Monitoring and Smart Decision Architecture for DRONE-FOG Integrated Environment

Abstract

Due to the limited computing resources of drones, it is difficult to handle computation-intensive tasks locally, hence, fog-based computation offloading has been widely adopted. The effectiveness of an offloading operation, however, is determined by its ability to infer where the execution of code/data represents less computational effort for the drone, so that, by deciding where to offload correctly, the device benefits. Thus, this paper proposes MonDroneFog, a novel fog-based architecture that supports image offloading, as well as monitoring and storing the performance metrics related to the drone, wireless network, and cloudlet. It takes advantage of the main machine-learning algorithms to provide offloading decisions with high levels of accuracy, F1, and G-mean. We evaluate the main classification algorithms under our database and the results show that Multi-Layer Perceptron (MLP) and Logistic Regression classifiers achieve 99.64% and 99.20% accuracy, respectively. Under these conditions, MonDrone-Fog works well in dense forests when weather conditions are favorable and can be useful as a support system for SAR missions by providing a shorter runtime for image operations.

Is the edge really necessary for drone computing offloading? An experimental assessment in carrier‐grade 5G operator networks

“…As a result, substantial work on the MEC paradigm has tackled this challenge, for example by optimising offloads to minimise the response time of the services; [7][8][9][10] or by seeking trade-offs between quality of experience (QoE) and service deployment costs 11 and the edge computing infrastructure these services require. 12 Other lines of research are the design of architectures for dynamic deployment of services on an edge computing scenario; [13][14][15] and task distribution among local and remote heterogeneous servers. 16 There also exists abundant literature on the performance of computing services depending on the relative location of the servers with respect to the users (e.g., network edge vs. cloud).…”