Fog computing has been merged with Internet of Vehicle (IoV) systems to provide computing resource for end users, by which low-latency and high computational capacity can be guaranteed. In this work, we propose a feasible solution that enables offloading for real-time traffic management in fog-based IoV systems, aiming at minimizing the average response time for events reported by vehicles. Specially, we construct a distributed city-wide traffic management system, in which vehicles close to RSUs can be utilized as fog nodes. Then, we model parked and moving-vehicle based fog nodes according to queueing theory, and draw the conclusion that moving-vehicle based fog nodes can be modeled as an M/M/1 queue. An approximate approach is developed to solve the formulated optimization problem by decomposing it into two subproblems. Performance analyses based on a real taxi-trace data set are conducted to demonstrate the superiority of our method.

Vehicular edge computing (VEC) is a promising paradigm based on the Internet of vehicles to provide computing resources for end users and relieve heavy traffic burden for cellular networks. In this paper, we consider a VEC network with dynamic topologies, unstable connections and unpredictable movements. Vehicles inside can offload computation tasks to available neighboring VEC clusters formed by onboard resources, with the purpose of both minimizing system energy consumption and satisfying task latency constraints. For online task scheduling, existing researches either design heuristic algorithms or leverage machine learning, e.g., deep reinforcement learning (DRL). However, these algorithms are not efficient enough because of their low searching efficiency and slow convergence speeds for large-scale networks. Instead, we propose an imitation learning enabled online task scheduling algorithm with near-optimal performance from the initial stage. Specially, an expert can obtain the optimal scheduling policy by solving the formulated optimization problem with a few samples offline. For online learning, we train agent policies by following the expert’s demonstration with an acceptable performance gap in theory. Performance results show that our solution has a significant advantage with more than 50 percent improvement compared with the benchmark.

In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model’s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-score (99.92%).

The marine Internet of things (MIoT) is the application of the Internet of things technology in the marine field. Nowadays, with the arrival of the era of big data, the MIoT architecture has been transformed from cloud computing architecture to edge computing architecture. However, due to the lack of trust among edge computing participants, new solutions with higher security need to be proposed. In the current solutions, some use blockchain technology to solve data security problems while some use federated learning technology to solve privacy problems, but these methods neither combine with the special environment of the ocean nor consider the security of task publishers. In this article, we propose a secure sharing method of MIoT data under an edge computing framework based on federated learning and blockchain technology. Combining its special distributed architecture with the MIoT edge computing architecture, federated learning ensures the privacy of nodes. The blockchain serves as a decentralized way, which stores federated learning workers to achieve nontampering and security. We propose a concept of quality and reputation as the metrics of selection for federated learning workers. Meanwhile, we design a quality proof mechanism [proof of quality (PoQ)] and apply it to the blockchain, making the edge nodes recorded in the blockchain more high-quality. In addition, a marine environment model is built in this article, and the analysis based on this model makes the method proposed in this article more applicable to the marine environment. The numerical results obtained from the simulation experiments clearly show that the proposed scheme can significantly improve the learning accuracy under the premise of ensuring the safety and reliability of the marine environment.

The IoT devices, backed by resourceful fog computing, are capable of meeting the requirements of computationally-intensive tasks. However, many existing IoT applications are unable to perform well, due to different QoS requirements, while communicating with the fog server. Besides, constantly changing traffic demands of applications is another challenge. For example, the demand for real-time applications includes communicating over a path that is less prone to delay, and applications that offload computationally-intensive tasks to the fog server need a reliable path that has a lower probability of link failure. This results in a trade-off between conflicting objectives that are constantly evolving, i.e., minimizing end-to-end delay and maximizing the reliability of paths between IoT devices and the fog server. We propose a novel approach that takes advantage of machine learning (ML) and multi-objective optimization (MOO) based techniques. The reliability of links is evaluated using an ML-based algorithm in an SDN-enabled multi-hop scenario for the IoT-Fog environment. By considering the two conflicting objectives, the MOO algorithm is used to find the Pareto-optimal paths. Our experimental evaluation considers two applications with different QoS requirements -a real-time application (App-1) using UDP sockets and a task offloading application (App-2) using TCP sockets. Our results show that: (i) the trade-off between the two objectives can be optimized, and (ii) the SDN controller was able to make adaptive decision on-the-fly to choose the best path from the Pareto-optimal set. The App-1, communicating over the selected path finished its execution in 13% less time than communicating over the shortest path. The App-2 had 41% less packet loss using the selected path compared to using the shortest path.

The joint of WiFi‐based and vision‐based human activity recognition has attracted increasing attention in the human‐computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi‐based and Kinect‐based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi‐based activity recognition dataset named WiAR to provide a benchmark for WiFi‐based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton‐based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.