Aggregation Transmission Strategy for Remote Sensing Data Based On Spatio-Temporal Correlation

In some application scenarios, strong spatio-temporal correlations exist between remote sensing data streams. For instance, during earthquake relief efforts, users in nearby locations may simultaneously request remote sensing data from an area of interest within the same time period. This leads to large data volumes transmitted in a limited spatio-temporal range, causing network traffic imbalance and reduced transmission efficiency. To address this issue, this article proposes the Aggregated Transmission Strategy for Remote Sensing Data based on Spatio-Temporal Association (AFRST). According to the spatio-temporal attributes of remote sensing data and the location of users, AFRST utilizes the mapping relationship between naming and demand in named data networking (NDN) and generates a demand association set when the demand between users in the same area reaches the association threshold, and the data in the overlapping area in the set is transmitted only once. We also uniformly assign transmission paths to the association set to improve the data transmission efficiency. Furthermore, AFRST takes into account the network state and user demand, constructing a transmission-load balancing control model (TLBCM) based on the network utility maximization framework. This model maximizes the data transmission rate and balances the network load under constraints, such as link capacity and other factors in each time slot, optimizing network service performance. The performance of AFRST is evaluated using ndnSIM, and the experimental results demonstrate the effectiveness of AFRST in terms of transmission latency, throughput, and number of completions. Compared to DCT and DPCCP, the average completion time per demand is increased by 29.9% and 18.1%, the overall transmission rate is increased by 43.5% and 22.8%, the overall average increase in the number of completions is about 42.4% and 22.9%, and the throughput is about 16.1% and 15.7% higher.