On Context Awareness for Multisensor Data Fusion in IoT

Gite, Shilpa; Agrawal, Himanshu

doi:10.1007/978-81-322-2526-3_10

Cited by 28 publications

(16 citation statements)

References 21 publications

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“…The architecture predicts combining both the elements from context frames and then propagating them further to the deep learning modules of the system. The system's working for driver activity prediction using a mix of algorithms is shown in Algorithm 1in figure 5, similar to presented in the papers [11,44]. Initially, the spatiotemporal and eye-gaze-based features are extracted, then combined to form the intermediate vector representation.…”

Section: A Manoeuvre Anticipation Using Admtmentioning

confidence: 99%

“…Owing to multiple action sequences, automated moderation of driver activity anticipation is challenging for these techniques [8]. The authors present approaches that build upon the previously proposed situation-aware mechanisms [9][10][11]. Thus, the problem objective can be framed as a system that can anticipate driver action and alert drivers with good accuracy of action anticipation.…”

Section: Introductionmentioning

confidence: 99%

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ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

et al. 2021

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Assistive driving is a complex engineering problem and is influenced by several factors such as the sporadic nature of the quality of the environment, the response of the driver, and the standard of the roads on which the vehicle is being driven. The authors track the driver's anticipation based on his head movements using Spatio-Temporal Interest Point (STIP) extraction and enhance the anticipation of action accuracy well before using the RNN-LSTM framework. This research tries to tackle a fundamental problem of lane change assistance by developing a novel model called Advanced Driver's Movement Tracking (ADMT). ADMT uses customized convolution-based deep learning networks by using Recurrent Convolutional Neural Network (RCNN). STIP with eye gaze extraction and RCNN performed in ADMT on brain4cars dataset for driver movement tracking. Its performance is compared with the traditional machine learning and deep learning models, namely Support Vector Machines (SVM), Hidden Markov Model (HMM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and provided an increment of almost 12% in the prediction accuracy and 44% in the anticipation time. ADMT systems outperformed all of the models in terms of both the accuracy of the system and the previously mentioned time of anticipation that is discussed at length in the paper. Thus it assists the driver with additional anticipation time to access the typical reaction time for better preparedness to respond to undesired future behavior. The driver is then assured of a safe and assisted driving experience with the proposed system. INDEX TERMS RCNN; advanced driver movement tracking system, spatio-temporal interest points, eye gaze tracking, deep neural networks.

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Section: A Manoeuvre Anticipation Using Admtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

et al. 2021

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“…Each one of those models was custom-made for specific undertakings and objectives. Different mechanical models, which have been made and executed by various consortia, seem, by all accounts, not to be adequate for developing fast analyses on IoT sensors [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…A few distinct sorts of questions on such system are additionally investigated, which are not the same as inquiries on conventional social database [5]. We utilize an occurrence of weather-based dataset to demonstrate the adequacy and productivity of association of linked data based approaches taking into account the IoT-based system [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

Malik

Ahmad

Farhan

et al. 2016

International Journal of Distributed Sensor Networks

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The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.

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“…According to published works, implementation of a multisensor-based data merging approach (i) improves the probability of proper detection, (ii) extends the spatial and temporal coverage, (iii) reduces ambiguity, (iv) enhance systems reliability, and (v) increases system robustness [5]. However, computational is a complicated task, especially when semantic merging is targeted.…”

mentioning

confidence: 99%

Recommendation function for smart data analytics toolbox to support semantic merging of middle-of-life data streams

Eddahab

Horváth

2020

2020 International Conference on Control, Automation and Diagnosis (ICCAD)

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Continuous enhancements of connected products make them able to generate and communicate a huge amounts of middle-of-life data streams to their producers. This affordance also creates a challenge for current data analytics tools unable to keep up with the heterogeneous nature and characteristics of these type of data. Accordingly, a function able to combine data from multiple data streams and analyze them as one source of information is definitely needed in a next-generation data analytics toolbox to support product enhancements by designers. As a result of a recent Ph.D. project, this paper presents the conceptualization and the implementation of a novel function of merging middle-oflife data streams. The implemented computational mechanism (i) acquires middle-of-life data streams, (ii) pre-processes them individually, (iii) merges information from the concerned streams, (iv) derives recommendation based on the merged information, and (v) send a recommendation as a message to the designer. The performance of the computational implementation was tested in an application case of data steaming and management to white goods designers for enhancing a connected washing machine. From a computational point of view, the testing proved that the set of proprietary algorithms designed for the realization of computational merging, together with the existing ones taken from the literature, were able to efficiently perform the subtasks. The advantages of merges were: (i) it provides more information than the one obtained by processing sensors' data individually, (ii) it reflects the condition of the product with a higher fidelity, (iii) it communicates information about the product while it is in use by the customer, (iv) it reduces the sensors analyses time and effort, and (v) it provides recommendation as an action plan concerning the product at hand. The outcomes of this study will be used in a follow up research to develop a comprehensive smart data analytics toolbox to support product designers in product innovation.

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On Context Awareness for Multisensor Data Fusion in IoT

Cited by 28 publications

References 21 publications

ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

ADMT: Advanced Driver’s Movement Tracking System Using Spatio-Temporal Interest Points and Maneuver Anticipation Using Deep Neural Networks

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

Recommendation function for smart data analytics toolbox to support semantic merging of middle-of-life data streams

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