Dynamic Service Discovery Research Articles

Context-Aware Service Discovery: Graph Techniques for IoT Network Learning and Socially Connected Objects

Adopting Internet-of-things (IoT) in large-scale environments such as smart cities raises compatibility and trustworthiness challenges, hindering conventional service discovery and network navigability processes. The IoT network is known for its highly dynamic topology and frequently changing characteristics (e.g., the devices’ status, such as battery capacity and computational power); traditional methods fail to learn and understand the evolving behavior of the network to enable real-time and context-aware service discovery in such diverse and large-scale topologies of IoT networks. The Social IoT (SIoT) concept, which defines the relationships among the connected objects, can be exploited to extract established relationships between devices and enable trustworthy and context-aware services. In fact, SIoT expresses the possible connections that devices can establish in the network and reflect compatibility, trustworthiness, and so on. In this paper, we investigate the service discovery process in SIoT networks by proposing a low-complexity context-aware Graph Neural Network (GNN) approach to enable rapid and dynamic service discovery. Unlike the conventional graph-based techniques, the proposed approach simultaneously embeds the devices’ features and their SIoT relations. Our simulations on a real-world IoT dataset show that the proposed GNN-based approach can provide more concise clusters compared to traditional techniques, namely the Louvain and Leiden algorithms. This allows a better IoT network learning and understanding and also, speeds up the service lookup search space. Finally, we discuss implementing the GNN-assisted context-service discovery processes in novel smart city IoT-enabled applications.

Service Orchestration for Object Detection on Edge and Cloud in Dependable Industrial Vehicles

Industrial applications, including autonomous systems and vehicles, rely on processing data on multiple physical devices. The composition of functionality across heterogeneous computing infrastructure is challenging, and will likely get even more challenging in the future as software in vehicles is updated to introduce new features and ensure the safety. New soft real-time use cases emerge and in such cases the model of offloading processing from a limited or malfunctioning device is a viable solution. This study examines orchestration of services across edge and cloud for an industrial vehicle application use case involving image based object detection using machine learning (ML) based models. First, service orchestration requirements are defined taking into account the dependable nature of industrial vehicle applications. Second, an implementation based on Arrowhead framework is presented and evaluated. The open Arrowhead framework offers means for dynamic service discovery, authorization and late binding of computational units. The feasibility of object detection as a service and the suitability of Arrowhead framework to support such orchestrations across edge and cloud is assessed.

A Hybrid Similarity Measure for Dynamic Service Discovery and Composition based on Mobile Agents

A Hybrid Similarity Measure for Dynamic Service Discovery and Composition based on Mobile Agents

Service Discovery and Selection in Internet of Things - A Review

The Internet of Things (IoT) is a novel technology that has opened doors to the new level of interaction between the things. This has resulted in an enhancement in the quality of life and optimized use of various resources. IoT uses various technologies related to networking, sensing, databases and artificial intelligence to enhance the lifestyle and makes business processes simpler. In the IoT based systems, the number of devices contributing to a particular application is very large, spreading to a large geographical area at various locations. The device can be considered as a resource used by a service in the application layer. IoT service is the most important entity. The real-world things generate data by perceiving the environment in large quantity and store it in the distributed databases or cloud databases. This manuscript analytically and statistically categorizes and analyze the current research techniques on the service discovery and selection in the IoT, published between 2010 and 2018. It finds that the discovery among a huge number of services requires fast, scalable and dynamic service discovery mechanism over the Internet. Once the services are discovered, the next step is to select the most appropriate service. This paper includes a comprehensive analysis of the discovery and selection of services in IoT. A patent related to service and discovery have been also discussed which would be beneficial in identifying the research gap to make the system more efficient.

Development of a mobile web services discovery and composition model

Mobile computing, a challenging paradigm aiming at using and providing services anywhere and anytime, places great challenges on dynamic service discovery and composition. These challenges are specially related to mobile context (terminal constraints, user mobility, network connection, etc.) In fact, the discovery model began with semantically rewriting and enriching services and user query with OWL-S ontology and its non-functional properties (context, QoWS and user preferences). Then a similarity calculation step has been performed between the enriched user query and the set of advertised web services to generate the most relevant ones. Furthermore, the resulting services will be the inputs of a mobile web service composition process. Mobile web service composition is based on a formal model aiming to compute dynamic context (Location, Bandwidth) uncertainty and web service sensitivity. These values contribute to select web services that will form the composition plan. The web service composition algorithm suggests to the end user, the most relevant plans sorted in ascending order according to their total sensitivity value. The implementation and comprehensive simulation experiments show the efficiency of the mobile web service discovery and composition models.

