Service Requests Research Articles

How to Protect of Reader Preference Privacy in Mobile Book Information Services: A Technical Method

Along with the wide popularization of mobile terminal devices such as smart phones, mobile libraries, which provide mobile book information services as the core business, have become an important part of people’s daily life. However, with the rapid development of emerging network technologies such as cloud computing, the server side of a mobile library has become more and more untrusted, and the problem of reader preference privacy associated with mobile book information services has become increasingly prominent. To this end, this paper proposes to construct a group of false requests of mobile book information service at a trusted client of a mobile library, which are then submitted to the untrusted server side together with a true request, so as to confuse the reader book preference privacy. Firstly, we define a theoretical model for the protection of reader preference privacy. It measures the confusion effect of false requests on a true request by introducing the concepts of book information entropy, location information entropy, and book location joint entropy. It measures the concealment effect of false requests on readers’ mobile book preferences by introducing the concepts of book category distance and location region distance. Secondly, we present a specific implementation algorithm of the theoretical model to generate a group of ideal false requests that meet the privacy constraints for readers’ true requests. Finally, theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed method, i.e., it can effectively improve the security of readers’ mobile book preference privacy on the untrusted server side, without compromising the availability of a mobile book information service.

MADDPG-Based Deployment Algorithm for 5G Network Slicing

One of the core features of 5G networks is the ability to support multiple services on the same infrastructure, with network slicing being a key technology. However, existing network slicing architectures have limitations in efficiently handling slice requests with different requirements, particularly when addressing high-reliability and high-demand services, where many issues remain unresolved. For example, predicting whether actual physical resources can meet network slice request demands and achieving flexible, on-demand resource allocation for different types of slice requests are significant challenges. To address the need for more flexible and efficient service demands, this paper proposes a 5G network slicing deployment algorithm based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG). Firstly, a new 5G network slicing deployment system framework is established, which measures resources for three typical 5G network slicing scenarios (eMBB, mMTC, uRLLC) and processes different types of slice requests by predicting slice request traffic. Secondly, by adopting the multi-agent approach of MADDPG, the algorithm enhances cooperation between multiple service requests, decentralizes action selection for requests, and schedules resources separately for the three types of slice requests, thereby optimizing resource allocation. Finally, simulation results demonstrate that the proposed algorithm significantly outperforms existing algorithms in terms of resource efficiency and slice request acceptance rate, showcasing the advantages of multi-agent approaches in slice request handling.

Adaptive ubiquitous learning for server deployment and distributed offloading in UAV-enhanced IoV

Through creating an environment rich in computational and communication capabilities, ubiquitous computing gradually integrates it with human activities. Inspired by adaptive ubiquitous learning, various intelligent devices (e.g., roadside units and infrared sensors) deployed in the Internet of Vehicles (IoV) are expected to be critical to mitigating urban traffic congestion and enhancing travel safety. In addition, benefiting from the advantages of high mobility and real-time response, Unmanned Aerial Vehicles (UAVs) embody substantial prospects to assist IoV in efficiently and flexibly handling latency-sensitive, computation-intensive tasks. Nevertheless, due to time-varying demands and heterogeneous computing resources, it is challenging to provide effective service for mobile devices while guaranteeing high-quality data transmission. Therefore, a distributed service offloading system framework in UAV-enhanced IoV is designed. To minimize the service latency, a game theory-based distributed service offloading algorithm, named G-DSO, is proposed to realize adaptive ubiquitous learning for service request distribution. Finally, numerous experiments are implemented based on real-world service requirement datasets. Experimental results demonstrate that the proposed G-DSO approach improves the hit rate by 2.68% to 74.42% compared with four existing service offloading methods, verifying the effectiveness and good scalability of G-DSO.

SISTEM INFORMASI HOME SERVICE DAN PENJUALAN SPARE PARTS MENGGUNAKAN MODEL WATERFALL

Yamaha Karya Laba Motor is a company specializing in motorcycle maintenance and parts sales. Despite its services, shortcomings persist such as verbal vehicle inspections upon service intake and manual recording of service queues and parts sales. Issues arise concerning inventory monitoring, particularly when parts are depleted, necessitating time-consuming manual checks that hinder mechanics' efficiency. Additionally, the lack of home service and online parts sales further complicates customer convenience. The development of a Home Service and online parts sales application at Yamaha Karya Laba Motor aims to address these challenges by enhancing operational efficiency and customer satisfaction. The application includes features such as transaction management, user administration, parts inventory, reporting, Home Service requests, and online parts sales. These functionalities empower Yamaha Karya Laba Motor employees to efficiently monitor parts availability and generate transaction reports. Simultaneously, customers benefit from streamlined processes, saving time and ensuring convenience. This study underscores the transformative impact of digital solutions in improving operational workflows and enhancing service.

