Search Results (948)

Energy replenishment in Wireless Rechargeable Sensor Networks (WRSNs) is challenging in obstacle-dense environments. It tightly couples two sub-problems: charging scheduling and trajectory planning. Existing heuristics and end-to-end deep reinforcement learning (DRL) methods cannot jointly solve these tasks with formal safety guarantees. To address this, we propose a hierarchical DRL framework that decouples the two sub-problems. At the high level, a Deep Q-Network (DQN) selects the next charging target based on the global energy state. At the low level, an Improved A* planner uses obstacle dilation and Bézier smoothing to generate a provably collision-free and mechanically feasible trajectory. This dilation mechanism strictly ensures physical safety. Our ablation study shows that disabling this mechanism introduces 25.20 ± 13.31 collisions per episode. We evaluated our framework on networks with 100 nodes and 10 obstacles. Compared to the end-to-end DDPG baseline, our approach reduces the Node Dead Ratio (NDR) from 0.950 to 0.785 and completely eliminates collisions. Furthermore, it reduces the median cumulative turning angle by 96% across 20 randomized topologies, and achieves 3.8× the charging efficiency of the EDF heuristic. Ultimately, this framework occupies a distinct Pareto-optimal point on the efficiency–reliability–safety frontier. Full article

Industrial Internet of Things (IIoT) networks require continuous, uninterrupted sensing operations despite the finite battery capacity of deployed IoT nodes. Conventional reactive energy management, where nodes switch to charging mode only after residual energy falls below a fixed threshold, cannot prevent depletion events and compromises network uptime. We propose a digital twin (DT)-enabled predictive scheduling framework in which a DT layer co-located with a multi-access edge computing (MEC) control center continuously mirrors the physical network state and generates H-slot look-ahead scheduling decisions before depletion can occur. The framework operates over a 5G network-sliced infrastructure with dedicated URLLC, eMBB, and mMTC slices. Two coupled integer programming problems are formulated, namely a predictive IoT node scheduling problem and a predictive energy transmitter scheduling problem. Optimal solutions are obtained via branch-and-bound with reliability branching (DT-PBB), and a low-complexity DT-Aware Greedy Priority Heuristic (DT-GPH) is also proposed. Evaluated against Earliest-Deadline-First (EDF-WPT), No-WPT (a baseline that disables wireless charging entirely), and Random baselines across three parameter configurations with K up to 200 nodes, DT-PBB achieves the highest sensing utility and the fewest energy depletion events in all scenarios. DT-GPH provides near-optimal depletion performance at substantially lower computation cost. EDF-WPT, the strongest reactive policy, incurs 2-4 times more depletion events than DT-PBB. Proactive DT-enabled look-ahead decisively outperforms reactive urgency-based scheduling, validating the zero-downtime paradigm for large-scale IIoT networks. Full article

Wireless Rechargeable Sensor Networks (WRSNs) rely on Wireless Charging Vehicles (WCVs) to replenish sensor nodes under limited mission budgets. Existing semi-on-demand strategies mainly emphasize node selection and service ordering, while charging depth is often fixed or treated as a secondary factor rather than being used to regulate future request timing. This work proposes HB-APC, a Hybrid-Budget Adaptive Partial Charging framework for WRSNs. HB-APC uses safeguarded charging and pressure-aware adaptive charging-depth allocation as a temporal request-redistribution mechanism. Safeguarded charging assigns request nodes a target energy level that supports survival beyond a future service window, while pressure-aware adaptive partial charging adjusts the proactive charging depth according to system pressure to balance per-node depth and service coverage. Rather than simply reducing request volume, HB-APC delays and disperses future request arrivals. NS-3 simulation results show that HB-APC maintains comparable performance under light and moderate loads while significantly improving survivability under high-pressure scenarios. In the 1000 × 1000 m, 1900-node static scenario, HB-APC increases First Node Dead Time (FND) from 19,705.07 s to 392,720.97 s and reduces dead nodes from 97.97 to 5.40. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

Macrophage polarization toward the pro-inflammatory M1 phenotype underlies an effective strategy for potentiating antitumor immune responses. Electrical stimulation has emerged as a potent modulator of immune cell polarization. However, conventional electrode-mediated electrical stimulation has limited penetration into deep tissues and relies on external power supplies. Here, we report on ultrasound (US)-responsive piezoelectric nanofibers, constructed by embedding manganese–titanium metal–organic frameworks (MT-MOF) within a polyacrylonitrile (PAN) matrix (MT-MOF/PAN). As a non-centrosymmetric bimetallic framework, MT-MOF generates a heterogeneous charge distribution under mechanical deformation, thereby enhancing the composite’s piezoelectric output. Furthermore, interfacial coupling between MT-MOF and the PAN nanofibers provides an additional contribution to this enhancement. Under US stimulation, MT-MOF/PAN nanofibers generates a peak voltage of 0.24 V, substantially exceeding the output of pure PAN nanofibers. In RAW-Blue cells, US-activated MT-MOF/PAN nanofibers significantly activate the nuclear factor-κB (NF-κB) pathway and promote the secretion of tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), whereas neither US nor nanofibers alone produce this effect. Mechanistic studies demonstrate that piezoelectric stimulation induces a transient intracellular Ca²⁺ influx, as visualized by Fluo-4 acetoxymethyl ester (Fluo-4 AM) imaging, whereas US alone or nanofibers alone produce no significant effects. These findings establish MT-MOF/PAN nanofibers as a wireless, electrode-free platform for antitumor immunotherapy. Full article

