Service Requests Research Articles

A policy gradient approach to solving dynamic assignment problem for on-site service delivery

The paper studies the resource allocation problem for delivering on-site services in urban areas. Requests for services are received spontaneously, with deliveries to be assigned dynamically. Real-life examples of such applications include the dispatch of traffic officers to scenes of accidents and the deployment of mechanics to sites of maintenance works. The dynamic assignment problem is to be solved via a policy gradient approach that dynamically assigns workers to different locations so that each customer involved would experience a minimum delay. Our solution framework adopts the transformer architecture with layers of inter-task and inter-agent communications as the approximator. This approximator is trained with the vanilla policy gradient algorithm. To improve computational effectiveness, we introduce an option of withholding an assignment, where workers may not be assigned at a decision point even if a service request is received, to enhance the flexibility of actions. Extensive computational experiments with a varying number of orders, order frequencies, and spatial sparsity are conducted. Our proposed method is shown to outperform other benchmarking methods, including the genetic algorithm and other online heuristics, in terms of stability of effectiveness, computational efficiency, and solution quality. Our experimental results suggest that the proposed method would have a reduced advantage over other benchmarking algorithms if the on-site service time is long.

Distributed Denial-of-Service (DDoS) Attack Detection using 1D Convolution Neural Network (CNN) and Decision Tree Model

The major problem of internet security is a Distributed Denial-of-Service (DDoS) attack, which can’t be detected easily. This attack is said to have occurred when lots of service requests are simultaneously received at a server on the internet. This makes the server too busy to provide normal services for others. The Distributed Denial of Service (DDoS) attacks nature on large networks on the Internet demanding to develop the effective detection and response methods. The deployment of these technique should perform not only at the network core but also at the edge. A DDoS attack detection framework is presented based on transfer learning model consisting of 1D Convolution Neural Network (CNN) and decision tree classifier. The 1D CNN model utilizes for features extraction from the input network traffic data. This operation also reduces the dimension of the data thereby removing the redundancy in the data. These features are given to the decision tree model for classification. The proposed framework identified the DDoS attacks with good accuracy. This system could identify attacks in real-time and provide network security.

A Distributed Pricing Strategy for Edge Computation Offloading Optimization in Autonomous Driving

The increase of on-vehicle applications has brought explosive computation demands to autonomous vehicles and overwhelmed their limited onboard resources. Edge computing can offload application load and effectively alleviate this problem. However, the introduction of edge computing faces significant challenges, including the considerable amount of resource contention due to the scarcity of edge resources and the competition among edge computing resource providers to earn users' services requests. We notice that the problem is not purely technical as solutions for these two problems can become conflicting to each other. In this paper, we propose a distributed pricing strategy to achieve full use of computing resources at the edge and maximize the revenue of service operators, both with guaranteed quality-of-service of on-vehicle applications. More specifically, we first use the multi-leader multi-follower Stackelberg game theory to model the pricing of on-vehicle task offloading under edge computing. Next, we propose a distributed pricing strategy to enable edge servers to adjust their local price distributions so that edge servers can bargain with offloading requesters independently. Experimental results confirm that the proposed distributed pricing strategy can provide more optimized server computing resource utilization while guaranteeing the performance of in-vehicle applications.

Cooperative Service Placement and Request Routing in Mobile Edge Networks for Latency-Sensitive Applications

The emergence of mobile applications like self-driving cars, networked gaming, virtual reality, and augmented reality has created a demand for computation-intensive and latency critical applications. The dense deployment of base stations (BSs) makes the overlapping coverage of BS that induces the placement of services and routing the requests to the appropriate node is a challenging issue. Further, the limited resources at the edge node invite additional problems of allocating the existing limited edge resources cooperatively to fulfil the user demands. To address the problems mentioned above, we formulate the cooperative service placement and routing problem by placing the diversified latency critical services to maximize the time utility with the deadline and resource constraints. The problem is formulated as an integer linear programming problem for cooperative service placement in mobile edge networks. A greedy and randomized rounding technique is presented to solve the service caching and request routing problems decomposed from the proposed problem. Further, a heuristic algorithm based on the circular convex set is presented to solve the proposed NP-hard problem. Extensive simulation results demonstrate that the proposed cooperative service placement schemes significantly improve the delay, cache hit ratio, load on cloud, and time utility performance compared with existing mechanisms.

