Investigation on Context-Aware Service Discovery in Pervasive Computing Environments

The decreasing cost of networking technology and network-enabled devices is driving the large scale deployment of such networks and devices so as to offer many new and innovative services to users in ubiquitous computing. For example, when you carry your mobile laptop or personal digital assistant (PDA) around, or drive on the road, various services have been made available, ranging from finding a local printer to print a file, to instantaneously knowing about the traffic situation from traffic-cameras and other sensors along a highway. To achieve the above, every participating network- enabled end-device must solve an interesting technical problem, i.e., to locate a particular network service or device out of hundreds of thousands of accessible services and devices. Such service advertising and discovery is important as mobile devices and mobile wireless devices proliferate on networks. For this reason, a service discovery and advertising protocol is an important tool to help these devices find services on the network wherever they connect, and to let other network users know about the services they are offering. Context-aware service discovery, on the other hand, would help users to find services that are most appropriate based on fast-changing client conditions, such as location. For example, most laptops are statically configured to print to dedicated office printers. With the help of the context-awareness, a laptop could find the nearest accessible printer attached to the network that the laptop is currently plugged into.

With the increasing use of web services in everyday tasks, we are entering an era of Internet of Services (IoS). Service discovery and selection has become critical issue in the area of web services. Traditional service discovery approaches are often supported by centralized registries that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, in this paper we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes, previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of the best available web service by taking into account the QoS properties of the web services. Experimental results were based on real world semantic web service dataset, the results showed that the approach achieved better performance and efficiency in both discovery and selection processes.

To make the revelation of ubiquity true different kind of network's integration is going on and this assimilation makes service discovery more challenging because different types of service discovery have been develop for the particular network. A Context-Aware Service Discovery for these amalgam networks is different from usual, especially when we talk about 6LoWPANs functioning with IP Networks, as 6LoWPAN are characterized by low power and low bandwidth, short range and low cost that make the challenge hard. Moreover Interworking of 6LoWPAN with IP networks brings in many challenges for Context-Aware based service discovery issues.In this paper we suggest an advanced service discovery architecture and mechanism that assist proximity and Context Aware based service discovery in IP network and LoWPAN interworked environment. The results show that our architecture helps to discover the closest and exact services from inside as well as outside the LoWPAN according to user requirement. It also reduces the traffic overhead for service discovery considerably.

Sensor network is a novel technology about acquiring and processing information. This paper introduces the basic interaction patterns between requesters and providers of services during a typical Service Discovery task. It discusses existing service discovery protocols and provides a preliminary analysis of the mechanisms for service and resource discovery in the Wireless Sensor Network domain.

Service discovery is the process to find a service that is best meet the user's requirement. Context of the user and the service plays an important role during the process because it takes into consideration more about the user and the service. In order to better implement the personalization of service discovery, and take into full consideration of the user's requirement and feature, a context-based framework for web services discovery is proposed in this paper. User Model and Intention Model are used in our framework to construct the user context. Service discovery and composition are carried out heuristically by considering both the user context and the web services context. And Service discovery is conducted by taking into account not only a published single web service but the combination of web services when a single web service can not be found. Service recommendation is also provided.

Web-hosted Internet of Things (IoT) applications are the next logical step in the recent endeavor by academia and industry to design and standardize new communication protocols for smart objects. Context Awareness is defined as the property of a system that employs context to provide related information or services to the user, where the relationship is based on the user's task. Therefore, context-aware service discovery can be defined as utilizing context information to discover the most relevant services for the user. Merging context-aware concepts with the IoT facilitates IoT system developments that depend on complex environments with many sensors and actuators, user, and their environment. The main objective of this study is to design an abstract framework for provisioning smart objects as a service based on context-aware concepts while considering constraints of bandwidth, scalability, and performance. The proposed framework building blocks include data acquisition and management service and data aggregation, and rules reasoning. The proposed framework is validated and evaluated by constructing an IoT network simulation and testing accessing the service in the traditional method and according to the proposed framework and comparing the results.

This paper is concerned with modelling the process of service and resource discovery and allocation with specific emphasis on the management of that process. Its scope ranges from local low level discrete resources, e.g. finding and using network elements, through network services, e.g. establishing communications facilities, on to the highest level global service offerings, e.g. discovering suitable VPNs and subsequent negotiation with them. The discovery and allocation of both services and resources may be treated symmetrically.

Resource discovery is the process of computing systems; it supports resource management and scheduling of applications. It searches for the apposite variety of resource that satisfies the user's requirements. Resource discovery in computing system is perplexing because of conventional centralized approaches and resilient availability of resources. This scenario becomes more complex in inter-clouds owing to the dynamism and heterogeneity of clouds involved. The inspiration driving this paper is to utilize peer-to-peer approach to circumvent the problem of resource provisioning and resource discovery that are predominantly associated with centralized approach. This paper introduces a system model of Resource discovery in Inter-Cloud and mechanism to joining Remote Resource Manager using reputation based algorithm. We attempt to minimize the data traffic and balance the load partly by using the reputation based algorithm.

Advanced mobile devices increasingly provide innovative services and applications that can be accessed anywhere and anytime. The actual challenge is to take into account the dynamically changing conditions of the heterogeneous resources (equipments, data sources, services, etc) required for providing in ubiquitous environments an optimized, self-adaptive and personalized task management according to the context of use. In addition, the constantly changing availability and capabilities of these distributed resources make the discovery and mobilization of the appropriate resources required by user tasks difficult. In this paper, we propose a framework based on ontological models, object oriented components, a rule base, and an inference engine, to support services and applications operation in mobile systems. We also present an application example from the mobile health (mHealth) field to demonstrate how the proposed framework can support resources discovering according to user tasks specifications and environment conditions as well as to the accessibility and usability of the discovered resources.

