Advanced Transport Management System

Many people go to their workplace by bus, train (public transportation), etc. While travelling from public transportation the problem of heavy traffic or waiting time for the bus for a longer time may occur. Even though the bus’s arrival and departure time are schedule, but we can’t assure that the bus will always come on time. Hence to overcome the problem of time loss because of waiting at the bus stops, we implemented the smart tracking system. In this project, any passenger who is having Android app can have access to the bus. The passenger can register and sign up to receive information about desired bus arrival times for the interested buses and related routes via SMS/map. Even passenger can book the ticket as well as seat through Android app

Using Transcoding for Hidden Communication in IP Telephony

The paper presents a new steganographic method for IP telephony called TranSteg (Transcoding Steganography). Typically, in steganographic communication it is advised for covert data to be compressed in order to limit its size. In TranSteg it is the overt data that is compressed to make space for the steganogram. The main innovation of TranSteg is to, for a chosen voice stream, find a codec that will result in a similar voice quality but smaller voice payload size than the originally selected. Then, the voice stream is transcoded. At this step the original voice payload size is intentionally unaltered and the change of the codec is not indicated. Instead, after placing the transcoded voice payload, the remaining free space is filled with hidden data. TranSteg proof of concept implementation was designed and developed. The obtained experimental results are enclosed in this paper. They prove that the proposed method is feasible and offers a high steganographic bandwidth. TranSteg detection is difficult to perform when performing inspection in a single network localisation.Comment: 17 pages, 16 figures, 4 table

Indoor outdoor detection

Abstract. This thesis shows a viable machine learning model that detects Indoor or Outdoor on smartphones. The model was designed as a classification problem and it was trained with data collected from several smartphone sensors by participants of a field trial conducted. The data collected was labeled manually either indoor or outdoor by the participants themselves. The model was then iterated over to lower the energy consumption by utilizing feature selection techniques and subsampling techniques. The model which uses all of the data achieved a 99 % prediction accuracy, while the energy efficient model achieved 92.91 %. This work provides the tools for researchers to quantify environmental exposure using smartphones

Ortsbezogene Anwendungen und Dienste: 9. Fachgespräch der GI/ITG-Fachgruppe Kommunikation und Verteilte Systeme ; 13. & 14. September 2012

Der Aufenthaltsort eines mobilen Benutzers stellt eine wichtige Information für Anwendungen aus den Bereichen Mobile Computing, Wearable Computing oder Ubiquitous Computing dar. Ist ein mobiles Endgerät in der Lage, die aktuelle Position des Benutzers zu bestimmen, kann diese Information von der Anwendung berücksichtigt werden -- man spricht dabei allgemein von ortsbezogenen Anwendungen. Eng verknüpft mit dem Begriff der ortsbezogenen Anwendung ist der Begriff des ortsbezogenen Dienstes. Hierbei handelt es sich beispielsweise um einen Dienst, der Informationen über den aktuellen Standort übermittelt. Mittlerweile werden solche Dienste kommerziell eingesetzt und erlauben etwa, dass ein Reisender ein Hotel, eine Tankstelle oder eine Apotheke in der näheren Umgebung findet. Man erwartet, nicht zuletzt durch die Einführung von LTE, ein großes Potenzial ortsbezogener Anwendungen für die Zukunft. Das jährlich stattfindende Fachgespräch "Ortsbezogene Anwendungen und Dienste" der GI/ITG-Fachgruppe Kommunikation und Verteilte Systeme hat sich zum Ziel gesetzt, aktuelle Entwicklungen dieses Fachgebiets in einem breiten Teilnehmerkreis aus Industrie und Wissenschaft zu diskutieren. Der vorliegende Konferenzband fasst die Ergebnisse des neunten Fachgesprächs zusammen.The location of a mobile user poses an important information for applications in the scope of Mobile Computung, Wearable Computing and Ubiquitous Computing. If a mobile device is able to determine the current location of its user, this information may be taken into account by an application. Such applications are called a location-based applications. Closely related to location-based applications are location-based services, which for example provides the user informations about his current location. Meanwhile such services are deployed commercially and enable travelers for example to find a hotel, a petrol station or a pharmacy in his vicinity. It is expected, not least because of the introduction of LTE, a great potential of locations-based applications in the future. The annual technical meeting "Location-based Applications and Services" of the GI/ITG specialized group "Communication and Dsitributed Systems" targets to discuss current evolutions in a broad group of participants assembling of industrial representatives and scientists. The present proceedings summarizes the result of the 9th annual meeting

