Context-awareness is the ability of software systems to sense and adapt to their physical environment. Many contemporary mobile applications adapt to changing locations, connectivity states, available computational and energy resources, and proximity to other users and devices. Nevertheless, there is little systematic support for context-awareness in contemporary mobile operating systems. Because of this, application developers must build their own context-awareness adaptation engines, dealing directly with sensors and polluting application code with complex adaptation decisions. In this paper, we introduce CAreDroid, which is a framework that is designed to decouple the application logic from the complex adaptation decisions in Android context-aware applications. In this framework, developers are required--only--to focus on the application logic by providing a list of methods that are sensitive to certain contexts along with the permissible operating ranges under those contexts. At run time, CAreDroid monitors the context of the physical environment and intercepts calls to sensitive methods, activating only the blocks of code that best fit the current physical context. CAreDroid is implemented as part of the Android runtime system. By pushing context monitoring and adaptation into the runtime system, CAreDroid eases the development of context-aware applications and increases their efficiency. In particular, case study applications implemented using CAreDroid are shown to have: (1) at least half lines of code fewer and (2) at least 10× more efficient in execution time compared to equivalent context-aware applications that use only standard Android APIs.

Time synchronization is a keystone of Wireless Sensor Networks (WSN). It is fundamental to coordinate the action of nodes in a network and it is also a critical element of several security mechanisms. In this paper, we discuss and evaluate the time synchronization strategy behind the Trustful Space-Time Protocol (TSTP), which explores the protocol's cross-layer architecture to speculatively peek through the timestamps and geographic info present in message headers, implementing high-accuracy clock synchronization with minimal insertion of explicit messages. We evaluate the protocol analytically and experimentally. The analytic evaluation is based on the model defined by Schmid [15] for the Virtual High-resolution Time (VHT), while the experimental evaluation was performed on the IEEE 802.15.4-compliant EPOSMote platform running EPOS and TSTP. Our results demonstrate that nodes in the network can be consistently synchronized with sub-microsecond precision while exchanging far less messages than they would with an ordinary, non-speculative implementation, resulting in energy savings. Indeed, precision and energy savings are higher for networks with higher traffic, since more messages are available for peeking. In an experiment scenario in which messages were exchanged between devices every 15 seconds, nodes in the network achieved a synchronization error of approximately 15 microseconds in the worst case, while in a scenario in which messages were exchanged every 3 seconds, synchronization error was less than 0.5 microseconds in the worst case, and approximately 0.25 microseconds on average.

Modern integrated circuits, fabricated in nanometer technologies, suffer from significant power/performance variation across-chip, chip-to-chip and over time due to aging and ambient fluctuations. Furthermore, several existing and emerging reliability loss mechanisms have caused increased transient, intermittent and permanent failure rates. While this variability has been typically addressed by process, device and circuit designers, there has been a recent push towards sensing and adapting to variability in the various layers of software. Current hardware platforms, however, typically lack variability sensing capabilities. Even if sensing capabilities were available, evaluating variability-aware software techniques across a significant number of hardware samples would prove exceedingly costly and time consuming. We introduce VarEMU, an extension to the QEMU virtual machine monitor that serves as a framework for the evaluation of variability-aware software techniques. VarEMU provides users with the means to emulate variations in power consumption and in fault characteristics and to sense and adapt to these variations in software. Through the use (and dynamic change) of parameters in a power model, users can create virtual machines that feature both static and dynamic variations in power consumption. Faults may be injected before or after, or completely replace the execution of any instruction. Power consumption and susceptibility to faults are also subject to dynamic change according to an aging model. A software stack for VarEMU features precise control over faults and provides virtual energy monitors to the operating system and processes. This allows users to precisely quantify and evaluate the effects of variations on individual applications. We show how VarEMU tracks energy consumption according to variation-aware power and aging models and give examples of how it may be used to quantify how faults in instruction execution affect applications.

Instance and temperature-dependent power variation has a direct impact on quality of sensing for battery-powered long-running sensing applications. We measure and characterize the active and leakage power for an ARM Cortex M3 processor and show that, across a temperature range of 20 -60, there is a 10% variation in active power, and a variation in leakage power. We introduce variability-aware duty cycling methods and a duty cycle (DC) abstraction for TinyOS which allows applications to explicitly specify the lifetime and minimum DC requirements for individual tasks, and dynamically adjusts the DC rates so that the overall quality of service is maximized in the presence of power variability. We show that variability-aware duty cycling yields a improvement in total active time over schedules based on worst case estimations of power, with an average improvement of across a wide variety of deployment scenarios based on the collected temperature traces. Conversely, datasheet power specifications fail to meet required lifetimes by 7%-15%, with an average 37 days short of the required lifetime of 1 year. Finally, we show that a target localization application using variability-aware DC yields a 50% improvement in quality of results over one based on worst case estimations of power consumption.