Accuracy-preserving Measurement Collection for Realistic Wireless Simulations

Abstract—The wireless research community continuously ques-tions the accuracy and the validity of wireless simulation models. While this led to a shift to testbeds for experimental evaluation, wireless testbeds only provide evaluations in a well-defined set of scenarios. Furthermore, even for the deployment of limited sce-narios, testbeds require a complete system setup, which is a time-consuming and challenging process. Hence, realistic simulation models are needed to get early performance results before going through the tedious work involved with testbed experimentation. In our prior work, we proposed the Berlin Open Wireless Lab (BOWL) Indoor Model (BIM) [1] to improve the accuracy of physical (PHY) layer simulation models. In this paper, we propose an optimization to the BIM model (OptBIM), which reduces the measurement complexity to build the model. We validate both BIM and OptBIM at the PHY and transport layers. We see that OptBIM shows similar performance to BIM at the PHY and transport layers. Also, the total number of measurements necessary was reduced by at least 18%. The improvement comes from reducing the number of measurements by 8 times (e.g., from 144 to 18 in our study). I

Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks

This chapter discusses the need of security and privacy protection mechanisms in aggregation protocols used in wireless sensor networks (WSN). It presents a comprehensive state of the art discussion on the various privacy protection mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA protocol and proposes a mechanism to plug that vulnerability. To demonstrate the need of security in aggregation process, the chapter further presents various threats in WSN aggregation mechanisms. A large number of existing protocols for secure aggregation in WSN are discussed briefly and a protocol is proposed for secure aggregation which can detect false data injected by malicious nodes in a WSN. The performance of the protocol is also presented. The chapter concludes while highlighting some future directions of research in secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table

Using Commercial Ray Tracing Software to Drive an Attenuator-Based Mobile WIreless Testbed

We propose and build a prototype architecture for a laboratory-based mobile wireless testbed that uses highly detailed, site-specific channel models to dynamically configure a many-to-many analog channel emulator. Unlike similar systems that have used abstract channel models with few details from the physical environment, we take advantage of commercial ray tracing software and high-performance hardware to make realistic signal power and characteristics predictions in a highly detailed environment. The ray tracing results are used to program a many-to-many analog channel emulator. Using this system, we can conveniently, repeatedly, and realistically subject real wireless nodes to the effects of mobility. We use our prototype system and a detailed CAD model of the University of Maryland campus to compare field test measurements to measurements made from the same devices in the same physical scenario in the testbed. This thesis presents the design, implementation, and validation phases of the proposed mobile wireless testbed

LocaRDS: A Localization Reference Data Set

The use of wireless signals for the purposes of localization enables a host of applications relating to the determination and verification of the positions of network participants ranging from radar to satellite navigation. Consequently, this has been a longstanding interest of theoretical and practical research in mobile networks and many solutions have been proposed in the scientific literature. However, it is hard to assess the performance of these in the real world and, more importantly, to compare their advantages and disadvantages in a controlled scientific manner. With this work, we attempt to improve the current state of art methodology in localization research and to place it on a solid scientific grounding for future investigations. Concretely, we developed LocaRDS, an open reference data set of real-world crowdsourced flight data featuring more than 222 million measurements from over 50 million transmissions recorded by 323 sensors. We demonstrate how we can verify the quality of LocaRDS measurements so that it can be used to test, analyze and directly compare different localization methods. Finally, we provide an example implementation for the aircraft localization problem and a discussion of possible metrics for use with LocaRDS

Privacy-Preserving Crowdsourcing-Based Recommender Systems for E-Commerce & Health Services

En l’actualitat, els sistemes de recomanació han esdevingut un mecanisme fonamental per proporcionar als usuaris informació útil i filtrada, amb l’objectiu d’optimitzar la presa de decisions, com per exemple, en el camp del comerç electrònic. La quantitat de dades existent a Internet és tan extensa que els usuaris necessiten sistemes automàtics per ajudar-los a distingir entre informació valuosa i soroll. No obstant, sistemes de recomanació com el Filtratge Col·laboratiu tenen diverses limitacions, com ara la manca de resposta i la privadesa. Una part important d'aquesta tesi es dedica al desenvolupament de metodologies per fer front a aquestes limitacions. A més de les aportacions anteriors, en aquesta tesi també ens centrem en el procés d'urbanització que s'està produint a tot el món i en la necessitat de crear ciutats més sostenibles i habitables. En aquest context, ens proposem solucions de salut intel·ligent (s-health) i metodologies eficients de caracterització de canals sense fils, per tal de proporcionar assistència sanitària sostenible en el context de les ciutats intel·ligents.En la actualidad, los sistemas de recomendación se han convertido en una herramienta indispensable para proporcionar a los usuarios información útil y filtrada, con el objetivo de optimizar la toma de decisiones en una gran variedad de contextos. La cantidad de datos existente en Internet es tan extensa que los usuarios necesitan sistemas automáticos para ayudarles a distinguir entre información valiosa y ruido. Sin embargo, sistemas de recomendación como el Filtrado Colaborativo tienen varias limitaciones, tales como la falta de respuesta y la privacidad. Una parte importante de esta tesis se dedica al desarrollo de metodologías para hacer frente a esas limitaciones. Además de las aportaciones anteriores, en esta tesis también nos centramos en el proceso de urbanización que está teniendo lugar en todo el mundo y en la necesidad de crear ciudades más sostenibles y habitables. En este contexto, proponemos soluciones de salud inteligente (s-health) y metodologías eficientes de caracterización de canales inalámbricos, con el fin de proporcionar asistencia sanitaria sostenible en el contexto de las ciudades inteligentes.Our society lives an age where the eagerness for information has resulted in problems such as infobesity, especially after the arrival of Web 2.0. In this context, automatic systems such as recommenders are increasing their relevance, since they help to distinguish noise from useful information. However, recommender systems such as Collaborative Filtering have several limitations such as non-response and privacy. An important part of this thesis is devoted to the development of methodologies to cope with these limitations. In addition to the previously stated research topics, in this dissertation we also focus in the worldwide process of urbanisation that is taking place and the need for more sustainable and liveable cities. In this context, we focus on smart health solutions and efficient wireless channel characterisation methodologies, in order to provide sustainable healthcare in the context of smart cities