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2017 | OriginalPaper | Buchkapitel

4. Scalable Context-Awareness

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Abstract

Since the pioneering work on context-aware computing by Schilit et al. [12] over two decades ago, there have been tremendous developments in context-aware mobile computing [1, 7], a mobile device is made aware of the current context of the user, including the circumstances or the surroundings as well as the user’s activity on the phone, or the phone’s current state (e.g., battery level, device properties and so on), and can take action based on such context information. Mobile sensing [4, 13] on the device is used to obtain information about the user, including the user’s location, objects nearby (e.g., via WiFi or Bluetooth scanning) as well as the current physical activity of the user (e.g., walking, on a bus, etc), i.e. the work on mobile activity recognition (e.g., [14]), and the current user interaction with the apps on the phone (e.g., what the user is looking at). A large range of data analysis techniques has been employed to process sensor data in order to learn to recognise activities—recently, Deep Convolutional Neural Networks have been employed achieving accuracy in recognition of up to 97–99% [5].

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Literatur
1.
Zurück zum Zitat Guanling Chen and David Kotz. A survey of context-aware mobile computing research. Technical report, Hanover, NH, USA, 2000. Guanling Chen and David Kotz. A survey of context-aware mobile computing research. Technical report, Hanover, NH, USA, 2000.
2.
Zurück zum Zitat Thang M. Do, Seng W. Loke, and Fei Liu. HealthyLife: An Activity Recognition System with Smartphone Using Logic-Based Stream Reasoning, pages 188–199. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. Thang M. Do, Seng W. Loke, and Fei Liu. HealthyLife: An Activity Recognition System with Smartphone Using Logic-Based Stream Reasoning, pages 188–199. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013.
3.
Zurück zum Zitat B. Faltings, J. J. Li, and R. Jurca. Incentive mechanisms for community sensing. IEEE Transactions on Computers, 63(1):115–128, Jan 2014. B. Faltings, J. J. Li, and R. Jurca. Incentive mechanisms for community sensing. IEEE Transactions on Computers, 63(1):115–128, Jan 2014.
4.
Zurück zum Zitat Geri Gay. Context-aware mobile computing: affordances of space, social awareness, and social influence / Geri Gay. Morgan and Claypool Publishers [San Rafael, Calif.], 2009. Geri Gay. Context-aware mobile computing: affordances of space, social awareness, and social influence / Geri Gay. Morgan and Claypool Publishers [San Rafael, Calif.], 2009.
5.
Zurück zum Zitat Wenchao Jiang and Zhaozheng Yin. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM International Conference on Multimedia, MM ’15, pages 1307–1310, New York, NY, USA, 2015. ACM. Wenchao Jiang and Zhaozheng Yin. Human activity recognition using wearable sensors by deep convolutional neural networks. In Proceedings of the 23rd ACM International Conference on Multimedia, MM ’15, pages 1307–1310, New York, NY, USA, 2015. ACM.
6.
Zurück zum Zitat Peter Kiefer. Mobile intention recognition. In Steffen Hölldobler, Abraham Bernstein, Klaus-Peter Löhr, Paul Molitor, Gustaf Neumann, Rüdiger Reischuk, Myra Spiliopoulou, Harald Störrle, and Dorothea Wagner, editors, Ausgezeichnete Informatikdissertationen 2011, Lecture Notes in Informatics. Gesellschaft für Informatik, 2012. Peter Kiefer. Mobile intention recognition. In Steffen Hölldobler, Abraham Bernstein, Klaus-Peter Löhr, Paul Molitor, Gustaf Neumann, Rüdiger Reischuk, Myra Spiliopoulou, Harald Störrle, and Dorothea Wagner, editors, Ausgezeichnete Informatikdissertationen 2011, Lecture Notes in Informatics. Gesellschaft für Informatik, 2012.
