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A low-cost hybrid textile/PCB wearable rectenna is designed for smart on-body applications. It is an integration of a textile patch antenna and a highly efficient rectifier on a small PCB substrate, where the feeding aperture of the antenna is significantly miniaturized by exploiting an LC loading technique to have an effective integration with the rectifier. The feeding-rectifying circuitry is attached to the back of the antenna by normal sewing threads. The essential components are soldered on the small rigid PCB for a robust connection. The size of the rectenna is $0.817\times 0.817\,\,\lambda {0}^{2}$ while the rigid substrate area for the feeding-rectifying circuitry is only $0.278\times 0.147\,\,\lambda {0}^{2}$ ( $3.4\times1.8$ cm) at 2.45 GHz. The intrinsic dielectric/conduction loss of textile feedlines, the loss of antenna-to-rectifier interconnectors, the loss of soldering or gluing on textiles, and the loss due to body absorption from typical large feeding slots are constitutively reduced. The soldering points and the consequent hard-to-be-characterized parasitic effects on textiles are avoided. The proposed integration approach reduces the cost and eases the rectenna design, fabrication, and integration. As a result, the power conversion efficiency (PCE) of the rectifier reaches up to 39.5% at -20 dBm input power level whilst the experimental rectenna prototype achieves the state-of-the-art PCE of 41% at an extremely low incident power density of $0.4~\mu \text{W}$ /cm2. The maximum PCE is 56% at the -5 dBm input power. Finally, the possibility of harvesting datalink energy from PC/mobile phones as power source for wearable electronics is investigated.

Smart space has emerged as a new paradigm that combines sensing, communication, and artificial intelligence technologies to offer various customised services. A fundamental requirement of these services is person identification. Although a variety of person identification approaches have been proposed, they suffer from several limitations in practical applications such as low energy efficiency, accuracy degradation, and privacy issue. This paper proposes an energy harvesting based privacy-preserving gait recognition scheme for smart space which is named . In , we extract discriminative features from one-dimensional gait signal and design an attention-based long short term memory (LSTM) network to classify different people. Moreover, we leverage a novel Bloom filter-based privacy-preserving technique to address the privacy leakage problem. To demonstrate the feasibility of , we design a proof-of-concept prototype using off-the-shelf energy harvesting hardware. Extensive evaluation results show that the proposed scheme outperforms state-of-the-art by 6–10% and incurs low system cost while preserving user’s privacy.

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Secondly, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

The wireless-powered communication paradigm brings self-sustainability to the on-vehicle sensors by harvesting the energy from radiated radio frequency (RF) signals. This paper proposes a novel transmission and resource allocation strategy for the scenario where multiple wireless powered vehicle area networks (VAN) co-existed with high density. The considered multi-VAN system consists of a remote master access point (MAP), multiple on-vehicle hybrid access points (HAPs) and sensors. Unlike previous works, we consider that the sensors can recycle the radiated radio frequency energy from all the HAPs when HAPs communicate with MAP, so the dedicated signals for energy harvesting (EH) are unnecessary. The proposed strategy can achieve simultaneous wireless information and power transfer (SWIPT) without complex receiver architecture requirements. The extra EH and interference caused by the dense distribution of VANs, which are rarely explored, are fully considered. To maximize the sum throughput of all the sensors while guaranteeing the transmission from HAPs to the MAP, we jointly optimize the time allocation, system energy consumption, power allocation, and receive beamforming. Due to the non-convexity of the formulated problem, we address the sub-problems separately through the Rayleigh quotient, Frobenius norm minimization and convex optimization. Then an efficient iterative algorithm to obtain sub-optimal solutions. The simulation results and discussions illustrate the proposed scheme's effectiveness and advantages. peerReviewed

