Several operations, e.g., military operations demonstrate the limitation of surveillance missions performed by the high attitude platforms such as satellite. These platforms are insufficient for identifying, with certainty, features on the ground, like vehicles or facilities even when these techniques equipped with state of the art sensors. Hence, to gain a clear covering on the ground, it is vital to use remote sensing devices (e.g., Wireless Sensor Network-WSN) placed around the features to be identified. Moreover, since these missions will be performed in critical regions, the placement of nodes of such WSN needs to be done without human personnel involved, instead they are dropped via aerial deployment from aircraft, and they must be connected to a High Energy Communication Node (HECN) that serves as a relay from the ground to a satellite or a high altitude aircraft. PSO is a pseudo- optimization method (heuristic) inspired by the collective intelligence of swarms of biological populations (like flocks birds). In this thesis, PSO is utilized for planning a WSN design. Four objective functions are used in the design problem. These are Sensor per Cluster head Error ( ), Sensor Out-of-range Error ( SORE ), Communication Energy ( ), and Operational Energy (OE ). Several variations are considered for the characteristic components of PSO. These include swarm size, velocity clamping, inertia weight, self-confidence and swarm-confidence factors, and neighborhood topology. SCE CE The implementation of PSO algorithm and simulation of results are done using Matlab 7.0. The obtained results show the importance of each component of the PSO. Over all results, it is found that fixing parameters at: =50, , , , size S w = 1.4 c1= 2 c2 = 2.5 4.0 max V = + , gbest PSO, 130, and max k = r = 12 give us the best PSO's performance.