The last two decades have seen a clear trend toward crafting intelligent vehicles based on the significant advances in communication and computing paradigms, which provide a safer, stress-free, and more enjoyable driving experience. Moreover, emerging applications and services necessitate massive volumes of data, real-time data processing, and ultrareliable and low-latency communication (URLLC). However, the computing capability of current intelligent vehicles is minimal, making it challenging to meet the delay-sensitive and computation-intensive demand of such applications. In this situation, vehicular task/computation offloading toward the edge cloud (EC) and vehicular cloudlet (VC) seems to be a promising solution to improve the network’s performance and applications’ Quality of Service (QoS). At the same time, artificial intelligence (AI) has dramatically changed people’s lives. Especially for vehicular task offloading applications, AI achieves state-of-the-art performance in various vehicular environments. Motivated by the outstanding performance of integrating reinforcement learning (RL)/deep RL (DRL) to the vehicular task offloading systems, we present a survey on various RL/DRL techniques applied to vehicular task offloading. Precisely, we classify the vehicular task offloading works into two main categories: 1) RL/ DRL solutions leveraging EC and 2) RL/DRL solutions using VC computing. Moreover, the EC section-based RL/DRL solutions are further subcategorized into multiaccess edge computing (MEC) server, nearby vehicles, and hybrid MEC (HMEC). To the best of our knowledge, we are the first to cover RL/DRL-based vehicular task offloading. Also, we provide lessons learned and open research challenges in this field and discuss the possible trend for future research.

This paper surveys the literature of opportunistic offloading. Opportunistic offloading refers to offloading traffic originally transmitted through the cellular network to opportunistic network, or offloading computing tasks originally executed locally to nearby devices with idle computing resources through opportunistic network. This research direction is recently emerged, and the relevant research covers the period from 2009 to date, with an explosive trend over the last four years. We provide a comprehensive review of the research field from a multidimensional view based on application goal, realizing approach, offloading direction, etc. In addition, we pinpoint the major classifications of opportunistic offloading, so as to form a hierarchical or graded classification of the existing works. Specifically, we divide opportunistic offloading into two main categories based on application goal: traffic offloading or computation offloading. Each category is further divided into two smaller categories: with and without offloading node selection, which bridges between subscriber node and the cellular network, or plays the role of computing task executor for other nodes. We elaborate, compare, and analyze the literatures in each classification from the perspectives of required information, objective, etc. We present a complete introductory guide to the researches relevant to opportunistic offloading. After summarizing the development of the research direction and offloading strategies of the current state-of-the-art, we further point out the important future research problems and directions.

In recent years, single modality-based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognized that each of the established approaches has different strengths and weaknesses. As an important motor symptom, gait disturbance is usually used for diagnosis and evaluation of diseases; moreover, the use of multimodality analysis of the patient’s walking pattern compensates for the one-sidedness of single modality gait recognition methods that only learn gait changes in a single measurement dimension. The fusion of multiple measurement resources has demonstrated promising performance in the identification of gait patterns associated with individual diseases. In this article, as a useful tool, we propose a novel hybrid model to learn the gait differences between three neurodegenerative diseases, between patients with different severity levels of Parkinson’s disease, and between healthy individuals and patients, by fusing and aggregating data from multiple sensors. A spatial feature extractor (SFE) is applied to generating representative features of images or signals. In order to capture temporal information from the two modality data, a new correlative memory neural network (CorrMNN) architecture is designed for extracting temporal features. Afterward, we embed a multiswitch discriminator to associate the observations with individual state estimations. Compared with several state-of-the-art techniques, our proposed framework shows more accurate classification results.

The combination of nonorthogonal multiple access (NOMA) using power-domain with backscatter communication (BC) is expected to connect large-scale Internet of things (IoT) devices in the future sixth-generation era. This article introduces a BC in a multicell IoT network, where a source in each cell transmits a superimposed signal to its associated IoT devices using NOMA. The backscatter sensor tag (BST) also transmits data to IoT devices by reflecting and modulating the superimposed signal of the source. A new optimization framework is provided that simultaneously optimizes the total power of each source, power allocation coefficient of IoT devices, and RC of BST under imperfect successive interference cancellation decoding. This work aims to maximize the total energy efficiency (EE) of the IoT network subject to the quality of services of each IoT device. The problem is first transformed using the Dinkelbach method and then decoupled into two subproblems. The Karush–Kuhn–Tucker conditions and dual Lagrangian method are employed to obtain efficient solutions. In addition, we also calculate the EE of the conventional NOMA network without BC as a benchmark framework. Simulation results unveil the advantage of our considered NOMA BC network over the conventional NOMA network in terms of system total EE.

Mobile edge computing (MEC) has become a research trend that solves effectively computationally intensive and latency-sensitive tasks. MEC environments in the real world are dynamic and uncertain and then the changes of the environments bring challenges to the generalization and robustness of offloading algorithms. In order to solve the above problem, we propose a meta-reinforcement learning task offloading algorithm GASTO based on Graph Neural Network and seq2seq network. Meta-learning can learn the optimal initialization parameter through several gradient descent steps and samples to adapt to new environments more quickly. The task generated in the user equipment is composed of multiple subtasks rather than a single task, and there are dependencies between the subtasks. Therefore, the task on the user equipment is modeled as a Directed Acyclic Graph (DAG). The connection relationship between the subtasks in DAG plays an important role. Drawing on the idea of message passing, Graph Neural Network is applied in DAG to extract the intrinsic correlation between subtasks in GASTO. In addition, Seq2Seq network can reduce the dimension of action space effectively, and the scheduling decisions of all subtasks can be generated simultaneously. Besides, in order to enhance the sampling efficiency of tasks and the robustness of GASTO, the priority of sampling tasks is adjusted dynamically during the training process. The experimental results of four algorithms in different environments show that the proposed algorithm GASTO can quickly adapt to the new environment.