International audience; Unmanned Aerial Vehicles (UAVs) have attracted much attention lately and are being used in a multitude of applications. But the duration of being in the sky remains to be an issue due to their energy limitation. In particular, this represents a major challenge when UAVs are used as base stations (BSs) to complement the wireless network. Therefore, as UAVs execute their missions in the sky, it becomes beneficial to wirelessly harvest energy from external and adjustable flying energy sources (FESs) to power their onboard batteries and avoid disrupting their trajectories. For this purpose, wireless power transfer (WPT) is seen as a promising charging technology to keep UAVs in flight and allow them to complete their missions. In this work, we leverage a multi-agent deep reinforcement learning (MADRL) method to optimize the task of energy transfer between FESs and UAVs. The optimization is performed by carrying out three essential tasks: (i) maximizing the sum-energy received by all UAVs based on FESs using WPT, (ii) optimizing the energy loading process of FESs from a ground BS, and (iii) computing the most energy-efficient trajectories of the FESs while carrying out their charging duties. Furthermore, to ensure highlevel reliability of energy transmission, we use directional energy transfer for charging both FESs and UAVs by using laser beams and energy beam-forming technologies, respectively. In this study, the simulation results show that the proposed MADRL method has efficiently optimized the trajectories and energy consumption of FESs, which translates into a significant energy transfer gain compared to the baseline strategies.

Piezoelectric energy harvester (PEH) arrays are promising in many application scenarios. However, few interface circuits have been developed to manage the multiple AC inputs from PEHs. This paper proposes an extensible P-SSHI (parallel synchronized switch harvesting on inductor) array interface scheme to realize a multi-input conversion from PEH arrays. We develop a split-inductor-capacitor topology that can extract electrical energy from multiple PEHs. The proposed circuit topology effectively handles the inductor access conflict in the cases of PEHs with close, identical, and opposite vibration phases, insensitive to AC input phase changes. For demonstrating the capability of dealing with multiple AC inputs from PEHs and the enhancement effect on power output, we construct and test a self-powered three-channel array circuit with the passive peak detection function. Excited by sinusoidal vibrations, our circuit effectively avoids the energy return and efficiently achieves the power combination of the three PEHs. Compared with the classic full-bridge circuit, the proposed circuit helps increase the power output by 4.8 times. Also, to show the universality of the circuit, the circuits operation under various excitations is tested and analyzed.

This paper proposes digitally controlled power management circuit (PMC) for vibration energy harvesting in vehicle suspension system. The PMC is tested with a mechanical motion rectifier based electromagnetic energy harvester that converts from bi-directional vibration to unidirectional rotation, with improved energy harvesting performance and efficiency. Topology of one-stage DC-DC converter is adapted, and it is digitally controlled while considering the uncertainty of vibration sources. In order to efficiently transfer the harvested power to the electrical load, the algorithm of digital controller is designed to have four operation modes, which are impedance matching mode, voltage regulation mode, generator protection mode, and low power mode, for increasing the PMC efficiency while reducing the power consumption of controller. Experimental results show that proposed circuit can successfully operate and switch between the four operation modes. In the evaluation of PMC performance with 2mm and 2Hz excitation, the efficiency of proposed one-stage power converter is 86%. The total power consumption of controller in active mode and low power mode are 109 mW and 2.52 mW respectively while harvesting about 3W vibration energy.

Through the Internet of Things, autonomous sensing devices can be deployed to regularly capture environmental and other sensor measurements for a variety of usage scenarios. However, for the market segment of stand-alone, self-sustaining small IoT nodes, long term deployment remains problematic due to the energy-constrained nature of these devices, requiring frequent maintenance. This article introduces Hypnos, an open hardware and software toolkit that aims to balance energy intake and usage through adaptive sensing rate for low-cost Internetconnected IoT nodes. We describe the hardware architecture of the IoT node, an open hardware board based on the Arduino Uno form-factor packing the energy measurement circuitry, and the associated open source software library, that interfaces with the sensing node’s microcontroller and provides access to the low-level energy measurements. Hypnos comes equipped with a built-in, configurable, modified sigmoid function to regulate duty cycle frequency based on energy intake and usage, yet developers may also plug in their custom duty/sleep balancing function. An experiment was set up, whereby two identical boards ran for two months: one with the Hypnos software framework and built-in energy balancing function to regulate sensing rate and the other with fixed sensing rate. The experiment showed that Hypnos is able to successfully balance energy usage and sensing frequency within configurable energy ranges. Hereby, it increases reliability by avoiding complete shutdown, while at the same time optimizing performance in terms of average amount of sensor measurements.

