Context-aware Service Selection Research Articles

A Two-Phase Machine Learning Framework for Context-Aware Service Selection to Empower People with Disabilities.

The use of software and IoT services is increasing significantly among people with special needs, who constitute 15% of the world’s population. However, selecting appropriate services to create a composite assistive service based on the evolving needs and context of disabled user groups remains a challenging research endeavor. Our research applies a scenario-based design technique to contribute (1) an inclusive disability ontology for assistive service selection, (2) semi-synthetic generated disability service datasets, and (3) a machine learning (ML) framework to choose services adaptively to suit the dynamic requirements of people with special needs. The ML-based selection framework is applied in two complementary phases. In the first phase, all available atomic tasks are assessed to determine their appropriateness to the user goal and profiles, whereas in the subsequent phase, the list of service providers is narrowed by matching their quality-of-service factors against the context and characteristics of the disabled person. Our methodology is centered around a myriad of user characteristics, including their disability profile, preferences, environment, and available IT resources. To this end, we extended the widely used QWS V2.0 and WS-DREAM web services datasets with a fusion of selected accessibility features. To ascertain the validity of our approach, we compared its performance against common multi-criteria decision making (MCDM) models, namely AHP, SAW, PROMETHEE, and TOPSIS. The findings demonstrate superior service selection accuracy in contrast to the other methods while ensuring accessibility requirements are satisfied.

QoS-Aware Mobile Service Selection Algorithm

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

Decision support for the usage of mobile information services: A context-aware service selection approach that considers the effects of context interdependencies

In mobile business, context information is utilised to select services mostly tailored to a user’s current situation and preferences. In existing context-aware service selection approaches, a service utility is determined by comparing its non-functional properties with current context information but without considering its integration in a service composition. This may cause suboptimal selection results, as context information and thus the determined utility of a certain service are usually dependent on its preceding and succeeding services. The latter we denote as context interdependencies. In this paper, we investigate how the effects of context interdependencies can be modelled for the context-aware service selection at planning time (i.e. before starting to accomplish a service composition). To develop this approach, we use the concept of states to model context information for the selection. In our evaluation, we find that our approach leads to superior results compared to current context-aware service selection approaches.

QoS-based approach for context-aware service selection with fuzzy preferences handling

The expected expansion of the number of context-aware services makes service selection engines, which are able to automatically recommend the most relevant service to users, in high demand. Yet, current approach and practice on service selection remain inadequate to meet the requirement posed by the user-centric computing paradigm. In our approach we consider that functional aspects of a service are already met and we focus on non-functional and QoS-related aspects in service description, indeed, QoS-aware computing is expected to be a key enabler for customising the selection of fast-growing context-aware services. We propose in this paper a workflow-based algorithm allowing the inexact matching between context and services descriptions, the service description and request are compared using four similarities measures: syntactic, linguistic, structural and QoS semantic. In the last a discussion is opened about possible methods for service ranking, and a case study is presented to demonstrate the effectiveness of our approach.

Social Context and QoS Aware Service Selection Optimization

Social Context and QoS Aware Service Selection Optimization

A context-aware multimedia service scheduling framework in smart homes

Abstract Many pervasive services are embedded in smart homes. It is a great challenge to schedule these services due to the heterogeneity and the mobility of devices. In this article, we elaborate a context-aware service scheduling framework (CASSF), which discovers available services hosted on those heterogeneous and mobile devices, selects suitable services that meet task requirements (TR) and ultimately enhances user experience with content adaptation. Contributions of this article are in three folds. First, the device ontology is designed to describe device features including built-in services, which benefits the knowledge sharing and facilitates the service discovery; second, a context-aware service selection mechanism is proposed, in which available services and contexts including user situations, TR, device capabilities, etc. are taken into consideration. Furthermore, due to the heterogeneity and the diversity of devices, the content adaptation is introduced in the framework to improve user satisfaction. Based on the CASSF, a multimedia prompting system is implemented to assist the elderly with intelligent services, and the evaluation of the proposed framework is performed in term of the execution time. The experimental results showed the framework is feasible and effective.

A Method for Context-aware Web Services Selection

Service-oriented computing (SOC) is emerging as a new, promising computing paradigm that centers on the notion of service as the fundamental element for developing software applications. Traditional service selection deals with applications which only consider functional matchmaking of services and do not put them into a context when selecting services. When recognizing the importance of contextual information, in this paper we mainly focus on the precise and effective context-aware service selection. At first we proposed a General Context Model (GCM) for service selection. After the construction of GCM, we gave a framework for context-aware service selection and discussed the method of service selection based on context and function. The experimental result shows that our method performs better than the methods without consideration of context.

Semantic Web-based context-aware service selection in task-computing

Semantic Web-based context-aware service selection in task-computing

SmartCon: a context-aware service discovery and selection mechanism using Artificial Neural Networks

In this paper, we present SmartCon, a context-aware system for the discovery and selection of mobile services using Artificial Neural Networks (ANNs). The solution we have developed is a mobile agent-enabled system that adaptively and iteratively learns to select the best available mobile service derived from the extraction of a series of features utilising contextual information such as the Composite Capabilities/Preference Profiles (CC/PP), service-specific and non-uniform user-specific features which are supplied to a Back-Propagation Neural Network. Based on the features provided, the neural network classifies the most relevant mobile service. In the present work, the system is also capable through iterative learning to generalise and gather information using cognitive feedback based on the user's decisions and interactivity with a Mobile Device. SmartCon is evaluated using a series of preliminary empirical data and results show an 87% success rate in the discovery and selection of the best or most relevant mobile service.

Guest editor's preface to the special section on discovery and interaction of mobile services

Mobile services are wireless mobile computing applications and services located on remote servers or peer's devices in a mobile ad hoc setting (MANET). In order to use these services, users of handheld wireless devices must be able to locate them first. One of the fundamental challenges in mobile computing is the discovery of relevant services in dynamically changing environments such as MANETs. Many of the available service discovery protocols were designed for infrastructure-based wired networks and therefore are not suitable for MANETs. The few discovery protocols designed for MANETs do not offer location-based and context-aware service selection, and feature minimal interaction methods.