Multi-Agent Information Fusion System to manage data from a WSN in a residential home

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

New platform for intelligent context-based distributed information fusion

Tesis por compendio de publicaciones[ES]Durante las últimas décadas, las redes de sensores se han vuelto cada vez más importantes y hoy en día están presentes en prácticamente todos los sectores de nuestra sociedad. Su gran capacidad para adquirir datos y actuar sobre el entorno, puede facilitar la construcción de sistemas sensibles al contexto, que permitan un análisis detallado y flexible de los procesos que ocurren y los servicios que se pueden proporcionar a los usuarios. Esta tesis doctoral se presenta en el formato de “Compendio de Artículos”, de tal forma que las principales características de la arquitectura multi-agente distribuida propuesta para facilitar la interconexión de redes de sensores se presentan en tres artículos bien diferenciados. Se ha planteado una arquitectura modular y ligera para dispositivos limitados computacionalmente, diseñando un mecanismo de comunicación flexible que permite la interacción entre diferentes agentes embebidos, desplegados en dispositivos de tamaño reducido. Se propone un nuevo modelo de agente embebido, como mecanismo de extensión para la plataforma PANGEA. Además, se diseña un nuevo modelo de organización virtual de agentes especializada en la fusión de información. De esta forma, los agentes inteligentes tienen en cuenta las características de las organizaciones existentes en el entorno a la hora de proporcionar servicios. El modelo de fusión de información presenta una arquitectura claramente diferenciada en 4 niveles, siendo capaz de obtener la información proporcionada por las redes de sensores (capas inferiores) para ser integrada con organizaciones virtuales de agentes (capas superiores). El filtrado de señales, minería de datos, sistemas de razonamiento basados en casos y otras técnicas de Inteligencia Artificial han sido aplicadas para la consecución exitosa de esta investigación. Una de las principales innovaciones que pretendo con mi estudio, es investigar acerca de nuevos mecanismos que permitan la adición dinámica de redes de sensores combinando diferentes tecnologías con el propósito final de exponer un conjunto de servicios de usuario de forma distribuida. En este sentido, se propondrá una arquitectura multiagente basada en organizaciones virtuales que gestione de forma autónoma la infraestructura subyacente constituida por el hardware y los diferentes sensores

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

[EN] This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings

Framework for integrated oil pipeline monitoring and incident mitigation systems

Wireless Sensor Nodes (motes) have witnessed rapid development in the last two decades. Though the design considerations for Wireless Sensor Networks (WSNs) have been widely discussed in the literature, limited investigation has been done for their application in pipeline surveillance. Given the increasing number of pipeline incidents across the globe, there is an urgent need for innovative and effective solutions for deterring the incessant pipeline incidents and attacks. WSN pose as a suitable candidate for such solutions, since they can be used to measure, detect and provide actionable information on pipeline physical characteristics such as temperature, pressure, video, oil and gas motion and environmental parameters. This paper presents specifications of motes for pipeline surveillance based on integrated systems architecture. The proposed architecture utilizes a Multi-Agent System (MAS) for the realization of an Integrated Oil Pipeline Monitoring and Incident Mitigation System (IOPMIMS) that can effectively monitor and provide actionable information for pipelines. The requirements and components of motes, different threats to pipelines and ways of detecting such threats presented in this paper will enable better deployment of pipeline surveillance systems for incident mitigation. It was identified that the shortcomings of the existing wireless sensor nodes as regards their application to pipeline surveillance are not effective for surveillance systems. The resulting specifications provide a framework for designing a cost-effective system, cognizant of the design considerations for wireless sensor motes used in pipeline surveillance