Sensing and Visualizing Social Context from Spatial Proximity

The concept of pervasive computing, as introduced by Marc Weiser under the name ubiquitous computing in the early 90s, spurred research into various kinds of context-aware systems and applications. There is a wide range of contextual parameters, including location, time, temperature, devices and people in proximity, which have been part of the initial ideas about context-aware computing. While locational context is already a well understood concept, social context---based on the people around us---proves to be harder to grasp and to operationalize. This work continues the line of research into social context, which is based on the proximity and meeting patterns of people in the physical space. It takes this research out of the lab and out of well controlled situations into our urban environments, which are full of ambiguity and opportunities. The key to this research is the tool that caused dramatic change in individual and collective behavior during the last 20 years and which is a manifestation of many of the ideas of the pervasive computing paradigm: the mobile phone. In this work, the mobile is regarded as a proxy for people. Through it, the social environment becomes accessible to digital measurement and processing. To understand the large amount of data that now becomes available to automatic measurement, we will turn to the discipline of social network analysis. It provides powerful methods, that are able to condense data and extract relevant meaning. Visualization helps to understand and interpret the results. This thesis contains a number of experiments, that demonstrate how the automatic measurement of social proximity data through Bluetooth can be used to measure variables of personal behavior, group behavior and the behavior of groups in relation to places. The principal contributions are: * A methodology to visualize personal social context by using an ego proximity network. Specific episodes can be localized and compared. * method to compare different days in terms of social context, e.g. to support automatic diary applications. * A method to compose social geographic maps. Locations of similar social context are detected and combined. * Functions to measure short-term changes in social activity, based on the distinction between strange and familiar devices. * The characterization of Bluetooth inquiries for social proximity sensing. * A dataset of Bluetooth sightings from an ego perspective in seven different settings. Additionally, some settings feature multiple stationary scanners and Cell-ID measurements. * Soft- and hardware to capture, collect, store and analyze Bluetooth proximity data

Estimating population density distribution from network-based mobile phone data

In this study we address the problem of leveraging mobile phone network-based data for the task of estimating population density distribution at pan-European level. The primary goal is to develop a methodological framework for the collection and processing of network-based data that can be plausibly applied across multiple MNOs. The proposed method exploits more extensive network topology information than is considered in most state-of-the-art literature, i.e., (approximate) knowledge of cell coverage areas is assumed instead of merely cell tower locations. A distinguishing feature of the proposed methodology is the capability of taking in input a combination of cell-level and Location Area-level data, thus enabling the integration of data from Call Detail Records (CDR) with other network-based data sources, e.g., Visitor Location Register (VLR). Different scenarios are considered in terms of input data availability at individual MNOs (CDR only, VLR only, combinations of CDR and VLR) and for multi-MNO data fusion, and the relevant tradeoff dimensions are discussed. At the core of the proposed method lies a novel formulation of the population distribution estimation as a Maximum Likelihood estimation problem. The proposed estimation method is validated for consistency with synthetically generated data in a simplified simulation scenario.JRC.H.6-Digital Earth and Reference Dat

Management Of Manufacturing Assets By Deploying Maintenance-free Wireless Sensors

In modern manufacturing systems there is ever increasing need for higher efficiency, performance, optimization and requirement for minimal errors in the production process. Advancements in technology have significantly improved the performance of the manufacturing systems and offered advance tools to track manufacturing assets such as pallets. Pallets are extensively used for transportation of raw materials or final products. There are different ways to monitor and track pallets, some of these include using Radio Frequency Identification (RFID) tags, Global Positioning Systems (GPS), barcodes, utilizing wireless sensors such as accelerometers and gyroscopes. This thesis proposes a methodology for managing manufacturing assets, such as products, by making use of maintenance free wireless sensors. The implementation of this work presents an application developed for detecting and measuring the strength of radio frequency (RF) signals in the surrounding environment. The application is developed as an independent android application that is interfaced with the manufacturing system under test. The android applications acts as a prerequisite for identifying possible radio frequency harvesting points at the assembly line. One of the features of an energy harvester is utilized to harvest the radio frequency signals from sources such as Global System for Mobile Communication (GSM) Signals or wireless access points installed in the environment. The solution was successfully deployed and tested on the FASTory line, an assembly line consisting of 12 work stations situated at the Factory Automation Systems and Technologies Laboratory (FAST-Lab). By utilizing pallets, the android application is used to create a signal strength map of the assembly line. Based on signal strength map, different locations at the assembly line are identified where radio frequency signals can be harvested for powering the wireless sensors. The energy harvested from the RF signals removes the need for battery replacement of wireless sensors