We demonstrate a system built using probabilistic techniques that allows for remarkably accurate localization across our entire office building using nothing more than the built-in signal intensity meter supplied by standard 802.11 cards. While prior systems have required significant investments of human labor to build a detailed signal map, we can train our system by spending less than one minute per office or region, walking around with a laptop and recording the observed signal intensities of our building's unmodified base stations. We actually collected over two minutes of data per office or region, about 28 man-hours of effort. Using less than half of this data to train the localizer, we can localize a user to the precise, correct location in over 95% of our attempts, across the entire building. Even in the most pathological cases, we almost never localize a user any more distant than to the neighboring office. A user can obtain this level of accuracy with only two or three signal intensity measurements, allowing for a high frame rate of localization results. Furthermore, with a brief calibration period, our system can be adapted to work with previously unknown user hardware. We present results demonstrating the robustness of our system against a variety of untrained time-varying phenomena, including the presence or absence of people in the building across the day. Our system is sufficiently robust to enable a variety of location-aware applications without requiring special-purpose hardware or complicated training and calibration procedures.

IEEE 802.11b wireless Ethernet is becoming the standard for indoor wireless communication. This paper proposes the use of measured signal strength of Ethernet packets as a sensor for a localization system. We demonstrate that off-the-shelf hardware can accurately be used for location sensing and real-time tracking by applying a Bayesian localization framework.

Language run-time systems are often called upon to safely execute mutually distrustful tasks within the same run-time, protecting them from other tasks' bugs or otherwise hostile behavior Well-studied access controls exist in systems such as Java to prevent unauthorized reading or writing of data, but techniques to measure and control resource usage are less prevalent. In particular, most language run-time systems include no facility to account for and regulate heap memory usage on a per-task basis. This oversight can be exploited by a misbehaving task, which might allocate and hold live enough memory to cause a denial-of-service attack, crashing or slowing down other tasks. In addition, tasks can legitimately share references to the same objects, and traditional approaches that charge memory to its allocator fail to properly account for this sharing. We present a method for modifying the garbage collector, already present in most modern language run-time systems, to measure the amount of live memory reachable from each task as it performs its regular duties. Our system naturally distinguishes memory shared across tasks from memory reachable from only a single task without requiring incompatible changes to the semantics of the programming language. Our prototype implementation imposes negligible performance overheads in a variety of benchmarks, yet provides enough information for the expression of rich policies to express the limits on a task's memory usage.

Language run-time systems are routinely used to host potentially buggy or malicious codelets-software modules, agents, applets, etc.-in a secure environment. A number of techniques exist for managing access control to system services and even for terminating codelets once they have been determined to be misbehaving. However because codelets can be terminated anywhere in their execution, a codelet's internal state might become inconsistent; restarting the codelet could result in unexpected behavior. Any state the codelet shares with other codelets may likewise become inconsistent, destabilizing those codelets as well. To address these problems, we have designed a mechanism, strictly using code-to-code transformations, which provides transactional rollback support for codelets. Each instance of a codelet is run in its own transaction, and standard (ACID) transactional semantics apply. All changes made by the codelet are automatically rolled back when the corresponding transaction aborts. We discuss a transactional rollback implementation for Java, and present its performance.

IEEE 802.11b wireless Ethernet is rapidly becoming the standard for in-building and short-range wireless communication. Many mobile devices such as mobile robots, laptops and PDAs already use this protocol for wireless communication. Many wireless Ethernet cards measure the signal strength of incoming packets. This paper investigates the feasibility of implementing a localization system using this sensor. Using a Bayesian localization framework, we show experiments demonstrating that off-the-shelf wireless hardware can accurately be used for location sensing and tracking with about one meter precision in a wireless-enabled office building.

A key subproblem in the construction of location-aware systems is the determination of the position of a mobile device. This paper describes the design, implementation and analysis of a system for determining position inside a building from measured RF signal strengths of packets on an IEEE 802.11b wireless Ethernet network. Previous approaches to location awareness with RF signals have been severely hampered by non-linearity, noise and complex correlations due to multi-path effects, interference and absorption. The design of our system begins with the observation that determining position from complex, noisy and non-linear signals is a well-studied problem in the field of robotics. Using only off-the-shelf hardware, we achieve robust position estimation to within a meter in our experimental context and after adequate training of our system. We can also coarsely determine our orientation and can track our position as we move. By applying recent advances in probabilistic inference of position and sensor fusion from noisy signals, we show that the RF emissions from base stations as measured by off-the-shelf wireless Ethernet cards are sufficiently rich in information to permit a mobile device to reliably track its location.

Language run-time systems are increasingly being embedded in systems to support run-time extensibility via mobile code. Such systems raise a number of concerns when the code running in such systems is potentially buggy or untrusted. Although sophisticated access controls have been designed for mobile code and are shipping as part of commercial systems such as Java, there is no support for terminating mobile code short of terminating the entire language run-time. This article presents a concept called "soft termination" that can be applied to virtually any mobile code system. Soft termination allows mobile code threads to be safely terminated while preserving the stability of the language run-time. In addition, function bodies can be permanently disabled, thwarting attacks predicated on system threads eventually calling untrusted functions. Soft termination guarantees termination by breaking any potential infinite loops in mobile code. We present a formal design for soft termination and an implementation of it for Java, built using Java bytecode rewriting, which demonstrates reasonable performance (3 to 25% slowdowns onbenchmarks).