A Survey on Context-Aware Mobile and Wireless Networking: On Networking and Computing Environments' Integration

Makris, Prodromos; Skoutas, Dimitrios N.; Skianis, Charalabos

doi:10.1109/surv.2012.040912.00180

Cited by 156 publications

(97 citation statements)

References 125 publications

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“…More comprehensively, Liu et al 9 surveyed the existing studies according to the entire spectrum of context-aware mobile recommendation systems and their specifications, seeking to predict and anticipate the needs of users and respond to their behavior. Makris et al 10 studied context-awareness with regard to mobile and wireless networking and provided a precise taxonomy scheme for those areas. Perera et al 11 thoroughly investigated contextawareness from the Internet of Things (IoT) perspective, which encompasses the preliminaries of the IoT, context-aware fundamentals and design principles, and the evaluation of a number of projects in this context.…”

Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…The surveys explore contexts, context types and context modeling in the recommender systems, and defines future challenges to be faced in this research area. [25] takes context-awareness in a scope of mobile and wireless networking. It surveys traditional context-aware computing areas, and makes a connection between them with mobile and wireless networking notions.…”

Section: It Ismentioning

confidence: 99%

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

Yurur

Liu

Sheng

et al. 2016

IEEE Commun. Surv. Tutorials

169

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“…Previous researches (Schilit, et al, 1994) resumed the context computing as a result on combination of three information domains regarding on where computational resources are, what kind of resources is provided and, who is using it. Modeling these characteristics in attributes, import and exchange it in form of relevant information to identify a context is a key process to support the context computing adaptive process (Makris, et al, 2013). The changing process of a context started by sensors, network events, mobile device location detection or the combination of all these examples, provides several new different set of attributes to describe the context and its related resources.…”

Section: A Definition and Computing Modelsmentioning

confidence: 99%

EducActiveCore: Computational Model to Educational Personalization Based on Multiagent and Context-Aware Computing

Santos

Notargiacomo²

2017

IJLTER

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Abstract. With the growth of online courses and, usage of mobile access allowing students execute educational activities in multiple locales, with variety of data and media content, new perspectives of educational support using different computing models can be observed. Some of most recent evolved computing models stand out in areas like Social Networks Analysis, Artificial Intelligence, Mobile Computing and Context-Aware Computing. Understanding the combination of these computing areas as complementary researches, this work investigates the applications of these computing technics to modeling an intelligent computational engine with educational personalization purposes. In this resume of a research in progress, a reduced implementation prepared as proof of concept simulating aspects of the target model, operates as centralized adaptive engine. The implemented engine, applied Artificial Neural Networks on classification tasks and routing recommendation. A group of 27 students participated in an experiment interacting with the adaptive engine using a mobile application provided. The mobile application allowed tracking of interface during usage flow by students, and provided to students the adaptive engine recommendation results. Around 59% of students confirmed the recommendation effectiveness of adaptive engine. In this experiment, at the end of each participation, students sent feedbacks about application features. The current results indicate the viability of computational model related to automation of classification tasks to environment identification and activity routing recommendation. In brief, the initial experiment presented encouraging results, indicating that the continuity of research could result in a useful tool to online educational platforms.

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A Survey on Context-Aware Mobile and Wireless Networking: On Networking and Computing Environments' Integration

Cited by 156 publications

References 125 publications

Context‐aware movement analytics: implications, taxonomy, and design framework

Context‐aware movement analytics: implications, taxonomy, and design framework

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

EducActiveCore: Computational Model to Educational Personalization Based on Multiagent and Context-Aware Computing

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