To perform large-scale monitoring of sensitive events, energy harvesting wireless sensor network is considered where a mobile data sink MS collects data while travelling on a fixed path Pms. The sensor nodes sense environmental data continuously at a pre-specified rate. The sensors close to Pms are referred as gateways. Sensors forward their data to the MS through the gateways. In practice, the usage of MS is not suitable for time-sensitive applications due to its long data gathering delay. Time-bound data gathering for path constrained environment is not accounted in literature. We aim at finding energy-efficient maximum data gathering sub-path for the MS for a given data gathering period T. To deal with the problem a novel optimal deterministic data collection sub-path finding algorithm is proposed which is based on the geometric properties of the sensors' communication disks and the data gathering path. It maximizes the data collection and reduces the energy consumption by jointly optimizing the data gathering sub-path selection and the data forwarding path optimization. The performance of the proposed algorithm is compared with an existing baseline algorithm DDGA and a heuristic algorithm H-DGSPF. The simulation results show that our proposed algorithm outperforms DDGA and H-DGSPF in terms of data collection, data delivery success ratio, and energy consumption.

A solid-liquid triboelectric nanogenerator (TENG) has attracted increasing research interest in relation to the development of regeneration energy based on water resources. The output of solid-liquid TENG remains unsolved, however, because of the low voltage output that impedes wide applications. To this end, in this work we developed a miniaturized microfluidic channel-based TENG device for highly efficient conversion of energy from the transport of a water droplet to an electrical output. We investigated an optimized design in a triboelectric material, the droplet transport and the electrostatic induction layer to provide a high voltage output and stable energy harvesting. The optimized device demonstrated maximum voltage amplitude 102 mV with an ultralow liquid consumption, 0.36 [Formula: see text], resulting in sample-energy conversion 283.33 mV/ [Formula: see text]. This novel device is expected potentially to address the limitations imposed by sample consumption in energy harvesting in the future.

Inspired by hand push whisk blenders that are widely used in the kitchen, we here propose an energy harvester that enables conversion of low-frequency translational vibration to high-speed rotations and further generates electricity via the electromagnetic effect. The whisk-inspired energy harvester (W-EH) is composed of an internal thread sleeve, an external thread driving shaft, a spring, and a buoy. Compared with transmission approaches used by electromagnetic power generators, the proposed design has the superiority of low cost, ultra-low operation frequency (lower than 1 Hz), and high power output. Tests in water indicate that the normalized power density is 300% higher than the state of the art. Excited by mimicking ocean waves at 1.5 Hz, the W-EH prototype generates a maximum power of 64.4 milliwatts and succeeds in lighting up 115 LEDs simultaneously, which power is enough for continuous operations of most wireless sensors.

To break through the limitation of battery-powered wireless sensor networks, a novel kind of network, named Battery-Free Wireless Sensor Network (BF-WSN), is proposed. Battery-free sensor nodes in BF-WSNs harvest energy from power sources in their ambient environment, such as solar power, wind power and radio frequency (RF) signal power, etc., instead of batteries. Therefore, the energy consumption of battery-free sensor nodes are not limited by the battery capacity anymore. However, they still have limited energy harvesting rates and energy capacities. Data aggregation is an fundamental operation in sensor networks where the sensory data gathered by the relay nodes can be merged by in-network computation, such as taking the maximum, average, or sum, etc., of them. Due to the energy features of BF-WSNs, the data aggregation scheduling problem in BF-WSNs is more complicated and the previous aggregation scheduling algorithms designed for battery-powered WSNs are no longer applicable. This paper investigates the Minimum-Latency Aggregation Scheduling problem in BF-WSNs, which is proved to be NP-hard. Then, we propose the Data Aggregation Scheduling algorithm to solve the problem. Finally, the theoretical analysis and extensive simulation results are provided to verify the performance of the proposed algorithm.

