A semantic service discovery approach for ubiquitous computing

Wang, Reen-Cheng; Chang, Yao-Chung; Chang, Ruay-Shiung

doi:10.1007/s10845-008-0213-2

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…This work can be roughly divided into two categories: directory-aided and directory-less. Resource directories were used to facilitate resource discovery in directory-aided strategies [5][6][7][8][11][12][13]. Two modes were defined for resource discovery in [5], i.e., service searching and service browsing.…”

Section: Related Workmentioning

confidence: 99%

“…A personal operating middleware (POM) in the framework was responsible for providing personalized response information. A framework of semantic service discovery for ubiquitous computing was proposed in [13]. Although different issues in service discovery and reciprocal work with current ontology languages were discussed, no theoretical models or prototype systems were mentioned by the authors.…”

Section: Related Workmentioning

confidence: 99%

“…This paper introduces an energy-efficient method of adaptive resource discovery to solve this problem. Much research on resource discovery has been published [5][6][7][8][9][10][11][12][13][14][15]. However, most of the existing work has adopted a fixed strategy for resource discovery and failed to adapt this strategy based on different network resource statuses, e.g., the degree of resource scarcity and the pattern of resource requirements (to differentiate it from "Network Status", which mainly refers to network traffic and bandwidth, Network Resource Status (NRS) is used in this paper to represent the characteristics of resource distribution and usage in the network).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive resource discovery in mobile cloud computing

Liu

Nishio

Shinkuma

et al. 2014

Computer Communications

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Mobile Cloud Computing (MCC) is aimed at integrating mobile devices with cloud computing. It is one of the most important concepts that have emerged in the last few years. Mobile devices, in the traditional agent-client architecture of MCC, only utilize resources in the cloud to enhance their functionalities. However, modern mobile devices have many more resources than before. As a result, researchers have begun to consider the possibility of mobile devices themselves sharing resources. This is called the cooperation-based architecture of MCC. Resource discovery is one of the most important issues that need to be solved to achieve this goal. Most of the existing work on resource discovery has adopted a fixed choice of centralized or flooding strategies. Many improved versions of energy-efficient methods based on both strategies have been proposed by researchers due to the limited battery life of mobile devices. This paper proposes a novel adaptive method of resource discovery from a different point of view to distinguish it from existing work. The proposed method automatically transforms between centralized and flooding strategies to save energy according to different network environments. Theoretical models of both energy consumption and the quality of response information are presented in this paper. A heuristic algorithm was also designed to implement the new adaptive method of resource discovery. The results from simulations demonstrated the effectiveness of the strategy and the efficiency of the proposed heuristic method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive resource discovery in mobile cloud computing

Liu

Nishio

Shinkuma

et al. 2014

Computer Communications

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“…The existing Service Discovery technologies mainly resolved problems in the second phase, such as DEAPspace [4] , INS [5] , Salutation, Service Location Protocol (SLP), Jini, UPnP, Secure Service Discovery Service (SSDS), and Bluetooth [6,7,8,9,10] . Service Discovery protocols need to talk with each other continually to discover services in the second phase; The existing Service Discovery Technologies still run with the second phase's modes in the third phase, and have no response to the Stable States, this brings unnecessary COS: although the services are discovered already, but the devices still talk with each other continually.…”

Section: Problem In Existing Service Discovery Technologies and Cmentioning

confidence: 99%

A Stable and Unstable States Detection Method for Service Discovery

Shancheng

2009

2009 International Forum on Computer Science-Technology and Applications

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Service Discovery technologies were developed to fulfil the Pervasive Computing's "disappearance". In Pervasive Computing the courses of devices using Service Discovery Technologies have stable and unstable states, the existing Service Discovery Technologies have a shortcoming of having no response to the stable states, and this brings unnecessary cost of service. To overcome this demerit a method of detecting stable and unstable states is introduced and proved. According to the method we experimented with pure DEAPspace to detect the stable and unstable states, and the results prove the correctness of the theory. Based on the theory we modified and experimented with DEAPspace, the results indicate that the broadcast-count is reduced 30% at least during stable-state-period in the states detection based DEAPspace, this will reduce the unnecessary cost of service Significantly.

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User-Centric Identification Management for Cross Realm Cloud and Ubiquitous Computing

Wang

2012

Lecture Notes in Electrical Engineering

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A semantic service discovery approach for ubiquitous computing

Cited by 8 publications

References 15 publications

Adaptive resource discovery in mobile cloud computing

Adaptive resource discovery in mobile cloud computing

A Stable and Unstable States Detection Method for Service Discovery

User-Centric Identification Management for Cross Realm Cloud and Ubiquitous Computing

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