Wireless Sensor Enabled Public Transportation System

Automatic Vehicle Identification (AVI) in real time is becoming an urgent necessity due to rapid increase in the number of vehicles on roads. The Radio Frequency Identification (RFID) Technology can be used for vehicle identification to gather information in real-time from roads by getting the vehicles location from RFID readers placed in the vehicle. This paper focuses on designing the Public Vehicle Location System (PVLS). The proposed structure consists of passive RFID tags placed at various locations on the chosen route, RFID reader on the Bus, wireless communication with a PC and commanding software (RFID reader and database structure), also PVLS applications and website. The designed system controls, manages and monitors the performance of RFID readers. It also filters and stores the information in an appropriate format so that it could be used without difficulty in the application system and website. The system implemented by using RFID is placed in the Bus which is programmed by Visual C# 2008 with Compact .Net Framework

Wireless Sensor Networks for Detection of IED Emplacement / 14th ICCRTS: C2 and Agility

14th International Command and Control Research and Technology Symposium (ICCRTS), June 15-17, 2009, Washington DC.This paper appeared in the Proceedings of the 14th International Command and Control Research and Technology Symposium, Washington, DC, June 2009.We are investigating the use of wireless nonimaging-sensor networks for the difficult problem of detection of suspicious behavior related to IED emplacement. Hardware for surveillance by nonimaging-sensor networks can cheaper than that for visual surveillance, can require much less computational effort by virtue of simpler algorithms, and can avoid problems of occlusion of view that occur with imaging sensors. We report on four parts of our investigation. First, we discuss some lessons we have learned from experiments with visual detection of deliberately-staged suspicious behavior, which suggest that the magnitude of the acceleration vector of a tracked person is a key clue. Second, we describe experiments we conducted with tracking of moving objects in a simulated sensor network, showing that tracking is not always possible even with excellent sensor performance due to the illconditioned nature of the mathematical problems involved. Third, we report on experiments we did with tracking from acoustic data of explosions during a NATO test. Fourth, we report on experiments we did with people crossing a live sensor network. We conclude that nonimaging-sensor networks can detect a variety of suspicious behavior, but implementation needs to address a number of tricky problems.supported in part by the National Science Foundation under the EXP Program and in part by the National Research Council under their Research Associateship Program at the Army Research Laborator

Accuracy and timing aspects of location information based on signal-strength measurements in Bluetooth

Abstract-Accurate positioning of wireless devices using shortrange link-technologies is interesting for a number of applications including tracking, location-based services, and context sensitive networking. In this paper, accuracy and timing aspects for an indoor positioning method based on triangulation using signal strength measurements of Bluetooth links are analyzed experimentally and via simulation models: in the approach chosen in this paper, the delay to obtain signal strength measurements is determined by complex interactions of the Bluetooth inquiry procedure, which are analyzed in detailed simulation experiments. The location accuracy analysis is performed experimentally for the setting of an indoor corridor scenario including multi-path propagation properties

Context-aware routing system in an indoor scenario

The main aim of this report is to develop, design and analyse a system to simulate a context-aware routing algorithm in an indoor scenario. The context-aware purpose of this project is to take advantage of the interaction of the routing system with an entity when it is relevant. From all the context entities, the spatial environment is one of the most important, and the one which more information can be taken advantage of. Benefits can be obtained from using context-awareness in many ways, which have a special interest in the Information Technologies area. The intention of this report is to create a new application using context information related to the space, to be more precise, the position of the entities within a concrete location, and its preferences. The primary target is to design a supermarket in which benefits can be obtained from the position of the customers, their preferences (concretely the shopping list) and also the location of the products, to create an intelligent and efficient supermarket for the customer (but also for the supermarket in itself) point of view. Knowing the position of the customers and the location of the products in the supermarket can be useful to draw up efficient routes that can guide the customers through the corridors to buy their products quickly, which is the main reason why the system uses a shortest path routing algorithm to find the best route from the customer to the wished product. This algorithm considers the shortest distance and also the position of the rest of the customers so the system is able to guide the customers through another path in cases where they reach congested zones in the supermarket. Bluetooth wireless technology is used to accomplish the localization and system communication task. In addition the routing algorithm is adapted to fit the requirements of the intelligent supermarket. The design and implementation of a GUI simulator written in Java that represents the designed system is the main goal of this project. This simulator serves as a tool to test the system operation offering the possibility to modify parameters such as the rate and distribution type of the arrival of customers, the number of customers, subjective criteria of congestion and speed of the simulation among other parameters. Different types of statistics and the possibility to generate files with the information of the simulation are the main outcomes of this project, besides the GUI. In addition, this information can be translated into a Matlab script using a parser designed for this purpose. Finally the results and conclusions of the system are presented, and the future lines to follow the development of this innovative project

Signal modelling based scalable hybrid Wi-Fi indoor positioning system

Location based services (LBS) such as advertising, navigation and social media require a mobile device to be aware of its location anywhere. Global Positioning System (GPS) is accurate outdoors. However, in case of indoor environments, GPS fails to provide a location due to non-line of sight. Even in cases where GPS does manage to get a position fix indoors, it is largely inaccurate due to interference of indoor environment. Wi-Fi based indoor positioning offers best solution indoors, due to wide usage of Wi-Fi for internet access. Wi-Fi based indoor positioning systems are widely based on two techniques, first Lateration which uses distances estimated based on signal properties such as RSS (Received Signal Strength) and second, Fingerprint matching of data collected in offline phase. The accuracy of estimated position using Lateration techniques is lower compared to fingerprinting techniques. However, Fingerprinting techniques require storing a large amount of data and are also computationally intensive. Another drawback of systems based on fingerprinting techniques is that they are not scalable. As the system is scaled up, the database required to be maintained for fingerprinting techniques increases significantly. Lateration techniques also have challenges with coordinate system used in a scaled-up system. This thesis proposes a new scalable positioning system which combines the two techniques and reduces the amount of data to be stored, but also provides accuracy close to fingerprinting techniques. Data collected during the offline/calibration phase is processed by dividing the test area into blocks and then stored for use during online/positioning phase. During positioning phase, processed data is used to identify the block first and then lateration techniques are used to refine the estimated location. The current system reduces the data to be stored by a factor of 20. And the 50th percentile accuracy with this novel system is 4.8m, while fingerprint system accuracy was 2.8m using same data. The significant reduction in database size and lower computational intensity benefits some of the applications like location-based search engines even with slightly lower performance in terms of accuracy