Optimizing Service Redeployment in Migration-Oriented IoT Networks

The Internet of Things (IoT) paradigm has established an effective platform to promote the collaboration of resource-limited and duty-cycle IoT nodes, in order to support relative complex service requests that can hardly be achieved by any single IoT node. The functionalities of IoT nodes are typically encapsulated into IoT services, and the satisfaction of service requests is implemented as IoT service composition. Generally, IoT nodes work in turn in terms of their pre-specific working cycles, and IoT network topology is constantly varied due to their active/sleeping behaviour switching, causing unscheduled response latency. IoT service composition instantiation should be dynamically adjusted and partially re-deployed on-demand for supporting request processing efficiently. To remedy this issue, this paper proposes a Migration-oriented Service re-Deployment (MSrD) mechanism by migrating certain IoT services from their hosted IoT nodes to neighboring ones, in order to support functionally continuous availability.We formulate this problem as a game-theoretic approach, which is reduced to a potential game, where a Nash equilibrium solution is searched for optimizing this service re-deployment game. Extensive experiments are conducted, and numerical results show that our MSrD mechanism is promising, compared with the state-of-art techniques, in achieving service re-deployment optimization with efficient energy consumption and timely response latency simultaneously.

Microservice-Oriented Service Placement for Mobile Edge Computing in Sustainable Internet of Vehicles

The integration of Mobile Edge Computing (MEC) and microservice architecture drives the implementation of the sustainable Internet of Vehicles (IoV). The microservice architecture enables the decomposition of a service into multiple independent, fine-grained microservices working independently. With MEC, microservices can be placed on Edge Service Providers (ESPs) dynamically, responding quickly and reducing service latency and resource consumption. However, the burgeoning of IoV leads to high computation and resource overheads, making service resource requirements an imminent issue. What’s more, due to the limited computation power of ESPs, they can only host a few services. Therefore, ESPs should judiciously decide which services to host. In this paper, we propose a Microservice-oriented Service Placement (MOSP) mechanism for MEC-enabled IoV to shorten service latency, reduce high resource consumption levels and guarantee long-term sustainability. Specifically, we formulate the service placement as an integer linear programming program, where service placement decisions are collaboratively optimized among ESPs, aiming to address spatial demand coupling, service heterogeneity, and decentralized coordination in MEC systems. MOSP comprises an upper layer to map the service requests to ESPs and a lower layer to adjust the service placement of ESPs. Evaluation results show that the microservice-oriented service deployment mechanism offers dramatic improvements in terms of resource savings, latency reduction, and service speed.

Renewable Energy Provision and Energy-Efficient Operational Management for Sustainable 5G Infrastructures

The energy consumption of mobile network infrastructures has been witnessed to rise significantly in recent years due to the massive growth of mobile traffic triggered by the increasing adoption of 5G networks and massive applications of the internet of things (IoT). The increase in network traffic not only demands network densification but also increases the operational expenditure (OPEX) of the mobile network operators as well as the environmental and sustainability concerns. To address such a challenge, green energy technology has received increasing attention. However, keeping the density of small cell base stations (SCBSs) and matching the dynamics of available energy variations to the user service request arrivals are non-trivial that need further investigation. This paper proposes to utilize the microgeneration of renewable energy (RE) infrastructure with traffic-aware load offloading integrated with advanced sleep mode (ASM) operation. The centralized renewable energy microgeneration approach is adopted to power the high density of SCBSs installed at a dispersed geographical location. The stochastic modeling-based traffic offloading can offload the macro base station (MBS) users to SCBSs depending on the traffic intensity. The advanced sleep mode stochastic model is presented to gradually manage the underutilized SCBSs into sleep modes based on the traffic load. The proposed scheme is developed to reduce the on-grid energy consumption whilst meeting the quality of service (QoS) requirement in terms of blocking probability and reactivation delay. The numerical results demonstrated that the proposed solution can achieve a significant amount of energy-saving depending upon the renewable energy availability and MBS traffic offloading with ASMs policies.