In grid environment where resources are generally owned by different people, communities or organizations with varied administration policies and capabilities, managing the grid resources is not a simple task. Resource brokers simplify this process by providing an abstraction layer for users to access heterogeneous resources transparently. However, discovery of grid resources that suits to the user's job requirements is always a difficult job as the probability of grid resources satisfying the user's requirements is very less. Conventionally, in order to run a job on the grid a user has to identify a set of platforms capable of running that job by the virtue of having the required installation of operating system, libraries, tools, and the configuration of environment variables, etc. In practice, the availability of such software environments will either be limited to a very narrow set, or the job has to be made compatible with an environment supported by a large resource provider, such as TeraGrid. Further, if we could identify such an environment, it is hard to guarantee that the resource will be available when needed, for as long as needed, and that user will get his or her fair share of that resource. This difficulty can be overcome by incorporating the concept of Virtualization in Grid environment that enables the creation of dynamic user defined execution environment in a grid resource.

Technologies for Web services facilitate the creation of business process solutions in an efficient, standard way. However, the automation of process integration with Web service technologies requires the automation of discovery and composition of Web services. In this paper, we focus on two problems of the Web service-based business process integration: (1) the discovery of Web services based on the capabilities and properties of published services, and (2) the composition of business processes based on the business requirements of submitted requests. We propose a solution to these problems, which comprises multiple matching algorithms, a micro-level matching algorithm, which matches the capabilities of services with activities in a process request, and macro-level matching algorithms, which are used to compose a business process by identifying services that satisfy the business requirements and constraints of the request. The solution from the macro-level matching algorithms is optimal in terms of meeting a certain business objective, e.g., minimizing the cost or execution time, or maximizing the total utility value of business properties of interest. Numerical examples are illustrated to show how to select the best Web service candidate for a chosen business process through the use of proposed macro-level matching algorithms. Furthermore, we show how existing Web service standards, UDDI and BPEL4WS, can be used and extended to specify the capabilities of services and the business requirements of requests, respectively.

There is a high interest on service discovery techniques, which minimize the cost of detecting services and provide users with convenience. For more dynamic and useful service discovery, middleware for context-aware service discovery is required. This paper proposes an intelligent middleware architecture for context-aware service discovery that is based on JADE, which is a FIPA-compliant agent platform. The proposed middleware provides not only service scalability among heterogeneous domains, but also modules processing context information. When a service is detected, context information relating to the user and environment is used. As a policy-based system, the proposed middleware also use predefined policy. In other words, user preference can be considered. Near the conclusion of this paper, we discuss future works.

With the development of Web services technology, the number of service increases rapidly, and it becomes a challenge task that how to efficiently discovery the services that exactly match the user's requirements from the large scale of services library. Many semantic Web services discovery technologies proposed by the recent literatures only focus on the keyword-based or primary semantic based service's matching. This paper studies the rules and rule reasoning based service matching algorithm in the background of large scale services library. Firstly, the formal descriptions of semantic web services and service matching is presented. The services' matching are divided into four levels: Exact, Plugin, Subsume and Fail and their formal descriptions are also presented. Then, the service matching is regarded as rule-based reasoning issues. A set of match rules are firstly given and the related services set is retrieved from services ontology base through rule-based reasoning, and their matching levels are determined by distinguishing the relationships between service's I/O and user's request I/O. Finally, the experiment based on two services sets show that the proposed services matching strategy can easily implement the smart service discovery and obtains the high service discovery efficiency in comparison with the traditional global traversal strategy.

In Service-Oriented Architecture (SOA), applications can be assembled by a set of existing distributed services spanning across organizations and platforms. SOA increases reuse and cooperation among services. Therefore, applications can be built in a rapid way with relatively low cost. With the increasing adoption of SOA solutions to application integration, the ability to compose services efficiently and effectively becomes more and more important. A lot of efforts have been devoted to service composition. Service composition integrates existing reusable services together as a process to fulfill business requirements, and exposes the process as a service (i.e., composite service). Specifically, a composition process involves the following five steps: 1. Specify business requirements: A requirement (e.g., purchase a book online) for composing services could be a declarative expression using formal or informal languages, an abstract process model (e.g., a business process model), or design models (e.g., UML diagrams). 2. Provide service description: Service description specifies the capability of services, such as inputs/outputs, exceptions, functional and non-functional description. 3. Discover relevant services: Service discovery is the process of locating a service that meets a certain searching criteria. For example, we search for an appropriate service to offer forecasting service. The searching criteria for the service discovery could be specified using keywords, such as weather forecast. 4. Generate composition process model: A composition process model contains a set of selected services, control flows and data flows among those services. Standards, such as BPEL (Business Process Execution Language), are used to formally specify process models. A business requirement usually cannot be fulfilled by a single service. Various services are integrated using composition process models. 5. Evaluate composition results: It is quite common that many returned services have the same or similar functionalities. It leads to more than one composition result to fulfill the business requirements. The different composition results can be evaluated to select the best one that meets the functional and nonfunctional requirements.

A resource discovery tree using bitmap for grids