2015
DOI: 10.1007/s11235-015-9984-x
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A context-aware search system for Internet of Things based on hierarchical context model

Abstract: In recent years, numerous sensing devices and wireless networks are immersed into our living environments, creating the Internet of Things (IoT) integrating the cyber and physical objects. Searching for objects in IoT is a challenging problem because the context relationships among IoT objects are various and complex. The traditional web search approaches cannot work well in the IoT search domain because they miss the critical characteristics of the context relationships. In addition, a user's dynamic and chan… Show more

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Cited by 50 publications
(22 citation statements)
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“…More complex relations may involve multiple objects distributed in different physical places. These relations can be used to search required objects for users or recommend objects of interest to users [4][5][6].…”
Section: Introductionmentioning
confidence: 99%
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“…More complex relations may involve multiple objects distributed in different physical places. These relations can be used to search required objects for users or recommend objects of interest to users [4][5][6].…”
Section: Introductionmentioning
confidence: 99%
“…Existing works that utilize relations in the IOT [4][5][6][7] assume that a central server exists and all the data are collected to it for processing. This may not be feasible since (1) in many scenarios there is no such central server; (2) if an object is predefined as the central server, it could suffer from one-point failure, computation bottleneck, and difficulties when moving away from other objects; (3) transmitting raw data to a central server for processing could incur much energy consumption.…”
Section: Introductionmentioning
confidence: 99%
“…Currently, most effects on self-adaptation are based on rules [37], swarm intelligence [18] or knowledge [83] or ontology [84]. With the improvement of big data analytics technologies, more and more machine learning based self-adapting solutions are researched, these approaches are smarter, and can adapt to various environments automatically.…”
Section: Self-adaptability and Validationmentioning
confidence: 99%
“…et al predicted the influence changes of facilities over dynamic vehicles with Bayes method and trajectory-based Markov chain model [85]. Chen Y.Y et al introduced and objects searching system for IoT with context-aware hidden Markov model and ontology method [84]. Martin M. et al compared the effect of deep learning and four multi-class classifiers which include multiclass neural network, multiclass decision jungle, multiclass logistic regression and multiclass decision forest on data processing in Industry 4.0 [86].…”
Section: Self-adaptability and Validationmentioning
confidence: 99%
“…RFID and WSN technology are used to realize the perception of Internet of things and achieve data acquisition [10]. WAMP tools are used to build the Internet of things server platform and complete the information through browser and remote client [11]. With the mobile Internet technology, this paper designs and implements the mobile Internet client platform and mobile client access which is connected with the Internet of things application platform [12].…”
Section: Literature Reviewmentioning
confidence: 99%