Fuzzy QoS requirement-aware dynamic service discovery and adaptation

The integration of coherent services plays a potential role in the field of Service Oriented Applications. Achieving this potential standard crucially depends on the ability to recognize and exploit the available services based on user requirements. In general, the user preferences on Quality of Service (QoS) requirements are fuzzy in nature. In addition to that, the QoS requirements are user dependent even if the functional requirements are the same. With a large number of available services, service selection for dynamic composition at run time is a challenge. Functional and non-functional assumptions made at design time may violate at run-time. These violations require run time reaction, by adopting a run-time process. Therefore, dynamic and fuzzy QoS-aware service discovery for run-time composition and continuous adaptation is a strong requirement in service oriented computing. Considering that different users follow different fuzzy reasoning in various contexts at different times, a fuzzy inference based service selection approach has been proposed in this paper. Continuous adaptation is done, as and when a design time assumption violation is reported by a run-time monitoring system. We have implemented and tested the proposed approach and the results show its effectiveness.

Extended Fault Taxonomy of SOA-Based Systems

Service Oriented Architecture (SOA) is considered as a standard for enterprise software development. The main characteristics of SOA are dynamic discovery and composition of software services in a heterogeneous environment. These properties pose newer challenges in fault management of SOA-based systems (SBS). A proper understanding of different faults in an SBS is very necessary for effective fault handling. A comprehensive three-fold fault taxonomy is presented here that covers distributed, SOA specific and non-functional faults in a holistic manner. A comprehensive fault taxonomy is a key starting point for providing techniques and methods for accessing the quality of a given system. In this paper, an attempt has been made to outline several SBSs faults into a well-structured taxonomy that may assist developers to plan suitable fault repairing strategies. Some commonly emphasized fault recovery strategies are also discussed. Some challenges that may occur during fault handling of SBSs are also mentioned.

Ensemble based spam detection in social IoT using probabilistic data structures

A social approach can be used for the Internet of Things (IoT) to connect large number of objects in social networks like Twitter, Facebook, Instagram, etc. Social networks within the IoT domain have simplified the task of dynamic discovery of services and information. Detecting spam in social media, especially when massive data flows continuously and large number of attributes are associated with it, is a daunting task which requires lot of technical insight. This paper proposes a semi-supervised technique for spam detection in Twitter by employing ensemble based framework comprising of four classifiers. The framework is based on usage of Probabilistic Data Structures (PDS) like Quotient Filter (QF) to query the URL database, spam users, spam words databases and Locality Sensitive Hashing (LSH) for similarity search, as classifiers in various stages which provide fast results with less computational effort. Performance of the framework has been evaluated by comparative analysis of PDS with the similar data structures and through the standard evaluation parameters which include precision, recall and F-score.

Quality model and metrics of ontology for semantic descriptions of web services

An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.

Using Event B to Specify Context Awareness for Service Discovery in Pervasive Environments

The environments of pervasive computing are open and dynamic. In order to ensure the dynamic discovery of services evolving in a heterogeneous and dynamic environment, specific extensions to WSDL, known as A-WSDL are suggested. These extensions permit to a service provider to define the context of service use and the behavior associated to each change of context. To verify and prove the expected behavior of the suggested discovery protocol in the design phase, the Event-B formalism is adopted. One of the advantages of the Event B formalism is the application of the refining techniques which permit to express complex features by means of mathematical proofs and moves from an abstract specification to a concrete specification by using the Rodin tool which offers a support for the refining and the proofs.

Blueprint Flow: A Declarative Service Composition Framework for Cloud Applications

Cloud applications provides users with services that can be accessed on demand through the Internet. Fertile service frameworks are considered one of the most critical ingredients for the envisaged benefits so as to further interactions among cloud computing resources and application components. Such foundations should lead to the proliferation of new innovative services and applications. The research community has been exploring the Open Service Gateway initiative’s (OSGi) potential as a top candidate for cloud application platforms. Although the current OSGi specification provides some level of support for dynamic service discovery, tracking, and composition, more should be done to be able to adequately address the need for diverse interaction patterns for cloud applications. This paper introduces a novel service framework built upon OSGi platforms that supports a directed-acyclic-graph style composition of constituent services. Given a declarative blueprint of service interconnections and interactions, the framework can find and assemble corresponding component services to form a real application. Our proposal can enable a realistic topology of service component interlinkings beyond linear chaining interactions as supported by the status quo. The design, implementation details, and validation results of our workflow-based service composition framework architecture are discussed in the paper.

