Adapting Applications in Mobile Terminals Using Fuzzy Context Information

Mäntyjärvi, Jani; Seppänen, Tapio

doi:10.1007/3-540-45756-9_9

Cited by 26 publications

(15 citation statements)

References 12 publications

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“…The ContextTorrent [10] is an Android-based framework that processes raw context into semantically searchable context by modeling context as an ontology. Fuzzy context information has been derived in case of uncertainty [11]. Naive Bayesian networks have also been used for context classification [12].…”

Section: Related Workmentioning

confidence: 99%

Context Management for Supporting Context-aware Android Applications Development

Elazhary

Althubyani

Ahmed

et al. 2017

Int. J. Interact. Mob. Technol.

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Abstract-Building context-aware mobile applications is one of the most ambitious areas of research. Such applications can change their behavior according to context or perform specific tasks in specific contexts. Regardless of the application, all context-aware mobile applications share the need to retrieve and process context information. This paper presents a Context Management tool for the Android platform (ACM). ACM allows easy access to internal onboard mobile sensors and hardware features extracting corresponding raw data. Raw context is processed into higher-level more human-readable context that is provided seamlessly to the mobile applications. Different methods are used for this purpose including fuzzy classifiers. Since different mobiles have different sensors and hardware features, ACM can adapt to the mobile device by deactivating access to unavailable ones. Information regarding the available sensors and hardware features and their specifications can also be queried. Additionally, applications can request notifications regarding context change or specific context values. In addition to providing developers with supporting classes and methods, ACM is accompanied by an application that allows developers to examine its functionality and capabilities before using it. The application can be also used to examine the readings of the different sensors in different situations and thus calibrate them as needed. Additionally, it can be used to modify and personalize default interpretations of raw context values to high-level ones. ACM has been tested empirically and the results show extreme interest of context-aware mobile application developers in its promising capabilities and that it is conducive to facilitating, speeding up and triggering development of many more of such applications.

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Section: Related Workmentioning

confidence: 99%

Context Management for Supporting Context-aware Android Applications Development

Elazhary

Althubyani

Ahmed

et al. 2017

Int. J. Interact. Mob. Technol.

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“…In [10], a fuzzy representation of context is introduced for adaptation of user interface application on mobile devices and the same fuzzy concept has been used in [11] for providing the user with an explicit and meaningful explanation for the system's proactive behavior. Alternatively, in [12][13] fuzzy logic is used for defining the 'context situations' and the rules for adaptation of the service policies according to their fitness degree.…”

Section: Related Workmentioning

confidence: 99%

“…Reviewing recent works [5][6][7][8][9][10][11][12][13][14] in context reasoning under uncertainty reveals that these works have limited capability in dealing with vagueness of real-life situations and reflecting gradual and delta changes in the results of situation inference. More importantly, they lack a rich theoretical basic for supporting pervasive computing scenarios.…”

Section: Introductionmentioning

confidence: 99%

Reasoning about Context in Uncertain Pervasive Computing Environments

Haghighi

Krishnaswamy

Zaslavsky

et al. 2008

Lecture Notes in Computer Science

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Abstract.Context-awareness is a key to enabling intelligent adaptation in pervasive computing applications that need to cope with dynamic and uncertain environments. Addressing uncertainty is one of the major issues in contextbased situation modeling and reasoning approaches. Uncertainty can be caused by inaccuracy, ambiguity or incompleteness of sensed context. However, there is another aspect of uncertainty that is associated with human concepts and realworld situations. In this paper we propose and validate a Fuzzy Situation Inference (FSI) technique that is able to represent uncertain situations and reflect delta changes of context in the situation inference results. The FSI model integrates fuzzy logic principles into the Context Spaces (CS) model, a formal and general context reasoning and modeling technique for pervasive computing environments. The strengths of fuzzy logic for modeling and reasoning of imperfect context and vague situations are combined with the CS model's underlying theoretical basis for supporting context-aware pervasive computing scenarios. An implementation and evaluation of the FSI model are presented to highlight the benefits of the FSI technique for context reasoning under uncertainty.

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“…The modelling is done by hand on a semantic level, which is not applicable in our case. Another human computer interface approach using fuzzy context can be found in [13]. One of the arguments stated here for using fuzzy logic, is that it provides a continuous control signal, whereas Boolean logic does only provide discontinuous threshold based control signals.…”

Section: Applications Using Fuzzy Logic And/or Statistical Methodsmentioning

confidence: 99%

AwarePen - Classification Probability and Fuzziness in a Context Aware Application

Berchtold¹,

Riedel²,

Beigl³

et al.

Ubiquitous Intelligence and Computing

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Abstract. Fuzzy inference has been proven a candidate technology for context recognition systems. In comparison to probability theory, its advantage is its more natural mapping of phenomena of the real world as context. This paper reports on our experience with building and using monolithic fuzzy-based systems (a TSK-FIS) to recognize real-world events and to classify these events into several categories. It will also report on some drawbacks of this approach that we have found. To overcome these drawbacks a novel concept is proposed in this paper. The concept incorporates fuzzy-based approaches with probabilistic methods, and separates the monolithic fuzzy-based system into several modules. The core advantage of the concept lays in the separation of detection complexity into distinct modules, each of them using fuzzy-based inference for context classification. Separation of detection functionality is supported by an automatic process using transition probabilities between context classifiers to optimize detection quality for the resulting detection system. This way our approach incorporates the advantages of fuzzy-based and probabilistic systems. This paper will show results of experiments of an existing system using a monolithic FIS approach, and reports on advantages when using a modular approach.

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Adapting Applications in Mobile Terminals Using Fuzzy Context Information

Cited by 26 publications

References 12 publications

Context Management for Supporting Context-aware Android Applications Development

Context Management for Supporting Context-aware Android Applications Development

Reasoning about Context in Uncertain Pervasive Computing Environments

AwarePen - Classification Probability and Fuzziness in a Context Aware Application

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