Service Selection Research Articles

A Survey of Trust Management Schemes for Social Internet of Things

Social Internet of Things (SIoT) involves integrating social networking concepts in the Internet of Things (IoT) to enhance social interactions among IoT objects and users. SIoT is envisaged to provide adequate service selection and discovery. Trust is an essential factor whenever social concepts are discussed in communication networks. Trust usually leads to a mutual relationship between two parties (i.e., the trustor and trustee) where they both enjoy mutual benefits. For secure social relationships, Trust management (TM) is a crucial feature of SIoT. The primary aim of this work is to provide a comprehensive review of trust management proposals/schemes available for SIoT. Four main trust calculation algorithms for trust management were selected for this review, and they were examined in detail. The IEEE Xplore, Scopus, ResearchGate, and Google Scholar databases were searched for articles containing the terms "Trust aggregation approaches in IoT", and "Trust computation in SIoT" with a particular emphasis on works published between 2018 and 2021. The paper also discussed the pros and cons of each TM technique, trust metrics/features, contributions, and limitations of the state-of-the-art SIoT TM proposals in the literature. The paper further provides open issues and possible research directions for entry-level researchers in the domain of SIoT.

Explaining consumer suspicion: insights of a vignette study on online product reviews

As part of the online product and service selection and purchase process, many consumers consult and rely on online product reviews. In order to persuade potential customers to buy their products, many organizations and businesses post deceptive product reviews of their own products on their own or third-party websites to their advantage. This creates consumer suspicion about the authenticity and veracity of online product reviews. To better understand how consumers’ experiences of having been deceived by deceptive online product reviews in the past and the density of deception characteristics in an online product review influence their level of suspicion about the review and, ultimately, their intention to buy the product, we conduct a 3 × 3 vignette study. Our results indicate that deceptive characteristics in online product reviews and prior encounters with deception in online marketplaces increase consumer suspicion. Furthermore, we show that preference for a specific product decreases consumer suspicion about reviews of that product. Lastly, we demonstrate that consumer suspicion towards a product decreases purchase intention.

Predicting the selection of industrial waste disposal service in cement kiln using a random parameters approach with heterogeneity in means and variances

Industrial waste disposal is conducted by coprocessing in the cement kiln. This process can save resources and dispose of industrial waste in a way that is beneficial to the environment by using renewable energy and being a viable alternative to the cement industry in several countries. Nowadays, there is competition among industrial waste processors due to their increasing numbers. Therefore, this study aimed to investigate the significant factors related to forecasting the selection of industrial waste disposal services in cement kilns by developing random parameters with heterogeneity in means and variances. To our knowledge, there are no studies analyzing the selection of industrial waste disposal in coprocessing. Thus, this analysis is a novel approach, able to reduce the least bias and incorrect inference that may lead to operation on effective dealing measures to explain individual relationships based on the differences of several customers. The questionnaires were completed by the customers. According to our findings, a statistically significant factor that customers considered was the image of industrial waste disposal processors by coprocessing in cement kiln, and factors of distances from the waste processors had a significant role in customer decision, whereas the logistics job position may choose the service due to the E-license convenience. The limitation of the study is findings in Thailand. Coprocessing in other countries can use the research results and incorporate them into their strategic business plan in the future to ensure the sustainability of their service by coprocessing cement kilns.

DeepLTSC: Long-Tail Service Classification via Integrating Category Attentive Deep Neural Network and Feature Augmentation

With the explosive growth in the number and diversity of Web services, correlative research has been investigated on Web service classification, as it fundamentally promotes advanced service-oriented applications, such as service discovery, selection, composition and recommendation. However, conventional approaches are restricted to indiscriminatingly classify Web services, which can trigger many challenges. First, they have not made full advantage of the implicit relationships among multi-dimensional information of Web services, such as the increasing number of service categories. Thus, it leads to low effectiveness of learning and representing service features, failing to ensure the overall accuracy of service classification. Second, the imbalance of service distributions has been ignored, while it is observed that service categories reveal distinct long-tail characteristics. That results in low accuracy on service classification for those categories that contain fewer Web services. To handle the challenges of more effectively learning implicit service features across the service repository, and with a particular concentration on those tail categories that contain fewer Web services, we propose a novel framework called DeepLTSC to more accurately perform the task of Web service classification under long-tail distributions. In DeepLTSC, we first present an improved label attentive convolutional deep neural network (LACNN) with service categories, which can generate deep service features to improve the overall classification performance. Then, a proposed service feature augmentation model (SFA) together with focal loss function is integrated into DeepLTSC to further optimize service features, aiming to boost the classification accuracy on tail service categories. Extensive experiments are conducted on three large-scale real-world services datasets with different long-tail distributions. The results demonstrate that DeepLTSC significantly outperforms state-of-the-art approaches for Web service classification on both overall and tail categories.