The Multi-Trip Autonomous Mobile Robot Scheduling Problem with Time Windows in a Stochastic Environment at Smart Hospitals

Autonomous mobile robots (AMRs) play a crucial role in transportation and service tasks at hospitals, contributing to enhanced efficiency and meeting medical demands. This paper investigates the optimization problem of scheduling strategies for AMRs at smart hospitals, where the service and travel times of AMRs are stochastic. A stochastic mixed-integer programming model is formulated to minimize the total cost of the hospital by reducing the number of AMRs and travel distance while satisfying constraints such as AMR battery state of charge, AMR capacity, and time windows for medical requests. To address this objective, some properties of the solutions with time window constraints are identified. The variable neighborhood search (VNS) algorithm is adjusted by incorporating the properties of the AMR scheduling problem to solve the model. Experimental results demonstrate that VNS generates high-quality solutions. Both enhanced efficiency and the meeting of medical demands are achieved through intelligently arranging the driving routes of AMRs for both charging and service requests, resulting in substantial cost reductions for hospitals and enhanced utilization of medical resources.

A Comprehensive Study of the CA-72.4 Tumor Marker Utilization and Cancer Detection: Analysis Based on Ministry of Health Data

Background: This study aims to explore the relevance and utilization of the CA 72.4 tumor marker in cancer diagnosis, with a particular focus on gastrointestinal tumors and ovarian cancer, based on data from the Minis-try of Health. Materials and Methods: We conducted a retrospective analysis of health records from the Ministry of Health spanning over five years. The data, consisting of various demographics, including gender, age, and clinical service requests, was statistically analyzed. The rates of reference range exceedance for the CA 72.4 tumor marker, diagnosis times, the ratio of cancer diagnosis, and the cost distribution were among the key aspects evaluated. Results: The analysis revealed a significant increase in the request for CA 72.4 tests over the years. It was also observed that cancer detection rates slightly increased over time, with a higher percentage in patients testing positive for CA 72.4, primarily requested before diagnosis. The highest requests for the test were made by the Medical Oncology clinic. Furthermore, it was observed that gender and age groups played significant roles in the prevalence of cancer diagnoses. Conclusions: Our study indicates that the use of CA 72.4 as a tumor marker could be valuable, especially in the detection of certain types of cancer. However, the correlation between a positive CA 72.4 test and a definitive cancer diagnosis requires further investigation, as the sensitivity and specificity of this marker are still subjects of debate. Despite certain limitations, this study offers substantial insights into the utility of CA 72.4 in the current healthcare setting.

Online Physician-Patient Interaction and Patient Satisfaction: Empirical Study of the Internet Hospital Service.

In China, a form of online health service called the internet hospital became a prominent means of patient care when face-to-face visits were not possible during the COVID-19 pandemic to minimize transmission of the SARS-CoV-2 virus. Patients' internet hospital experiences largely depend on online physician-patient interaction. Yet, little is known about how physicians can improve patient satisfaction by using specific communication strategies online. This study aimed to identify specific communication strategies to help physicians deliver better quality internet hospital services. We also outline recommendations for hospitals to operate internet hospital platforms more effectively. A longitudinal data set was collected from an internet hospital platform operated by a top hospital in China. By extracting communication patterns from approximately 20,000 records of online health care services and by controlling the features of service requests, we tested the impacts of response load, more detailed style, and emotional comfort on patient satisfaction. We further explored the effects of these communication patterns in different service contexts. Physicians with a low response load, a more detailed style, and expressions of emotional comfort received more positive patient feedback. Response load did not affect patient satisfaction with free online health service, whereas a more detailed style and emotional comfort enhanced satisfaction with free service. Response load significantly reduced patient satisfaction with paid online health service, while a more detailed style had no effect. Compared with free service, emotional comfort more strongly promoted patient satisfaction with paid service. The communication strategies identified can help physicians provide patients with a better internet hospital experience. These strategies require hospitals to schedule each physician's online service period more appropriately. In addition, tailoring the strategies to service situations can facilitate more targeted and effective internet hospital service for patients.

Analysis of a Collision-Affected M/GI/1/ /N Retrial Queuing System Considering Negative Customers and Transmission Errors

This paper considers a retrial G-queue with collisions, transmission errors, and a finite number of sources, where service and repair time are both general distributions. The number of sources (terminals) is finite and a source cannot generate new requests until the channel (server) finishes its work, i.e., the rate at which new primary requests are generated varies inversely with the number of data frame (customer) in the system. A collision occurs when service requests arrive at a busy channel, and transmission errors prevent data frames from leaving the system after completing service. Two types of arrivals are considered. Negative customers will break down the system in the busy state and remove the customer under service. The application of our model is indicated, with a particular emphasis on communication networks such as the local-area networks (LAN) with CSMA/CD protocol. Recursive formulas have been derived to calculate the stationary joint distributions and the Laplace transform of reliability function by applying the discrete transformations method along with the supplementary variables technique (SVT). Furthermore, the comparative performance and reliability analysis have been conducted numerically. Numerical examples are provided to investigate the sensitivity of different parameters on performance measures and reliability indicators.