Radio frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multi-antenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this paper, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as $0.85\sqrt{M}$, where $M$ is the number of PB's antenna elements. Moreover, a rotation-specific power control mechanism was proposed to i) fairly optimize the WET process if devices' positioning information is available, and/or ii) to avoid hazards to human health in terms of specific absorption rate (SAR). We show that RAB performance even approaches quickly (or surpasses, for scenarios with sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology., Comment: 12 pages, 10 figures. Accepted for publication in IEEE IoT Journal

The fourth industrial revolution is paving the way for Industrial Internet of Things applications where large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This paper proposes and analyses LoRaIN, a network architecture and MAC-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.

A passive field conversion-amplification scheme, is proposed and demonstrated for the first time, by integrating a magnetic cantilever with a TMR (Tunnel magnetoresistance) for current monitoring. In the scheme, a magnetic structure with a strong field, is driven and vibrated by the interaction with a weak field. The passive movement of magnetic structure physically results in a movement of strong field. The weak field, is converted by the movement and further passively amplified into a strong one. The degree of amplification is determined by the structure geometry, the position of magnetic structure, as well as the weak field. A TMR cantilever current sensor, mainly composed of two units, is developed to demonstrate the proposed scheme. One is a cantilever with a permanent magnet fixed at its free end as a sensing unit and the other is the TMR as a detecting unit. Experimental measurements demonstrate that a higher detection sensitivity can be achieved, compared to that with only TMR detection. The gain of the sensitivity reaches more than an order of magnitude. If an energy harvesting unit is integrated into the current sensor, a self-powered sensing node can be accomplished for potential wireless sensor network (WSN) applications.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

The emerging hybrid backscatter and energy harvesting devices have been regarded as a promising scheme for green Internet-of-Things (IoT). In the interwoven primary and secondary wireless powered IoT networks, we develop a novel hybrid scheme that integrates the backscatter communication and the harvest-then-transmit (HTT) protocol. In order to mitigate the adverse effect on the primary user (PU), the secondary users (SUs) are classified into ‘Cooperation Scenario’ and ‘Coexistence Scenario’ based on their different levels of interference to the PU. In ‘Cooperation Scenario’, we propose a cooperation protocol where the SUs operate in the backscatter mode so as to relay information to the PU. Therefore, the SUs are rewarded for harvesting energy and obtaining the spectrum access time from the primary system. Also, in ‘Coexistence Scenario’, a coexistence strategy is developed to enable the SUs to operate in either ambient backscatter or energy harvesting mode during the channel busy time. When the primary channel becomes idle, the SUs are capable of active transmission by using the harvested energy. For each scenario, we investigate the sum-throughput maximization problem of the secondary system. Employing the Lambert W function and the block coordinate descent method, the optimal time allocation can be obtained via the Lagrangian dual method. Numerical results validate that the proposed hybrid backscatter and HTT scheme improves the performance of the secondary system evidently compared with benchmark methods.

In this paper, we investigate the joint task assignment and power control problems for Device-to-Device (D2D) offloading communications with energy harvesting. Exploiting the D2D links for data offloading allows reducing the traffic load of the cellular base stations. The energy consumed by the D2D transmitters for data offloading can be compensated by energy harvesting. The main objective is to maximize the energy efficiency (EE) under energy causality and delay constraints, assuming a harvest-transmit model. Hence, the proposed model results in a non-convex problem. We first derive an equivalent and more tractable optimization problem by exploiting nonlinear fractional programming, also known as the Dinkelbach method. We propose a layered optimization method by decoupling the energy efficiency maximization problem into power allocation and offloading assignment. The first step consists of computing the optimal power values by applying the conjugate gradient method. In the second step, the problem of the D2D pair formation for data offloading amounts to the bipartite graph matching. It can be solved to optimality using the Hungarian algorithm. Extensive simulations were performed on various network scenarios. Numerical results show that the proposed resource allocation scheme achieves remarkable improvements in terms of network EE.

