We previously developed an indoor localization system based on Wi-Fi received signal strength (RSS), achieving room-, floor-, and corridor-level localizations within the Toyohashi University of Technology (TUT) campus. However, the error in corridor localization can be significant in certain areas, depending on the placement of access points (APs), due to the inherent limitations of weighted centroid localization (WCL). This study aims to improve localization errors by combining WCL with radio fingerprint localization (FPL). To achieve this combination, we develop a section estimation technique to determine whether to apply WCL or FPL for corridor-level localization. We demonstrate that the combined scheme achieves smaller localization errors than WCL. Furthermore, we show that it has been implemented and performs well on Android devices.

We present PARR, a perception-aware, risk-constrained replication-and-roaming policy for wireless links carrying time-sensitive-networking (TSN) flows in mobile robotics. PARR is not a MAC scheduler or a standalone predictor: IEEE 802.1CB frame replication (FRER) provides reliability, while LiDAR geometry and an online-calibrated shadow-fading residual decide when, where, and over which access points (APs) to replicate at minimum airtime. On a 3GPP TR 38.901 indoor-factory channel with correlated shadowing, PARR matches blanket-FRER reliability at about one-third of its airtime, stays robust as shadowing grows where threshold and reactive baselines fail, and, unlike blanket FRER which is harmful under contention, scales to sixteen coexisting robots.

This paper studies a reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and energy harvesting system. The sensing beampattern gain is maximized by jointly optimizing the base station beamforming and RIS phase shifts under communication and energy constraints. A semidefinite relaxation (SDR) benchmark and a low-complexity (LC) alternating algorithm are developed. The SDR method provides a performance reference, while the LC design relies on Lagrangian dual updates and coordinate descent for practical implementation. Numerical results further show that the proposed design maintains sensing performance under different communication and harvesting requirements. Simulations show that the LC design achieves near-SDR performance with lower complexity and effectively captures the tradeoff among sensing, communication, and energy harvesting.

The proliferation of counterfeit electronic parts, including integrated circuits (ICs) and other components, threatens system functionality and can lead to critical failures. This paper presents a device identification method based on spectral emission differences as a fundamental step toward board-level authenticity assurance. The proposed method actively extracts device-to-device differences originating in semiconductor manufacturing and observes them together with additional variations introduced by components, interconnects, and printed circuit boards (PCBs). Specifically, a marker signal in the MHz band is generated inside the IC. As this signal propagates through and radiates from the PCB, the characteristics of the source, path, and antenna are superimposed on the observed spectrum. The peak amplitudes of the fundamental and harmonic components are then used as identification features. An evaluation using six commercial microcontrollers showed that all devices were correctly identified by averaging three measurements, and that the maximum misidentification rate was limited to 5.6% even for a single measurement.