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

Sistem Informasi Permintaan Barang dan Jasa Fasilitas Umum

Abstract
 The Banten Province Land Transportation Management Office (BPTD) Region VIII oversees many Service Units (SATPEL) such as type A bus terminals, crossing ports, and weigh stations in Banten Province. The General Bureau is a Work Unit (Satker) tasked with handling requests for goods/services. Some problems occur in the process of requesting goods/services in the Office of the Land Transportation Management Center (BPTD) Region VIII of Banten Province, namely the submission process is still manual, so it takes a long time and the status of approval from each party who has a decision-making policy cannot be known directly. So to overcome the problems faced, it is necessary to design an information system for requests for goods and services that use the Waterfall method and the PHP and MySQL programming languages ​​to create an expected information system, so that data processing can be processed more quickly and in control. Thus each Service Unit (Satpel) can submit a letter of submission of goods by accessing the goods/services procurement web online.
 
 Keywords: Service Goods, Demand, Information Systems, Transportation, Waterfall.

Collaborative on-demand dynamic deployment via deep reinforcement learning for IoV service in multi edge clouds

In vehicular edge computing, the low-delay services are invoked by the vehicles from the edge clouds while the vehicles moving on the roads. Because of the insufficiency of computing capacity and storage resource for edge clouds, a single edge cloud cannot handle all the services, and thus the efficient service deployment strategy in multi edge clouds should be designed according to the service demands. Noticed that the service demands are dynamic in temporal, and the inter-relationship between services is a non-negligible factor for service deployment. In order to address the new challenges produced by these factors, a collaborative service on-demand dynamic deployment approach with deep reinforcement learning is proposed, which is named CODD-DQN. In our approach, the number of service request of each edge clouds are forecasted by a time-aware service demands prediction algorithm, and then the interacting services are discovered through the analysis of service invoking logs. On this basis, the service response time models are constructed to formulated the problem, aiming to minimize service response time with data transmission delay between services. Furthermore, a collaborative service dynamic deployment algorithm with DQN model is proposed to deploy the interacting services. Finally, the real-world dataset based experiments are conducted. The results show our approach can achieve lowest service response time than other algorithms for service deployment.

Does Race Affect Public Evaluations of Constituent Messages in Local Government Meetings? Results from an Experiment

Constituents in the United States have used local public meetings in recent years to shape policy on some of the most high-profile race-related issues. However, public meeting participation remains less studied relative to other modes of participation. This study investigates the extent to which race shapes the way a message is heard and evaluated by the public audience and the degree to which its impact depends on the issue of the message. To carry out the study, I set up a 2 × 3 experiment containing six short treatment videos in which I manipulated the race of the actor/speaker (Black or White) and the message issue (innocuous service request, race-related policy, and race-neutral policy). Subjects were randomly assigned to watch one of the six videos and then asked to evaluate the message in the video. The early results of this study show that unlike previous studies revealing racial bias in a range of political contexts in the United States, race exerts neither independent nor conditional effects on the evaluation of the message as reasonable. I discuss the limitations and implications of these findings and useful considerations to think about in expanding this study.

Cohort-based kernel principal component analysis with Multi-path Service Routing in Federated Learning

Federated Learning (FL) is a machine learning (ML) strategy that is performed in a decentralized environment. The training is performed locally by the client on the global model shared by the server. Federated learning has recently been used as a service (FLaaS) to provide a collaborative training environment to independent third-party applications. However, the widespread adoption in distributed settings of FL has opened venues for a number of security attacks. A number of studies have been performed to prevent multiple FL attacks. However, sophisticated attacks, such as label-flipping attacks, have received little or no attention. From the said perspective, this research is focused on providing a defense mechanism for the aforesaid attack. The proposed approach is based on Type-based Cohorts (TC) with Kernel Principal Component Analysis (KPCA) to detect and defend against label-flipping attacks. Moreover, to improve the performance of the network, we will deploy Multi-path Service Routing (MSR) for edge nodes to work effectively. The KPCA will be used to secure the network from attacks. The proposed mechanism will provide an effective and secure FL system. The proposed approach is evaluated with respect to the following measures: execution time, memory consumption, information loss, accuracy, service request violations, and the request’s waiting time.

