Embodiments are directed to a system and method for authenticating a user of a client computer making a request to a server computer providing access to a network resource through an authentication platform that issues a challenge in response to the request requiring authentication of the user identity through a reply from the client computer, determining one or more items of context information related to at least one of the user, the request, and the client computer, and determining a disposition of the request based on the reply and the one or more items of context information. The reply includes a user password and may be provided by an authorizing client device coupled to the client computer over a wireless communications link.

Multi-factor authentication and comprehensive login system for client-server networks

Managing energy efficiently is paramount in modern smartphones. The diverse range of wireless interfaces and sensors, and the increasing popularity of power-hungry applications that take advantage of these resources can reduce the battery life of mobile handhelds to few hours of operation. The research community, and operating system and hardware vendors found interesting optimisations and techniques to extend the battery life of mobile phones. However, the state of the art of lithium-ion batteries clearly indicates that energy efficiency must be achieved both at the hardware and software level. In this survey, we will cover the software solutions that can be found in the research literature between 1999 and May 2011 at six different levels: energy-aware operating systems, efficient resource management, the impact of users' interaction patterns with mobile devices and applications, wireless interfaces and sensors management, and finally the benefits of integrating mobile devices with cloud computing services.

Energy Management Techniques in Modern Mobile Handsets

In this paper, we propose a system for context- aware battery management that warns the user when it detects that the phone battery can run out before the next charging opportunity is encountered. At the heart of this system, are algorithms that predict: (1) when the next charging opportunity will be available, (2) how much call-time will be required by the user in the interim, and (3) how long the battery will last if the current set of applications continue to execute. We propose algorithms that process user's location traces and call-logs for making some of these predictions. We also propose a technique to predict battery consumption of applications. We present the design of the system and demonstrate its feasibility by experimentally showing that each of the prediction algorithms can perform with fairly high accuracy.

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Context-aware Battery Management for Mobile Phones

KleeNet : Discovering Insidious Interaction Bugs in Wireless Sensor Networks Before Deployment Categories and Subject Descriptors

Complex interactions and the distributed nature of wireless sensor networks make automated testing and debugging before deployment a necessity. A main challenge is to detect bugs that occur due to non-deterministic events, such as node reboots or packet duplicates. Often, these events have the potential to drive a sensor network and its applications into corner-case situations, exhibiting bugs that are hard to detect using existing testing and debugging techniques.In this paper, we present KleeNet, a debugging environment that effectively discovers such bugs before deployment. KleeNet executes unmodified sensor network applications on symbolic input and automatically injects non-deterministic failures. As a result, KleeNet generates distributed execution paths at high-coverage, including low-probability corner-case situations. As a case study, we integrated KleeNet into the Contiki OS and show its effectiveness by detecting four insidious bugs in the μIP TCP/IP protocol stack. One of these bugs is critical and lead to refusal of further connections.

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KleeNet: discovering insidious interaction bugs in wireless sensor networks before deployment

This paper presents a novel, history-based, statistical technique for online battery lifetime prediction. The approach first takes a one-time, full cycle, voltage measurement of a constant load, and uses it to transform the partial voltage curve of the current workload into a form with robust predictability. Based on the transformed history curve, we apply a statistical method to make a lifetime prediction. We investigate the performance of the implementation of our approach on a widely used mobile device (HP iPAQ) running Linux, and compare it to two similar battery prediction technologies: ACPI and Smart Battery. We employ twenty-two constant and variable workloads to verify the efficacy of our approach. Our results show that this approach is efficient, accurate, and able to adapt to different systems and batteries easily.

Online prediction of battery lifetime for embedded and mobile devices

Mobile, battery-powered devices such as personal digital assistants and web-enabled mobile phones have successfully emerged as new access points to the world's digital infrastructure. However, the growing gap between device capabilities and battery technology requires novel techniques that extend battery life. Key to the success of such techniques, is our ability to accurately predict the power consumption of a program. In this paper, we investigate the degree to which battery dissipation induced by program execution can be measured by application-level software tools and predicted by a compiler and runtime system. We present a novel technique with which we can accurately estimate whole-program power-consumption for an arbitrary program by composing battery dissipation rates of benchmarks. We empirically evaluate our technique using an iPAQ hand-held device and a number of MiBench and other programs.

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Application-level prediction of battery dissipation

Simulation is widely used for developing, evaluating and analyzing sensor network applications, especially when deploying a large scale sensor network remains expensive and labor intensive. However, due to its computation intensive nature, existent simulation tools have to make trade-offs between fidelity and scalability and thus offer limited capabilities as design and analysis tools. In this paper, we introduce DiSenS (DIstributed SENsor network Simulation) -- a highly scalable distributed simulation system for sensor networks. DiSenS does not only faithfully emulates an extensive set of sensor hardware and supports extensible radio/power models, so that sensor network applications can be simulated transparently with high fidelity, but also employs distributed-memory parallel cluster system to attack the complex simulation problem. Combining an efficient distributed synchronization protocol and a sophisticated node partitioning algorithm (based on existent research), DiSenS achieves greater scalability than even many discrete event simulators. On a small to medium size cluster (16-64 nodes), DiSenS is able to simulate hundreds of motes in realtime speed and scale to thousands in sub-realtime speed. To our knowledge, DiSenS is the first full-system sensor network simulator with such scalability.

Disens: scalable distributed sensor network simulation

Software development for sensor network is made difficult by resource constrained sensor devices, distributed system complexity, communication unreliability, and high labor cost. Simulation, as a useful tool, provides an affordable way to study algorithmic problems with flexibility and controllability. However, in exchange for speed simulation often trades detail that ultimately limits its utility. In this paper, we propose a new development paradigm, simulation-based augmented reality, in which simulation is used to enhance development on physical hardware by seamlessly integrating a running simulated network with a physical deployment in a way that is transparent to each. The advantages of such an augmented network include the ability to study a large sensor network with limited hardware and the convenience of studying a part of the physical network with simulation's debugging, profiling and tracing capabilities. We implement the augmented reality system based on a sensor network simulator with high fidelity and high scalability. Key to the design are "super" sensor nodes which are half virtual and half physical that interconnect simulation and physical network with fine-grained traffic forwarding and accurate time synchronization. Our results detail the overhead associated with integrating live and simulated networks and the timing accuracy between virtual and physical parts of the network. We also discuss various application scenarios for our system.

Simulation-based augmented reality for sensor network development

/pdf/online-prediction-of-battery-lifetime-for-embedded-and-3ehhtatwer.pdf

Ye Wen

Papers

Online prediction of battery lifetime for embedded and mobile devices

Application-level prediction of battery dissipation

Disens: scalable distributed sensor network simulation

Simulation-based augmented reality for sensor network development

Online prediction of battery lifetime for embedded and mobile devices