Abstract
Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.
Highlights
Remote sensing has the advantage of being able to monitor a wide range of areas over long distances
We propose a novel computation offloading considering the real-time constraints and delays in the server. e delay can be caused by requests from multiple drones to the server. e proposed technique calculates the time to perform deep learning-based analysis algorithms to the specific layer, the time to transmit intermediate result data to the server, and the time to wait on the server. en, it calculates the energy consumed by performing the analysis algorithm on drones and the energy consumption required to transmit the intermediate result data to the server
We find the optimal layer to minimize energy consumption while meeting the deadline
Summary
Remote sensing has the advantage of being able to monitor a wide range of areas over long distances. E existing computational offloading finds an optimal layer that minimizes the sum of the energy for performing deep learning-based analysis algorithms up to a specific layer and the energy for transferring intermediate result data to the server. This does not consider the real-time constraints at all, which may result in exceeding the deadline during computation up to the optimal layer or during computation on the server. E proposed technique calculates the time to perform deep learning-based analysis algorithms to the specific layer, the time to transmit intermediate result data to the server, and the time to wait on the server.
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