Inspired from natural self-managing behavior of the human body, autonomic systems promise to inject self-managing behavior in software systems. Such behavior enables self-configuration, self-healing, self-optimization and self-protection capabilities in software systems. Self-configuration is required in systems where efficiency is the key issue, such as real time execution environments. To solve self-configuration problems in autonomic systems, the use of various problem-solving techniques has been reported in the literature including case-based reasoning. The case-based reasoning approach exploits past experience that can be helpful in achieving autonomic capabilities. The learning process improves as more experience is added in the case-base in the form of cases. This results in a larger case-base. A larger case-base reduces the efficiency in terms of computational cost. To overcome this efficiency problem, this paper suggests to cluster the case-base, subsequent to find the solution of the reported problem. This approach reduces the search complexity by confining a new case to a relevant cluster in the case-base. Clustering the case-base is a one-time process and does not need to be repeated regularly. The proposed approach presented in this paper has been outlined in the form of a new clustered CBR framework. The proposed framework has been evaluated on a simulation of Autonomic Forest Fire Application (AFFA). This paper presents an outline of the simulated AFFA and results on three different clustering algorithms for clustering the case-base in the proposed framework. The comparison of performance of the conventional CBR approach and clustered CBR approach has been presented in terms of their Accuracy, Recall and Precision (ARP) and computational efficiency.

In this paper, we describe several approaches to address the challenges of the network of the future. Our main hypothesis is that the Future Internet must be designed for the environment of applications and transport media of the 21st century, vastly different from the initial Internet's life space. One major requirement is the inherent support for mobile and wireless usage. A Future Internet should allow for the fast creation of diverse network designs and paradigms and must also support their co-existence at run-time. We detail the technical and business scenarios that lead the development in the EU FP7 4WARD project towards a framework for the Future Internet.

In a wireless sensor network (WSN), a large number of sensor nodes are deployed over a wide area and multi-hop communications are required between the nodes. Managing numerous sensor nodes is a very complicated task, especially when the energy issue is involved. Even though a number of ad-hoc management and network structuring approaches for WSNs have been proposed, a management framework covering the entire network management infrastructure from the messaging protocol to the network structuring algorithm has not yet been proposed. In this paper we introduce a management framework for WSNs called SNOWMAN (SeNsOr netWork MANagement) framework. It employs the policy-based management approach for letting the sensor nodes autonomously organize and manage themselves. Moreover, a new light-weight policy distribution protocol called TinyCOPS-PR and policy information base (PIB) are also developed. To facilitate scalable and localized management of sensor networks, the proposed SNOWMAN constructs a 3-tier hierarchy of regions, clusters, and sensor nodes. The effectiveness of the proposed framework is validated through actual implementation and simulation using ns-2. The simulation results reveal that the proposed framework allows smaller energy consumption for network management and longer network lifetime than the existing schemes such as LEACH and LEACH-C for practical size networks.