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The control of consumption in homes and workplaces is an increasingly important aspect if we consider the growing popularity of smart cities, the increasing use of renewable energies, and the policies of the European Union on using energy in an efficient and clean way. These factors make it necessary to have a system that is capable of predicting what devices are connected to an electrical network. For demand management, the system must also be able to control the power supply to these devices. To this end, we propose the use of a multiagent system that includes agents with advanced reasoning and learning capacities. More specifically, the agents incorporate a case-based reasoning system and machine learning techniques. Besides, the multiagent system includes agents that are specialized in the management of the data acquired and the electrical devices. The aim is to adjust the consumption of electricity in networks to the electrical demand, and this will be done by acting automatically on the detected devices. The proposed system provides promising results; it is capable of predicting what devices are connected to the power grid at a high success rate. The accuracy of the system makes it possible to act according to the device preferences established in the system. This allows for adjusting the consumption to the current demand situation, without the risk of important home appliances being switched off. [ABSTRACT FROM AUTHOR]

There has been continuous research in the energy distribution sector because of its huge impact on modern societies. Nonetheless, aerial high voltage power lines are still supported by old transmission towers which involve some serious risks. Those risks may be avoided with periodic and expensive reviews. The main objective of this work is to reduce the number of these periodic reviews so that the maintenance cost of power lines is also reduced. More specifically, the work is focused on reducing the number of periodic reviews of transmission towers to avoid step and touch potentials, which are very dangerous for humans. A virtual organization-based multi-agent system is proposed in conjunction with different artificial intelligence methods and algorithms. The developed system is able to propose a sample of transmission towers from a selected set to be reviewed. The system ensures that the whole set will have similar values without needing to review all the transmission towers. As a result of this work, a website application is provided to manage all the review processes and all the transmission towers of Spain. It allows the companies that review the transmission towers to initiate a new review process for a whole line or area, while the system indicates the transmission towers to review. The system is also able to recommend the best place to locate a new transmission tower or the best type of structure to use when a new transmission tower must be used. [ABSTRACT FROM AUTHOR]

The management of heterogeneous distributed sensor networks requires new solutions that can address the problem of automatically fusing the information coming from different sources in an efficient and effective manner. In the literature it is possible to find different types of data fusion and information fusion techniques in use today, but it is still a challenge to obtain systems that allow the automation or semiautomation of information processing and fusion. In this paper, we present a multiagent system that based on the organizational theory proposes a new model to automatically process and fuse information in heterogeneous distributed sensor networks. The proposed architecture is applied to a case study for indoor location where information is taken from different heterogeneous sensors. [ABSTRACT FROM AUTHOR]

The early detection and monitoring of kidney disease continues being an important problem in medicine. The diagnosis and treatment of patients with this disease usually require expensive medical equipment that is difficult to install. Patients or medical centers may not always be able to afford such equipment. This work proposes the creation of a wireless sensor network for medical environments; it will assist medical professionals in the diagnosis and monitoring of patients with renal symptomatology. This work will focus on the analysis of symptoms that accompany this disease and the design of a system which will help determine types of kidney diseases. The proposed system will incorporate new hardware mechanisms and an intelligent system. It will be designed through a multi-agent architecture based on virtual organizations. This architecture will include a new model of agents, specifically designed to be incorporated into computationally limited devices. This hardware will be characterized by its low cost and ease of use. A case study has been carried out in order to validate the proposed architecture. In order to validate the proposed architecture, we designed a case study that aims to provide a technological tool for medical professionals and makes it possible to determine any diseases related to diuresis. The initial results are promising. [ABSTRACT FROM AUTHOR]

With the increase of intelligent systems based on Multi-Agent Systems (MAS) and the use of Wireless Sensor Networks (WSN) in context-aware scenarios, information fusion has become an essential part of this kind of systems where the information is distributed among nodes or agents. This paper presents a new MAS specially designed to manage data from WSNs, which was tested in a residential home for the elderly. The proposed MAS architecture is based on virtual organizations, and incorporates social behaviors to improve the information fusion processes. The data that the system manages and analyzes correspond to the actual data of the activities of a resident. Data is collected as the information event counts detected by the sensors in a specific time interval, typically one day. We have designed a system that improves the quality of life of dependant people, especially elderly, by fusioning data obtained by multiple sensors and information of their daily activities. The high development of systems that extract and store information make essential to improve the mechanisms to deal with the avalanche of context data. In our case, the MAS approach results appropriated because each agent can represent an autonomous entity with different capabilities and offering different services but collaborating among them. Several tests have been performed to evaluate this platform and preliminary results and the conclusions are presented in this paper. [ABSTRACT FROM AUTHOR]