7.
Zurück zum Zitat Seng W. Loke. Context-Aware Pervasive Systems. Auerbach Publications, Boston, MA, USA, 2006.CrossRef Seng W. Loke. Context-Aware Pervasive Systems. Auerbach Publications, Boston, MA, USA, 2006.CrossRef
8.
Zurück zum Zitat Chuishi Meng, Wenjun Jiang, Yaliang Li, Jing Gao, Lu Su, Hu Ding, and Yun Cheng. Truth discovery on crowd sensing of correlated entities. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, SenSys ’15, pages 169–182, New York, NY, USA, 2015. ACM. Chuishi Meng, Wenjun Jiang, Yaliang Li, Jing Gao, Lu Su, Hu Ding, and Yun Cheng. Truth discovery on crowd sensing of correlated entities. In Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems, SenSys ’15, pages 169–182, New York, NY, USA, 2015. ACM.
9.
Zurück zum Zitat Tuan Nguyen, Seng W. Loke, Torab Torabi, and Hongen Lu. On the practicalities of place-based virtual communities: Ontology-based querying, application architecture, and performance. Expert Syst. Appl., 41(6):2859–2873, 2014.CrossRef Tuan Nguyen, Seng W. Loke, Torab Torabi, and Hongen Lu. On the practicalities of place-based virtual communities: Ontology-based querying, application architecture, and performance. Expert Syst. Appl., 41(6):2859–2873, 2014.CrossRef
10.
Zurück zum Zitat Goran Radanovic, Boi Faltings, and Radu Jurca. Incentives for effort in crowdsourcing using the peer truth serum. ACM Trans. Intell. Syst. Technol., 7(4):48:1–48:28, March 2016. Goran Radanovic, Boi Faltings, and Radu Jurca. Incentives for effort in crowdsourcing using the peer truth serum. ACM Trans. Intell. Syst. Technol., 7(4):48:1–48:28, March 2016.
11.
Zurück zum Zitat Nirmalya Roy, Archan Misra, and Diane J. Cook. Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments. J. Ambient Intelligence and Humanized Computing, 7(1):1–19, 2016.CrossRef Nirmalya Roy, Archan Misra, and Diane J. Cook. Ambient and smartphone sensor assisted ADL recognition in multi-inhabitant smart environments. J. Ambient Intelligence and Humanized Computing, 7(1):1–19, 2016.CrossRef
12.
Zurück zum Zitat Bill Schilit, Norman Adams, and Roy Want. Context-aware computing applications. In IEEE Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, US, 1994. Bill Schilit, Norman Adams, and Roy Want. Context-aware computing applications. In IEEE Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, US, 1994.
13.
Zurück zum Zitat Sougata Sen, Archan Misra, Rajesh Balan, and Lipyeow Lim. The Case for Cloud-Enabled Mobile Sensing Services. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, MCC ’12, pages 53–58, New York, NY, USA, 2012. ACM. Sougata Sen, Archan Misra, Rajesh Balan, and Lipyeow Lim. The Case for Cloud-Enabled Mobile Sensing Services. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, MCC ’12, pages 53–58, New York, NY, USA, 2012. ACM.
14.
Zurück zum Zitat Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten, and Paul J.M. Havinga. A survey of online activity recognition using mobile phones. Sensors, 15(1):2059, 2015. Muhammad Shoaib, Stephan Bosch, Ozlem Durmaz Incel, Hans Scholten, and Paul J.M. Havinga. A survey of online activity recognition using mobile phones. Sensors, 15(1):2059, 2015.
15.
Zurück zum Zitat Dong Wang, Tarek Abdelzaher, and Lance Kaplan. Social Sensing: Building Reliable Systems on Unreliable Data. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition, 2015. Dong Wang, Tarek Abdelzaher, and Lance Kaplan. Social Sensing: Building Reliable Systems on Unreliable Data. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1st edition, 2015.
Metadaten
Titel
Scalable Context-Awareness
verfasst von
Seng W. Loke
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-54436-6_4