OPTCLOUD: An Optimal Cloud Service Selection Framework Using QoS Correlation Lens.

Cloud computing has grown as a computing paradigm in the last few years. Due to the explosive increase in the number of cloud services, QoS (quality of service) becomes an important factor in service filtering. Moreover, it becomes a nontrivial problem when comparing the functionality of cloud services with different performance metrics. Therefore, optimal cloud service selection is quite challenging and extremely important for users. In the existing approaches of cloud service selection, the user's preferences are offered by the user in a quantitative form. With fuzziness and subjectivity, it is a hurdle task for users to express clear preferences. Moreover, many QoS attributes are not independent but interrelated; therefore, the existing weighted summation method cannot accommodate correlations among QoS attributes and produces inaccurate results. To resolve this problem, we propose a cloud service framework that takes the user's preferences and chooses the optimal cloud service based on the user's QoS constraints. We propose a cloud service selection algorithm, based on principal component analysis (PCA) and the best-worst method (BWM), which eliminates the correlations between QoS and provides the best cloud services with the best QoS values for users. In the end, a numerical example is shown to validate the effectiveness and feasibility of the proposed methodology.

Application Analysis: Unit Price Contract System In The Procurement Of Goods And Services At Pt Xyz

This applied research describe how the process of procurement of goods and services at PT. XYZ. This research is descriptive qualitative research where the method used is a literacy study to find out the previous research, then observation, interview, and observation of document data. Selection of good and services providers at PT. XYZ is done by way of Unit Price Contract (UPC). The stages of the procurement of good and services in UPC system include two stages, at the first stage is carried out to select the procurement participant administratively. The second stage is determining the winner of the UPC partner. In its implementation, the procurement of good and services at PT. XYZ rarely find significant obstacles.

A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

In general, objective and subjective QoS assessment data forms the major data source for service evaluations. Especially, in the most dynamic service-oriented computing ecosystem like cloud environments, such assessment data is subjected to high uncertainty due to the dynamics in the number of cloud services and related QoS values. In such cases, the accuracy and reliability of the most efficient cloud service selection model remain questionable. Further, the complex interdependencies among the multiple QoS parameters and the trustworthiness of the cloud services complicate the process of handling uncertainty during cloud service selection. This work presents Fuzzy-Multi Attribute Decision Making (FMADM), a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data. FMADM employs (i) Picture Fuzzy Sets (PFSs): to capture inconsistency, uncertainty, and ambiguity in the QoS data, (ii) Naïve Bayes: to recompute the weights of the QoS attributes, (iii) Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS): to model the non-linear relationship between the QoS values of cloud services and their corresponding trust result, and (iv) Random Forest Classifier (RFC): to predict the trust value of cloud services. The performance of FMADM was evaluated using Quality Web Service (QWS) v1.0 dataset under different scenarios using various quality metrics.

New Mobile Cloud Computing Approach Based Multi-Agent System For OWL-S Services

Using mobile cloud technology, local application resources can move into the cloud computing resource pool in the form of web services. The web services discovery and execution processes in the mobile environment are considered as a very difficult challenge. Moreover, these processes may degrade network performance due to the mobile environment. To overcome these issues, we present a new approach for better discovery, selection, execution, and negotiation of OWL-S services by utilizing a multi-agent system in mobile cloud computing. By using the Multi-Agent System (mobile agent and fixed agent), these processes are made with the minimum utilization of resources because the mobile agents migrate to targeted services and interact locally with them. Moreover, the use of mobile agent overcomes the problems related to the mobile environment and wireless devices. Besides, it automatizes the discovery, selection, execution, and negotiation of OWL-S services. The obtained results demonstrate the effectiveness of the proposed system. Moreover, we conclude that the mobile agent is a good solution to eliminate the problem of wireless devices and the mobile environment, and reduced the execution time of service discovery and invocation. Also, the finding indicates that cloud computing is a good solution to eliminate the problem of storage and execution in mobile devices. Besides, the context-aware provides a more accurate service selection.