Understanding Gen Z shoppers' interaction with customer-service robots: a cognitive-affective-normative perspective

PurposeLarge supermarket chains are adopting customer-service robots to improve service delivery in physical stores. Successful deployment of these robots depends on shoppers' willingness to interact with them, requiring an understanding of influencing factors. This study, grounded in the Cognitive-Affective-Normative (CAN) theory, seeks to systematically explore the factors influencing Gen Z shoppers' willingness to interact with customer-service robots.Design/methodology/approachA hybrid approach combining Structural Equation Modeling (SEM) and Necessary Condition Analysis (NCA) was employed to analyze survey data collected from 945 Gen Zs in the Czech Republic.FindingsThe results from SEM highlight significant cognitive, normative, and affective factors that influence the intention of Gen Z shoppers to interact with a customer-service robot. Specifically, cognitive factors such as effort and performance expectancy, along with normative factors like subjective norms, emerged as critical determinants. Furthermore, affective factors such as technology anxiety and positive emotions significantly influence users' readiness to use customer-service robots for service requests. The study also underscores that positive emotions, effort expectancy, performance expectancy, and subjective norms are vital prerequisites for interacting with customer-service robots.Originality/valueThe originality of this work lies in its two significant contributions to the burgeoning field of SRs in retail literature. First, it extends the CAN theory to the context of SRs among Gen Z shoppers in Czechia, thereby enriching the existing literature on SRs in retail. Second, by employing a hybrid analytical approach, our research offers both empirical and methodological advancements, providing rigorous insights crucial for enhancing the understanding of the pivotal factors influencing shoppers' interactions with SRs in physical store environments.

Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion

Research on Public Service Request Text Classification Based on BERT-BiLSTM-CNN Feature Fusion

BPNN‐based flow classification and admission control for software defined IIoT

AbstractFlow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Transport SDN architecture for multi-layer transport slicing

The proposed architecture advances the concept of network slicing, crucial for beyond 5G services, by enabling dynamic resource allocation and customized partitioning in managed network infrastructures. This architecture addresses the challenges of provisioning end-to-end (E2E) slices across diverse network domains, which is complicated by technological heterogeneity and the variety of vendor solutions. By introducing a standardized transport network solution, we ensure seamless integration, equitable treatment of service requests, and the ability to meet diverse demands. The architecture is centered around a multi-layer transport network slicing architecture, which allows for the division of transport networks into virtual autonomous segments, each tailored for specific services or applications. This segmentation is essential for providing differentiated and personalized 5G services, optimizing network performance, and maximizing resource application. A key component of this architecture is the transport slice controller (TSC), which controls the provision and life-cycle management of transport slices, ensuring a standardized approach in the industry for the definition and realization of slices.

A REVIEW OF THE IMPACT OF COVID-19 ON SERVERLESS COMPUTING TECHNOLOGY

The Covid-19 pandemic had a profound effect on technology in general, and serverless computing is no exception. Covid-19 pandemic has shaped the field of serverless computing, and how technology has evolved in response. Serverless computing technology have been adapted to meet the need for remote working, and how the technology has changed in terms of scalability and cost-effectiveness. This pandemic has affected virtually every aspect of daily life as significant measures are being taken to limit the spread of the virus. The pandemic has changed not only the way companies operate, but also the way they have been able to survive. Studies indicate increased requests for cloud services ranging from resident users, particularly for telecommuting, entertainment, commerce, to education, and as a result, causing traffic shifts at the core of the Internet. Covid-19 had such a significant impact on cloud services that there is an unprecedented amount of demand for cloud service providers like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. This study used data from a variety of sources to analyse the impact of serverless computing during the pandemic and to justify its significance for a pandemic-affected business. It also reviewed the pre-Covid, Covid and post-Covid-19 era. Two survey reports were used in this study and the effect of Covid-19 on Serverless computing. This paper emphasizes the benefits and adoption of Serverless computing during the pandemic, in contrast to other studies that concentrated on the impact of the Covid-19 epidemic on the cloud computing environment.

