A Collaborative Planning Method of Space-Ground Sensor Network Coverage Optimization for Multiparameter Observation Tasks

Optimizing Coverage and Deployment of Wireless Sensor Networks Based on Image Processing

“…A Multi-parameter Observation Task requires monitoring of several essential parameters simultaneously; however, limited observation sensor resources can lead to spatial misalignment of the coverage area of each parameter, thus decreasing the observation efficiency, especially in a space-to-ground sensor array. To address this issue, Ke Wang et al [7]proposed a collaborative planning method for the planning phase of the sensors, known as the Multi-parameter Space-Ground Maximum Coverage Model (SGMC-MP). This method seeks to maximize the overlap coverage range among task parameters, thereby reducing spatial misalignment and improving sensor utilization.…”

Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response

“…Zhang et al (2019) proposed a model for evaluating the effectiveness of observations and data downlinks for low-orbiting satellites. Hu et al (2019) constructed the observation capability information association model (OCIAM) for the selection of sensors and their combinations and further proposed the sensor observation capability object field (SOCO-Field) to construct sensor associations for a specific emergent geographical environment observation task (GeoTask) (Hu et al, 2020), and Wang et al (2020) introduced the space-ground maximal coverage model with multiple parameters (SGMC-MP) to complete sensor mission planning. The current research data on remote sensor capabilities are relatively scarce and focus on evaluating the inherent capabilities of individual satellite remote sensors with a single object of evaluation, making it difficult to meet the needs of multisensor and multigeohazard emergency response tasks.…”