Scalable and adaptive context delivery mechanism for context‐aware computing

Scalable and adaptive context delivery mechanism for context‐aware computing Purpose – Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems. Design/methodology/approach – The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme. Findings – The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance. Originality/value – The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Pervasive Computing and Communications Emerald Publishing

Scalable and adaptive context delivery mechanism for context‐aware computing

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Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1742-7371
DOI
10.1108/17427370810890265
Publisher site
See Article on Publisher Site

Abstract

Purpose – Presence of innumerable sensors, complex deduction of contexts from sensor data, and reusability of contextual information impose the requirement of middleware for context aware computing. Smart applications, hosted in myriad devices (e.g. PDA, mobile, PCs), acquire different contexts from the middleware and act intelligently based on the available contexts in a context‐aware computing environment. As the system grows larger, scalable delivery of contexts from the middleware to numerous context‐aware applications will be inevitable. However, pure unicast based or pure broadcast‐based dissemination cannot provide high scalability as well as low‐average latency. The purpose of this paper is to present a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems. Design/methodology/approach – The proposed scheme is based on hybrid data dissemination technique where the most frequently requested data (e.g. HOT contexts) are delivered through multicast and the rest (e.g. COLD contexts) are delivered through unicast to reduce network traffic. The paper dynamically prioritizes and classifies the HOT and COLD context data depending on the number of requests and longest waiting time. Moreover, the division of bandwidth between the delivery of HOT and COLD contexts reduces average latency. Polling traffic is decreased by incorporating leasing mechanism. Extensive simulation is conducted to evaluate the proposed scheme. Findings – The mechanism dynamically prioritizes and classifies the hot and cold context data depending on the request rate and longest waiting time. The solution addresses the push popularity problem that occurs in the passive as the passive clients access data without sending explicit requests. The leasing mechanism is incorporated to reduce the periodical requests (polling) for better performance. Originality/value – The paper is of value in presenting a scalable context delivery mechanism for the middlewares to facilitate the development of larger context‐aware computing systems and also in presenting implementation details of a prototype that is developed using Jini framework and Java reliable multicast service (JRMS) library.

Journal

International Journal of Pervasive Computing and CommunicationsEmerald Publishing

Published: Jun 27, 2008

Keywords: Computer software; Data communication equipment; Bandwidths; Computer applications; Adaptability

References

  • Scalable dissemination: what's hot and what's not
    Beaver, J.; Morsillo, N.; Pruhs, K.; Chrysanthis, P.K.
  • Polychannel systems for mass digital communications
    Gifford, D.
  • The context toolkit: aiding the development of context‐enabled applications
    Salber, D.; Dey, A.K.; Abowd, G.D.
  • High performance data broadcasting systems
    Triantafillou, P.; Harpantidou, R.; Paterakis, M.

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