Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm.

Digital Twin (DT) and Intelligent Reflective Surface (IRS), the most two promising technologies of 6G make the Internet of Vehicles (IoV) more adaptive. However, future autonomous driving needs powerful networking resources and high-quality wireless communications to guarantee the Quality of Service (QoS). Especially considering the time-varying physical operating environments of IoV, it is extremely urgent to improve resource utilization and wireless channel quality. In this work, we propose a Digital Twin-Driven Vehicular Task Offloading and IRS Configuration Framework (DTVIF) to efficiently monitor, learn, and manage the IoV. Specifically, we adopt Mobile Edge Computing (MEC) and IRS to provide augmented computing capacities for vehicles and improve transmission performance when vehicles communicate to MEC servers. DT is employed to achieve real-time data collection and digital representation of physical operating environments of IoV to better support decisions making. In order to reduce the overall delay and energy consumption of DTVIF, we propose a Two-Stage Optimization for Jointly Optimizing Task Offloading and IRS Configuration (TSJTI) algorithm based on Deep Reinforcement Learning (DRL) and Transfer Learning (TFL). In the first stage, we introduce Double Deep $Q$ -learning Networks (DDQN) to find the optimal offloading decision. In the second stage, based on the parameters learned from the first stage, we migrate the parameters from the first stage to find the optimal IRS configuration based on the Deep Deterministic Policy Gradient (DDPG) method. The simulations demonstrate that the proposed algorithm can effectively reduce the processing latency of task offloading and reduce the average energy consumption in DTVIF.

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. In view of the delay-sensitive property of these promising applications, as well as the high expense by using infrastructures and roadside units (RSU), the task offloading among vehicles has gained enormous popularity considering its free-of-charge and timely response. In this paper, by utilizing the gathering period of vehicles in urban environment due to stopped by traffic lights or Area of Interest (AOI), a task offloading scheme merely relying on vehicle-to-vehicle (V2V) communication is proposed by fully exploring the idle resources of gathered vehicles for task execution. Through formulating the task execution as a Min-Max problem among one task and several cooperative vehicles, the task executing time is optimized with the Max-Min Fairness scheme, which is further solved by the Particle Swarm Optimization (PSO) Algorithm. Extensive simulation demonstrate that our model could well meet the delay requirement of delay-sensitive application by cooperative computing among vehicles.

Heart disease seriously threatens human life due to high morbidity and mortality. Accurate prediction and diagnosis become more critical for early prevention, detection, and treatment. The Internet of Medical Things and artificial intelligence support healthcare services in heart disease monitoring, prediction, and diagnosis. However, most prediction models only predict whether people are sick, and rarely further determine the severity of the disease. In this article, we propose a machine learning based prediction model to achieve binary and multiple classification heart disease prediction simultaneously. We first design a Fuzzy-GBDT algorithm combining fuzzy logic and gradient boosting decision tree (GBDT) to reduce data complexity and increase the generalization of binary classification prediction. Then, we integrate Fuzzy-GBDT with bagging to avoid overfitting. The Bagging-Fuzzy-GBDT for multiclassification prediction further classify the severity of heart disease. Evaluation results demonstrate the Bagging-Fuzzy-GBDT has excellent accuracy and stability in both binary and multiple classification predictions.

The rapid expansion of wearable medical devices and health data of Internet of Medical Things (IoMT) poses new challenges to the high Quality of Service (QoS) of intelligent health care in the foreseeable 6G era. Healthcare applications and services require ultra reliable, ultra low delay and energy consumption data communication and computing. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies empowered IoMT to deal with huge data sensing, processing and transmission in high QoS. However, traditional frame aggregation schemes in WBAN generate too much control frames during data transmission, which leads to high delay and energy consumption and is not flexible enough. In this paper, a Deep Q-learning Network (DQN) based Frame Aggregation and Task Offloading Approach (DQN-FATOA) is proposed. Firstly, different service data were divided into queues with similar QoS requirements. Then, the length of the frame aggregation was selected dynamically by the aggregation node according to the delay, energy consumption, and throughput by DQN. Finally, the number of tasks offloaded was selected due to the current state. Compared with the traditional scheme, the simulation results show that the proposed DQN-FATOA has effectively reduced delay and energy consumption, and improved the throughput and overall utilization of WBAN.

The Internet of Medical Things (IoMT) and Artificial Intelligence (AI) have brought unprecedented opportunities to meet massive behavioral data access and personalization requirements for Internet of Behavior (IoB). They facilitate the communication and computing resource allocation to guarantee low delay and energy consumption demands in healthcare. This article presents an improved offloading algorithm for Mobile Edge Computing (MEC) based on Deep Q Network (DQN) and Simulated Annealing (SA) for IoB. Firstly, we analyze the network model and establish a task cost function based on processing delay and energy consumption. Secondly, we define a Distributed Optimization Problem (DOP) to maximize individual utilities and system utility, which is proved to be a potential countermeasure. Thirdly, we conduct Markov modeling for the current offloading strategy-making scheme and define the objectives and constraints of the optimization function. At the same time, the SA is introduced into the DQN Algorithm, which improves the capacity of the algorithm by focusing on the exploration in the early stage and following the experience value in the later stage. From the simulation results, we can see that compared with the traditional scheme, the proposed strategy can maximize the utilization of the system and reduce processing delay and energy consumption.