By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.

Motivated by age of information (AoI), we introduce the age of leaked information (AoLI) to characterize the freshness of information eavesdropped by an unintended receiver (UR). To ensure that the intended receiver acquires the fresh information while preventing UR from overhearing, a multi-objective optimization problem is formulated to jointly minimize the time-averaged AoI and maximize the time-averaged AoLI. By modeling their evolution as Markov chains, the theoretical AoI and AoLI are analyzed to derive the closed-form optimal transmit and jamming power. Simulation results unveil the inherent tradeoff between the AoI and AoLI.

This paper focuses on the interplay between secrecy throughput and fairness for a wireless powered secure internet of things (IoT) network, where IoT devices harvest energy from an access point (AP), and securely upload their data to the AP by exploiting physical layer security (PLS) technology. To achieve it, a multi-objective stochastic optimization problem is formulated to simultaneously maximize long-term averaged secrecy sum-rate and minimum rate (min-rate) of devices, subject to devices’ energy sustainability and AP’s energy budget constraints, where energy beamforming, power allocation, and time scheduling policies are jointly optimized. In order to ensure network fairness, we construct a virtual fairness queue for each device to characterize the gap between its averaged secrecy rate and the expected secrecy min-rate, and incorporate its status into the transmission design. Due to the non-convexity of original problem and massive number of optimization variables, Lyapunov optimization framework is leveraged to decompose it into multiple online resource allocation problems, each of which is dependent on the system status of current slot. Accordingly, a fairness-energy-aware secure transmission (FEaST) scheme is proposed, where the closed-form energy beamforming, power allocation and time scheduling are derived according to the instantaneous status of fairness queue, energy consumption and wireless channels. Finally, simulation results validate that the gain of secrecy min-rate is over 84% and 123% under linear and nonlinear energy harvesting models respectively, and that of the weighted sum of secrecy sum-rate and min-rate is over 30% and 50% respectively, when compared with fixed scheduling scheme. Moreover, the inherent tradeoff between long-term secrecy sum-rate and min-rate is unveiled.

In this paper, we study a buffer-aided TDMA uplink network, where multiple status-update devices and throughput-demand devices are supposed to upload their data to one information access point (AP), and all devices are assumed to be provisioned with a data buffer to temporarily store the randomly generated data from either the installed sensor or upper-layer applications. To fulfill the communication requirements using two types of devices, the average Age of Information (AoI) is utilized to characterize the data freshness of the status-update devices, while the average sum rate is employed to capture the average transmission performance of the throughput-demand devices. On this basis, a joint-optimization problem was formulated to minimize the average AoI for status-update devices and to maximize the average sum rate for the throughput-demand devices. Lyapunov optimization framework was used to solve the problem of obtaining an AoI-aware adaptive TDMA uplink scheme. Numerical results are presented to show that an AoI-aware adaptive TDMA uplink scheme can effectively fulfill the heterogeneous service requirements using status-update devices and throughput-demand devices.