The area surveillance problem is the problem of surveying a known or an unknown area with the main purpose of detecting objects. This thesis will tackle the problem of how to employ a group of mobile-sensors for surveying an unobstructed area in an optimal manner. The mobile-sensors should make use of their onboard computers and they should iteratively compute their waypoints until they successfully surveyed the area. To solve the area surveillance problem in an optimal manner, the mobile-sensors should be able to coordinate their actions. The Distributed Constraint Optimization Problem (DCOP) framework will be employed to define the area surveillance problem. In the DCOP, the mobile-sensors can define their optimal position for the next discrete time step by communicating with the other mobile-sensors that participate in the problem.
For solving this DCOP, the Distributed Pseudo-tree Optimization Procedure (DPOP) will be utilized. The DPOP is a complete solver that can find the optimal solution of a DCOP in a decentralized manner. However, the main limitation of DPOP is that the size of the largest message that the mobile-sensors will have to exchange is space-exponential in the induced width of the pseudo-tree. Considering that the mobile-sensors have to use their onboard computers for solving the problem, exchanging huge size of messages is not desired. To overcome this limitation of DPOP, a new extension, known as the MST-DPOP will be presented in this Thesis. The MST-DPOP makes use of the Maximum Spanning Tree (MST) algorithm to reduce the size of the largest message that the mobile-sensors have to exchange. Employing MST along with DPOP can bound the size of the largest message and the required computations for constructing the utility messages. However, the new extension cannot guarantee that its solution will be the optimal. The experimental results shows that the MST-DPOP is able to define a solution with an average error less than $2\%$. Moreover, the MST-DPOP requires on average around $1$ discrete time step more than the DPOP to solve the area surveillance problem. Consequently, given that the MST-DPOP overcomes the high memory requirements of DPOP, it is preferred for solving the area surveillance problem.