Handling Efficient VNF Placement with Graph-Based Reinforcement Learning for SFC Fault Tolerance

Network functions virtualization (NFV) has become the platform for decomposing the sequence of virtual network functions (VNFs), which can be grouped as a forwarding graph of service function chaining (SFC) to serve multi-service slice requirements. NFV-enabled SFC consists of several challenges in reaching the reliability and efficiency of key performance indicators (KPIs) in management and orchestration (MANO) decision-making control. The problem of SFC fault tolerance is one of the most critical challenges for provisioning service requests, and it needs resource availability. In this article, we proposed graph neural network (GNN)-based deep reinforcement learning (DRL) to enhance SFC fault tolerance (GRL-SFT), which targets the chain graph representation, long-term approximation, and self-organizing service orchestration for future massive Internet of Everything applications. We formulate the problem as the Markov decision process (MDP). DRL seeks to maximize the cumulative rewards by maximizing the service request acceptance ratios and minimizing the average completion delays. The proposed model solves the VNF management problem in a short time and configures the node allocation reliably for real-time restoration. Our simulation result demonstrates the effectiveness of the proposed scheme and indicates better performance in terms of total rewards, delays, acceptances, failures, and restoration ratios in different network topologies compared to reference schemes.

A multi-UAV assisted task offloading and path optimization for mobile edge computing via multi-agent deep reinforcement learning

To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi-agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software-defined networking technology. Subsequently, considering UAV load and fairness in user equipment-related services, we employ the multi-agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method’s effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users’ service requests while utilizing computing resources efficiently.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Advancing orchestration synthesis for contract automata

Contract automata allow to formally define the behaviour of service contracts in terms of service offers and requests, some of which are moreover optional and some of which are necessary. A composition of contracts is said to be in agreement if all service requests are matched by corresponding offers. Whenever a composition of contracts is not in agreement, it can be refined to reach an agreement using the orchestration synthesis algorithm. This algorithm is a variant of the synthesis algorithm used in supervisory control theory and it is based on the fact that optional transitions are controllable, whereas necessary transitions are at most semi-controllable and cannot always be controlled. In this paper, we present advancements of the orchestration synthesis for contract automata. Notably, we identify the existing limits of the orchestration synthesis and propose a novel orchestration synthesis along with additional constructs to enhance the expressiveness and scalability of contract automata. The proposed advancements have been implemented and experimented on two case studies, one of which originates from the railway domain and the other is a card game.

Overcoming Communication Barriers in Clinical Care: A Digital Translation Platform.

Language barriers in healthcare can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients' relatives for interpretation, compromising the quality of care. This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating: 1. how healthcare professionals overcome language barriers and their acceptance of an on-demand video telephony platform, 2. the feasibility of the platform during medical consultations, and 3. identifying potential challenges for future development. The study included ethnographic interviews with healthcare professionals and an observational pilot to assess the use of the Tranlatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system's backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that healthcare professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer non-professional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari and Arabic. Ethnographic research conducted by interviewing healthcare professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools like Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5, 31.25%) and emergency (4, 25%). Challenges were identified around translator availability, with 23 (59%) of requests going unanswered, which was further evidenced by user feedback. The pilot study demonstrates the feasibility of the Translatly platform in real-world healthcare settings. It shows potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development.

Enhancing intelligent transport systems: A cutting-edge framework for context-aware service management with hybrid deep learning

This study presents a comprehensive framework for optimizing intelligent transport systems (ITS) by integrating advanced communication and information technologies into vehicles, roads, and infrastructure. The primary goal is to enhance transportation efficiency, safety, and environmental sustainability while improving overall mobility for people and goods. Leveraging contextual information, the framework offers personalized, proactive services such as real-time traffic updates, route recommendations, and parking availability. Additionally, it enhances safety and security by providing early hazard warnings and adapting to changing road conditions. Our proposed framework utilizes the enhanced coral reef optimization (ECRO) algorithm to efficiently group vehicles for energy-saving data collection, maximizing information gathering efficiency. Collected data is then transmitted to a central data gathering center via a sink node optimized through the modified pelican optimization (MPO) algorithm, considering various vehicle node design constraints. An incident detection module accurately classifies and detects road incidents, enabling timely emergency service requests and alternate route recommendations. To facilitate incident detection, we introduce the deep Rigdelet neural network (DRNN), a novel deep learning technique tailored for decision-making in incident classification. We validate our framework's performance through NS-2 simulations using the SUMO traffic generator, demonstrating its effectiveness in meeting quality of service (QoS) metrics. Through comparative analysis with existing frameworks, our proposed approach stands out for its superior performance and ability to optimize ITS operations.

Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network

Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.