With the development of the Internet of Things (IoT), demands of power and data for IoT devices increase drastically. In order to resolve the supply–demand contradiction, simultaneous wireless information and power transfer (SWIPT) has been envisioned as an enabling technology by providing high-power energy transfer and high-rate data delivery concurrently. In this paper, we analyze and design a high-efficiency resonant beam (RB) charging and communication scheme. The scheme is based on semiconductor materials for the gain medium, which provide a better energy absorption capacity compared with the traditional solid-state one. Moreover, the telescope internal modulator (TIM), which can concentrate beams to match the gain size, is adopted in the scheme, reducing the transmission loss. To evaluate the scheme’s SWIPT performance, we establish an analytical model and study the influence factors of its beam transmission, energy conversion, output power, and spectral efficiency. Numerical results show that the proposed RB system can realize 16 W electric power output with 11% end-to-end conversion efficiency, and it can support 18 bit/s/Hz spectral efficiency for communication. Full article

Compact wireless electronic devices require charging interfaces that can support different receiver positions and orientations within limited spaces. In this context, a double-sided mixed-coupling structure can provide independent magnetic- and electric-field power-transfer paths by combining coil-based and plate-based coupling mechanisms. This paper proposes a double-sided mixed-coupling wireless power transfer (DMPT) coupler for compact wireless electronic devices related to the Internet of Things (IoT) and the Internet of Drones (IoD). The proposed coupler integrates an upper coil-based magnetic-field coupling path and a lower stacked-plate-based electric-field coupling path within a single transmitter structure. Through this configuration, inductive wireless power transfer (IPT) and capacitive wireless power transfer (CPT) are implemented as independent double-sided power-transfer paths. To analyze the resonant behavior, a three-port equivalent circuit including mutual inductance and mutual capacitance is developed, and the resonance splitting under the uncompensated condition is investigated using even/odd mode decomposition. The predicted resonant frequencies agree with the ANSYS HFSS results with errors of 0.16% and 1.12%. After series-L compensation, the 60 × 60 × 7.31 mm³ coupler operates at the 6.78 MHz industrial, scientific, and medical band, showing S₁₁ ≈ 0.042, S₂₁ ≈ 0.68, and S₃₁ ≈ 0.64 under the double-sided aligned condition. Field and transient waveform analyses further verify that the upper H-coupling region and lower E-coupling region operate simultaneously while being spatially separated. The proposed DMPT coupler provides a coupler-level design framework for implementing IPT and CPT as independent double-sided coupling paths. Full article

To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization. Four detection-coil voltages form an induced-voltage fingerprint database organized as a multi-scale spatial graph. A graph attention network (GAT) teacher model is trained offline to learn neighborhood correlations in the voltage–position mapping, and its spatial knowledge is distilled into a lightweight Tiny-MLP student model for microcontroller unit (MCU)-based online inference. Experimental results show that the GAT teacher achieves a mean absolute error (MAE) of 0.589 cm, while the distilled Tiny-MLP reduces the MAE of the directly trained Tiny-MLP from 1.548 cm to 1.148 cm (a 25.8% reduction under a fixed seed). In 2000 closed-loop alignment trials with random initial positions, the system achieves an 85.5% success rate under a 0.5 cm threshold, indicating that the method supports low-complexity closed-loop self-alignment for UAV wireless charging. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

This paper presents a 3D FEM-based comparative study of circular and rectangular planar spiral coils for electric vehicle (EV) wireless power transfer (WPT) at $85\mathrm{kHz}$. A geometry-consistent simulation framework is adopted, where both coil types are evaluated under identical conductor material, conductor diameter, inner area, turn spacing, and excitation current, allowing the isolated effect of coil geometry to be analyzed. Three operating conditions are investigated: turn-number variation ($10\le N\le 30$) under perfect alignment, air-gap variation (130–$200\mathrm{mm}$), and lateral misalignment variation ($\Delta x\in [-30,30]\mathrm{mm}$). The transmitter self-inductance $L_1$, receiver self-inductance $L_2$, mutual inductance M, and coupling coefficient k are evaluated. The main contribution of this work is the identification of a geometry-dependent trade-off between coupling performance and robustness. The results show that increasing the number of turns enhances $L_1$, $L_2$, M, and k for both geometries, while self-inductances remain nearly insensitive to air gap and misalignment. The circular coil demonstrates higher robustness to coil separation and misalignment, whereas the rectangular coil can achieve higher coupling in configurations with a high number of turns under controlled misalignment conditions. These findings provide a solid basis for selecting the most suitable geometry for each wireless charging use case. Full article

