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Recently Smart Mobile Access Point (SMAP) based architectures have emerged as a promising solution for creating smart solutions supporting monitoring of special phenomena. SMAP allow us to predict communication activities in a system using the information collected from the network, and select the best approach to support the network at any given time. To improve the network performance, SMAPs can autonomously change their positions. They communicate with each other and carry out distributed computing tasks, constituting a mobile fog-computing platform. However, the communication cost becomes a critical factor. In this paper, we propose a compound method to select the best near-optimal placement of SMAPs with the goal to maximize the monitoring coverage and to minimize the communication cost. Our approach combines a parallel implementation of the Imperialist Competitive Algorithm (ICA) with Kruskal's Algorithm.