Chapter 4 – Applications and Impacts 185 Data Compression for Tele-Monitoring of Buildings A.Salatian 1 , F.Adepoju 1 , B.Taylor 2 and L.Oborkhale 1 1 School of Information Technology and Communications, American University of Nigeria, Yola, Nigeria 2 Scott Sutherland School, Robert Gordon University, Aberdeen, UK e-mail: {apkar.salatian|rancis.adepoju@aun.edu.ng}, b.taylor@rgu.ac.uk, lawrence.oborkhale@aun.edu.ng Abstract In Africa there has been an increase in construction of new buildings. In many cases, these buildings are constructed at a rate where the local authorities have no capacity to enforce any building code, ethics or standards. To compound this problem, there is a predominant lack of qualified staff on the ground to conduct proper physical inspections in the building sites. One solution to this problem is to utilise tele-monitoring of buildings whereby building data is transmitted over a network for remote interpretation by an expert in a different location. A common form of telecommunication is broadband which is not always straightforward to use in Africa. In this paper we propose wavelet analysis as a data compression technique to transform the building monitor data into trends to address the challenges of broadband in Africa for data transmission and allow qualitative reasoning at the receiving site for building decision support. Keywords Wavelets, data compression, building, tele-monitoring 1. Introduction In Africa, there is a lack of building expertise and a lack of standards in the construction industry (Wells, 1986; Abate, 1997). One solution to this problem is to use tele-monitoring of buildings whereby building data is transmitted over a network for remote interpretation by an expert in another location. One of the mediums for tele-monitoring of buildings is broadband, which, in turn, presents further challenges in Africa which need to be addressed. Due to the capital costs, a common problem associated with broadband in Africa is the lack of telecommunication infrastructure. Consequently, bandwidth demand can easily outstrip the revenue realizable that is needed to pay for the network infrastructure investment (Freeman, 2005). As a result, many rural areas in Africa generally have lower bandwidth than urban areas because it is cheaper – this makes data transfer slow. Moreover, there will be service contention on the restricted bandwidth even if core bandwidth exists to deliver the services because aggregate bandwidth will be generally greater than can be delivered over the access connection (Stallings, 2007). One approach to deal with these challenges is to use data compression. Data compression can be defined as the act of encoding large files in order to shrink them down in size and in doing so the intelligence present in the information is preserved (Ahmad, 2002).