Emerging applications in smart cities and communities require massive IoT deployments using sensors/actuators (things) that can enhance citizens' quality of life and public safety. However, budget constraints often lead to limited instrumentation and/or the use of low-cost sensors that are subject to drift and bias. This raises concerns of robustness and accuracy of the decisions made on uncertain data. To enable effective decision-making while fully exploiting the potential of low-cost sensors, we propose to send mobile units (e.g., trained personnel) equipped with high-quality (more expensive) and freshly-calibrated reference sensors so as to carry out calibration in the field. We design and implement an efficient cooperative approach to solve the calibration planning problem, which aims at minimizing the cost of the recurring calibration of multiple sensor types in the long-term operation. We propose a two-phase solution that consists of a sensor selection phase that minimizes the average cost of recurring calibration, and a path planning phase that minimizes the travel cost of multiple calibrators which have load constraints. We provide fast and effective heuristics for both phases. We further build a prototype that facilitates the mapping of the deployment field and provides navigation guidance to mobile calibrators. Extensive use-case-driven simulations show our proposed approach significantly reduces the average cost compared to naive approaches: up to 30% in a moderate-sized indoor case, and higher in outdoor cases depending on the scale.