SenST*: Approaches for Reducing the Energy Consumption of Smartphone-Based Context Recognition

Schirmer, Maximilian; Höpfner, Hagen

doi:10.1007/978-3-642-24279-3_27

Cited by 15 publications

(5 citation statements)

References 28 publications

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“…In [30][31][32], the authors propose to optimise the sensing process in terms of power saving. In particular, Nath [30] exploits reasoning techniques for learning associations among context attributes for optimising the internal logic of the framework.…”

Section: Related Workmentioning

confidence: 99%

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

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Zambonelli

2019

Mobile Information Systems

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Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms.

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Section: Related Workmentioning

confidence: 99%

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

Bicocchi

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Zambonelli

2019

Mobile Information Systems

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“…In order to allow each device to handle universal messaging, there have to be defined informational exchange standards. As the IoT is one of the key parts of Industry 4.0 [11], current trends in automation and data exchanges in manufacturing, we can also predict the benefits of sensorial networks. In order to obtain the latest research on Industry 4.0, we have employed a systematic literature review (SLR) to identify the research areas related to sensorial networks [12].…”

Section: Trends and Standardsmentioning

confidence: 99%

“…Future Internet 2019, 11,215 15 of 20 used for tuning and optimizing the implementation on the Android client. On the server side, the Java Mission Control (JMC) was used for profiling Java Virtual Machines (JVM).…”

Section: Tuning and Testing Of The Developed Solutionmentioning

confidence: 99%

Sensorial Network Framework Embedded in Ubiquitous Mobile Devices

et al. 2019

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: Today’s digital society is interconnected and networked, with modern smart devices ubiquitously built into and embedded within smart environments and other environments, where people (their users) typically live. It is very important to mention that sensorial awareness of an environment depends on one’s current location and equipment, as well as the equipment’s real-time capabilities. Personal sensorial information is considered to be the key factor for progress in the improvement of the productivity of everyday life and creation of a smart surrounding environment. This paper describes the design, implementation, and testing process of a new sensorial framework based on the current possibilities created by ubiquitous smart mobile devices with sensors, which involves computing power and battery power issues. The two parts of the proposed framework have been designed, implemented, and tested. The client part is represented by a front-end mobile application, and the back-end part is represented by a server-side application. The analysis of the data, captured during the testing phase, involves the analysis of the processing time, battery consumption, and transmitted data amount. This analysis reveals that Transmission Control Protocol (TCP) and user datagram protocol (UDP) protocols have a comparable performance, although TCP is preferable for use in local networks. In comparison to other solutions such as MobiSense or Feel the World framework, the final solution of the proposed and developed sensorial framework has two main capabilities, which are the security support and social networking possibilities. The advantage of the MobiSense platform is the existence of several real-world applications, whereas the proposed sensorial framework needs to be verified in the massive context of many users in real time.

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“…The framework doesn't focus on a generic approach enabling re-configuration and runtime adaptation. In [9], [4] and [10] authors propose to optimise the sensing process in terms of power saving. In particular, [9] exploits a specification-driven reasoning technique to learn relationship among context attributes to optimise the internal logic of an awareness framework.…”

Section: Related Workmentioning

confidence: 99%

Human aware superorganisms

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Zambonelli

2014

Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication

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Massive networks of wearable devices have recently become a key scenario for pattern recognition technologies. Applications range from implicit human-machine interactions, to autonomous monitoring of user habits and activities. This paper presents a framework providing developers with tools to orchestrate the continuous process of collecting and classifying data streams in aware-systems. It supports service oriented, reconfigurable components and provides a solid background to put at joint work specification-and data-driven approaches. It also provides an innovative meta-classification scheme allowing to implement applications by editing a simple state automata. Experimental results suggest that the approach could be integrated in a number of applications for: (i) improving energy efficiency, (ii) improving classification accuracy and (iii) improving software engineering of aware systems.

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SenST*: Approaches for Reducing the Energy Consumption of Smartphone-Based Context Recognition

Cited by 15 publications

References 28 publications

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

Sensorial Network Framework Embedded in Ubiquitous Mobile Devices

Human aware superorganisms

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