inTrack: High Precision Tracking of Mobile Sensor Nodes

Radio-interferometric ranging is a novel technique that allows for fine-grained node localization in networks of inexpensive COTS nodes. In this paper, we show that the approach can also be applied to precision tracking of mobile sensor nodes. We introduce inTrack, a cooperative tracking system based on radio-interferometry that features high accuracy, long range and low-power operation. The system utilizes a set of nodes placed at known locations to track a mobile sensor. We analyze how target speed and measurement errors affect the accuracy of the computed locations. To demonstrate the feasibility of our approach, we describe our prototype implementation using Berkeley motes. We evaluate the system using data from both simulations and field tests

User-Friendly Surveying Techniques for Location-aware Systems.

Many location-aware applications rely on data from fine-grained location systems. During deployment such systems require a survey, specifying the locations of their environment-based components. Most current surveying methods are time-consuming, and require costly and bulky equipment. This paper presents the concept of self-surveying, i.e. methods by which a location system can survey itself. Such methods are user-friendly, fast, and require little or no extra equipment. Experimental results show self-survey accuracies comparable to the accuracy of the underlying location system

Siren: Context-aware Computing for Firefighting

Based on an extensive field study of current firefighting practices, we have developed a system called Siren to support tacit communication between firefighters with multiple levels of redundancy in both communication and user alerts. Siren provides a foundation for gathering, integrating, and distributing contextual data, such as location and temperature. It also simplifies the development of firefighting applications using a peer-to-peer network of embedded devices through a uniform programming interface based on the information space abstraction. As a proof of concept, we have developed a prototype context -aware messaging application in the firefighting domain. We have evaluated this application with firefighters and they have found it to be useful for improving many aspects of their current work practices

Hallway Monitoring: Distributed Data Processing with Wireless Sensor Networks

We present a sensor network testbed that monitors a hallway. It consists of 120 load sensors and 29 passive infrared sensors (PIRs), connected to 30 wireless sensor nodes. There are also 29 LEDs and speakers installed, operating as actuators, and enabling a direct interaction between the testbed and passers-by. Beyond that, the network is heterogeneous, consisting of three different circuit boards—each with its specific responsibility. The design of the load sensors is of extremely low cost compared to industrial solutions and easily transferred to other settings. The network is used for in-network data processing algorithms, offering possibilities to develop, for instance, distributed target-tracking algorithms. Special features of our installation are highly correlated sensor data and the availability of miscellaneous sensor types

Mobile Software Agents for Location-based Systems

As mobile computing matures, location-awareness as part of context-awareness gains more attention; especially, location-aware services for mobile users. This paper concentrates on the software engineering issues of location-aware services and presents Mobile Shadow as an successful example of a design and an implementation of a scalable, fault tolerant, and component-based service infrastructure for location-aware services. The paper presents the basic concepts used in Mobile Shadow, the requirements, and the resulting component architecture

RF-Based Initialisation for Inertial Pedestrian Tracking

Abstract. Location information is an important source of context for ubiquitous computing systems. We have previously developed a wearable location system that combines a foot-mounted inertial unit, a detailed building model and a particle filter to locate and track humans in indoor environments. In this paper we present an algorithm in which a map of radio beacon signal strengths is used to solve two of the major problems with the original system: scalability to large environments and uncertainty due to environmental symmetry. We show that the algorithm allows the deployment of the system in arbitrarily large buildings, and that uncertainty due to environmental symmetry is reduced. This reduction allows a user to be located after taking an average of 38 steps in a 8725 m 2 three-storey building, compared with 76 steps in the original system. Finally, we show that radio maps such as those required by the algorithm can be generated quickly and automatically using the wearable location system itself. We demonstrate this by building a radio map for the 8725 m 2 building in under two and a half hours