This paper presents an agent-based and context-oriented approach that supports the composition of Web services. A Web service is an accessible application that other applications and humans can discover and invoke to satisfy multiple needs. To reduce the complexity featuring the composition of Web services, two concepts are put forward, namely, software agent and context. A software agent is an autonomous entity that acts on behalf of users and the context is any relevant information that characterizes a situation. During the composition process, software agents engage in conversations with their peers to agree on the Web services that participate in this process. Conversations between agents take into account the execution context of the Web services. The security of the computing resources on which the Web services are executed constitutes another core component of the agent-based and context-oriented approach presented in this paper.

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This paper presents an approach that aims at personalizing Web services composition and provisioning using context. Composition addresses the situation of a user's request that cannot be satisfied by any available service, and thus requires the combination of several Web services. Provisioning focuses on the deployment of Web services according to users' preferences. A Web service is an accessible application that other applications and humans can discover and trigger. Context is the information that characterizes the interactions between humans, applications, and the surrounding environment. Web services are subject to personalization if there is a need of accommodating users' preferences during service performance and outcome delivery. To be able to track personalization in terms of what happened, what is happening, and what might happen three types of context are devised, and they are referred to as user-, Web service-, and resource-context.

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A self-organising system functions without central control, and through contextual local interactions. Components achieve a simple task individually, but a complex collective behaviour emerges from their mutual interactions. Such a system modifies its structure and functionality to adapt to changes to requirements and to the environment based on previous experience. Nature provides examples of self-organisation, such as ants food foraging, molecules formation, or antibodies detection. Similarly, current software applications are driven by social interactions (negotiations, transactions), based on autonomous entities or agents, and run in highly dynamic environments. The issue of engineering applications, based on the principles of self-organisation to achieve robustness and adaptability, is gaining increasing interest in the software research community. The aim of this paper is to survey natural and artificial complex systems exhibiting emergent behaviour, and to outline the mechanisms enabling such behaviours

-One of the major challenges in cognitive radio (CR) networks is the need to sample signals as efficiently as possible without incurring the loss of vital information. Compressive Sensing (CS) is a new sampling paradigm which provides a theoretical framework for sub-sampling signals which are characterized as being sparse in the frequency domain. The random demodulator (RD) is a CS-based architecture which has been employed to acquire frequency sparse, bandlimited signals which typify the signals which often occur in many CR-related applications. This paper investigates the impact of precolouring upon CS performance by combining the RD with an autoregressive (AR) filter model to enhance compressive spectral estimation. Quantitative results with quadrature phased shift keying (QPSK) modulated multiband signals, corroborate that adopting a precolouring strategy both reduces the spectral leakage in the power spectrum, and concomitantly improves the overall signal-to-noise ratio (SNR) performance of the compressive spectrum estimator.

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This chapter presents a context-based approach for Web services personalization so that user preferences are accommodated. Preferences are of different types, varying from when the execution of a Web service should start to where the outcome of this execution should be delivered according to user location. Besides user preferences, it will be discussed in this chapter that the computing resources on which the Web services operate have an impact on their personalization. Indeed, resources schedule the execution requests that originate from multiple Web services. To track the personalization of a Web service from a temporal perspective (i.e., what did happen, what is happening, and what will happen), three types of contexts are devised and referred to as user context, Web service context, and resource context.