Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Proceedings of the Third International Mobile Satellite Conference (IMSC 1993)

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial cellular communications services. While the first and second International Mobile Satellite Conferences (IMSC) mostly concentrated on technical advances, this Third IMSC also focuses on the increasing worldwide commercial activities in Mobile Satellite Services. Because of the large service areas provided by such systems, it is important to consider political and regulatory issues in addition to technical and user requirements issues. Topics covered include: the direct broadcast of audio programming from satellites; spacecraft technology; regulatory and policy considerations; advanced system concepts and analysis; propagation; and user requirements and applications

Data Acquisition Applications

Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

Design and Evaluation of Compression, Classification and Localization Schemes for Various IoT Applications

Nowadays we are surrounded by a huge number of objects able to communicate, read information such as temperature, light or humidity, and infer new information through ex- changing data. These kinds of objects are not limited to high-tech devices, such as desktop PC, laptop, new generation mobile phone, i.e. smart phone, and others with high capabilities, but also include commonly used object, such as ID cards, driver license, clocks, etc. that can made smart by allowing them to communicate. Thus, the analog world of just a few years ago is becoming the a digital world of the Inter- net of Things (IoT), where the information from a single object can be retrieved from the Internet. The IoT paradigm opens several architectural challenges, including self-organization, self-managing, self-deployment of the smart objects, as well as the problem of how to minimize the usage of the limited resources of each device. The concept of IoT covers a lot of communication paradigms such as WiFi, Radio Frequency Identification (RFID), and Wireless Sensor Network (WSN). Each paradigm can be thought of as an IoT island where each device can communicate directly with other devices. The thesis is divided in sections in order to cover each problem mentioned above. The first step is to understand the possibility to infer new knowledge from the deployed device in a scenario. For this reason, the research is focused on the web semantic, web 3.0, to assign a semantic meaning to each thing inside the architecture. The sole semantic concept is unusable to infer new information from the data gathered; in fact, it is necessary to organize the data through a hierarchical form defined by an Ontology. Through the exploitation of the Ontology, it is possible to apply semantic engine reasoners to infer new knowledge about the network. The second step of the dissertation deals with the minimization of the usage of every node in a WSN. The main purpose of each node is to collect environmental data and to exchange hem with other nodes. To minimize battery consumption, it is necessary to limit the radio usage. Therefore, we implemented Razor, a new lightweight algorithm which is expected to improve data compression and classification by leveraging on the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. Data compression is performed studying the well-know Vector Quantization (VQ) theory in order to create the codebooks necessary for signal compression. At the same time, it is requested to give a semantic meaning to un- known signals. In this way, the codebook feature is able not only to compress the signals, but also to classify unknown signals. Razor is compared with both state-of-the-art compression and signal classification techniques for WSN . The third part of the thesis covers the concept of smart object applied to Robotic research. A critical issue is how a robot can localize and retrieve smart objects in a real scenario without any prior knowledge. In order to achieve the objectives, it is possible to exploit the smart object concept and localize them through RSSI measurements. After the localization phase, the robot can exploit its own camera to retrieve the objects. Several filtering algorithms are developed in order to mitigate the multi–path issue due to the wireless communication channel and to achieve a better distance estimation through the RSSI measurement. The last part of the dissertation deals with the design and the development of a Cognitive Network (CN) testbed using off the shelf devices. The device type is chosen considering the cost, usability, configurability, mobility and possibility to modify the Operating System (OS) source code. Thus, the best choice is to select some devices based on Linux kernel as Android OS. The feature to modify the Operating System is required to extract the TCP/IP protocol stack parameters for the CN paradigm. It is necessary to monitor the network status in real-time and to modify the critical parameters in order to improve some performance, such as bandwidth consumption, number of hops to exchange the data, and throughput

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

A supporting infrastructure for Wireless Sensor Networks in Critical Industrial Environments

