Table 1.

Summary of recent trends on computation offloading.

	Device Type	Problem/Approach	Deep Learning Applications	Validation	Main Result
(Kang et al., 2017) [20]	mobile	Analyze the calculation and data features of 8 Deep Neural Networks (DNN) architectures, Computer vision, speech, and processing applications for natural languages and demonstrate the balance between partitioning computation at many points within the network.	Yes	Experimental	Improves end-to-end latency, reduces mobile energy consumption and improves datacenter throughput.
(Wamser et al., 2017) [21]	drones	Demonstrate the effect of network condition on video streaming from drones over the cloud	No	Experimental	Improve the quality-of-service of the streaming over the cloud
(Van Le et al., 2018) [17]	adhoc mobile	reinforcement learning for offloading of ad-hoc mobile applications to the cloud using cellular networks	No	Simulation	Obtain optimal offloading
(Tan et al., 2018) [22]	mobile	Joint optimal connectivity, storage and computing resource management system for vehicular network using deep reinforcement learning approach	Yes	Simulation	Significant performance by optimum selection of parameters.
(Wang et al., 2019) [23]	mobile	Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge system	Yes	Simulation	Achieves near-optimal performance
(Chaari et al., 2019) [24]	robots	Kafka broker for offloading computer vision applications from robots to cloud	No	Experimental	Communication delays may increase execution times
(Xu et al., 2019) [25]	Mobile	offloading deep learning mobile applications of 5G networks	Yes	Simulation	Reduces delay for deep learning tasks
(Qi et al., 2019) [26]	vehicles	Deep reinforcement learning to obtain optimal offloading decisions	No	Simulation	online learning of computation offloading from vehicular services
(Alelaiwi et al., 2019) [27]	mobile	Deep-learning-based response-time prediction computation offloading method	Yes	Simulation	Reaches a Mean Absolute Percentage Error (MAPE) below 0.1 and an R-square greater than 0.6
(Alam et al., 2019) [28]	mobile	Deep Q-learning based code offloading method of computation in mobile edge/fog.	Yes	Simulation	The proposed offloading performs better for time and latency execution and energy consumption.
(Ning et al., 2019) [29]	mobile	Nonorthogonal Multiple Access (NOMA) system for mobile edge computing (MEC) vehicular network.	Yes	Simulation	Under the various network circumstances the scheme can increase transfer rate gain and offload efficiency.
(Ning et al., 2020) [30]	mobile	Deep-reinforcement-learning-based framework for 5G-enabled vehicle networks	Yes	Simulation	Achieved an overall better offloading cost.
(Chen et al., 2020) [31]	drones	Intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network using a splitting Deep Neural Network (sDNN)	Yes	Simulation	Improves service latency performance by 33% and 60%, respectively.
(Wu et al., 2020) [32]	drones	Three-layer UAV-based Mobile Edge Computing (MEC) network architecture and the functions of task offloading and data communication are analyzed in IoT device layer, UAV based edge computing layer and MEC server layer	Yes	Simulation	The energy consumption of UAV is reduced, and the proposed algorithm is used to dynamically schedule the task offloading strategy.
(Wang et al., 2020) [33]	drones	Framework of task scheduling is presented in the unmanned aerial vehicle-aided mobile edge computing (UMEC)	Yes	Simulation	The implementation of the agent in computing tasks would reduce delays and energy consumption significantly.
(Alioua et al., 2020) [34]	drones	A new device architecture for offloading and exchanging computations. Then, a new device utility function is developed which combined calculation time, overhead energy, link quality, communications and computing costs	Yes	Experimental	More efficient time and energy average for data processing which ranges from 43 % to 97 % according to the calculation approach.
DeepBrain	drones	Design and develop a full-stack cloud-based architecture for computation offloading of deep learning applications in Internet-of-Drones	Yes	Experimental Performance Evaluation	Demonstrate the feasibility and performance of computation offloading of deep learning applications from drones connected through the Internet