Adaptive data dissemination schemes for location-aware mobile services

  • Authors:
  • KwangJin Park;MoonBae Song;Chong-Sun Hwang

  • Affiliations:
  • Department of Computer Science and Engineering, Korea University 5-1, Anam-dong, Seongbuk-Ku, Seoul 136-701, Republic of Korea;Department of Computer Science and Engineering, Korea University 5-1, Anam-dong, Seongbuk-Ku, Seoul 136-701, Republic of Korea;Department of Computer Science and Engineering, Korea University 5-1, Anam-dong, Seongbuk-Ku, Seoul 136-701, Republic of Korea

  • Venue:
  • Journal of Systems and Software - Special issue: Quality software
  • Year:
  • 2006

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Abstract

Broadcasting is the natural method of propagating information in wireless links, which guarantees scalability in the case of bulk data transfers. It is particularly attractive for resource limited mobile clients in asymmetric communications. To facilitate power saving via wireless data broadcast, index information is typically broadcast along with the data. By first accessing the broadcast index, the mobile client is able to predict the arrival time of the desired data. However, it suffers from the drawback that the client has to wait and tune for an index segment, in order to conserve battery power consumption. In location-aware mobile services (LAMSs), it is important to reduce the query response time, since a late query response may contain out-of-date information. This paper proposes a new broadcast-based spatial query processing method, called BBS designed to support NN query processing. In the BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependent data along with an index segment. In this method, since the data objects broadcasted by the server are sequentially ordered based on their location, it is not necessary for the client to wait for an index segment, if it has already identified the desired data items before the associated index segment has arrived. The performance of this scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients and the size of the service area.