Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures.

This study presents an appraisal of mineralization potential and the geologic record of Kaoje and environs using high-resolution aeromagnetic data. The data were analyzed using the fast Fourier transform technique for reduction-to-equator (RTE), analytic signal (AS), total horizontal derivative (THDR), tilt derivative (TDR), spectral analysis and Euler deconvolution at varying spectral indices. The reduced to equator residual magnetic intensity range from -73.4 to 205.6 nT and are associated with contrasting lithology of the Sedimentary and Basement Complex rocks. The anomalies and analytic signal amplitude unfold and update the extension of Zuru Schist to the southern region of Kaoje, elongated banded gneiss within the schist body, pegmatite intrusion through the migmatite and NNW-SSE trending schist hosted manganite and sandstone-hosted goethite. Estimated depth to shallow and deeper magnetic sources ranged from 0 to 59 m and 225-415 m respectively, and the corresponding Euler solutions revealed perfect clustering along notable geologic features and minerals. The iron mineralization are revealed as sourced from the magmatic bodies that lie beneath the sedimentary rocks and a corresponding sphere geologic model within northwest and southwest of Kaoje at a depth range of 0-225 m. The structural trends suggested the tectonic events in the area and indicate an imprint of Zungeru-Anka transcurrent fault that serves as a conduit for iron mineralizing fluid to Kaoje.

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems.

The challenge of optimally allocating transmit power among multiple beams remains a central problem in multi-beam beamforming for massive MIMO networks, especially as user density and interference become increasingly dynamic in next-generation wireless deployments. Conventional normalization techniques, such as Global and Per-Beam Normalization, are unable to adapt to varying channel conditions and user demands, often resulting in inefficient power distribution and limited system performance. To address this, we propose Power Redistribution Normalization (PRN), a novel approach that dynamically reallocates transmit power across multiple beams in real time, optimizing both desired signal peaks and interference nulls. The PRN framework is integrated with diverse beamforming algorithms—including Least Mean Square (LMS), Minimum Variance Distortionless Response (MVDR), Recursive Least Squares (RLS), Zero Forcing (ZF), and Sample Matrix Inversion (SMI)—and is evaluated under realistic antenna configurations, user densities, and interference scenarios. Results demonstrate that PRN-MVDR achieves the highest spectral efficiency (~25 bps/Hz), SINR (~8.7 dB), and energy efficiency (~25 bps/Hz/W) compared to existing normalization methods. Moreover, PRN-LMS provides comparable performance with significantly lower computational complexity (~0.4 ms latency), making it attractive for real-time and hardware-constrained applications. The proposed PRN method is thus well-suited for dense urban 5G/6G networks and large-scale IoT deployments, where robust power allocation and interference management are critical to system capacity and reliability.