This paper studies the complete targets coverage problem in a novel and practical context: sensor nodes operating in an Internet of Things (IoT) network that have a dual-battery system with non-ideal properties. Specifically, sensor nodes have batteries that cannot be charged and discharged simultaneously. Also, sensor nodes must fully discharge/charge a battery before it is charged/discharged again. We outline a Mixed Integer Linear Program (MILP) and use it to optimize the activation schedule of sensor nodes. Its objective is to maximize complete targets coverage lifetime. We also propose a heuristic solution named Battery Switching Aware Algorithm (BSAA) to solve large problem instances. Simulation results show the performance of BSAA achieves approximately 98% of the coverage lifetime of MILP. In addition, equipping sensor nodes with a dual battery system prolongs the coverage lifetime by up to 70.2%.

Owing to the shorter wavelength in the millimeter-wave (mmWave) spectrum, miniaturized antennas can receive power with a higher efficiency than UHF bands, promising sustainable mmWave-powered Internet of Things (IoT) devices. Nevertheless, the performance of a mmWave power receiver has not been compared, numerically or experimentally, to its sub-6 GHz counterpart. In this paper, the performance of mmWave-powered receivers is evaluated based on a novel wearable textile-based higher-order mode microstrip antenna, showing the benefits of wireless power transmission (WPT). Firstly, a broadband antenna is proposed maintaining a stable wearable measured bandwidth from 24.9 to 31.1 GHz, over three-fold improvement compared to a conventional patch. The proposed antenna has a measured 8.2 dBi co-polarized gain with the highest thickness-normalized efficiency of a wearable antenna. When evaluated for compact power receivers, the measured path gain shows that WPT at 26 GHz outperforms 2.4 GHz by 11 dB. A rectenna array based on the proposed antenna is then evaluated analytically showing the potential for up to 6.3x higher power reception compared to a UHF patch, based on the proposed antenna's gain and an empirical path-loss model. Both use cases demonstrate that mmWave-powered rectennas are suitable for area-constrained and large-area wearable IoT applications.

Vibration energy harvesting using multiple piezoelectric transducers (multi-PZTs) can increase its output power and environmental adaptability. At present, there are still few researches on multi-input interface circuits for multi-PZTs, especially those using the parallel synchronized switching harvesting on inductor (PSSHI) technique. Therefore, this article presents a self-powered dual-inductor MI-PSSHI-VDR interface circuit. It can process multi-PZT voltages with arbitrary phases input. The measurements demonstrate that the prototyped MI-PSSHI-VDR circuit can extract a peak power of 1.93 mW from four PZTs under an excitation of 1.5 g and 75.5 Hz. The energy integration efficiency and energy extraction efficiency of the prototyped circuit are 93.2% and 75.1%. Compared with a multi-input full-bridge rectifier, the proposed circuit can achieve 3.22 power improvement and 3.58 voltage improvement, which grasps a good balance between the indexes of peak output power and load voltage range. Additionally, the MI-PSSHI-VDR circuit is easy to implement and shows a good scalability, which can be an alternative solution for multi-PZTs energy harvest

In a piezoelectric energy harvesting (PEH) system, the dynamics and harvested power vary with different base excitation. Accurately predicting the energy harvesting capability under different types of excitation is of importance for the analysis and design of PEH systems. Many studies started the modeling and analysis of such an electromechanically coupled system under harmonic excitation. However, in real-world scenarios, environmental vibration might be irregular and impulsive. This paper extends the equivalent impedance analysis from single harmonic to multiple harmonics for describing the complex dynamics and harvested power of a PEH system under non-harmonic base excitation. The proposed multiple harmonic analysis is based on the extended impedance method (EIM), which uniforms the impedance expressions of both linear and nonlinear components in a matrix form. The modeling principle and procedures of EIM are provided in detail. The power flow in the steady-state PEH systems and energy flow in the transient PEH systems are numerically discussed. Experiments based on a base-excited piezoelectric cantilever, as the energy harvester, and a full-wave bridge rectifier, as the power conditioning circuit, validate the EIM based analysis, in terms of harvested power/energy prediction and dynamics description.

