In today's world, we are witnessing the launch, maintenance, control and exploitation of various projects. Projects that have high costs and optimal use of this capital in the current economy of the country are highly regarded by managers. On the other hand, each project is expected to achieve its predetermined goals at a specified time. Today, the necessity of rapid construction of subway lines in Tehran's metropolitan area, which is involved in severe traffic jams, is not covered by anyone. Despite the understanding of this issue by the authorities and the good actions taken so far in this direction. However, more effort and a greater determination to reach the acceptable speed in the construction and completion of metro lines is necessary. Quick implementation of projects is based on the principles of concurrent engineering, called fast Tracking, in construction projects. In the fast delivery method, solutions such as resource enhancement, synchronous engineering, overlapping phases, value engineering, teamwork by the multi-functional team, improving communication and information transfer, and accelerating decision making, using efficient contractual methods (transferring risks as Effective way, which by using the correct methods, accelerates the implementation of the project. Therefore, in this article, management strategies for implementing fast Tracking projects in the development projects of the country have been investigated.

We study the problem of power efficient Tracking interval management for distributed target Tracking wireless sensor networks (WSNs). We first analyze the performance of a distributed target Tracking network with one moving object, using a quantitative mathematical analysis. We show that previously proposed algorithms are efficient only for constant average velocity objects; however, they do not ensure an optimal performance for moving objects with acceleration. Towards an optimal performance, first, we derive a closed-form mathematical expression for the estimation of the minimal achievable power consumption by an optimal adaptive Tracking interval management algorithm. This can be used as a benchmark for energy efficiency of these adaptive algorithms. Second, we describe our recently proposed energy efficient blind adaptive time interval management algorithm called Adaptive Hill Climbing (AHC) in more detail and explain how it tries to get closer to the derived optimal performance. Finally, we provide a comprehensive performance evaluation for the recent similar adaptive time interval management algorithms using computer simulations. The simulation results show that using the AHC algorithm, the network has a very good performance with the added advantage of getting 9 % closer to the calculated minimal achievable power consumption compared with that of the best previously proposed energy efficient adaptive time interval management algorithm.