14,426 research outputs found

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

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    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

    A new QoS routing algorithm based on self-organizing maps for wireless sensor networks

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    For the past ten years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore and compare the performance of two very well known routing paradigms, directed diffusion and Energy- Aware Routing, with our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

    EYES - Energy Efficient Sensor Networks

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    The EYES project (IST-2001-34734) is a three years European research project on self-organizing and collaborative energy-efficient sensor networks. It will address the convergence of distributed information processing, wireless communications, and mobile computing. The goal of the project is to develop the architecture and the technology which enables the creation of a new generation of sensors that can effectively network together so as to provide a flexible platform for the support of a large variety of mobile sensor network applications. This document gives an overview of the EYES project

    OLIMPO, An Ad-Hoc Wireless Sensor Network Simulator for Public Utilities Applications

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    This paper introduces OLIMPO, an useful simulation tool for researchers who are developing wireless sensor communication protocols. OLIMPO is a discreteevent simulator design to be easily recon gured by the user, providing a way to design, develop and test communication protocols. In particular, we have designed a self-organizing wireless sensor network for low data rate. Our premise is that, due to their inherent spread location over large areas, wireless sensor networks are well-suited for SCADA applications, which require relatively simple control and monitoring. To show the facilities of our simulator, we have studied our network protocol with OLIMPO, developing several simulations. The purpose of these simulations is to demonstrate, quantitatively, the capability of our network to support this kind of applications

    Piezoelectric vibration energy harvesting from airflow in HVAC (Heating Ventilation and Air Conditioning) systems

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    This study focuses on the design and wind tunnel testing of a high efficiency Energy Harvesting device, based on piezoelectric materials, with possible applications for the sustainability of smart buildings, structures and infrastructures. The development of the device was supported by ESA (the European Space Agency) under a program for the space technology transfer in the period 2014-2016. The EH device harvests the airflow inside Heating, Ventilation and Air Conditioning (HVAC) systems, using a piezoelectric component and an appropriate customizable aerodynamic appendix or fin that takes advantage of specific airflow phenomena (vortex shedding and galloping), and can be implemented for optimizing the energy consumption inside buildings. Focus is given on several relevant aspects of wind tunnel testing: different configurations for the piezoelectric bender (rectangular, cylindrical and T-shaped) are tested and compared, and the effective energy harvesting potential of a working prototype device is assessed

    An efficient self-organizing node deployment algorithm for mobile sensor networks

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    Using artificial intelligence in routing schemes for wireless networks

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    For the latest 10 years, many authors have focused their investigations in wireless sensor networks. Different researching issues have been extensively developed: power consumption, MAC protocols, self-organizing network algorithms, data-aggregation schemes, routing protocols, QoS management, etc. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically discarded. However, in some special scenarios the features of neural networks are appropriate to develop complex tasks such as path discovery. In this paper, we explore the performance of two very well-known routing paradigms, directed diffusion and Energy-Aware Routing, and our routing algorithm, named SIR, which has the novelty of being based on the introduction of neural networks in every sensor node. Extensive simulations over our wireless sensor network simulator, OLIMPO, have been carried out to study the efficiency of the introduction of neural networks. A comparison of the results obtained with every routing protocol is analyzed. This paper attempts to encourage the use of artificial intelligence techniques in wireless sensor nodes

    Architectures for Wireless Sensor Networks

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    Various architectures have been developed for wireless sensor networks. Many of them leave to the programmer important concepts as the way in which the inter-task communication and dynamic reconfigurations are addressed. In this paper we describe the characteristics of a new architecture we proposed - the data-centric architecture. This architecture offers an easy way of structuring the applications designed for wireless sensor nodes that confers them superior performances

    Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks

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    It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.Comment: Accepted for publication: Special Issue on Security and Performance of Networks and Clouds (The Computer Journal
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