Logistics Service Provider Evaluation and Selection: Hybrid SERVQUAL–FAHP–TOPSIS Model

Production and business enterprises are aiming to improve their logistics activities in order to increase competitiveness. Therefore, the criteria and decision support models for selecting logistics service providers are significant to businesses. Fuzzy theory has been applied to almost all industrial engineering fields, such as decision making, operations research, quality control, project scheduling and many more. In this research, the authors combined fuzzy theory and a Multicriteria Decision Making (MCDM) model for the evaluation and selection of potential third-party logistics (3PL) providers. The goal is to take the advantages of these approaches and allow for more accurate and balanced (symmetric) decision making through their integration. The main contribution of this study is that it develops a complete approach to assessing the quality of the logistics service industry. The combined method of the SERVQUAL and FAHP–TOPSIS models not only provides reasonable results, but it also allows decision makers to visualize the impact of different criteria on the final outcome. Furthermore, this integrated model can provide valuable insights and methods for other areas to define service quality.

Towards a new semantic middleware for service description, discovery, selection, and composition in the Internet of Things

AbstractThe emergence of the Internet of Things (IoT) allows the integration of everyday devices such as tags, sensors, and actuators into the Internet. IoT also provides handy and automatic techniques to interoperate ad‐hoc, heterogeneous, and mobile networks and simplify data sharing among devices by abstracting their capabilities as services. Nevertheless, the large number and heterogeneity of encompassed networks/devices and the diversity of the offered resources/services make the IoT service description, discovery, selection, and composition a challenging task. To deal with these issues, we introduce, in this article, a semantic middleware for IoT applications based on service composition. Such a solution supports the semantic description of IoT resources, including services and user requests, and provides a modular, end‐to‐end, and loosely coupled request resolution process that comprises a context‐aware service discovery, a semantic service selection, and an automatic lightweight service composition. The proposal is illustrated and extensively evaluated on a restricted smart‐city scenario. Obtained results show that the proposed service discovery, selection, and composition sub‐processes improve the scalability of equivalent state‐of‐the‐art solutions by around 15, 20, and 40, respectively.

Balancing Privacy Risk and Benefit in Service Selection for Multiprovision Cloud Service Composition

The popularity of cloud computing has fueled the growth in multiprovision cloud service composition (MPCSC), where each cloud service provider (CSP) can fulfill multiple tasks, i.e., offer multiple services, simultaneously. In the MPCSC, users would rather disclose some private data for more benefits (e.g., personalized services). However, the more private data is released, the more serious the privacy risk faced by users. In particular, the multiservice provision characteristic of MPCSC further exacerbates the privacy risk. Therefore, how to balance the privacy risk and benefit in service selection for MPCSC is a challenging research problem. In this paper, firstly we explore the service selection problem of balancing privacy risk and benefit in MPCSC (SSBM), then we propose an improved Kuhn–Munkres (KM) algorithm solution to the SSBM problem. Furthermore, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the proposed approach is both efficient and effective for solving the SSBM problem.

Performance Comparison of WDM MIMO RoFSO Links for 5G Applications

The ubiquitous deployment of 5G systems is expected shortly. The 5th generation systems are envisioned to deliver a comprehensive selection of services with revolutionary data rates through a single network. Optical fiber networks are going to be the backbone of such 5G systems endowing huge data transfers. Radio over Fiber (RoF) is a promising network architecture that can provide all the provisions envisaged in the development of 5G systems. However, in certain scenarios, these fiber optic networks may encounter empirical problems in deployment. Radio over Free Space Optical (RoFSO) links can be used as an alternate in such environments. To increase the data carrying capacity of the link, Wavelength Division Multiplexing (WDM) communication equipping multiple wavelengths can be used. The free space link may suffer from atmospheric disturbances like rain, fog, and haze. In such adverse conditions, Multiple Input Multiple Output (MIMO) transceivers can be used to increase the link performance. In this work, the performance analysis of such WDM MIMO RoFSO links carrying 5G frequencies is carried out. The work also presents a comparison of single path and MIMO RoFSO links, single channel and WDM RoFSO links, and a combination of these. It is found that, transporting data over WDM channels can increase the capacity of the RoFSO system tremendously while the MIMO link setup can maintain the transmission range and link performance even when high data rates are used.
 HIGHLIGHTS
 
 A quantitative performance comparison of Radio over Free Space Optical links
 WDM increases the capacity while the MIMO links help to maintain the transmission range
 A combination of WDM and MIMO RoFSO links is identified to be the best

Emotional intelligence in military medical officers in the Defence Medical Services

BackgroundEmotional intelligence (EI) is a concept describing an individual’s ability to understand, process and act accordingly on others’ and one’s own emotions. It is a desirable quality for people working...

