Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare

Abstract—Ambient intelligence has acquired great importance in recent years and requires the development of new innovative solutions. This paper presents a distributed telemonitoring system, aimed at improving healthcare and assistance to dependent people at their homes. The system implements a service-oriented architecture based platform, which allows heterogeneous wireless sensor networks to communicate in a distributed way independent of time and location restrictions. This approach provides the system with a higher ability to recover from errors and a better flexibility to change their behavior at execution time. Preliminary results are presented in this paper. Index Terms—Ambient intelligence (AmI), healthcare, servicesoriented architectures (SOAs), wireless sensors networks (WSNs)

Wireless Sensor Networks and Real-Time Locating Systems to Fight against Maritime Piracy

There is a wide range of military and civil applications where Wireless Sensor Networks (WSNs) and Multi-Agent Systems (MASs) can be used for providing context-awareness for troops and special corps. On the one hand, WSNs comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work properly. On the other hand, agent-based architectures allow building autonomous and robust systems that are capable of working on highly dynamic scenarios. This paper presents two piracy scenarios where the n-Core platform can be applied. n-Core is a hardware and software platform intended for developing and deploying easily and quickly a wide variety of WSNs applications based on the ZigBee standard. In the first scenario a RTLS is deployed to support boarding and rescue operations. In the second scenario a multi-agent system is proposed to detect the unloading of illegal traffic of merchandise at ports

Eco-Epidemiology of Chagas Disease in Chile

There are four vector species of Chagas disease in Chile: Triatoma infestans, responsible for the domestic cycle; Mepraia spinolai, the main wild vector; and M. gajardoi and M. parapatrica, two coastal wild species whose importance as vectors is not well known. They are species of dry environments of the central-north region of Chile, whose best predictors of distribution are warm average temperatures and low rainfall. They are found in rock quarries, nests of birds, and small mammals, and T. infestans has sylvatic foci associated with a Bromeliaceae species. While human blood represents 70% of the diet of T. infestans, in M. spinolai this value is 7%, which means that a large part of Chagas disease in Chile is due to T. infestans. However, all species have high percentages of T. cruzi infection. Chagas disease in Chile follows the distribution of T. infestans, and although the cycle of domestic transmission by this vector is interrupted, there is still a constant prevalence and mortality and ascending incidences. Models predict that although climate change will not vary greatly the north-south distribution of vectors, it could increase the reproductive number of the disease, increasing risk areas of Chagas disease

Self-organizing multi-agent system for management and planning surveillance routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study.Web of Science3151100108

Mitigation of the ground reflection effect in real-time locating systems based on wireless sensor networks by using artificial neural networks

Wireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks.Wireless sensor networks (WSNs) have become much more relevant in recent years, mainly because they can be used in a wide diversity of applications. Real-time locating systems (RTLSs) are one of the most promising applications based on WSNs and represent a currently growing market. Specifically, WSNs are an ideal alternative to develop RTLSs aimed at indoor environments where existing global navigation satellite systems, such as the global positioning system, do not work correctly due to the blockage of the satellite signals. However, accuracy in indoor RTLSs is still a problem requiring novel solutions. One of the main challenges is to deal with the problems that arise from the effects of the propagation of radiofrequency waves, such as attenuation, diffraction, reflection and scattering. These effects can lead to other undesired problems, such as multipath. When the ground is responsible for wave reflections, multipath can be modeled as the ground reflection effect. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the ground reflection effect by using multilayer perceptron artificial neural networks

Self-Organizing Multi-Agent System for Management and Planning Surveillance Routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study

A Distributed Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care

This chapter presents ALZ-MAS (Alzheimer multi-agent system), an ambient intelligence (AmI)-based multi-agent system aimed at enhancing the assistance and health care for Alzheimer patients. The system makes use of several context-aware technologies that allow it to automatically obtain information from users and the environment in an evenly distributed way, focusing on the characteristics of ubiquity, awareness, intelligence, mobility, etc., all of which are concepts defined by AmI. ALZ-MAS makes use of a services oriented multi-agent architecture, called flexible user and services oriented multi-agent architecture, to distribute resources and enhance its performance. It is demonstrated that a SOA approach is adequate to build distributed and highly dynamic AmI-based multi-agent systems

3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008

The Symposium on Ubiquitous Computing and Ambient Intelligence (UCAmI) began as a workshop held in 2003 in San Sebastián (Spain) under the Spanish Artificial Intelligence Conference. This event gathered 32 attendees and 18 papers were presented. The second edition, already as a Symposium, took place in Granada (Spain) under the first Spanish Computer Science Conference (CEDI). Later, in 2006, a second workshop was celebrated in Ciudad Real and, in 2007; the second Symposium was organized in Zaragoza by the CEDI conference. Now we continue to work on the organization of this event in Salamanca, a beautiful Spanish city. The European Community and the Sixth and Seventh Framework Programs encourage researchers to explore the generic scope of the AmI vision. In fact, some researchers have a crucial role in this vision. Emile Aarts from Philips describes Ambient Intelligence as "the integration of technology into our environment, so that people can freely and interactively utilize it". This idea agrees with the proposal of Mark Weiser regarding the Ubiquitous Computing paradigm. The UCAmI community tries to join experts around the world in order to promote collaborations and to put into practice studies for involving people into intelligent environments so that the "Everyday Computing" concept can be a reality. The UCAmI technical program includes 40 papers (31 long paper, 6 short paper and 3 doctoral consortium) selected from a submission pool of 56 papers, from 11 different countries

ALZ-MAS: Alzheimer’s special care multi-agent system.

This paper presents a multi-agent based solution for Alzheimer's residential health care. The carers work in a very dynamic environment and the use of planning agents helps them to adapt to changes and optimizes their working day. The ALZ MAS system includes agents that take care of the patients and of the security of the environment. Some of the agents use RFID technology to control the location of the patients, necessary to create and execute dynamic plans and to guarantee the security of the residence. The deliberative agents used cooperate with each other and have learning capabilities. This paper also shows how to construct multi-objective driven agents using a case-based reasoning methodolog