Camera Placement in Smart Cities for Maximizing Weighted Coverage With Budget Limit

This paper addresses the camera placement problem for smart cities in 3-D space and proposes a heuristic algorithm that maximizes the weighted coverage rate while satisfying the budget constraint. We first discuss about the quantization of surveillance target and camera locations into discrete grid points and the setting of the weights of target grid points. We then present the visibility analysis with field of view in consideration of occlusions and different camera specifications. Based on these characteristics and constraints, we formulate the camera placement problem and propose a new heuristic algorithm called collaboration-based local search algorithm, which incorporates the local search into collaborative allocation. We evaluate the performance of the proposed algorithm in comparison with the greedy algorithm, binary genetic algorithm, and binary particle swarm optimization through simulation experiments with small and large problem sets. The simulation results show that the proposed algorithm outperforms the three existing algorithms in terms of the average weighted coverage rate and computation time.