In today’s dynamic technological landscape, wireless communication networks have become an important part of economic development. The emergence of wireless technologies raises hopes to extend communication to remote areas that have not seen any tangible deployment to date. As developing nations pin their hopes to wireless technologies, cost models for wireless communication networks are becoming vital to support the emerging technologies. However, varying cost changes raise critical challenges to the estimation of both capital expenditure and operational expenditure. The network deployment process has numerous events that may cause adjustments to initially estimated project costs. These adjustments are necessary for a cost management plan and this plan includes monitoring cost performance and ensuring that only appropriate changes are made to the network project. The incidents that may cause cost changes can not be entirely predicted as their distribution tend to change dynamically from time to time. Estimating network deployment costs in such a dynamic environment necessitates cost models that can adapt to random occurrence of cost changes. Widely used cost models are usually performed by experienced personnel whose engineering experience is derived from deploying similar networks. In this approach experienced personnel add a certain percentage to the cost estimate to cater for contingency costs. Certainly such an approach depends on individual opinion, making it subjective and void of mathematical estimating relationships which are of paramount importance in ensuring that estimated deployment costs are sufficient to deal with cost uncertainties. We observe that existing approaches can only explore a limited solution space and hence can lead to cost overruns if implemented in dynamically cost changing environments. This thesis presents a wireless communication network deployment cost model that incorporates uncertainties into the final cost estimate. The model is adaptive to unpredictable cost changes since it allows adjustments of confidence levels when calculating contingency costs. This allows dynamically updating the cost changes without the cost model being reconstructed from scratch. We make use of the Poisson process in modeling the occurrence of incidents that are responsible for causing cost changes during network deployment. We also show that the occurrence of the incidents causing cost change are random and tend to follow the Poisson distribution. Using different levels of confidence we model various cost contingencies and make sensitivity analyses to identify the probability of cost overrun when given different contingencies. The dynamic adaptive cost model can be used either at the strategic level to understand the cost of a particular technique or at the operational level, as a way to show how Poisson process in network deployment can compare with engineering experience and other estimating techniques. We believe that the model is useful for remote areas where deployment costs are volatile and the distribution of incidents causing cost change to original cost estimates are diverse and dynamically changing. Further we expect that our research improves the knowledge base of information about the costs for rural communities to connect to the Internet, consequently providing useful input to future policy debates. This work is further poised to be a utility function to help those planning internet infrastructure deployments in least developed regions.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ufh/vital:11389 |
| Date | January 2010 |
| Creators | Sibanda, Khulumani |
| Publisher | University of Fort Hare, Faculty of Science & Agriculture |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Doctoral, PhD (Computer Science) |
| Format | 171 leaves, pdf |
| Rights | University of Fort Hare |
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