Hybrid Scheduling for Aggregated Data Delivery in Wireless Networks

Abstract

Wireless networks have long ago surpassed their infancy, becoming eligible for sophisticated applications beyond common Web browsing. As the volume of data managed by such applications relentlessly grows over time, the underlying network infrastructure has to keep up with the escalating demand. The multidimensional data modeling is extensively used in data warehouses, in order to facilitate decision making. Since ubiquitous access has become a critical success factor in most enterprises, wireless networks are expected to host data warehousing applications. In this paper, we propose a hybrid scheduling algorithm, explicitly suited for on demand delivery of aggregated data in wireless networks. By selecting between highly compressed data structures, the size of data transmitted into the network is minimized, however without any loss of semantic information. In this way, not only users posing multidimensional queries experience reduced access time and device energy consumption, but generated traffic is minimized too