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 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.

A method for detecting human motion in complex scenarios based on Channel State Information (CSI) is presented. First, the sensitivity of CSI phase information to human motion is explored, especially to the strenuous motion. Through a large number of experiments, the influence of human motion on CSI phase is found out, and the characteristics of signal changes are extracted. The One-class Support Vector Machine (OSVM) in machine learning is used to detect the multi-target strenuous human motion. Line-Of-Sight (LOS) and Non-Line-Of-Sight (NLOS) conditions are studied in the case of obstacles appearing in the wireless link when human motion occurred. LOS and NLOS are identified by the skewness of the channel impulse response (CIR) distribution. After identifying the LOS condition and NLOS condition in the current environment, the human motion is analyzed and detected, which further improves the accuracy of human motion detection from 70% to 91%.

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

Recently, using unmanned aerial vehicles (UAVs) to collect information from distributed sensors has become one of the hotspots in the Internet of Things (IoT) research. However, previous studies on the UAV-assisted data acquisition systems focused mainly on shortening the acquisition time, reducing the energy consumption, and increasing the amount of collected data, but it lacked the optimization of data freshness. Moreover, we hope that UAVs can perform long-term data collection tasks in dynamic scenarios within a constantly changing age of information (AoI) and within their own power levels. Therefore, we aim to maximize the quality of service (QoS) based on the freshness of data, while considering the endurance of the UAVs. Since our scenario is not an inertial order decision process with uniform time slots, we first transform the optimization problem into a semi-Markov decision process (SMDP) through modeling, and then we propose a hierarchical deep Q-network (DQN)-based path-planning algorithm to learn the optimal strategy. The simulation results show that the algorithm is better than the benchmark algorithm, and the tradeoff between the system QoS and the safe power state can be achieved by adjusting the parameter ${\beta }_e$.

Task scheduling on edge computing servers is a critical concern affecting user experience. Current scheduling methods attain an overall appealing performance through centralized control. Nevertheless, forcing users to act based on a centralized control is impractical. Hence, this work suggests a game theory-based distributed edge computing server task scheduling model. The proposed method comprehensively considers the mobile device-server link quality and the server’s computing resource allocation and balances link quality and computing resources requirements when selecting edge computing servers. Furthermore, we develop a time series prediction algorithm based on IndRNN and LSTM to accurately predict link quality. Once Nash equilibrium is reached quickly through our proposed acceleration scheme, the proposed model provides various QoS for different priority users. The experimental results highlight that the developed solution provides differentiated services while optimizing computing resource scheduling and ensuring an approximate Nash equilibrium in polynomial time.