No abstract

No abstract

The Internet of Things (IoT) has become one of the most widely research paradigms, having received much attention from the research community in the last few years. IoT is the paradigm that creates an internet-connected world, where all the everyday objects capture data from our environment and adapt it to our needs. However, the implementation of IoT is a challenging task and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). EC allows for more secure and efficient data processing in real time, achieving better performance and results. Energy efficiency is one of the most interesting IoT scenarios. In this scenario sensors, actuators and smart devices interact to generate a large volume of data associated with energy consumption. This work proposes the use of an Edge-IoT platform and a Social Computing framework to build a system aimed to smart energy efficiency in a public building scenario. The system has been evaluated in a public building and the results make evident the notable benefits that come from applying Edge Computing to both energy efficiency scenarios and the framework itself. Those benefits included reduced data transfer from the IoT-Edge to the Cloud and reduced Cloud, computing and network resource costs.

Abstract. The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. In this sense, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. One of the most important aspects in Ambient Intelligence is the use of context-aware technologies in order to perceive stimuli from both the users and the environment. Thus, the information obtained must be managed by intelligent and self-adaptable technologies in order to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies.The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and proactivity which make them appropriate for developing dynamic and distributed systems based on Ambient Intelligence, as they possess the capability of adapting themselves to the users and environmental characteristics. This paper presents several case studies where agents and contextaware technologies have been implemented to build AmI-based systems. These case studies expand the possibilities of Ambient Intelligence and get closer to its vision.

The challenge of promoting behavioral changes in users that leads to energy savings in public buildings has become a complex task requiring the involvement of multiple technologies. Wireless sensor networks have a great potential for the development of tools, such as serious games, that encourage acquiring good energy and healthy habits among users in the workplace. This paper presents the development of a serious game using CAFCLA, a framework that allows for integrating multiple technologies, which provide both context-awareness and social computing. Game development has shown that the data provided by sensor networks encourage users to reduce energy consumption in their workplace and that social interactions and competitiveness allow for accelerating the achievement of good results and behavioral changes that favor energy savings.