Tese de doutoramento no Programa de Doutoramento em Ciências e Tecnologias da Informação apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra.As Redes de Sensores Sem Fios (RSSFs) têm uma aplicabilidade muito elevada nas mais diversas áreas, como na indústria, nos sistemas militares, na saúde e nas casas inteligentes. No entanto, continuam a existir várias limitações que impedem que esta tecnologia tenha uma utilização extensiva. A fiabilidade é uma destas principais limitações que tem atrasado a adopção das RSSFs em ambientes industriais, principalmente quando sujeitos a elevadas interferências e ruídos. Por outro lado, a interoperabilidade é também um dos principais requisitos a cumprir nomeadamente com o avanço para o paradigma da Internet of Things. A determinação da localização dos nós, principalmente dos nós móveis, é, também ele, um requisito crítico em muitas aplicações. Esta tese de doutoramento propõe novas soluções para a integração e para a localização de RSSFs que operem em ambientes industriais e críticos. Como os nós sensores são, na maioria das vezes, instalados e deixados sem intervenção humana durante longos períodos de tempo, isto é, meses ou mesmo anos, é muito importante oferecer processos de comunicação fiável. No entanto, muitos problemas ocorrem durante a transmissão dos pacotes, nomeadamente devido a ruídos, interferências e perda de potência do sinal. A razão das interferências deve-se à existência de mais do que uma rede ou ao espalhamento espectral que ocorre em determinadas frequências. Este tipo de problemas é mais severo em ambientes dinâmicos nos quais novas fontes de ruído pode ser introduzidas em qualquer instante de tempo, nomeadamente com a chegadas de novos dispositivos ao meio. Consequentemente, é necessário que as RSSFs tenham a capacidade de lidar com as limitações e as falhas nos processos de comunicação. O protocolo Dynamic MAC (DunMAC) proposto nesta dissertação utiliza técnicas de rádio cognitivo (CR) para que a RSSF se adapte, de forma dinâmica, a ambientes instáveis e ruidosos através da selecção automática do melhor canal durante o período de operação. As RSSFs não podem operar em isolação completa do meio, e necessitam de ser monitoradas e controladas por aplicações externas. Apesar de ser possível adicionar a pilha protocolar IP aos nós sensores, este procedimento não é adequado para muitas aplicações. Para estes casos, os modelos baseados em gateway ou proxies continuam a apresentar-se preferíveis para o processo de integração. Um dos desafios existentes para estes processos de integração é a sua adaptabilidade, isto é, a capacidade da gateway ou do proxy poder ser reutilizado sem alterações por outras aplicações. A razão desta limitação deve-se aos consumidores finais dos dados serem aplicações e não seres humanos. Logo, é difícil ou mesmo impossível criar normas para as estruturas de dados dada a infinidade de diferentes formatos. É então desejável encontrar uma solução que permita uma integração transparente de diferentes RSSFs e aplicações. A linguagem Sensor Traffic Description Language (STDL) proposta nesta dissertação propõe uma solução para esta integração através de gateways e proxies flexíveis e adaptados à diversidade de aplicações, e sem recorrer à reprogramação. O conhecimento da posição dos nós sensores é, também ele, crítico em muitas aplicações industriais como no controlo da deslocação dos objectos ou trabalhadores. Para além do mais, a maioria dos valores recolhidos dos sensores só são úteis quando acompanhados pelo conhecimento do local onde esses valores foram recolhidos. O Global Positioning Systems (GPS) é a mais conhecida solução para a determinação da localização. No entanto, o recurso ao GPS em cada nó sensor continua a ser energeticamente ineficiente e impraticável devido aos custos associados. Para além disso, os sistemas GPS não são apropriados para ambientes in-door. Este trabalho de doutoramento propõe-se actuar nestas áreas. Em particular, é proposto, implementado e avaliado o protocolo DynMAC para oferecer fiabilidade às RSSFs. Para a segunda temática, a linguagem STDL e o seu motor são propostos para suportar a integração de ambientes heterogéneos de RSSFs e aplicações. As soluções propostas não requerem reprogramação e suportam também serviços de localização nas RSSFs. Diferentes métodos de localização foram avaliados para estimar a localização dos nós. Assim, com estes métodos as RSSFs podem ser usadas como componentes para integrar e suportar a Futura Internet. Todas as soluções propostas nesta tese foram implementadas e validadas tanto em simulação com em plataformas práticas, laboratoriais e industriais.The Wireless Sensor Network (WSN) has