Improving wireless network performance using sensor hints

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 59-62).Users of wireless devices often switch between being stationary and in motion while transferring data. Protocols that perform well in the static setting (where the channel conditions are relatively stable), however, tend to perform poorly when in motion (where channel conditions change rapidly), and vice versa. To circumvent this problem, we note that commodity smartphones and tablet devices come equipped with a variety of sensors, including accelerometers, multiple positioning sensors, magnetic compasses, and inertial sensors (gyros) that can provide hints about the device's mobility. In this thesis, we posit that these sensors can be profitably used to improve the performance of wireless network protocols running on these mobile devices and introduce an architecture for using external sensor hints for this purpose. We validate this idea with many different wireless protocols. First, we show how access points can perform better rate adaptation by changing strategies when they receive hints about a client's mobility. Second, we show how probing protocols for topology maintenance in mesh networks can increase both their efficiency and accuracy by adaptively probing based on movement. Third, we show how vehicular mesh networks can use directionality hints to improve the connectivity of routes. Finally, we outline several other novel applications of external sensor hints for improving wireless network performance. We evaluate our protocols using trace-driven simulation and real-world experiments. We show that our hint-aware rate adaptation protocol increases throughput by 30% to 50% on average over frame-based and SNR-based protocols. Our hint-aware probing protocol reduces the bandwidth consumed by probing to accurately estimate link delivery probabilities-by a factor of 20 in our experiments. And, our hint-aware route selection in vehicular mesh networks increases route stability by a factor of 4 to 5 compared to a hint-free approach in our simulations.by Lenin Ravindranath Sivalingam.S.M

Optimal Power Flow using Ant Colony Search Algorithm to Evaluate Load Curtailment Incorporating Voltage Stability Margin Criterion

This paper proposes a method to compute load curtailment evaluation using ACSA based optimal power flow incorporating voltage stability margin criterion. . In a deregulated environment, congestion alleviation could mean load curtailment in certain situations.  The utilities would definitely prefer to curtail a load as lower as possible during a viability crisis situation. A criterion based on the voltage stability indicator is them incorporated as an additional constraint into the optimal power flow using ACSA algorithm and it is evaluated in a WSCC 9-bus test system.DOI:http://dx.doi.org/10.11591/ijece.v3i5.2738  

Code In The Air: Simplifying Sensing and Coordination Tasks on Smartphones

A growing class of smartphone applications are tasking applications that run continuously, process data from sensors to determine the user's context (such as location) and activity, and optionally trigger certain actions when the right conditions occur. Many such tasking applications also involve coordination between multiple users or devices. Example tasking applications include location-based reminders, changing the ring-mode of a phone automatically depending on location, notifying when friends are nearby, disabling WiFi in favor of cellular data when moving at more than a certain speed outdoors, automatically tracking and storing movement tracks when driving, and inferring the number of steps walked each day. Today, these applications are non-trivial to develop, although they are often trivial for end users to state. Additionally, simple implementations can consume excessive amounts of energy. This paper proposes Code in the Air (CITA), a system which simplifies the rapid development of tasking applications. It enables non-expert end users to easily express simple tasks on their phone, and more sophisticated developers to write code for complex tasks by writing purely server-side scripts. CITA provides a task execution framework to automatically distribute and coordinate tasks, energy-efficient modules to infer user activities and compose them, and a push communication service for mobile devices that overcomes some shortcomings in existing push services.National Science Foundation (U.S.) (Grant 0931550

Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applicationsComment: ICSE 201

Designing a Context-Sensitive Context Detection Service for Mobile Devices

This paper describes the design, implementation, and evaluation of Amoeba, a context-sensitive context detection service for mobile devices. Amoeba exports an API that allows a client to express interest in one or more context types (activity, indoor/outdoor, and entry/exit to/from named regions), subscribe to specific modes within each context (e.g., "walking" or "running", but no other activity), and specify a response latency (i.e., how often the client is notified). Each context has a detector that returns its estimate of the mode. The detectors take both the desired subscriptions and the current context detection into account, adjusting both the types of sensors and the sampling rates to achieve high accuracy and low energy consumption. We have implemented Amoeba on Android. Experiments with Amoeba on 45+ hours of data show that our activity detector achieves an accuracy between 92% and 99%, outperforming previous proposals like UCLA* (59%), EEMSS (82%) and SociableSense (72%), while consuming 4 to 6× less energy

Dolphin Echolocation Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper proposes Dolphin echolocation Algorithm (DEA) for solving the multi-objective reactive power dispatch problem. Echolocation is the genetic sonar used by dolphins and more than a few kinds of other animals for direction-finding and hunting in different environments. This aptitude of dolphins is mimicked in this paper to develop a new process for solving optimal reactive power dispatch problem. A detailed study has shown that meta-heuristic algorithms have certain overriding rules. These rules will facilitate to get enhanced results. Dolphin echolocation algorithm takes reward of these rules and outperforms many active optimization methods. The new approach DEA leads to outstanding results with little computational efforts. In order to evaluate the efficiency of the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other specified algorithms. Simulation results show that DEA is superior to other algorithms in tumbling the real power loss and enhancing the voltage stability.

Improving the performance and reliability of mobile applications

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 129-133).The mobile application ("app") ecosystem has grown at a tremendous pace with millions of apps and hundreds of thousands of app developers. Mobile apps run across a wide range of network, hardware, location, and usage conditions that are hard for developers to emulate or even anticipate during lab testing. Hence, app failures and performance problems are common in the wild. Scarce resources, shift away from familiar synchronous programming models, and poor development support has made it more difficult for app developers to overcome these problems. This dissertation focuses on systems that make it significantly easy for app developers to diagnose and improve their mobile apps. To reduce user annoyance and survive the brutally competitive mobile app marketplace, developers need systems that (i) identify potential failures before the app is released, (ii) diagnose problems after the app is deployed in the wild, and (iii) provide reliable app performance in the face of varying conditions in the wild. This dissertation presents systems that satisfy these needs. VanarSena makes it easy to diagnose common failures in mobile apps before deployment, AppInsight makes it easy to monitor mobile apps after deployment, and Timecard allows mobile apps to adapt to conditions in the wild and provide consistent performance. For the legion of amateur app developers with fewer resources at hand, these systems significantly reduce the barrier for diagnosing and improving mobile apps. The systems are built on top of a binary instrumentation framework that automatically rewrites app binary at bytecode level. Hence, using them requires minimal effort on part of the app developer. The systems include novel instrumentation techniques to automatically track the runtime behavior of the app. To cope with the scarcity of resources, they include resource-aware mechanisms that incur negligible overhead. To make them immediately deployable, they are designed to require no modification to the OS or runtime. We have built VanarSena, AppInsight, and Timecard for the Windows Phone platform. VanarSena does automated app testing by systematically emulating user interactions and fault conditions from the wild to uncover app failures. VanarSena uncovered 2,969 distinct crashes in more than 1,100 apps in the app store. AppInsight does light-weight monitoring of mobile apps in the wild. It automatically instruments the app binary to track performance and failures. AppInsight uncovered several performance bottlenecks and crashes in the wild and has provided useful feedback to developers. Timecard enables apps to adapt at runtime and provide consistent performance in the face of varying conditions in the wild. Timecard can tightly control the response time around a desired user-perceived delay.by Lenin Ravindranath Sivalingam.Ph. D