In this article, we study resource optimization issues for hybrid energy harvesting massive multiple-input–multiple-output (MIMO) systems, where renewable energy harvested from surrounding environments is introduced as additional energy supply to the base station (BS) together with the grid power. Our purpose is to maximize system energy efficiency with the preferential use of renewable energy under several practical restrictions, such as limited battery capacity, energy causality, quality-of-service (QoS) requirement, and transmit power constraints. An offline policy is first presented under ideal assumptions, i.e., noncausal knowledge of the channel state information and energy harvesting dynamics are known in advance, which causes it to become a performance upper bound. In particular, the offline policy is designed in an iterative manner with the use of the fractional programming technique. Moreover, inspired by the offline policy, we exploit statistical information to present two practical online algorithms. Specifically, the Markov chain method predicts prospective information through use of the finite-state Markov model, whereas the timing detection scheme focuses only on the resource optimization of the current time slot. Numerical results illustrate that both online algorithms perform closely to the offline policy, which indicates that they can achieve a good tradeoff between performance and implementation complexity.

In this paper, a single-stage rectifier-less boost converter circuit (SSRBC) for piezoelectric energy harvesting from ambient vibration was proposed. The proposed rectifier-less circuit acted as a boost converter to extract energy from a piezoelectric device (PD). It combined the conventional boost, buck-boost methods using two split inductors and single filter capacitor. The proposed integrated circuit topology functioned in both positive and negative half cycles generated by the PD. In the proposed topology, inductors were invigorated by being enveloped with the current, which was produced by the PD through the switches. This facilitated active rectification of ultra-low AC (amplitude < 0.5 VP). Theoretical analysis, control strategies, simulation and experimental study, were presented. The proposed circuit was capable of converting a low amplitude AC voltage of 0.5 VP into 5.1 Vdc. The highest output power extracted by the proposed circuit was 281.1 µW, which outperformed existing circuits. It could potentially facilitate the advancement of vibration-based energy harvesting system for low power demand applications such as sensors, quartz watches and portable charging devices.

For a downlink communication system, we propose a nonorthogonal multiple access scheme with two energy harvesting relays. The base station (BS) transmits the data of both near and far users simultaneously using the superposition coding technique. Two relays alternately amplify and forward the data to the far-user. Each relay can harvest wireless energy from the signals of both the BS and the other transmitting relay, so each relay can achieve more opportunities to accumulate more energy for a higher-quality cooperative relaying. The near-user performs adaptive interference cancelation to retrieve its desired data. Numerical results show that the nonorthogonal scheme greatly outperforms the orthogonal scheme in terms of throughput.

The emerging energy harvesting technology facilitates the development of ubiquitous and everlasting battery-free motion detectors. This paper introduces a robust design of the transient-motion-powered motion detector, which is called ViPSN-pluck. “ViPSN” is the acronym for vibration-powered sensing node while “pluck” stands for the plucking-motion energy harvester. By using a piezo-magneto-elastic structure, ViPSN-pluck can efficiently harvest energy from a transient motion. By properly making good use of this tiny harvested energy, ViPSN-pluck can effectively carry out motion detection and Bluetooth low-energy (BLE) wireless communication. Given the concurrency of mechanical potential energy pre-charging and motion detection, the transient-motion plucking energy harvester used in ViPSN-pluck has the merit of high energy reliability. This unique feature is unprecedented in the solar and RF energy harvesting cases, which might suffer from energy outages under fluctuating irradiance or RF signal strength, respectively. The working principle of ViPSN-pluck, in particular, the dynamic characteristics of the plucking energy harvester and the energy matching between generation and utilization, are discussed in detail to demonstrate the robustness in operation. The cyber-electro-mechanical synergy among the mechanical dynamics, power conditioning circuit, and low-power embedded system is highlighted. The design methodology of ViPSN-pluck provides a valuable reference for the developments of future motion-powered IoT devices.

