Wireless Indoor Location Estimation Based on Neural Network RSS Signature Recognition (LENSR)

Location Based Services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological Counter Propagation Network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results

Geometric sensitivity of beacon placement using airborne mobile anchors

Locating fixed sensing devices with a mobile anchor is attractive for covering larger deployment areas. However, the performance sensitivity to the geometric arrangement of anchor beacon positions remains unexplored. Therefore, localization using new RSSI-based localization algorithm, which uses a volumetric probability distribution function is proposed to find the most likely position of a node by information fusion from several mobile beacon radio packets to reduce error over deterministic approaches. This paper presents the guidelines of beacon selection that leads to design the most suitable trajectory, as a trade-off between the energy costs of travelling and transmitting the beacons versus the localization accuracy

Collaborative Localization Algorithms for Wireless Sensor Networks with Reduced Localization Error

Localization is an important research issue in Wireless Sensor Networks (WSNs). Though Global Positioning System (GPS) can be used to locate the position of the sensors, unfortunately it is limited to outdoor applications and is costly and power consuming. In order to find location of sensor nodes without help of GPS, collaboration among nodes is highly essential so that localization can be accomplished efficiently. In this paper, novel localization algorithms are proposed to find out possible location information of the normal nodes in a collaborative manner for an outdoor environment with help of few beacons and anchor nodes. In our localization scheme, at most three beacon nodes should be collaborated to find out the accurate location information of any normal node. Besides, analytical methods are designed to calculate and reduce the localization error using probability distribution function. Performance evaluation of our algorithm shows that there is a tradeoff between deployed number of beacon nodes and localization error, and average localization time of the network can be increased with increase in the number of normal nodes deployed over a region

Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks

Das Hauptproblem von Lokalisierungsalgorithmen für WSNs basierend auf Ankerknoten ist die Abhängigkeit von diesen. Mobilität im Netzwerk kann zu Topologien führen, in denen einzelne Knoten oder ganze Teile des Netzwerks temporär von allen Ankerknoten isoliert werden. In diesen Fällen ist keine weitere Lokalisierung möglich. Dies wirkt sich primär auf den Lokalisierungsfehler aus, der in diesen Fällen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstärkt zusätzlich das Problem von nicht verfügbaren Ankerknoten während des Netzwerkbetriebs. In dieser Arbeit werden zunächst die Vor- und Nachteile der beiden großen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft für range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten für ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingeführt. Beide haben zum Ziel das Problem der temporär nicht verfügbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die Positionsschätzung vom letzten bekannten Standort weiter zu führen. Dies geschieht mit Hilfe von zusätzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur Verfügung gestellt werden. PO-MCL hingegen nutzt das Mobilitätsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primär auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird außerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgeführt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.

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

Probabilistic localization for outdoor wireless sensor networks

Recent advances in wireless communication, low power sensors and microcontrollers enable the deployment of large-scale wireless sensor networks. Localization is a fundamental service required by many wireless sensor network applications. We consider a distributed, probabilistic approach, suitable for outdoor systems with inaccurate range measurements. The approach restricts the possible locations of the nodes by using a combination of positive and negative constraints. We reduce the computational complexity of the algorithm by using two-dimensional fast Fourier transforms (FFTs). We evaluated the proposed probabilistic approach through simulations based on real-world measurements; the results are compared with two other localization schemes and the Cramer-Rao lower bound (CRLB). The results show that, for inaccurate range measurements, the proposed probabilistic approach outperforms existing methods and approaches the CRLB. I