Rahul Balani

Multi-level software reconfiguration for sensor networks

In-situ reconfiguration of software is indispensable in embedded networked sensing systems. It is required for re-tasking a deployed network, fixing bugs, introducing new features and tuning the system parameters to the operating environment. We present a system that supports software recon-figuration in embedded sensor networks at multiple levels. The system architecture is based on an operating system consisting of a fixed tiny static kernel and binary modules that can be dynamically inserted, updated or removed. On top of the operating system is a command interpreter, implemented as a dynamically extensible virtual machine, that can execute high-level scripts written in portable byte code. Any binary module dynamically inserted into the operating systems can register custom extensions in the virtual machine interpreter, thus allowing the high-level scripts executed by the virtual machine to efficiently access services exported by a module, such as tuning module parameters. Together these system mechanisms permit the exibility of selecting the most appropriate level of reconfiguration. In addition to detailing the system architecture and the design choices, the paper presents a systematic analysis of exibility versus cost tradeoffs provided by these mechanisms.

Variability-aware duty cycle scheduling in long running embedded sensing systems

Instance and temperature-dependent leakage power variability is already a significant issue in contemporary embedded processors, and one which is expected to increase in importance with scaling of semiconductor technology. We measure and characterize this leakage power variability in current microprocessors, and show that variability aware duty cycle scheduling produces 7.1× improvement in sensing quality for a desired lifetime. In contrast, pessimistic estimations of power consumption leave 61% of the energy untapped, and datasheet power specifications fail to meet required lifetimes by 14%. Finally, we introduce a duty cycle abstraction for TinyOS that allows applications to explicitly specify lifetime and minimum duty cycle requirements for individual tasks, and dynamically adjusts duty cycle rates so that overall quality of service is maximized in the presence of power variability.

Hardware Variability-Aware Duty Cycling for Embedded Sensors

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.

Programming Support for Distributed Optimization and Control in Cyber-Physical Systems

Large-scale actuator control problems in Cyber-Physical Systems (CPSs) are often expressed within the networked optimization model. While significant advances have taken place in optimization techniques, their widespread adoption in practical implementations is impeded by the complexity of inter-node coordination and lack of programming support that is necessary for sharing information coherently between distributed and concurrent controller processes. In this paper, we propose a distributed shared memory (DSM) architecture that abstracts away the details of inter-node coordination from the programmer resulting in simplified application design. It maintains data coherency through explicit use of mutual exclusion lock primitives that serialize access to coarse subsets of shared variables using fine-grained read/write permissions. The underlying lock protocol is deadlock-free, fair and safe, and reduces response time and message cost by 81.6% and 72.8% respectively over a conventional DSM implementation with coarse access permissions. Moreover, in a representative application example, the proposed framework reduces application code size by 76% and total latency by 22% over a hand-crafted implementation.

Distributed programming framework for fast iterative optimization in networked cyber-physical systems

Large-scale coordination and control problems in cyber-physical systems are often expressed within the networked optimization model. While significant advances have taken place in optimization techniques, their widespread adoption in practical implementations has been impeded by the complexity of internode coordination and lack of programming support for the same. Currently, application developers build their own elaborate coordination mechanisms for synchronized execution and coherent access to shared resources via distributed and concurrent controller processes. However, they typically tend to be error prone and inefficient due to tight constraints on application development time and cost. This is unacceptable in many CPS applications, as it can result in expensive and often irreversible side-effects in the environment due to inaccurate or delayed reaction of the control system. This article explores the design of a distributed shared memory (DSM) architecture that abstracts the details of internode coordination. It simplifies application design by transparently managing routing, messaging, and discovery of nodes for coherent access to shared resources. Our key contribution is the design of provably correct locality-sensitive synchronization mechanisms that exploit the spatial locality inherent in actuation to drive faster and scalable application execution through opportunistic data parallel operation. As a result, applications encoded in the proposed Hotline Application Programming Framework are error free, and in many scenarios, exhibit faster reactions to environmental events over conventional implementations. Relative to our prior work, this article extends Hotline with a new locality-sensitive coordination mechanism for improved reaction times and two tunable iteration control schemes for lower message costs. Our extensive evaluation demonstrates that realistic performance and cost of applications are highly sensitive to the prevalent deployment, network, and environmental characteristics. This highlights the importance of Hotline, which provides user-configurable options to trivially tune these metrics and thus affords time to the developers for implementing, evaluating, and comparing multiple algorithms.

Diagnostic quality driven physiological data collection for personal healthcare

We believe that each individual is unique, and that it is necessary for diagnosis purpose to have a distinctive combination of signals and data features that fits the personal health status. It is essential to develop mechanisms for reducing the amount of data that needs to be transferred (to mitigate the troublesome periodically recharging of a device) while maintaining diagnostic accuracy. Thus, the system should not uniformly compress the collected physiological data, but compress data in a personalized fashion that preserves the “important” signal features for each individual such that it is enough to make the diagnosis with a required high confidence level. We present a diagnostic quality driven mechanism for remote ECG monitoring, which enables a notation of priorities encoded into the wave segments. The priority is specified by the diagnosis engine or medical experts and is dynamic and individual dependent. The system pre-processes the collected physiological information according to the assigned priority before delivering to the backend server. We demonstrate that the proposed approach provides accurate inference results while effectively compressing the data.

Distributed coordination for fast iterative optimization in wireless sensor/actuator networks

Large-scale coordination and control problems in sensor/actuator networks are often expressed within the networked optimization model. While significant advances have taken place in both first- and higher-order optimization techniques, their widespread adoption in practical implementations has been hindered by a lack of adequate programming and evaluation support. This motivates the two major contributions of this paper. First, we extend the distributed programming framework proposed in [1] with a synchronization primitive to implement different versions of the subgradient technique and perform extensive evaluation with varying deployment and algorithmic parameters. Second, the insights — obtained by observing the variability in practical metrics such as response time and incurred message cost — lead us to exploit the spatial locality inherent in these large-scale actuator control applications, and propose a novel consensus algorithm applied to the subgradient method. We show using simulations that there is at least 99% improvement in response time and the message cost is reduced by more than 90% over prior consensus based algorithms.

Columbus: Configuration Discovery for Clouds

Low-cost, accurate and scalable software configuration discovery is the key to simplifying many cloud management tasks. However, the lack of standardization across software configuration techniques has prevented the development of a fully automated and application independent configuration discovery solution. In this work, we present Columbus, an application-agnostic system to automatically discover environmental configuration parameters or Points of Variability (PoV) in clustered applications with high accuracy. Columbus uses the insight that even though configuration mechanisms and files vary across different software, the PoVs are encoded using a few common patterns. It uses a novel rule framework to annotate file content with PoVs and a Bayesian network to estimate confidence for annotated PoVs. Our experiments confirm that Columbus can accurately discover configuration for a diverse set of enterprise and cloud applications. It has subsequently been integrated in three real-world systems that analyze this information for discovery of distributed application dependencies, enterprise IT migration and virtual application configuration.