility management mechanism will help in the development of dual-mode mobile handsets with QoE adaptability.

Artificial Intelligence Agents for Materials Sciences.

The artificial intelligence (AI) tools based on large-language models may serve as a demonstration that we are reaching a groundbreaking new paradigm in which machines themselves will generate knowledge autonomously. This statement is based on the assumption that the ability to master natural languages is the ultimate frontier for this new paradigm and perhaps an essential step to achieving the so-called general artificial intelligence. Autonomous knowledge generation implies that a machine will be able, for instance, to retrieve and understand the contents of the scientific literature and provide interpretations for existing data, allowing it to propose and address new scientific problems. While one may assume that the continued development of AI tools exploiting large-language models, with more data used for training, may lead these systems to learn autonomously, this learning can be accelerated by devising human-assisted strategies to deal with specific tasks. For example, strategies may be implemented for AI tools to emulate the analysis of multivariate data by human experts or in identifying and explaining patterns in temporal series. In addition to generic AI tools, such as Chat AIs, one may conceive personal AI agents, potentially working together, that are likely to serve end users in the near future. In this perspective paper, we discuss the development of this type of agent, focusing on its architecture and requirements. As a proof-of-concept, we exemplify how such an AI agent could work to assist researchers in materials sciences.

Online Learning for Failure-Aware Edge Backup of Service Function Chains With the Minimum Latency

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to place redundant SFC backups at the edge for quickly recovering from any failures. The existing research largely overlooks the influences of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SFC popularity, backup completeness,</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">failure rate</i> on the optimal deployment of SFC backups on edge servers. In this paper, we comprehensively consider from the perspectives of both the end users and edge system to backup SFCs for providing popular services with the lowest latency. To overcome the challenges resulted from unknown SFC popularity and failure rate, as well as the known system parameter constraints, we take advantage of the online bandit learning technique to cope with the uncertainty issue. Combining the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Prim</i> -inspired method with the greedy strategy, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Real-Time Selection and Deployment</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RTSD</i> ) algorithm. Extensive simulation experiments are conducted to demonstrate the superiority of our proposed algorithms.

KVFL: Key-Value-Based Persistent Fuzzing for IoT Web Servers

Abstract As the number of Internet of Thing (IoT) devices increases, attacks against their vulnerabilities have become a serious threat. The web servers (WSs) in IoT devices provide management services for end-users, which are currently the major attack surface. Several fuzzing solutions for identifying vulnerabilities in IoT devices have been proposed, but there is currently no grey-box fuzzer specifically designed for the unique features of WSs in IoT to effectively detect memory corruption vulnerabilities. We design and implement KVFL, an efficient grey-box fuzzer, to address the issues of low throughput and slow exploration of deep code when fuzzing for IoT WSs. Firstly, KVFL employs a delicate hooking technology that heuristically hijacks and emulates hardware-dependent functions, ensuring WSs can be accurately and efficiently emulated in user-mode. On this basis, KVFL fully utilizes the loop parsing HTTP requests feature of WSs through a redesigned fork-server, to minimize nonessential rebooting losses of the target, thereby significantly improving fuzzing throughput. Secondly, KVFL leverages code coverage feedback to automatically infer a set of valid Keys and derive a Key-Value mutation. This enables the generation of high-quality test cases that can facilitate deeper code exploration of WSs. The evaluation results show that compared to the state-of-the-art IoT grey-box fuzzer FIRM-AFL, KVFL improves the throughput by over 2× and explores 4.5× more edges. Additionally, it identifies all 1-day vulnerabilities with over 7× faster speed than the baseline and detects three previously unknown 0-day vulnerabilities. These all indicate that KVFL is effective and efficient at fuzzing IoT WSs.