OSWMI: An objective-subjective weighted method for minimizing inconsistency in multi-criteria decision making

In Multi-Criteria Decision Making (MCDM), alternatives are evaluated by considering different criteria. In MCDM, there is a requirement to integrate the objective and subjective weights, since the objective weighting methods ignore the decision-maker’s (DM’s) experiences and the subjective weighting methods ignore the performance ratings of the alternatives with respect to different criteria. To integrate the two types of weights and evaluate the best alternative, three well-established methods, namely “CRiteria Importance Through Intercriteria Correlation (CRITIC)”, “Best Worst Method (BWM)”, and “LINear programming techniques for Multidimensional Analysis of Preferences (LINMAP)” are considered in our study. Based on these methods, we have proposed a new method, namely “Objective-Subjective Weighted method for Minimizing Inconsistency (OSWMI)” which considers both pairwise comparisons of the criteria and alternatives along with their corresponding performance ratings. We have first improved both the methods, CRITIC (named as improved CRITIC) and LINMAP (named as LINMAP II). Finally, the proposed OSWMI method is developed by integrating the improved CRITIC method, BWM, and LINMAP II using a multi-objective non-linear programming (MONLP) model. The OSWMI method may reduce the problem of strategic weight manipulation, since the integrated weights and the two ideal solutions are priori unknown and obtained simultaneously for selecting the best alternative. A case study of the web service selection is used to demonstrate the implementation of the OSWMI method. From the analysis, the proposed OSWMI method reveals a promising result. Further, sensitivity of the OSWMI method is checked by using the standard regression coefficients obtained by multiple linear regression.

Study of a linear-physical-programming-based approach for web service selection under uncertain service quality

Abstract To promote the practical use of web service technologies, it is critical to select a proper web service that will meet users’ preferences from a pool of web services with similar functions. It is usually a core issue during web service selection to describe and evaluate the quality of these web services. Average value can be employed to describe certain service quality and to evaluate the quality with multiple criteria through the Simple Additive Weight Method. Based on an analysis of various deficiencies in existing approaches proposed in some studies, four indicators were used: Price, Response Time, Reliability and Credibility. On top of that, recent sampling data sets were introduced to describe the uncertain quality of service (QoS) criteria; linear physical programming was used to evaluate QoS with comprehensive criteria to meet the need to objectively describe uncertain QoS criteria, and preference regions were set to adapt to interval characteristics of criteria. Finally, a computational example is provided to demonstrate the feasibility and effectiveness of the method.

Consumer Empowerment Index in the Telecommunications Sector: The Role Of Socio-Demographic and Lifestyle

Consumer empowerment is a benchmark for business, especially in the telecommunications sector, for economic fundamentals. This study aims to analyze the effect of social, demographic and economic characteristics and lifestyles on consumer empowerment in the telecommunications sector. This study applied a cross-sectional study with data collection by online survey. Cluster analysis and logistic regression were used to analyze 100 data. The results showed that consumer empowerment in the telecommunications sector was still relatively low. The average level of consumer empowerment in the telecommunications sector was 51.61, including the capable category. Based on dimensions, the index of information seeking was 80.8, knowledge of laws and consumer protection institutions was 52.8, service selection was 63.8, usage behavior was 71.0, tendency to talk was 43.7, and complaint behavior was 27.7. The full-filled lifestyle had a significant negative effect on consumer empowerment in the telecommunications sector. Thus, respondents who had a lifestyle other than a fulfilled lifestyle had more opportunities to be empowered than a fulfilled lifestyle. This research is a breakthrough in the study of thematic consumer empowerment, so it needs to be developed in the future.
 Keywords—Consumer Empowerment; Demographics; Economic; Lifestyle; Social

Predicting the Reviews of the Restaurant Using Natural Language Processing Technique

Recommendation programs are finally being used to provide customers with a customized selection of services. To put it another way, they're set up to produce recommendations (such as restaurants or tourist attractions) that cater to the needs of the customer and may be used to a variety of situations. In order to make recommendation processes more efficient and effective, and to deal with any problems that may arise, a variety of helpful methods to data management may be used. In this article, a machine learning method is proposed to address the issue of customizing restaurant preferences based on tripadvisor.com search data. The hotel's services are used, and client feedback is taken into account.