Towards a dynamic discovery of smart services in the social internet of things

The paradigm of the Social Internet of Things (SIoT) boosts a new trend wherein the connectivity and user friendliness benefits of Social Network Services (SNS) are exhibited within the network of connected objects, i.e. the Internet of Things (IoT). The SIoT exceeds the more traditional paradigm of IoT with an enhanced intelligence and context-awareness. In this paper, a novel service framework based on a cognitive reasoning approach for dynamic SIoT services discovery in smart spaces is proposed. That is, reasoning about users’ situational needs, preferences, and other social aspects along with users’ surrounding environment is proposed for generating a list of situation-aware services which matches users’ needs. This reasoning approach is then implemented as a proof-of-concept prototype, namely Airport Dynamic Social, within a smart airport. Finally, an empirical study to evaluate the reasoning approach's efficiency shows improved services adaptability to situational needs compared to common approaches proposed in literature.

A Novel Rubric and Feature-based Appraisaland Comparison Framework for the Evaluationof Semantic Web Services Composition Approaches

Background/Objectives: This article proposes a novel methodology based on rubrics and feature-based schemes for the appraisal and comparison of approaches to semantic Web services composition and discovery. Methods/Statistical analysis: In order to evaluate the Semantic Web services composition and discovery approaches we created a new framework called RFSWS. This framework is the combination of traditional feature-based evaluation schemes and newly developed analytic rubrics tables. Five recently introduced prominent Semantic Web services composition approaches were identified, explained, and then evaluated/compared using RFSWS. Findings: In this work we determined aspects of Semantic Web services composition and discovery processes that can be evaluated as performance criteria in rubric tables. This is a novel application of rubrics, which have traditionally been used for grading student performance by teachers. We created a novel framework called RFSWS consisting of rubric tables and a feature-based evaluation scheme for the evaluation and comparison of Semantic Web service discovery and composition approaches, and applied it in the evaluation of five recently introduced prominent Semantic Web services composition approaches. Considering the shortcomings of existing Semantic Web services composition approaches that were discovered through this evaluation, we proposed an idealized dynamic Semantic Web service discovery and composition method, a yardstick by which all future Semantic Web services composition approaches can be evaluated. Application/ Improvements: Our novel RFSWS framework can be applied in the comprehensive and systematic evaluation/comparison of Semantic Web services discovery and composition approaches.

A large-scale web QoS prediction scheme for the Industrial Internet of Things based on a kernel machine learning algorithm

Cloud computing plays an essential role in enabling practical applications based on the Industrial Internet of Things (IIoT). Hence, the quality of these services directly impacts the usability of IIoT applications. To select or recommend the best web and cloud based services, one method is to mine the vast data that are pertinent to the quality of service (QoS) of such services. To enable dynamic discovery and composition of web services, one can use a set of well-defined QoS criteria to describe and distinguish functionally similar web services. In general, QoS is a nonfunctional performance index of web services, and it might be user-dependent. Hence, to fully assess the QoS of all available web services, a user normally would have to invoke every one of them. This implies that the QoS values for services that the user has not invoked would be missing. If the number of web services available is large, it is virtually inevitable for this to happen because invoking every single service would be prohibitively expensive. This issue is typically resolved by employing some predication algorithms to estimate the missing QoS values. In this paper, a data-driven scheme of predicting the missing QoS values for the IIoT based on a kernel least mean square algorithm (KLMS) is proposed. During the data prediction process, the Pearson correlation coefficient (PCC) is initially introduced to find the relevant QoS values from similar service users and web service items for each known QoS entry. Next, KLMS is used to analyze the hidden relationships between all the known QoS data and corresponding QoS data with the highest similarities. We therefore can apply the derived coefficients for the prediction of missing web service QoS values. An extensive performance study based on a public data set is conducted to verify the prediction accuracy of our proposed scheme. This data set includes 200 distributed service users on 500 web service items with a total of 1,858,260 intermediate data values. The experiment results show that our proposed KLMS-based prediction scheme has better prediction accuracy than traditional approaches.

