Many sensor network applications require location awareness, but it is often too expensive to include a GPS receiver in a sensor network node. Hence, localization schemes for sensor networks typically use a small number of seed nodes that know their location and protocols whereby other nodes estimate their location from the messages they receive. Several such localization techniques have been proposed, but none of them consider mobile nodes and seeds. Although mobility would appear to make localization more difficult, in this paper we introduce the sequential Monte Carlo Localization method and argue that it can exploit mobility to improve the accuracy and precision of localization. Our approach does not require additional hardware on the nodes and works even when the movement of seeds and nodes is uncontrollable. We analyze the properties of our technique and report experimental results from simulations. Our scheme outperforms the best known static localization schemes under a wide range of conditions.

Robots must complete their tasks in spite of unreliable actuators and limited, noisy sensing. In this paper, we consider the information requirements of such tasks. What sensing and actuation abilities are needed to complete a given task? Are some robot systems provably “more powerful, ” in terms of the tasks they can complete, than others? Can we find meaningful equivalence classes of robot systems? This line of research is inspired by the theory of computation, which has produced similar results for abstract computing machines. The basic idea is a dominance relation over robot systems that formalizes the idea that some robots are stronger than others. This comparison, which is based on the how the robots progress through their information spaces, induces a partial order over the set of robot systems. We prove some basic properties of this partial order and show that it is directly related to the robots’ ability to complete tasks. We give examples to demonstrate the theory, including a detailed analysis of a limited-sensing global localization problem. 1

Meerkats is a wireless network of battery-operated camera nodes, that can be used for monitoring and surveillance of wide areas. This paper describes the Meerkats architecture, including: (1) a number of application-level visual sensor acquisition and processing techniques such as image acquisition policies (including cooperative, event-driven policies); visual analysis for event detection, parameter estimation, and hierarchical representation; (2) resource management strategies that dynamically assess the power versus application-level requirements to make decisions on the tasks to be performed by the system (e.g., what data representation level to use in transmitting data at a given point in time); and (3) network-level techniques for bandwidth- and power-adaptive routing as well as media scaling. We also report results from the statistical models we developed for image acquisition scheduling which, given a probability of mis-detection, try to minimize power consumption by deciding when cameras should wake up and acquire images. We present results from our power consumption characterization of the Meerkats sensor node which is based on the Crossbow Stargate [13]. Finally, we describe our visual processing approach based on motion analysis to perform event detection, motion orientation, ground plane positioning, and velocity determination. I.

Abstract. A major challenge faced by the blind and visually impaired population is that of wayfinding – the ability of a person to find his or her way to a given destination. We propose a new wayfinding aid based on a camera cell phone, which is held by the user to find and read aloud specially designed machine-readable signs in the environment (labeling locations such as offices and restrooms). Our main technical innovation is that we have designed these machine-readable signs to be detected and located in fractions of a second on the cell phone CPU, even at a distance of several meters. A linear barcode printed on the sign is read using novel decoding algorithms that are robust to noisy images. The information read from the barcode is then read aloud using pre-recorded or synthetic speech. We have implemented a prototype system on the Nokia 7610 cell phone, and preliminary experiments with blind subjects demonstrate the feasibility of using the system as a real-time wayfinding aid. 2 1

We present an algorithm enabling localization of moving wireless devices in an indoor setting. The method uses only RF signal strength and can be implemented without specialized hardware. The mobility of the users is modeled by learning a function mapping a short history of signal strength values to a 2D position. We use radial basis function (RBF) fitting to learn a reliable estimate of a mobile node’s position given its past signal strength measurements. Even though we deal with extremely noisy measurements in a cluttered indoor setting, nodes are not required to be stationary during measurement or learning. We evaluate our algorithm in a real indoor setting using MicaZ motes, achieving an average localization accuracy of 1.3 m. In our experiments, using historical data improves the localization accuracy by almost a factor of two compared to using only the most current measurements.

... (ICN) is a challenging problem due to the uncertainty and time varying nature of network connectivity. In this work, we focus on a special class of ICN formed by mobile ad hoc users called ICMAN. We first consider a new and practical probabilistic mobility model where the nodes move between a set of “hubs ” in a partially repetitive and nondeterministic pattern to form the so-called “sociological orbits”. Second, to leverage the sociological orbit based mobility pattern in routing within ICMAN, we propose a series of multi-path Sociological Orbit aware Location Approximation and Routing (SOLAR) protocols. We present theoretical analysis of the mobility model and routing algorithms under consideration, and show that the proposed routing algorithms can outperform other conventional routing approaches in an ICN by taking advantage of the sociological orbit based mobility pattern.

Abstract. Overthepastdecadewehavewitnessedtheevolutionof wireless sensor networks, with advancements in hardware design, communication protocols, resource efficiency, and other aspects. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. In this paper, we present a survey on localization methods for mobile wireless sensor networks. We provide taxonomies for mobile wireless sensors and localization, including common architectures, measurement techniques, and localization algorithms. We conclude with a description of real-world mobile sensor applications that require position estimation. 1

This article addresses the problem of mobile robot localization using Ultra-Wide-Band (UWB) range measurements. UWB is a radio technology widely used for communications that recently is receiving increasing attention also for positioning applications. In these cases, the position of a mobile transceiver is determined from the distances to a set of fixed, well-localized beacons. Though this is a well-known problem in the scientific literature (the trilateration problem), the peculiarities of UWB range measurements (basically, distance errors and multipath effects) demand a different treatment to other similar solutions as for example those based on laser. This work presents a thorough experimental characterization of UWB ranges within a variety of environments and situations. From these experiments we derive a probabilistic model which is then used by a particle filter to combine different readings from UWB beacons as well as the vehicle odometry. To account for the possible offset error due to multipath effects, the state tracked by the particle filter includes the offset of each beacon in addition to the planar robot pose (x, y,φ), both estimated sequentially. We show navigation results for a robot moving in indoor scenarios covered by three UWB beacons that validate our proposal. Key words: Mobile robot localization, Sensor characterization, Particle filter, Ultra-Wide-Band 1.

doi:10.3906/elk-0904-3 An RFID based indoor tracking method for navigating visually impaired people

location for safety applications using wireless networks