Design of a Decentralized Identifier-Based Authentication and Access Control Model for Smart Homes

In recent years, smart homes have garnered extensive attention as a prominent application scenario of IoT technology. However, the unique characteristics of smart homes have brought forth serious security threats, emphasizing the paramount importance of identity authentication and access control. The conventional centralized approach is plagued by the issue of having a “single point of failure,” while existing distributed solutions are constrained by limited device resources and the complexities of identity authentication. To tackle these challenges, this paper proposes a smart home authentication and access control model based on decentralized identifiers (DIDs). By leveraging the inherent decentralization of DIDs, which rely on blockchain, a distributed environment is constructed, effectively mitigating the problem of the “single point of failure.” In this model, every participant in the smart home system, including users and smart devices, is uniquely identified by DIDs and through the integration of an improved capability-based access control scheme, which streamlines the user identity authentication process, reduces authentication complexity, and enables convenient cross-household access with a single registration. Our experimental results demonstrate that the application of decentralized identifiers provides the model with various security attributes, including confidentiality, integrity, and traceability. Additionally, the model exhibits low time costs for each module, ensuring timely responses to access service requests and incurring lower gas consumption compared to other Ethereum-based methods. Thus, our research proposes a lightweight authentication and access control solution suitable for smart home environments.

How is primary care access changing? A retrospective, repeated cross-sectional study of patient-initiated demand at general practices in England using a modern access model, 2019–2022

ObjectivesTo explore trends in patient-initiated requests for general practice services and the association between patient characteristics including demographics, preferences for care and clinical needs and modes of patient contact (online...

Performance Analysis of a Retrial Queueing System with Optional Service, Unreliable Server, Balking and Feedback

This paper considers a Markovian retrial queueing system with an optional service, unreliable server, balking and feedback. An arriving customer can avail of immediate service if the server is free. If the potential customer encounters a busy server, it may either join the orbit or balk the system. The customers may retry their request for service from the orbit after a random amount of time. Each customer gets the First Essential Service (FES). After the completion of FES, the customers may seek the Second Optional Service (SOS) or leave the system. In the event of unforeseen circumstances, the server may encounter a breakdown, at which point an immediate repair process will be initiated. After the service completion, the customer may leave the system or re-join the orbit if not satisfied and demand regular service as feedback. In this investigation, the stationary queue size distributions are framed using a recursive approach. Various system performance measures are derived. The effects induced by the system parameters on the performance metrics are numerically and graphically analysed.

Implementation of Blockchain-Assisted Source Routing for Traffic Management in Software-Defined Networks

The control and infrastructure layers are split into Software-Defined Networks (SDNs). With the control and infrastructure planes split, new network applications may be developed with more simplicity and greater independence. On the other hand, the disadvantages of SDN create a slew of questions. In large-scale networks, such as Wide Area Networks (WANs) covering huge areas, more propagation delays substantially contribute to network convergence time. In addition, traditional SDN restricts network design flexibility due to the influence of controller location on network performance in large-scale networks. SDN-based source routing (SR) has emerged as a viable solution to the issues above, where the packet header field is used to specify a packet's route. This study presents an SR-based End-to-End (E2E) traffic management framework called SoRBlock. In SoRBlock, inter-domain routing uses blockchain technology, while intra-domain routing relies on the SR technique in SDNs. The simulation results show that the proposed SR-based SoRBlock framework outperforms the traditional hierarchical routing approach, HRA, in SDN networks by lowering path setup time (PST) and the number of controller messages. While the same (i.e., identical origin and target) service requests were used for all runs in the simulations, the proposed SoRBlock architecture presents almost three times less total PST between 45ms and 65ms than the HRA method between 130ms and 200ms due to the HRA approach's increased node-controller and controller-controller latencies. On the other hand, SoRBlock shows two times less PST ([75ms – 90ms]) than HRA ([150ms – 175ms]) when different service requests (i.e., different origin and target) were used. Concerning Controller Messages Processed (CMP), the HRA deals nearly 50% more controller messages between 7 and 15 than the SoRBlock between 3 and 10 when the number of domains varies, while the CMP in the SoRBlock scheme ([10 - 17]) approaches that in the HRA framework ([15 - 20]) regarding the ratio while the count of nodes rises in domains.