EC-NTD: Efficient countermeasure against DrDoS attacks with NAPT and two-stage detection in SDN-based networks

Distributed reflection denial of service (DrDoS) attacks are a prevalent and troublesome form of DDoS attack. Fake service requests trigger a flood of services responses, typically in large packet sizes, that are sent to targeted hosts via public servers. As public servers are legitimate, and their services are necessary for targeted hosts, DrDoS attacks cannot be effectively blocked through firewalls or most solutions to DDoS floods. This paper leverages the software-defined networking (SDN) technique and proposes an efficient countermeasure with NAPT and two-stage detection (EC-NTD) scheme to safeguard against DrDoS attacks, where NAPT refers to network address port translation. We consider that attack sources (i.e., botnet members that send fake requests to public servers) may be outside or inside an SDN-based network where DrDoS targeted hosts reside. To guard against external attacks, filtering rules and NAPT are used in gateways to distinguish normal responses from those caused by attacks. To recognize internal attacks, the controller detects anomalies in the quantity of requests and their source IP addresses. Additionally, the adaptive adjustment of the attack detection period length helps alleviate the controller’s load while maintaining effective defense. Simulation results reveal that the EC-NTD scheme can efficiently safeguard against DrDoS attacks with different services.

Towards a quantum-safe 5G: Quantum Key Distribution in core networks

The Service-Based Architecture (SBA) of the fifth generation (5G) of cellular networks introduced great advancements in flexibility, efficiency, and performance of modern networks, thanks to the distribution and softwarization of core network entities called Network Functions (NFs). Due to the distributed nature of 5G systems, these NFs are usually located in different sites to provide better Quality of Service (QoS) or specific services, which poses a great challenge regarding security. However, even though the 3GPP standard identifies security requirements by leveraging Transport Layer Security (TLS) authentication and OAuth2.0 authorization, current experimental and commercial deployments often disregard these aspects for the sake of simplicity and performance. In fact, even though data and communication protection is crucial, the implementation of security protocols and mechanisms complicates the deployments and may reduce the efficiency of 5G systems. This paper presents a novel solution that implements TLS for authentication and encryption leveraging entanglement-based Quantum Key Distribution (QKD), ensuring that all the communications between NFs in the 5G core network are secured by QKD keys, and are therefore quantum-safe. Additionally, this paper presents a QKD key repository using the Network Repository Function (NRF), which stores and relays TLS session keys to authenticated consumer NFs to access producer NFs in a fast but quantum-safe process. The solution was validated in a real hardware environment, and obtained results demonstrate that the proposed solution enhances the security of the SBA communications in an efficient way, reducing an 85% the service request time compared to the traditional TLS-based procedure, as well as 29.96% fewer bytes transmitted throughout the process.

A distance adaptive dual-hop routing algorithm for underwater optical wireless networks

Three main kinds of underwater wireless communication, which employ acoustic waves, radio frequency and optical waves, have attracted intensive research interests in recently years. Among them, the underwater optical wireless communication (UOWC) is characterized by high propagation speed and large transmission bandwidth. But, the optical waves in underwater environment are significantly affected by absorption and scattering effects, which limit their transmission range. In order to enhance the performance of UOWC, designing a transmission and energy efficiency routing algorithm has become a non-ignorable issue in UOWC. In this paper, a transmission distance adaptive dual-hop (TDAD) routing algorithm is proposed for underwater optical wireless networks (UOWNs) to improve their efficiency in packet-delivery and energy-consumption. Unlike the existing routing algorithms designed for UOWNs, which pre-set the transmission range of network nodes, the proposed TDAD algorithm adaptively selects the transmission range for each node according to the diversity of heterogeneous service requests and employs location and energy information in its dual-hop based routing procedure. Simulation results indicate that the proposed TDAD algorithm remarkably improves packet delivery rate with more balanced energy consumption when compared to the deviation angle-based single-hop (DAS) algorithm and the distributed sector-based (DS) routing algorithm.

Effectiveness of distributed stateless network server selection under strict latency constraints

We consider a set of network users (nodes), each generating latency-constrained service requests corresponding to the execution of computational tasks on servers positioned either within a cloud infrastructure or at the network edge. Within this framework, we systematically assess the efficacy of a distributed stateless server selection strategy, strategically performed by individual nodes. Leveraging principles from game theory, our study allows for a comparative analysis between the optimality achieved through globally orchestrated stateless allocation and a decentralized stateless server selection mechanism driven by the self-interested objectives of individual nodes.Our emphasis on stateless server allocation, rooted in a probabilistic selection framework between edge and cloud servers, stems from prior empirical revelations demonstrating the advantageous outcomes of determining the optimal distribution of edge and cloud tasks based on static network characteristics. Importantly, this determination occurs irrespective of the real-time network state.The suboptimal nature of the selfish allocation is quantified by the so called “price of anarchy,” a metric shown to approximate unity closely. This observation substantiates the justification for a distributed strategic implementation of stateless policies. This elucidation serves as a pivotal guide for crafting algorithms governing server selection, providing a quantitative validation of the efficacy inherent in distributed self-interested approaches.