As radio-frequency (RF) based wireless energy harvesting technology can provide remote and continuous power to low-power devices, e.g., wireless sensors, it may be a substitute for batteries and extend the lifetime of the wireless sensor networks. In this paper, we propose a wireless energy harvesting localization system (WEHLoc), which contains batteryless wireless sensors as anchors and an energy access point (E-AP) to transfer power to the anchors. We consider a passive target localization scenario, in which the anchors monitor the target and send the sensed ranging data back to the E-AP. Additionally, we formulate the optimal estimation accuracy problem which is a 0–1 mixed-integer programming problem and relates to the energy beam, target transmitted power, and deployed anchor density. Then, we develop the power allocation scheme of the E-AP to solve the objective. In order to reduce the complexity, we propose a heuristic method that converts the maximum estimation accuracy problem into the energy efficiency problem and use linear programming to solve them. The simulations demonstrate that WEHLoc can be massively deployed in a wide area, and the estimation error and the power consumption are relatively low.

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Passive wireless sensor network (PWSN) requires high positioning for network management. The harvested energy of the passive sensor is modulated as the ranging data and the position is derived accordingly. Thus, the wireless power transfer (WPT) is a dominant factor for such localization. With the help of intelligent reconfigurable surface (IRS), the WPT efficiency can be significantly improved. In this paper, we propose the Fisher information matrix (FIM) and the Cramér–Rao lower bound (CRLB) analyzing model of the PWSN localization. We prove the impacts of phase modulation of IRS on the localization performance. Based on our analysis, we develop an approximation algorithm and a genetic algorithm to control the IRS phases. Then, the localization accuracy of PWSN can be further improved. The simulation results demonstrate that the phase modulation based on GA can achieve high accurate localization for PWSN using IRS.

In coupled magnetic resonance (CMR) wireless energy transfer systems, the energy transfer power is low and the power transfer efficiency changes with the coil position. One reason for this reduction in power and efficiency is the impedance mismatching (IM) between the Tx and Rx coils; achieving impedance matching for multiple-input multiple-output (MIMO) CMR IM wireless power transmission (WPT) is quite complex due to the uncertainty in the number of coils and the interaction between coils. In this paper, we provide an analytical model of MIMO CMR which fully formulates the complex relationship between multiple Tx and Rx channels. Then, we design an impedance matching network (IMN) for MIMO CMR and derive an optimal IM solution. Base on this solution, we also develop an adaptive impedance matching scheme to control IMN, based on an automatic analysis of MIMO CMR system; the resulting control scheme achieves optimal values for transmission power and efficiency through IMN and coil selection. The simulation results indicate that the scheme is able to automatically adjust the impedance matching network according to the changes of the relative positions between Tx and Rx coils to achieve high energy transfer power and efficiency.

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