Abstract-Wireless Sensor Networks (WSNs) have attracted a great deal of research interest during the last few years. Potential applications make them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Localization is a key aspect of such networks, since the knowledge of a sensor's location is critical in order to process information originating from this sensor, or to actuate responses to the environment, or to infer regarding an emerging situation etc. Indoor localization in the literature is based on various techniques, ranging from simple Received-Signal-Strength (RSS) to the more demanding Time-of-Arrival (ToA) or Directionof-Arrival (DoA) of the incoming signals. In the context of several EU research projects, various WSN platforms for indoor localization have been developed, evaluated and tested within real-world emergency medical services applications. These platforms were selected in order to deal with all principal localization techniques, namely RSSI, ToA and DoA. Deployment and real-world considerations are discussed, measurements results are presented and overall system evaluation conclusions are drawn regarding indoor localization capabilities of WSNs.

Wireless sensor networks (WSNs) have attracted a great deal of research interest during the last few years, with potential applications making them ideal for the development of the envisioned world of ubiquitous and pervasive computing. Energy and computational effi ciency constraints are the main key issues when dealing with this type of network. The main research effort has been channeled towards routing and distributed processing, in order to achieve better quality of service (QoS) provisions, lower interference, and a lower power-consumption rate while data dissemination is carried out. The embedment of smart antennas on wireless-sensor nodes is proposed herein as an alternative and novel approach at the physical layer, with the potential for relieving traditional challenges faced by current wireless-sensornetwork architectures. Studying the behavior of wireless sensor networks consisting of different types of antennas (omnidirectional or adaptive directional) yielded unexpectedly favorable results that improved the operation of networking systems of this type. In the test cases presented herein, the incorporation of smart antennas resulted in approximate improvements in the quality of service by 20%, the effi ciency by 50%, the percentage of active nodes by 20%, and the energy consumption by 50%, depending on the simulation setup.

Abstract-A system consisting of a smart antenna and a processor can perform filtering in both the time and space domain, thus reducing the sensitivity of the receiver to interfering directional noise sources. Smart antennas can be used for further increase in the capacity of a communication system and for variable speed of transmission for multimedia information. Switched beam antenna arrays are a subset of smart antennas that cover either the x-y plane or a portion of it with multiple radiation patterns. A processor can decide which pattern to use for reception or transmission. In this paper the use of genetic algorithms (GAs) is examined in the design of switched beam antenna arrays. The antenna consists of five or six elements and the radiation patterns vary from 4 to 8, covering the x-y plane with the main beams of the radiation patterns pointing at 0

Abstract-Location information is critical for the development of value-added location-based services, such as fraud protection, locationaware network access, person/asset tracking etc. Herein, a method for the enhancement of localization systems in terms of achieved accuracy is proposed, which can be applied to new as well as existing systems regardless the underlying localization technique. The method is based on modeling the position measurement error introduced by the localization algorithm using a polynomial approximation approach. Measurements results demonstrate the applicability of the proposed technique in enhancing accuracy in a low cost and efficient manner.

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STORM is an ongoing European research project that aims at developing an integrated platform for monitoring, protecting, and managing cultural heritage sites through technical and organizational innovation. Part of the scheduled preventive actions for the protection of cultural heritage is the development of wireless acoustic sensor networks (WASNs) that will be used for assessing the impact of human-generated activities as well as for monitoring potentially hazardous environmental phenomena. Collected sound samples will be forwarded to a central server where they will be automatically classified in a hierarchical manner; anthropogenic and environmental activity will be monitored, and stakeholders will be alarmed in the case of potential malevolent behavior or natural phenomena like excess rainfall, fire, gale, high tides, and waves. Herein, we present an integrated platform that includes sound sample denoising using wavelets, feature extraction from sound samples, Gaussian mixture modeling of these features, and a powerful two-layer neural network for automatic classification. We contribute to previous work by extending the proposed classification platform to perform low-level classification too, i.e., classify sounds to further subclasses that include airplane, car, and pistol sounds for the anthropogenic sound class; bird, dog, and snake sounds for the biophysical sound class; and fire, waterfall, and gale for the geophysical sound class. Classification results exhibit outstanding classification accuracy in both high-level and low-level classification thus demonstrating the feasibility of the proposed approach.