Sensor networks and the Internet of Things have driven the evolution of traditional electric power distribution networks towards a new paradigm referred to as Smart Grid. However, the different elements that compose the Information and Communication Technologies (ICTs) layer of a Smart Grid are usually conceived as isolated systems that typically result in rigid hardware architectures which are hard to interoperate, manage, and to adapt to new situations. If the Smart Grid paradigm has to be presented as a solution to the demand for distributed and intelligent energy management system, it is necessary to deploy innovative IT infrastructures to support these smart functions. One of the main issues of Smart Grids is the heterogeneity of communication protocols used by the smart sensor devices that integrate them. The use of the concept of the Web of Things is proposed in this work to tackle this problem. More specifically, the implementation of a Smart Grid's Web of Things, coined as the Web of Energy is introduced. The purpose of this paper is to propose the usage of Web of Energy by means of the Actor Model paradigm to address the latent deployment and management limitations of Smart Grids. Smart Grid designers can use the Actor Model as a design model for an infrastructure that supports the intelligent functions demanded and is capable of grouping and converting the heterogeneity of traditional infrastructures into the homogeneity feature of the Web of Things. Conducted experimentations endorse the feasibility of this solution and encourage practitioners to point their efforts in this direction.

The Smart Grid is an example of a cyber-physical system where the physical power grid is surrounded by many intelligent and communication devices that allow for an enhanced management of the power network itself. The Smart Grid may bring great benefits by massively introducing renewable energy sources in the power grid, reducing carbon emissions and improving sustainability. However, it may also bring big challenges regarding reliability, latency and even cybersecurity, since it opens the power system to at least the same threats faced by the Internet. In fact, vulnerabilities may be still larger, considering the novel, heterogeneous and distributed nature of the Smart Grid. Furthermore, cybersecurity is essential for its survival and feasibility, thus making the risks still more relevant.Such Information and Communication Technologies and computer networks supporting the Smart Grid need to comply with very stringent requirements. They also need to efficiently integrate and manage in a single network a vast array of technologies which diverse link layer technologies, meshed and non-meshed Ethernet networks, different cybersecurity protocols, networking at different layers, cognitive systems and storage and replication of data.The objective is to provide a system capable of providing adequate service to the wide array of applications foreseen for the Smart Grid but the complexity of the problem is impressive and it is not possible to focus all of its aspects in a single paper or even project.The present paper presents these requirements, the solutions and results developed and tested in the FP7 European Project INTEGRIS, especially in the security domain, as well as the future challenges and research lines identified and some prospective solutions.

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Abstract. There are problems that present a huge volume of information or/and complex data as imprecision and approximated knowledge. Consequently, a Case-Based Reasoning system requires two main characteristics. The first one consists of offering a good computational time without reducing the accuracy rate of the system, specially when the response time is critical. On the other hand, the system needs soft computing capabilities in order to construct CBR systems more tractable, robust and tolerant to noise. The goal of this paper is centred on achieving a compromise between computational time and complex data management by focusing on the case memory organization (or clustering) through unsupervised techniques. In this sense, we have adapted two approaches: 1) neural networks (Kohonen Maps); and 2) inductive learning (X-means). The results presented in this work are based on datasets acquired from medical and telematics domains, and also from UCI repository.

FINESCE is the Smart Energy use case project of the Future Internet Public Private Partnership Programme. It aims at defining an open infrastructure based on Information and Communications Technology (ICT) used to develop new solutions and applications in all fields of Future Internet related to the energy sector. To accomplish this goal a cloud-based environment is proposed, providing high scalability, fast provisioning, resilience and cost efficiency, while facilitating the deployment of applications and services for utilities.The proposed solution for Smart Energy system encompasses Cloud Computing technologies taking advantage of the service delivery models that it provides (Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS)) over different cloud deployment solutions (Private, Public, Hybrid, Community). Therefore, it is necessary to study their implications, particularly with regard to security and data privacy, whether in transit or stored data, of the cloud solution chosen.The present paper aims to gather basic security requirements in deploying a solution based on Cloud Computing highlighting issues in hybrid clouds because this is the deployment model used in Smart Energy use case. It also exposes attacks and vulnerabilities related to Cloud Computing to be considered for implementing a secure environment for FIDEV, the private platform implementation. Moreover, the security requirements for Smart Energy use case are defined. And, finally, the results of a security audit performed over the testbed platform that simulates a distributed storage solution for FINESCE project are presented.