Dynamic Service Discovery, Composition and Reconfiguration in a Model Mapping Business Process to Web Services

In any organization where SOA has been implemented, all of the web services are registered in UDDI and users’ needs are served by using appropriate web services. So in this paper, we will try to discover a service from repository first that can provide the required output to the user. The process becomes difficult when a single service is not able to fulfill a user’s need and we need a combination of services to answer complex needs of users. In our paper, we will suggest a simpler approach for dynamic service composition using a graph based methodology. This will be a design time service composition. This approach uses the functional and non-functional parameters of the services to select the most suitable services for composition as per user’s need. This approach involves “service classification” on the basis of functional parameters, “service discovery” on the basis of user’s need and then “service composition” using the selected services on the basis of non-functional parameters like response time, cost, security and availability. Another challenge in SOA implementation is that, once the composition has performed, some services may become faulty at runtime and may stop the entire process of serving a user’s need. So, we will also describe a way of “dynamic service reconfiguration” in our approach that will enable us to identify and replace a faulty service that is violating the SLA or is not accessible anymore. This service reconfiguration is done without redoing or reconfiguring the entire composition. In the end, to simulate the proposed approach, we will represent a prototype application built on php 5.4 using My SQL database at backend.

Event-Aware Framework for Dynamic Services Discovery and Selection in the Context of Ambient Intelligence and Internet of Things

The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users’ requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.

A Collaborative Self-Governing Privacy-Preserving Wireless Sensor Network Architecture Based on Location Optimization for Dynamic Service Discovery in MANET Environment

Due to the characteristics of a MANET, none of the existing solutions for service discovery work well in decentralized mobile environments. In this paper, we propose a collaborative self-governing privacy-preserving wireless sensor network architecture to address the issue of service discovery in MANET environment. The proposed architecture is able to dynamically adjust the working modes between directory-based and directory-less modes according to the network status of a MANET. The dynamic network status of a MANET is monitored by a wireless sensor network. Two location optimization algorithms are developed for topology control and the preservation of user location privacy. Simulation results show that the proposed architecture can greatly improve the performance of service discovery.

A Novel Approach for Mobile Devices in Accessing Web Services

years Mobile devices & Web Services have become highly popular . It is a challenging task to host the web services in mobile devices over a wireless network, Due to different limitations of mobile devices like limited battery, memory space, and low processor speed. So it's becoming necessary to integrate web services into applications that run on these mobile devices. We are introducing an efficient proxy based approach which overwhelms technical challenges involved in the dynamic discovery of web services & generates client side proxies at runtime. We describe an effective architecture which shows how mobile devices dynamically discovers & invocate web services by limited intervention with network, service client just need to enter search phrase & values for method parameters on the dynamically generated GUI. KeywordsDevices, Mobile computing, Proxy Server, Web Services.

A Dynamic Services Discovery Model for Better Leveraging BPM and SOA Integration

This work addresses the problem of BPM-SOA integration using the services discovery activity as the main driver for that. The developed model is able to select the most adequate (web) services for given business transactions that are modeled at BPM level, regarding their business' contexts and QoS restrictions. It considers an inter-organization scenario, where services are published and made available by disparate providers over largely distributed and heterogeneous repositories, which can also be accessed under the SaaS model. Key elements of the model include a business processes catalog and an ontology based on the UBL open standard, and a QoS ontology. The implemented framework strongly relies on open ICT standards at all tackled levels, facilitating interoperability and the integration of services discovery environment in wider enterprise architecture projects. A software prototype has been implemented and results are described at the end. In spite of a set of assumptions, it was realized that a focus on the dynamic services discovery activity can be a feasible approach for better levering BPM-SOA integration, providing a more flexible basis for enterprises when executing their daily business transactions.

Quantitative trust management with QoS-aware service selection

As the number of services available on the web increases, discovering which services are trustworthy becomes vital. Given the black-box nature of services, selecting a trustworthy service that best fits users' requirements is critical. Once a set of services fulfilling users' functional requirements has been found, which one a user chooses depends mostly on the Quality of Services QoS, particularly security, trust and reputation. This paper proposes a trust management model to support service discovery and selection based on QoS. We define a quantitative trust-evaluation method for dynamic service discovery and selection and show experimental results. The proposed model allows service consumers to get the most trustworthy services possible. Our mechanism uses consumers' feedback to describe how trustworthy they have found services and service providers to be. Choosing services using a quantitative measurement rather than their intuition will allow consumers to select a highly reliable service that meets their quality and technical requirements.