a countless number of applications in almost all of the fields including military, industrial, healthcare, and smart home environments. However, there are several problems that prevent the widespread of sensor networks in real situations. Among them, the reliability of communication especially in noisy industrial environments is difficult to guarantee. In addition, interoperability between the sensor networks and external applications is also a challenge. Moreover, determining the position of nodes, particularly mobile nodes, is a critical requirement in many types of applications. My original contributions in this thesis include reliable communication, integration, localization solutions for WSNs operating in industrial and critical environments. Because sensor nodes are usually deployed and kept unattended without human intervention for a long duration, e.g. months or even years, it is a crucial requirement to provide the reliable communication for the WSNs. However, many problems arise during packet transmission and are related to the transmission medium (e.g. signal path-loss, noise and interference). Interference happens due to the existence of more than one network or by the spectral spread that happens in some frequencies. This type of problem is more severe in dynamic environments in which noise sources can be introduced at any time or new networks and devices that interfere with the existing one may be added. Consequently, it is necessary for the WSNs to have the ability to deal with the communication failures. The Dynamic MAC (DynMAC) protocol proposed in this thesis employs the Cognitive Radio (CR) techniques to allow the WSNs to adapt to the dynamic noisy environments by automatically selecting the best channel during its operation time. The WSN usually cannot operate in complete isolation, but it needs to be monitored, controlled and visualized by external applications. Although it is possible to add an IP protocol stack to sensor nodes, this approach is not appropriate for many types of WSNs. Consequently, the proxy and gateway approach is still a preferred method for integrating sensor networks with external networks and applications. The problem of the current integration solutions for WSNs is the adaptability, i.e., the ability of the gateway or proxy developed for one sensor network to be reused, unchanged, for others which have different types of applications and data frames. One reason behind this problem is that it is difficult or even impossible to create a standard for the structure of data inside the frame because there are such a huge number of possible formats. Consequently, it is necessary to have an adaptable solution for easily and transparently integrating WSNs and application environments. In this thesis, the Sensor Traffic Description Language (STDL) was proposed for describing the structure of the sensor networks’ data frames, allowing the framework to be adapted to a diversity of protocols and applications without reprogramming. The positions of sensor nodes are critical in many types of industrial applications such as object tracking, location-aware services, worker or patient tracking, etc. In addition, the sensed data is meaningless without the knowledge of where it is obtained. Perhaps the most well-known location-sensing system is the Global Positioning System (GPS). However, equipping GPS sensor for each sensor node is inefficient or unfeasible for most of the cases because of its energy consumption and cost. In addition, GPS is not appropriate in some environments, e.g., indoors. Similar to the original concept of WSNs, the localization solution should also be cheap and with low power consumption. This thesis aims to deal with the above problems. In particular, in order to add the reliability for WSN, DynMAC protocol was proposed, implemented and evaluated. This protocol adds a mechanism to automatically deal with the noisy and changeable environments. For the second problem, the STDL and its engine provide the adaptable capability to the framework for interoperation between sensor networks and external applications. The proposed framework requires no reprogramming when deploying it for new applications and protocols of WSNs. Moreover, the framework also supports localization services for positioning the unknown position sensor nodes in WSNs. The different localization methods are employed to estimate the location of mobile nodes. With the proposed framework, WSNs can be used as plug and play components for integrating with the Future Internet. All the proposed solutions were implemented and validated using simulation and real testbeds in both the laboratory and industrial environments