Energy harvesting (EH) enabled cellular internet of things (IoT) is a promising solution to handle the charging and accessing of massive IoT nodes. However, limited by the high frequency band of future 5G, the radius of the small base station (SBS) is reduced, hence greatly increasing the cost of the network operators (NOs). In this paper, we consider the joint cell association, cell sleeping, and incentive decision problem for EH aided cellular IoT with full duplex (FD) self-backhauls. We formulate a Stackelberg game to investigate the coordination between the utilities of NO and energy transmitters (ETs), where both the features of FD self-backhauls and cell sleeping are introduced to reduce the expense of NO. We then propose an alternative direction algorithm to solve the equilibrium of the game efficiently, where the relationship of the formulated constraints and variables are utilized to transform the original problem into two sub-problems. We propose a two-level Lagrangian relaxation to solve the first sub-problem, while the other is proved to be convex and solve by an efficient iteration. Simulation results demonstrate the benefits of our algorithm in utility improvement and expense reduction. Moveover, it shows that our algorithm can obtain high efficiency by adjusting the tradeoff between the number of active SBS and transmitting power of ETs according to the network deployment.

Green wireless communications have been extensively studied in wireless sensor networks, including the use of new energy, renewable energy, and low power consumption and energy saving technologies for years. In these networks, due to channel fading, insufficient and random energy arrival, some possible bad deployment of sensors, etc. the communication among sensor nodes in a wireless sensor networks (WSNs) will inevitably be affected or even interrupted sometimes, which may result in unacceptable performance in entire network. In order to solve this problem, we propose a wireless sensor network composing of several local subnetworks with amplified forwarding relay and specially designed working time cycle. In this network, we study our resource allocation policies to manage both power and time for throughput maximization. We use deep reinforcement learning (DRL) to develop our resource allocation policies under the model constructed as a Markov decision process for this optimization problem in the subnetwork. We apply actor-critic strategy to find our optimal solution in continuous state and action space and adaptively achieve maximum throughput of this network based on energy harvesting, causal information of battery state and channel gains. The simulation results demonstrate that the proposed transmission policies can produce higher throughput in the local network and finally improve overall system performance in comparison with greedy policy, random policy and conservative policy.

In this paper, we propose a cooperative non-orthogonal multiple access (NOMA)-enabled simultaneous wireless information and power transfer (SWIPT) Internet-of-Things (IoT) networks, where one information source harvests energy from a multi-antennas power beacon (PB) to serve two IoT users via the help of multiple energy-limited relay nodes. To improve the performance of far IoT user, we propose reactive and proactive relay selection protocols together with time-power energy harvesting mechanism under imperfect successive interference cancellation. Closed-form expressions for the outage probability (OP), throughput, and energy efficiency (EE) of the proposed system are obtained, from which the asymptotic analysis for the throughput is also carried out. To further enhance the system performance, we propose a low-complexity method to optimize the outage and throughput performance subject to power allocation, time, and power splitting parameters. Towards real-time configurations in IoT networks, we design a deep learning framework for the sum-throughput and EE predictions with low computation complexity and high accuracy. The influences of antennas setting at PB, time-switching ratio, power-splitting ratio, power allocation factor, and the number of relays on the system OP, throughput, and EE are evaluated and discussed along with numerical results.

We propose a battery-free temperature monitoring device that can be fitted inside the ear for an accurate body temperature measurement of a subject. The proposed application consists of two primary systems: 1) a battery-free temperature sensing ultra-high-frequency radio frequency identification sensory tag and 2) an auxiliary energy harvesting system, which enhances the sensing device's measurement accuracy and precision. The system can record changes in the localized body temperature of authenticated users with an average latency of 501 ms. The assembly demonstrated a temperature average accuracy of ± 0.14 °C operating at 866 MHz. The system performance demonstrated high stability and repeatability of reported temperature measurements. The device's dimension is a form factor that can easily fit in a front shirt pocket, with a wire tethered earbud temperature sensor. The system is developed to make sensor measurements without requiring a battery for the device. Measurements are made remotely as users pass by checkpoints installed throughout a building. The device is a cost-effective solution for monitoring body temperature in work environments.

