Distributed network intelligence: A prerequisite for adaptive and personalised service delivery

M. J. O’Grady (), G. M. P. O’Hare, J. Chen and D. Phelan
Additional contact information
M. J. O’Grady: University College Dublin
G. M. P. O’Hare: University College Dublin
J. Chen: University College Dublin
D. Phelan: University College Dublin

Information Systems Frontiers, 2009, vol. 11, issue 1, No 5, 73 pages

Abstract: Abstract Mobile computing is undoubtedly one of the predominant computer usage paradigms in operation today. The implications of what might be cautiously termed a usage paradigm shift have still not crystallised fully, either for society, or those envisaging a new raft of applications and services for mobile users. However, fundamental to the current and future success of mobile computing are mobile telecommunications networks. Such networks have been a success story in their own right in recent years, both as traditional voice carriers and, increasingly importantly, as a conduit of mobile data. The potential for new mobile data applications is immense, but, crucially, this potential is severely compromised by two factors inherent in mobile computing: limited bandwidth and computationally restricted devices. Hence, the academic and commercial interest in harnessing intelligent techniques as a means of mitigating these concerns, and ensuring the user experience is a satisfactory one. In this paper, the broad area of intelligence in telecommunications networks is examined, and issues relating to the deployment of intelligent technologies are explored. In particular, the potential of intelligent agents is identified as a viable mechanism for realising a full end-to-end deployment of intelligence throughout the network, including possibly the most crucial component: the end user’s device. As an illustration of the viability of this approach, a brief description of a mobile blogging application is presented.

Keywords: Mobile computing; Mobile telecommunications; Intelligent agents; Context-aware computing; Personalisation (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10796-008-9140-1

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