An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform

With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations

Reefer container monitoring system based on WSN and cloud technology

Reefer containers are the main transportation method for the import and export of food and medicine. For high-quality products is necessary to monitor the condition of the reefer containers in order to avoid affecting goods quality due to environmental variations. Monitoring the reefer containers which are used to transport fruits, vegetables, and dairy products is one of the examples. In this context appears the necessity to develop this work expressed by a distributed sensor system for monitoring reefer containers. With the support of the WSN (wireless sensor network) including a set of sensors, it is possible to obtain the information about the temperature, humidity and location data of the reefer container and upload those data to a cloud platform expressed in the case of the purposed system by The Things Network platform. Based on LEACH (Low Energy Adaptive Clustering Hierarchy) routing algorithm, the embedded software was developed to guarantee a well-balanced distribution of the energy load among WSN end-nodes. A web application and a mobile application has been developed to display the data coming from the WSN node. To check if the reefer container working in a good condition, an alarm software module has been developed to highlight abnormal data coming for the system. The routing algorithm has been simulated and the effectiveness of the algorithm is verified by simulation results. The effectiveness of the proposed system was experimentally tested, and several results are included in this dissertation.Os contentores frigoríficos são o principal método de transporte para a importação e exportação de alimentos e medicamentos. Em produtos de alta qualidade, é necessário monitorizar as condições dos contentores frigoríficos, a fim de evitar a perda da qualidade das merca dorias devido a variações térmicas. Por exemplo, monitorarizando os contentores frigoríficos usados para transportar frutas, vegetais e laticínios. Neste contexto, aparece a necessidade do desenvolvimento deste projeto descrito por um sistema de sensores distribuídos para monitorizar contentores frigoríficos. Com o suporte da rede de sensores sem fios, incluindo um conjunto de sensores, é possível obter informações sobre os dados da temperatura, humidade e localização do contentor refrigerado e fazer uplo ad desses dados numa plataforma em cloud expressa no caso do sistema proposto por plataforma de rede de coisas. Com base no algoritmo de roteamento LEACH, o software incorporado foi desenvolvido para garantir uma distribuição equilibrada da carga de energi a entre os nós de WSN. Uma aplicação Web e uma aplicação móvel foram desenvolvidas para mostrar os dados provenientes do nó WSN. Para verificar a qualidade dos dados, um módulo de software de alarme foi também desenvolvido para destacar dados anormais que chegam ao sistema. O algoritmo de roteamento foi simulado e a eficiência do algoritmo é verificada pelos resultados da simulação. A eficiência do sistema proposto foi testada experimentalmente e os vários resultados estão incluídos nesta dissertação

Dynamic privacy management in pervasive sensor networks

This paper describes the design and implementation of a dynamic privacy management system aimed at enabling tangible privacy control and feedback in a pervasive sensor network. Our work began with the development of a potentially invasive sensor network (with high resolution video, audio, and motion tracking capabilities) featuring different interactive applications that created incentive for accepting this network as an extension of people’s daily social space. A user study was then conducted to evaluate several privacy management approaches – an active badge system for both online and on-site control, on/off power switches for physically disabling the hardware, and touch screen input control. Results from a user study indicated that an active badge for on-site privacy control is the most preferable method among all provided options. We present a set of results that yield insight into the privacy/benefit tradeoff from various sensing capabilities in pervasive sensor networks and how privacy settings and user behavior relate in these environments.Things That Think Consortiu

Evaluating the n-Core Polaris Real-Time Locating System in an Indoor Environment

Context-aware technologies allow Ambient Assisted Living developments to automatically obtain information from users and their environment in a distributed and ubiquitous way. One of the most important technologies used to provide context-awareness is Wireless Sensor Networks (WSN). Wireless Sensor Networks comprise an ideal technology to develop Real-Time Locating Systems (RTLS) aimed at indoor environments, where existing global navigation satellite systems do not work correctly. In this regard, n-Core Polaris is an indoor and outdoor RTLS based on ZigBee WSNs and an innovative set of locating and automation engines. This paper presents the main components of the n-Core Polaris, as well as some experiments made in a real scenario whose results demonstrate the effectiveness of the system in indoor environments

A Novel Interpolation Fingerprint Localization Supported by Back Propagation Neural Network

In view of people's increasing demand for location-aware service, high-accuracy indoor localization has been considered the top priority of location-based service (LBS), therefore, the compact and cost-effective ZigBee technology with low power dissipation will undoubtedly be taken as one of the options for indoor localization within small area. As the accuracy cannot satisfy the application requirement, traditional localization ZigBee-based algorithm is abandoned gradually. This paper proposes a novel ZigBee-based indoor fingerprint localization algorithm and optimizes it through back propagation neural network (BPNN) interpolation method. Simulation result shows that this algorithm can significantly reduce the number of fingerprints and improve localization accuracy

How the Internet of Things Technology Enhances Emergency Response Operations

The Internet of Things (IoT) is a novel paradigmthat connects the pervasive presence around us of a variety of things or objects to the Internet by using wireless/wired technologies to reach desired goals. Since the concept of the IoT was introduced in 2005, we see the deployment of a new generation of networked smart objects with communication, sensory and action capabilities for numerous applications, mainly in global supply chain management, environment monitoring and other non-stress environments. This paper introduces the IoT technology for use in the emergency management community. Considering the information required for supporting three sequential and distinct rhythms in emergency response operations: mobilization rhythm, preliminary situation assessment rhythm, and intervention rhythm, the paper proposes a modified task-technology fit approach that is used to investigate how the IoT technology can be incorporated into the three rhythms and enhance emergency response operations. The findings from our research support our two hypotheses: H1: IoT technology fits the identified information requirements; and H2: IoT technology provides added value to emergency response operations in terms of obtaining efficient cooperation, accurate situational awareness, and complete visibility of resources

E-Flow: A communication system for user notification in dynamic evacuation scenarios

Most of the current evacuation plans are based on static signaling, fixed monitoring infrastructure, and limited user notification and feedback mechanisms. These facts lead to lower situation awareness, in the case event of an emergency, such as blocked emergency exits, while delaying the reaction time of individuals. In this context, we introduce the E-Flow communication system, which improves the user awareness by integrating personal, mobile and fixed devices with the existing monitoring infrastructure. Our system broadens the notification and monitoring alternatives, in real time, among, safety staff, end-users and evacuation related devices, such as sensors and actuators