Reasoning with Imprecise Context Using Improved Dempster-Shafer Theory

Lyu, Chang Hoon; Choi, Min‐Kyung; Li, Zhong Yuan; Youn, Hee Yong

doi:10.1109/wi-iat.2010.190

“…In [171], [177], it has been used to understand whether there is a meeting in the room. Other example applications are presented in [178], [179]. hidden Markov Models [180] are also a probabilistic technique that allows state to be represented using observable evidence without directly reading the state.…”

Section: Context Reasoning Decision Modelsmentioning

confidence: 99%

Context Aware Computing for The Internet of Things: A Survey

Perera

¹

,

Zaslavsky

²

,

Christen

³

et al. 2014

IEEE Commun. Surv. Tutorials

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As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

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“…One of the difficulties in the implementation of Dempster Shafer Theory is the normalization process and may lead to an opposite result than are expected [11]. In In a study conducted by [10] and [14], proposed a concept called ullage and evidence tendency factor as an extension of Xu Ling-yu's rule.…”

Section: Combining Evidencementioning

confidence: 99%

“…Assume evidence resources are 100% reliable, the evidence tendency factor η can be calculated by Eqs. (5): (5) In the study conducted by [10] and [14], Combination Rule used is an extension of Xu Ling-yu's rule, is defined by Eqs. …”

Section: Evidence Tendency Factormentioning

confidence: 99%

Mobile-based CBR System using Dempster-Shafer Modification Rule for Tourist Spots Recommendations in Buleleng Regency

MadeAgusWirawan¹,

Hartati²

2013

IJCA

0

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Buleleng Regency has many unique cultures and beauty of natural sceneries which are always the charm and attraction for tourists. It requires the effort to increase the number of visits of the tourists by providing the information of tour object in accordance with the tourists' interest and also easily for accessible. This study aimed at designing a mobile-based CBR system using Dempster-Shafer modification rule to provide the recommendation of tourist spots in Buleleng Regency. The process of recommendation based on the data tourist trips in the past as represented into the case based. The data contained in the case-based consisted of a traveler profile and tourist spots that are visited. Traveler profile included gender, country, age, occupation, income per month, and the frequency of visits. The results of this study indicated that the mobile-based CBR system using the Dempster-Shafer modification rule can be applied in providing recommendations of tourist spots for tourists who visited Buleleng Regency. Based on tests performed using K-Fold Cross Validation, were showed that the accuracy average of recommendations of tourist spots was 1) 18% for fully accordance, 2) 62% for partial accordance and 3) 18% of error rate.

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“…In [171], [177], it has been used to understand whether there is a meeting in the room. Other example applications are presented in [178], [179]. hidden Markov Models [180] are also a probabilistic technique that allows state to be represented using observable evidence without directly reading the state.…”

Section: Context Reasoning Decision Modelsmentioning

confidence: 99%

Context Aware Computing for The Internet of Things: A Survey

Perera,

Zaslavsky,

Christen

et al. 2013

Preprint

0

View full text Add to dashboard Cite

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

show abstract

Reasoning with Imprecise Context Using Improved Dempster-Shafer Theory

Cited by 6 publications

References 10 publications

Context Aware Computing for The Internet of Things: A Survey

Context Aware Computing for The Internet of Things: A Survey

Mobile-based CBR System using Dempster-Shafer Modification Rule for Tourist Spots Recommendations in Buleleng Regency

Context Aware Computing for The Internet of Things: A Survey

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