Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing

Krause, Andreas; Ihmig, Matthias; Rankin, Elly; Leong, Derek; Gupta, Smriti; Siewiorek, D.P.; Smailagic, Asim; Deisher, Michael; Sengupta, Ulysses

doi:10.1109/iswc.2005.52

Cited by 119 publications

(90 citation statements)

References 7 publications

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“…Mutual authentication of wearables becomes therefore a glaring problem, including the risk of mismatch (accidental or premeditated). Finally, most of the wearables of today are optimized based on their energy consumption [65]. To this end, utilizing the conventional RSA-like information security solutions may be unacceptable for battery-constrained devices and thus new lightweight primitives should be proposed and developed.…”

Section: Constraintsmentioning

confidence: 99%

Secure and Connected Wearable Intelligence for Content Delivery at a Mass Event: A Case Study

Ometov

Solomitckii

Olsson

et al. 2017

JSAN

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Presently, smart and connected wearable systems, such as on-body sensors and head-mounted displays, as well as other small form factor but powerful personal computers are rapidly pervading all areas of our life. Motivated by the opportunities that next-generation wearable intelligence is expected to provide, the goal of this work is to build a comprehensive understanding around some of the user-centric security and trust aspects of the emerging wearable and close-to-body wireless systems operating in mass events and under heterogeneous conditions. The paper thus intends to bring the attention of the research community to this emerging paradigm and discuss the pressing security and connectivity challenges within a popular consumer context. Our selected target scenario is that of a sports match, where wearable-equipped users may receive their preferred data over various radio access protocols. We also propose an authentication framework that allows for delivery of the desired content securely within the considered ecosystem.

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Section: Constraintsmentioning

confidence: 99%

Secure and Connected Wearable Intelligence for Content Delivery at a Mass Event: A Case Study

Ometov

Solomitckii

Olsson

et al. 2017

JSAN

View full text Add to dashboard Cite

show abstract

“…In the former approach, downsampling sensors is appealing because it is straightforward to implement. However, downsampling alone leads to suboptimal configurations, since the remainder of the pipeline is not optimized for the lower sample rate [16,14]. Conversely, upsampling when extra energy is available is likely to yield negligible performance improvement since the rest of the pipeline is unprepared to use the extra samples.…”

Section: Energy and Latencymentioning

confidence: 99%

“…Many proposals have surfaced for abstracting and dynamically adapting sampling rate [16,14,32]. Optimizations that rely on sampling rate adaptation have commonalities in their techniques with work investigating energy efficient mobile localization (e.g., [15]).…”

Section: Related Workmentioning

confidence: 99%

Balancing energy, latency and accuracy for mobile sensor data classification

Chu

Lane

Lai

et al. 2011

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems

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Sensor convergence on the mobile phone is spawning a broad base of new and interesting mobile applications. As applications grow in sophistication, raw sensor readings often require classification into more useful applicationspecific high-level data. For example, GPS readings can be classified as running, walking or biking. Unfortunately, traditional classifiers are not built for the challenges of mobile systems: energy, latency, and the dynamics of mobile.Kobe is a tool that aids mobile classifier development. With the help of a SQL-like programming interface, Kobe performs profiling and optimization of classifiers to achieve an optimal energy-latency-accuracy tradeoff. We show through experimentation on five real scenarios, classifiers on Kobe exhibit tight utilization of available resources. For comparable levels of accuracy traditional classifiers, which do not account for resources, suffer between 66% and 176% longer latencies and use between 31% and 330% more energy. From the experience of using Kobe to prototype two new applications, we observe that Kobe enables easier development of mobile sensing and classification apps.

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“…Sampling at high rates, however, incurs high power consumption (Krause et al, 2005) by not allowing sensors to enter low power modes and by increasing the amount of data processed by applications. Reducing the sample rate can decrease application power consumption considerably, but also reduces accuracy.…”

Section: Challenge 2: Accurately Predicting Power Consumption Of Sensmentioning

confidence: 99%

Analyzing Mobile Application Software Power Consumption via Model-Driven Engineering

2011

Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems

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Abstract:Smartphones are mobile devices that travel with their owners and provide increasingly powerful services. The software implementing these services must conserve battery power since smartphones may operate for days without being recharged. It is hard, however, to design smartphone software that minimizes power consumption. For example, multiple layers of abstractions and middleware sit between an application and the hardware, which make it hard to predict the power consumption of a potential application design accurately. Application developers must therefore wait until after implementation (when changes are more expensive) to determine the power consumption characteristics of a design. This paper provides three contributions to the study of applying model-driven engineering to analyze power consumption early in the lifecycle of smartphone applications. First, it presents a model-driven methodology for accurately emulating the power consumption of smartphone application architectures. Second, it describes the System Power Optimization Tool (SPOT), which is a model-driven tool that automates power consumption emulation code generation and simplifies analysis. Third, it empirically demonstrates how SPOT can estimate power consumption to within ∼3-4% of actual power consumption for representative smartphone applications.

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Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing

Cited by 119 publications

References 7 publications

Secure and Connected Wearable Intelligence for Content Delivery at a Mass Event: A Case Study

Secure and Connected Wearable Intelligence for Content Delivery at a Mass Event: A Case Study

Balancing energy, latency and accuracy for mobile sensor data classification

Analyzing Mobile Application Software Power Consumption via Model-Driven Engineering

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