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Software developers who develop applications to run on large powerful systems become used to the luxury virtually unlimited memory and processing power. They face a new set of obstacles when […]

Wireless sensors generate a large volume of data that require a highly scalable framework that enables storage, processing, and analysis. Cloud computing technology can provide unlimited storage in addition to a flexible processing infrastructure, allowing for the management and analysis of vast amounts of sensor data. This paper presents a framework for integrating wireless sensors and cloud computing. This framework can provide scalability and high availability for applications that use wireless sensors. Moreover, this cloud-based framework is designed to immediately make decisions based on real-time sensor and historical data, and a list of sensor and user policies are defined by the system administrator. In order to evaluate the framework performance after applying scalability and availability techniques, a load testing environment was built in the cloud to simulate a large number of virtual users. This environment was created in order to examine the quality of the services as provided by Windows Azure. The results have shown that the use of scalability techniques can significantly increase availability and performance.

Designing, implementing and executing algorithms have become a relevant and important element in various fields. Public users and data researchers are interested in analysing and interpreting data with shorter execution time and higher performance. Cloud computing is an environment that provides scalable and high-end virtual resources to achieve high quality services. This paper presents the design, implementation and evaluation of a framework for provisioning algorithms as a service in the cloud. This framework introduces solutions to help clients overcome different concerns and difficulties, such as looking for an appropriate algorithm, understanding algorithm source code, installing and configuring specific libraries, and achieving high algorithmic performance. The framework provides clients the possibility to discover available algorithms and/or deploy new algorithms over multiple scalable platforms. It also allows clients to analyse data, compare results, and measure algorithm's performance. A prototype implementation of the framework has been developed to demonstrate the feasibility of the solution. Evaluating results demonstrate that providing multiple scalability models and high-end web servers will improve algorithm performance and achieve availability and reliability using the framework.

Mobile software agents represent an ideal mechanism for dealing with information overload and disconnected computing. Currently, however, they are not integrated into the existing web infrastructure and therefore no websites are agent-enabled. Two main reasons why operators are unwilling to let agents run on their websites: (1) lack of business incentives and (2) security issues. In this paper we discuss the design and implementation of the Havana agent platform that addresses the above issues, and can be seamlessly integrated into the existing web infrastructure.

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.

The modularity of web services has left an open problem in composition – a scenario that involves an amalgamation of two or more web services to fulfil a request that no one web service is able to provide. This paper presents the design and implementation of a system that enables users of any device to dynamically discover context-based web services that will be automatically composed to satisfy a user's request. Existing web services can be easily adapted and new web services can be easily deployed. The system uses a custom UDDI-like registry that we have designed and implemented to support context-based adaptive composition.

Abstract Service discovery is an integral part of constructing self-configuring Mobile Ad Hoc Networks (MANETs). While several service discovery protocols have been developed, most of them are designed for infrastructure-based networks and thus not suitable to be used in MANETs. Due the volatile nature of MANET, service discovery protocols designed for MANETs often suffer from problems. Firstly, they have limited scalability due to the extensive use of broadcast communication. Secondly, they usually lack context-aware selection mechanisms and feature only reduced interaction standards. This paper proposes a network layer supported comprehensive service discovery solution that addresses the above issues and provides a solution in two parts. First it discusses a location-aware network layer routing protocol that groups mobile nodes into clusters while a gateway in each cluster is responsible for routing. Secondly, this paper proposes a service discovery protocol that utilizes directories for service discovery that interact with lower network layer gateway configurations. In this architecture, gateways are also used as directories for service discovery to improve performance. In doing so, we improve the system’s overall efficiency by eliminating the use of complex graph-based algorithms for maintaining clusters and establishing virtual backbones. Furthermore, this service discovery solution includes an agent-based context-aware service selection, and provides standard mechanisms for different types of service interaction.

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.