Survey of context information fusion for ubiquitous
Internet-of-Things (IoT) systems

Borges, Vijay

doi:10.1515/comp-2016-0003

Cited by 15 publications

(12 citation statements)

References 61 publications

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“…After pattern extraction is complete, these patterns are further fused. At the Decision stage, decisions are made and threats are tracked, where the best plan is chosen and executed at the Act stage [14]. …”

Section: Omnibus Modelmentioning

confidence: 99%

“…6 gives an overview of the ObjectOriented model. These roles as follows: the Actor has the role of interacting with the world, the Perceiver has the role of evaluating and analyzing the collected data, the Director has the role of creating plans based on the system set goals, and finally the Manger has the role of performing the set plans by the director [14].…”

Section: E Object-oriented Modelmentioning

confidence: 99%

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Data fusion models in WSNs: Comparison and analysis

Almasri

Elleithy

2014

Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education

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Abstract-In WSNs, hundreds of sensors collect data from the environment but these sensors have limited energy. Therefore, energy consumption is a very challenging issue in the design of WSNs. Sometimes, sensors fail as they got affected by the pressure or temperature. Such failure can lead to misleading measurements which in turn are waste of energy. As a result, data fusion is needed to overcome such confusion where it assures data's efficiency and eliminates data's redundancy. This paper provides an analysis of the state-of-the-art data fusion models along with their architectures. It also presents a comparison between these models to highlight the main objectives of each. In addition, it analyzes the advantages and the limitation of these models.

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Section: Omnibus Modelmentioning

confidence: 99%

Section: E Object-oriented Modelmentioning

confidence: 99%

Data fusion models in WSNs: Comparison and analysis

Almasri

Elleithy

2014

Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education

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“…2 The events belong to various stages such as data collection, processing, modeling, storage, reasoning, and validation. These resources are defined with context, ie, a set of inter-related events that share logical and timing relation among each other to provide relevant information such as location and type to characterize them.…”

Section: Introductionmentioning

confidence: 99%

“…2 The events belong to various stages such as data collection, processing, modeling, storage, reasoning, and validation. These requirements face challenges like interoperability, heterogeneity, etc, with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics.…”

mentioning

confidence: 99%

Context‐aware search optimization framework on the internet of things

Bharti

Kumar

Saxena

2018

Concurrency and Computation

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The resource discovery on IoT paradigm requires to be efficient with respect to modeling, storage, processing, and validation of the gathered data. These requirements face challenges like interoperability, heterogeneity, etc, with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics. It leads resource discovery to emerge as a non-linear constrained-specific problem that need to be linearized for its optimization with reduced complexity. Keeping the perspective, a context-aware search optimization framework on the internet of things is introduced, which targets knowledge presentation through schema, discovery via a multi-modal search algorithm, and its optimization through an Iterative Gradient Descent algorithm. The multi-modal search algorithm through keywords, value or spatial-temporal indices performs resource discovery by finding the suited matches as a search set from a search-space. The search set is further evaluated via the iterative gradient descent algorithm for optimization through the usage of iterative and convergence properties of the gradient descent. The search efficiency is tested using various objective functions and resources on MATLAB and is compared with Newton and Quasi-Newton methods. The obtained results depict the efficiency of the algorithm graphically with reference to the searching time, such as validate the system performance. KEYWORDS electronic toll plaza, Internet of Things, iterative gradient descent, multi-modal search, resource discovery, search optimization INTRODUCTIONThe Internet of Things (IoT) paradigm is expected to have almost 212 billion resources, as reported by Gartner, 1 which require to be identifiable to communicate and to interact with each other. These resources are defined with context, ie, a set of inter-related events that share logical and timing relation among each other to provide relevant information such as location and type to characterize them. 2 The events belong to various stages such as data collection, processing, modeling, storage, reasoning, and validation. Each stage with respect to context has their own needs, models, and techniques, which are to be studied thoroughly.For the Context Collection, the IoT platform requires various type of sensors that are compatible with different applications. For example, a temperature tracking system is designed that uses a client-server architecture having a Raspberry Pi and system-on-a-chip (SoC) device. It senses the temperature and forwards it to the end user using an Android mobile application. 3 Another novel service, Social Internet of Things framework (SIoT)is proposed to enhance intelligence, for dynamic IoT service discovery in smart spaces, and is based on a cognitive reasoning approach. 4Moving to the Context Modeling, there exist various techniques such as key-value, mark-up scheme, graphical, logic-based, and ontology-based to model the gathered data. The key-value-based modeling technique creates text or binary file a...

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“…Environments having Ubiquitous Computing facilities are called as Smart Environments [1]. This work focuses on Activity Recognition (AR), that would use the underlying smart environment to infer/reason the activities of living entities (mostly humans) in an integrated manner.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy kNN Adaptation to Learning by Example in Activity Recognition Modeling

Borges

Jeberson

2015

Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

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Activity Recognition is a complex task of the Human Computer Interaction (HCI) domain. k-Nearest Neighbors (kNN) a non-parametric classifier, mimics human decision making, using experiences for segregating a new object. Fuzzy Logic mimics human intelligence to make decisions; but suffers from requiring domain expertise to propose novel rules. In this paper a novel technique is proposed that comes with efficient fuzzy rules from the training data. The kNN classifier is modified by incorporating fuzzification of the feature space by learning from the data and not relying solely on domain experts to draw fuzzy rules. Additional novelty is the efficient use of the Fuzzy Similarity Relations and Fuzzy Implicators for hybridization of the kNN Classifier. The proposed hybridized fuzzy kNN classifier is shown to perform 5.6 % better than the classical kNN counterpart.

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Survey of context information fusion for ubiquitous Internet-of-Things (IoT) systems

Cited by 15 publications

References 61 publications

Data fusion models in WSNs: Comparison and analysis

Data fusion models in WSNs: Comparison and analysis

Context‐aware search optimization framework on the internet of things

Fuzzy kNN Adaptation to Learning by Example in Activity Recognition Modeling

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