Situation identification techniques in pervasive computing: A review

Ye, Juan; Dobson, Simon; McKeever, Susan

doi:10.1016/j.pmcj.2011.01.004

Cited by 383 publications

(251 citation statements)

References 69 publications

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“…The objective of the contextualized inference is to infer a sub-taxonomy of situations (from the very abstract to the very specific) of a system that is being observed or taxonomy of activities being performed [13], [14]. Specifically, contextualized inference methods [14] are generally applied in situation-and context-aware systems [17], [16], where a more specific situation (positioned at the lower levels of the situational taxonomy) is represented by the logical conjunction of situational components [12], [15]. Let us adopt the by far popular IF-THEN situational knowledge representation inference rule, i.e., the logical conjunc-…”

Section: Motivationmentioning

confidence: 99%

Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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On-line statistical and machine learning analytic tasks over largescale contextual data streams coming from e.g., wireless sensor networks, Internet of Things environments, have gained high popularity nowadays due to their significance in knowledge extraction, regression and classification tasks, and, more generally, in making sense from large-scale streaming data. The quality of the received contextual information, however, impacts predictive analytics tasks especially when dealing with uncertain data, outliers data, and data containing missing values. Low quality of received contextual data significantly spoils the progressive inference and on-line statistical reasoning tasks, thus, bias is introduced in the induced knowledge, e.g., classification and decision making. To alleviate such situation, which is not so rare in real time contextual information processing systems, we propose a progressive time-optimized data quality-aware mechanism, which attempts to deliver contextual information of high quality to predictive analytics engines by progressively introducing a certain controlled delay. Such a mechanism progressively delivers high quality data as much as possible, thus eliminating possible biases in knowledge extraction and predictive analysis tasks. We propose an analytical model for this mechanism and show the benefits stem from this approach through comprehensive experimental evaluation and comparative assessment with qualityunaware methods over real sensory multivariate contextual data.

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Section: Motivationmentioning

confidence: 99%

Quality-optimized predictive analytics

Anagnostopoulos

2016

Appl Intell

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“…However, most of the stateof-the-art activity models are built upon static machine learning models, the reader is referred to [30] for more details.…”

Section: Related Workmentioning

confidence: 99%

Sensor-based adaptive activity recognition with dynamically available sensors

Wen

Wang

2016

Neurocomputing

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An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.

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“…Moving towards situation-awareness, it is useful to refer to the work of Dobson et al [58] who give an overview of techniques available for situation identification. Situation identification as characterized there deals with (i) tracking the sequence of actions performed in the environment; (ii) checking them against temporal and spatial constraints; and (iii) predicting next situation.…”

Section: System Planningmentioning

confidence: 99%

Introduction to the inaugural issue of the Journal of Reliable Intelligent Environments

Augusto

Coronato

2015

J Reliable Intell Environ

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As envisioned by Weiser, computing is in the process of being everywhere and becoming invisible. As Milner noticed, the question now is whether we shall understand this ubiquitous computer we are building. This is especially true as designers are more and more using complex techniques for every component of the system and building systems which are made of increasingly heterogenous parts. With this extended editorial, we embark on an exploration journey into the exciting new area of "reliable intelligent environments" (RIEs). Taking the perspective of an RIE engineer, we present a selection of approaches that have been put forward to design, verify, and operate IEs in a manner so that users can rely on Intelligent Environment systems. We outline crucial challenges: the situatedness which exposes IE to challenges similar to those known from robotics and control systems, the embedding of human users and the safety, privacy, and usability requirements thus entailed, and the amounts of data produced by sensors and actuators, which require advanced reasoning and learning mechanisms to handle them in a reliable way in real-time. We also sketch the opportunities reliable IEs provide to developing new markets and products.

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Situation identification techniques in pervasive computing: A review

Cited by 383 publications

References 69 publications

Quality-optimized predictive analytics

Quality-optimized predictive analytics

Sensor-based adaptive activity recognition with dynamically available sensors

Introduction to the inaugural issue of the Journal of Reliable Intelligent Environments

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