The emerging Internet of Things (IoT) applications, such as smart manufacturing and smart home, lead to a huge demand on the provisioning of low-cost and high-accuracy positioning and navigation solutions. Inertial Measurement Unit (IMU) can provide an accurate inertial navigation solution in a short time but its positioning error increases fast with time due to the cumulative error of accelerometer measurement. On the other hand, Ultra-Wideband (UWB) positioning and navigation accuracy will be affected by the actual environment and may lead to uncertain jumps even under Line-of-Sight (LOS) conditions. Therefore, it is hard to use a stand alone positioning and navigation system to achieve high-accuracy in indoor environments. In this paper, we propose an integrated Indoor Positioning System (IPS) combining IMU and UWB through the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) to improve the robustness and accuracy. We also discuss the relationship between the geometric distribution of the base stations (BSs) and the Dilution of Precision (DOP) to reasonably deploy the BSs. The simulation results show that the prior information provided by IMU can significantly suppress the observation error of UWB. It is also shown that the integrated positioning and navigation accuracy of IPS significantly improves that of least squares (LS) algorithm, which only depends on UWB measurements. Moreover, the proposed algorithm has high computational efficiency and can realize real time computation on general embedded devices. In addition, two random motion approximation model algorithms are proposed and evaluated in the real environment. The experimental results show that the two algorithms can achieve certain robustness and continuous tracking ability in the actual IPS.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

In this paper, we investigate the Unmanned Aerial Vehicles (UAVs)-assisted communications in three dimensional (3-D) environment, where one UAV is deployed to serve multiple user equipments (UEs). The locations and quality of service (QoS) requirement of the UEs are varying and the flying time of the UAV is unknown which depends on the battery of the UAVs. To address the issue, a proximal policy optimization 2 (PPO2)-based deep reinforcement learning (DRL) algorithm is proposed, which can control the UAV in an online manner. Specifically, it can allow the UAV to adjust its speed, direction and altitude so as to minimize the serving time of the UAV while satisfying the QoS requirement of the UEs. Simulation results are provided to demonstrate the effectiveness of the proposed framework.

Capacitive proximity sensor uses electric fields to detect contactless objects like human body. Owing to its simplicity and low cost, it is an attractive choice for the application of human-computer interaction. For existing capacitive proximity sensors systems, their detection ranges are less than 20 cm that cannot meet the requirements of the applications for long distance sensing. This motivates us to develop a new capacitive proximity sensor system for human-computer interaction in this work. A capacitive sensor array, which consists of 4 × 4 sensor units with comb shapes, is first designed. The dimensions of the whole array and a single unit are respectively 30 cm × 30 cm and 4 cm × 4 cm, which are comprehensively considered with hand swing distance in natural state and the sensing distance. More specifically, a outer guard with a lattice-like structure encloses sensor cells is designed to reduce the crosstalk from its neighboring cells, and a simple capacitance measuring circuit is presented. Comparing to the charge/discharge, AC-based and CDC-chip-based capacitance measuring circuit, the proposed measuring circuit offers plain design, highest sensitivity (3.53 V/pF) and fastest speed (4 μs). The experimental results show that the system can capture hand motion up to 45 cm and get hand motion trajectory using the hand position tracking algorithm. These features indicate the proposed system is very suitable for human-computer interaction applications in the future, such as rehabilitation training and somatosensory game.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.