A Net Present Cost Minimization Framework for Wireless Sensor Networks
Abstract
Minimizing the cost of deploying and operating a wireless sensor network (WSN) involves deciding how to partition a budget between competing expenses such as node hardware, energy, and labour. To determine if funds are given to a specific project or invested elsewhere, companies often use interest rates to sum the project's cash flows in terms of present-day dollars. This provides an incentive to defer expenditures when possible and use the returns to reduce future costs. In this thesis, a framework is proposed for minimizing the net present cost (NPC) of a WSN by optimizing the number of, cost of, and time between expenditures. The proposed framework balances competing expenses and defers expenditures when possible. A similar strategy does not appear to be available in the literature, and has likely not been developed in industry as no commercial WSN operators currently exist.
In general, NPC minimization is a non-linear, non-convex optimization problem. However, if the time until the next expenditure is linearly proportional to the cost of the current expenditure, and the number of maintenance cycles is known in advance, the problem becomes convex and can be solved to global optimality. If non-deferrable recurring costs are low, then evenly spacing the expenditures can provide near-optimal results.
The NPC minimization framework is most effective when non-deferrable recurring costs, such as labour, are low. High labour costs limit the number of times that a WSN operator can use the returns from investing deferrable costs to decrease future expenditures. This thesis therefore proposes vehicle routing problems (VRPs) to reduce labour costs by delivering nodes with drones. Unlike similar VRPs, drone costs are reduced by reusing vehicles, and low-cost, feasible routes are ensured by modelling energy consumption as a function of drone battery and payload weight. The problems are modelled as mixed integer linear programs (MILPs). As these MILPs are NP-hard, simulated annealing algorithms are proposed for finding sub-optimal solutions to large instances of the problems.
Description
Keywords
Engineering--Electronics and Electrical
Citation
Dorling, K. (2016). A Net Present Cost Minimization Framework for Wireless Sensor Networks (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/24855