The search functionality is under construction.

IEICE TRANSACTIONS on Communications

User Location in Picocells -- A Paging Algorithm Derived from Measured Data

Stephan WANKE, Hiroshi SAITO, Yutaka ARAKAWA, Shinsuke SHIMOGAWA

  • Full Text Views

    0

  • Cite this

Summary :

We present a new paging algorithm for wireless networks with ultra-short-range radio access links (picocells). The ubiquitous office (u-office) network is a good example of such a network, and we present some u-office example applications. In addition, we show that conventional paging algorithms are not feasible in such networks. Therefore, we derived a new paging algorithm from the measurement results of an experimental sensor network with short-range wireless links deployed in our office. We equipped persons with sensors and deployed sensor readers at selected places in our office. The sensors transmit messages to the sensor readers at regular intervals. If no sensor reader is in range, the message is lost. Our main observation is that, if a picocell shows an attraction property to a certain person, the residence time of an attached mobile terminal is not gamma distributed (as described in the literature) and the probability of long-lasting residences increases. Thus, if the residence time is larger than a certain threshold, the probability of a long-lasting residence time increases if a sensor reader location has an attraction property to a person. Based on this observation, our proposed paging algorithm registers the location of the mobile terminal only when the residence time in the cell is longer than a predetermined constant. By appropriately setting this constant, we can significantly reduce the registration message frequency while ensuring that the probability of the network successfully connecting to a mobile terminal remains high.

Publication
IEICE TRANSACTIONS on Communications Vol.E93-B No.9 pp.2291-2298
Publication Date
2010/09/01
Publicized
Online ISSN
1745-1345
DOI
10.1587/transcom.E93.B.2291
Type of Manuscript
PAPER
Category
Network

Authors

Keyword