Dynamic Wireless Power Transfer (DWPT) is attracting increasing interest as a promising solution to extend the operating range of battery electric vehicles while reducing stationary charging needs. In this study, a DWPT system for Electric Vehicle charging is investigated through a comparative simulation-based case study focused on the Italian A4 highway, a strategic transport corridor characterized by high traffic intensity and long-distance mobility demand. The proposed system is based on a segmented magnetic coupling architecture with planar circular coils installed along the roadway and a vehicle-side pickup coil. Under common roadway, vehicle, and magnetic coupling assumptions, a benchmark Tesla Model 3 Long Range traveling at a constant speed of 90 km/h and characterized by an estimated energy consumption of 0.129 kWh/km is considered. Two compensation solutions are comparatively assessed, namely the Series–Series (SS) topology and the Inductor-Capacitor-Capacitor (LCC) topology. The methodology evaluates the two topologies under the same benchmark conditions in terms of peak power, average transferred power, transferred energy per kilometer, and effect on vehicle State Of Charge (SOC). The SS topology provides a peak power of 22.52 kW, an average power of 12.30 kW, and an energy transfer of 0.14 kWh/km, whereas the LCC topology reaches a peak power of 20.44 kW, an average power of 13.47 kW, and an energy transfer of 0.15 kWh/km. Starting from an initial SOC of 30%, the final SOC after traveling through the usable electrified highway section reaches 37.48% with SS compensation and 44.28% with LCC compensation. The results show that both topologies enable effective dynamic charging, with the LCC solution exhibiting better energy transfer capability and higher operational stability, while the SS topology delivers higher instantaneous power peaks. From a comparative simulation perspective, the study supports the technical feasibility of DWPT deployment in highway environments and provides useful design insights for selecting compensation topologies in dynamic electric vehicle charging applications. Full article

Direct grid-connected wireless charging based on direct AC–AC conversion is attractive for electric vehicles (EVs) because it can reduce power conversion stages and improve charger compactness. In matrix-converter-based wireless power transfer (WPT) systems, the grid-frequency AC voltage can be directly converted into high-frequency AC voltage without using bulky DC-link electrolytic capacitors. However, the removal of the intermediate energy-storage stage also makes the EV wireless charger more sensitive to grid-voltage fluctuation. For an LCC-S compensated WPT system, the voltage-source output characteristic makes the charging-side voltage sensitive to grid-voltage disturbance, resulting in severe MC output-current and battery charging-current overshoot. This transient overcurrent may threaten both the power converter and the EV battery charging process. In this paper, a dual-frequency state-space model is developed for the matrix-converter-based electrolytic-capacitor-less LCC-S WPT system to analyze the disturbance propagation from the grid side to the high-frequency resonant stage and the EV battery side. Based on the model, the current-overshoot suppression capability and bandwidth limitation of the conventional dual closed-loop control strategy are investigated. To further enhance transient current protection, a grid-voltage feedforward strategy is proposed to compensate for the disturbance before severe current overshoot is formed. Finally, experimental results verify that the proposed method effectively suppresses the MC output-current and battery charging-current overshoot under grid-voltage fluctuation, thereby improving the grid-disturbance resilience and dynamic safety of direct grid-connected EV wireless charging systems. Full article

Wireless power transfer (WPT) systems for electric vehicles (EVs) are increasingly being studied in the MHz range to increase power density and reduce the size of passive components. However, operation at higher frequencies significantly changes electromagnetic interference (EMI) behaavior. Fast switching in SiC- and GaN-based inverters, high-Q resonant operation, and frequency-dependent parasitic capacitances create conductive, capacitive, and magnetic interference mechanisms that are less significant in conventional kHz-range systems. Although many existing studies focus on power-transfer efficiency and converter optimization, EMI mechanisms in MHz-range EV WPT systems remain insufficiently systematized from a system-level electromagnetic perspective. This paper presents a state-of-the-art review of EMI generation mechanisms and system-level effects in high-frequency WPT systems for electric vehicles. The review considers the main interference sources and coupling paths, including switching-induced common-mode currents, resonant amplification of current and voltage stress, capacitive coupling between the coupler and nearby conductive structures, and magnetic-field redistribution caused by coil misalignment. Special attention is given to the transition from lumped-element assumptions to more distributed electromagnetic behavior at higher frequencies. The review also discusses the possible impact of these mechanisms on vehicle electronic subsystems and highlights the need for frequency-aware electromagnetic design, integrated modeling, and more rigorous EMC assessment for reliable MHz-range wireless EV charging systems. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems. Full article