Fuzzy Based Optimal QoS Constraint Services Composition in Mobile Ad Hoc Networks

In recent years, computational capability of mobile devices such as Laptops, mobile phones, PDAs, etc., are greatly improved. Implementation of SOA ("Service Oriented Architectures") in mobile ad hoc networks increases the flexibility of using mobile devices. On composing different available services to satisfy end user requirement is a critical challenge in MANETs ("Mobile Ad Hoc Network") due to dynamic topology, Resource heterogeneity, Band width constraint and highly distributed service providers. Existing composition services approaches are not suitable for MANETs due to lack of constraints consideration while choosing services. In this paper, we proposed Fuzzy based optimal QoS constrained Service Composition in MANETs. We consider Energy constraint, hop count, Response time & service throughput as QoS Constraints composing optimal services. We proposed fuzzy logic based system to provide rating to the services for optimal selection of services. We also considered that each node can provide one or more services. The service composition failure rate will be reduced by selecting optimal services in available services. The simulation result demonstrates that the proposed method outperformed than the traditional AODV in terms of average packet delay, energy constraint, throughput and turnaround time.

Empirical Matching-Based Computation Offloading Optimization for 5G and Edge Computing-Integrated EIoT

Electric Internet of things (EIoT) that integrates 5G and edge computing can provide data transmission and processing guarantee for smart grid. However, computation offloading optimization including joint optimization of server selection and computation resource allocation still faces several challenges such as difficulty in tradeoff balance among various quality of service (QoS) parameters, coupling between server selection and computation resource allocation, and multi-device competition. To address these challenges, we propose an empirical matching-based computation offloading optimization algorithm for 5G and edge computing-integrated EIoT. The optimization objective is to minimize the computation offloading delay by jointly optimizing large timescale server selection and small timescale computation resource allocation. We first model the large timescale server selection problem as a many-to-one matching problem, which can be decoupled from small timescale computation resource allocation by establishing a matching preference list based on empirical performance. Then, the large timescale server selection problem is solved by pricing-based matching with a quota algorithm. Furthermore, based on the obtained suboptimal result of large timescale server selection, the small timescale computation resource allocation problem is subsequently solved by Lagrange dual decomposition, the result of which is used to update large timescale empirical performance. Finally, extensive simulations are carried out to demonstrate the superior performance of the proposed algorithm by comparing it with existing algorithms.

Features of the course of arterial hypertension in military servicemen in the conditions of the Аrctic region

Purpose.To study the frequency of occurrence of cardiovascular risk factors and the course of arterial hypertension in military personnel in the Arctic region.Materials and methods.251 male servicemen aged 23–44 years (mean age 33.7±4.9 years) were examined and studied medical records. Depending on the place of service, all the examined were divided into two groups: I — 159 military personnel serving in the Arctic, II — 92 military personnel from the Western Military District. The studied cardiovascular risk factors included dyslipidemia, tobacco smoking, high blood pressure, abdominal obesity, psychosocial factors, dietary disorders, regular alcohol consumption, physical inactivity. We also analyzed the frequency of occurrence of a confirmed diagnosis of neurocirculatory asthenia and stage I–II hypertension.Results and its discussion.The survey showed a significant increase in the incidence of most of the studied predictors of cardiovascular risk (dyslipidemia, smoking, negative psychosocial factors, dietary disorders, and regular alcohol consumption) in the Arctic region group compared to the temperate climate group. This is realized in the form of a higher incidence of arterial hypertension and a trend towards an increase in the number of cases of generalized atherosclerosis among military personnel in the Arctic. The data obtained may indicate the need for a more thorough selection for service in the Arctic region with a mandatory in-depth examination of the circulatory and metabolic systems, including electrocardiography, echocardiography, stress treadmill test, ultrasound examination of the brachiocephalic arteries with the determination of the thickness of the intima-media complex, sphygmomanometry with the determination of the stiffness and biological age of the vessels, the study of the lipid profile and fasting glycemia. It is advisable to conduct an annual examination of the military personnel of the Arctic group at the place of service with the involvement of visiting military medical commissions, which should include a cardiologist and a specialist in functional diagnostics.