Using Magentix2 in Smart-Home Environments

[EN] In this paper, we present the application of a multi-agent platform Magentix2 for the development of MAS in smart-homes. Specificallly, the use of Magentix2 (http://gti-ia.upv.es/sma/tools/magentix2/index.php) platform facilitates the management of the multiple occupancy in smart living spaces. Virtual organizations provide the possibility of defining a set of norms and roles that facilitate the regulation and control of the actions that can be carried out by the internal and external agents depending on their profile. We illustrate the applicability of our proposal with a set of scenarios. © Springer International Publishing Switzerland 2015.This work is supported by the Spanish government grants CONSOLIDER INGENIO 2010 CSD2007-00022, MINECO/FEDER TIN2012-36586-C03-01, TIN2011-27652-C03-01, and SP2014800.Valero Cubas, S.; Del Val Noguera, E.; Alemany Bordera, J.; Botti, V. (2015). Using Magentix2 in Smart-Home Environments. En 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer Verlag. 27-37. https://doi.org/10.1007/978-3-319-19719-7_3S2737Bajo J, Fraile JA, Pérez-Lancho B, Corchado JM (2010) The thomas architecture in home care scenarios: a case study. Expert Syst Appl 37(5):3986–3999Cetina C, Giner P, Fons J, Pelechano V (2009) Autonomic computing through reuse of variability models at runtime: The case of smart homes. Computer 42(10):37–43Cook DJ (2009) Multi-agent smart environments. J Ambient Intell Smart Environ 1(1):51–55Crandall AS, Cook DJ (2010) Using a hidden markov model for resident identification. In: 6th international conference on intelligent environments, pp 74–79. IEEECriado N, Argente E, Botti V (2013) THOMAS: an agent platform for supporting normative multi-agent systems. J Logic Comput 23(2):309–333Davidoff S, Lee MK, Zimmerman J, Dey A (2006) Socially-aware requirements for a smart home. In: Proceedings of the international symposium on intelligent, environments, pp 41–44Grupo de Tecnología Informática e Inteligencia Artificial (GTI-IA) (2015). http://www.gti-ia.upv.es/sma/tools/magentix2/archivos/Magentix2UserManualv2.1.0.pdf . Magentix2 User’s Manual v2.0Loseto G, Scioscia F, Ruta M, di Sciascio E (2012) Semantic-based smart homes: a multi-agent approach. In: 13th Workshop on objects and agents (WOA 2012), vol 892, pp 49–55Rodriguez S, Julián V, Bajo J, Carrascosa C, Botti V, Corchado JM (2011) Agent-based virtual organization architecture. Eng Appl Artif Intell 24(5):895–910Rodríguez S, Paz JFD, Villarrubia G, Zato C, Bajo J, Corchado JM (2015) Multi-agent information fusion system to manage data from a WSN in a residential home. Inf Fusion 23:43–57Such JM, Garca-Fornes A, Espinosa A, Bellver J (2012) Magentix2: a Privacy-enhancing Agent Platform. Eng Appl Artif IntellSun Q, Yu W, Kochurov N, Hao Q, Hu F (2013) A multi-agent-based intelligent sensor and actuator network design for smart house and home automation. J Sens Actuator Netw 2(3):557–588Val E, Criado N, Rebollo M, Argente E, Julian V (2009) Service-oriented framework for virtual organizations. 1:108–114Wu C-L, Liao C-F, Fu L-C (2007) Service-oriented smart-home architecture based on osgi and mobile-agent technology. IEEE Trans Syst Man Cybern Part C Appl Rev 37(2):193–205Yin J, Yang Q, Shen D, Li Z-N (2008) Activity recognition via user-trace segmentation. ACM Trans Sens Netw (TOSN) 4(4):1

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Multi-agent systems applications in energy optimization problems: a state-of-the-art review

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings

Automatic UAVs path planning

My work at the University of Salamanca took place between 14th September 2017 and 1st December 2017. During these months, I have had the opportunity to work with the BISITE Research Group, attend different congresses held in Spain and learn new computer techniques related to artificial intelligence. The work has been focused on the development of software that implements algorithms for the control of UAVs (Unmanned Aerial Vehicles) autonomously. The algorithms are capable of guiding each UAV in such a way that they make an optimal route when travelling the area covered by a perimeter introduced by the user. As an important part of the algorithms, it is emphasized that when calculating changes of direction in the route, it is necessary to take into account the type of camera and its opening. This ensures that the captured images do not overlap or overlap with the minimum required to avoid spaces in 3D reconstruction software. As part of the work, the bibliography indicated in the References section has been used