Balancing energy, latency and accuracy for mobile sensor data classification

Chu, David; Lane, Nicholas D.; Lai, Ted Tsung-Te; Pang, Cong; Meng, Xiangying; Guo, Qing; Li, Fan; Zhao, Feng

doi:10.1145/2070942.2070949

Cited by 97 publications

(69 citation statements)

References 34 publications

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“…On the other hand, "high-level context" is obtained later through the combination of low-level and/or highlevel contexts, which is called "composition". Some mobile classifier development tools such as "Kobe" [61],"WEKA" [62], and former toolkits "The Context Toolkit" [45] can deal with low-level context acquisition from raw sensory. They infer high-level semantic outcomes while exhibiting efficient utilization of available resources, and achieving an optimal balance among energy, latency and accuracy tradeoffs.…”

Section: E Context Inferencementioning

confidence: 99%

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Yurur

Liu

Sheng

et al. 2016

IEEE Commun. Surv. Tutorials

170

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Section: E Context Inferencementioning

confidence: 99%

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Yurur

Liu

Sheng

et al. 2016

IEEE Commun. Surv. Tutorials

170

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“…Other approaches [22] [23] use only smartphone sensors for activity classification. A phone-only classification technique [8] provides an explicit energy-latency-accuracy tradeoff, while other smartphone methods [25] [31] [29] achieve energy savings with adaptive sampling.…”

Section: Related Workmentioning

confidence: 99%

Remora: Sensing resource sharing among smartphone-based body sensor networks

Keally

Zhou

Xing

et al. 2013

2013 IEEE/ACM 21st International Symposium on Quality of Service (IWQoS)

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In many body sensor network (BSN) applications, such as activity recognition for assisted living residents or physical fitness assessment of a sports team, users spend a significant amount of time with one another while performing many of the same activities. We exploit this physical proximity with Remora, a smartphone-based Body Sensor Network activity recognition system which shares sensing resources among neighboring BSNs. Compared to individual BSNs, Remora resource sharing provides increased accuracy and significant energy savings. To increase classification accuracy, Remora BSNs share sensors by overhearing neighbors' sensor data transmissions. When sharing, fewer on-body sensors are needed to achieve high accuracy, resulting in energy savings by turning off unneeded sensors. To save phone energy, neighboring BSNs share classifiers: only one classifier is active at a time classifying activities for all neighbors. Remora addresses three major challenges of sharing with physical neighbors: 1) Sharing only when the energy benefit outweighs the cost, 2) Finding and utilizing the shared sensors and classifiers which produce the best combination of accuracy improvement and energy savings, and 3) Providing a lightweight and collaborative classification approach, without the use of a backend server, which adapts to the dynamics of available neighbors. In a two week evaluation with 6 subjects, we show that sharing provides up to a 30% accuracy increase while extending phone battery lifetime by over 65%.

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“…Also related to CQue are efforts to develop a context-sensing engine for phone that can be used by applications to request contexts [4,13]. The Jigsaw context sensing engine [13] comprises of a set of sensing pipelines for accelerometer, microphone and GPS.…”

Section: Related Workmentioning

confidence: 99%

Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing

Parate

Chiu

Ganesan

et al. 2013

Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services

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The proliferation of sensors on mobile phones and wearables has led to a plethora of context classifiers designed to sense the individual's context. We argue that a key missing piece in mobile inference is a layer that fuses the outputs of several classifiers to learn deeper insights into an individual's habitual patterns and associated correlations between contexts, thereby enabling new systems optimizations and opportunities. In this paper, we design CQue, a dynamic bayesian network that operates over classifiers for individual contexts, observes relations across these outputs across time, and identifies opportunities for improving energy-efficiency and accuracy by taking advantage of relations. In addition, such a layer provides insights into privacy leakage that might occur when seemingly innocuous user context revealed to different applications on a phone may be combined to reveal more information than originally intended. In terms of system architecture, our key contribution is a clean separation between the detection layer and the fusion layer, enabling classifiers to solely focus on detecting the context, and leverage temporal smoothing and fusion mechanisms to further boost performance by just connecting to our higher-level inference engine. To applications and users, CQue provides a query interface, allowing a) applications to obtain more accurate context results while remaining agnostic of what classifiers/sensors are used and when, and b) users to specify what contexts they wish to keep private, and only allow information that has low leakage with the private context to be revealed. We implemented CQue in Android, and our results show that CQue can i) improve activity classification accuracy up to 42%, ii) reduce energy consumption in classifying social, location and activity contexts with high accuracy(>90%) by reducing the number of required classifiers by at least 33%, and iii) effectively detect and suppress contexts that reveal private information.

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Balancing energy, latency and accuracy for mobile sensor data classification

Cited by 97 publications

References 34 publications

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Context-Awareness for Mobile Sensing: A Survey and Future Directions

Remora: Sensing resource sharing among smartphone-based body sensor networks

Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing

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