The rapidly developing energy harvesting (EH) technology is a promising solution to the durability issue in the battery-powered Internet of things (IoT) systems. In this paper, underlaid device-to-device (D2D) transmission powered by radio signals harvested from cellular systems is studied. By considering the dilemmas among energy harvesting, D2D transmission opportunity, and interference management, we propose two transmission policies: Policy 1 requires that the available power in the battery should be no less than the D2D transmission power; Policy 2 not only sets the constraint on available power, but also introduces the guard zone rule to protect D2D transmissions from severe interference. The employment of a guard zone in this situation is technically challenging since the original distribution of energy arrival will thereby be changed. We derive expressions in closed or semi-closed forms for the considered D2D transmission performance metrics with the stochastic geometry framework and Poisson hole process. With numerical simulation results, the influences of varying network parameters on D2D performances are illustrated. The results show that by introducing a guard zone, the D2D successful transmission rate can be increased by 41.2%. All the developed D2D framework and the summarized useful remarks are used to provide meaningful design insights and guidelines for the deployment strategies of EH based D2D wireless networks.

Backscatter communication is an emerging paradigm for pervasive connectivity of low-power communication devices. Wirelessly powered backscattering wireless sensor networks (WSNs) become particularly important to meet the upcoming era of the Internet of Things (IoT), which requires the massive deployment of self-sustainable and maintenance-free low-cost sensing and communication devices. This article will introduce the state-of-the-art antenna design and radio frequency (RF) system integration for wirelessly powered backscatter communications, covering both the node and the base unit. We capture the latest development in ultralow-power RF front ends and coding schemes for μW-level backscatter modulators, as well as the latest progress in wireless power transfer (WPT) and energy harvesting (EH) techniques. Newly emerged rectenna system, waveform design, and channel optimization are reviewed in light of the opportunities for adaptively optimizing the WPT/EH efficiency for low-power signals with varying conditions. In addition, advanced device packaging and integration technologies in, e.g., additively manufactured RF components and modules for microwave and millimeter-wave ubiquitous sensing and backscattering energy-autonomous RF structures are reported. Inkjet printing for the sustainable and ultralow-cost fabrication of flexible RF devices and sensors will be reviewed to provide a prospective insight into the future packaging of backscatter communications from the chip-level design to complete system integration. Finally, this article will also address the challenges in fully wireless powered backscatter radio networks and discuss the future directions of backscatter communication in terms of ``Green IoT'' and ``Low Carbon'' smart home, smart city, smart skin, and machine-to-machine (M2M) applications.

Wireless powered communication networks (WPCNs) will act as a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. The MTC networks will be deployed by using low-power transceivers with finite discrete configurations. This paper considers minimum length scheduling problem for full-duplex WPCNs, where users transmit information to a hybrid access point at a rate chosen from a finite set of discrete-rate levels. The optimization problem considers energy causality, data and maximum transmit power constraints, and is proven to be NP-hard. As a solution strategy, we define the minimum length scheduling (MLS) slot, which is slot of minimum transmission completion time while starting transmission at anytime after the decision time. We solve the problem optimally for a given transmission order based on the optimality analysis of MLS slot. For the general problem, we categorize the problem based on whether the MLS slots of users overlap over time. We propose optimal algorithm for non-overlapping scenario by allocating the MLS slots, and a polynomial-time heuristic algorithm for overlapping scenario by allocating the transmission slot to the user with earliest MLS slot. Through simulations, we demonstrate significant gains of scheduling and discrete rate allocation., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Energy harvesting (EH) powered sensor nodes can achieve theoretically unlimited lifetime by scavenging energy from ambient power sources, such as radio frequency (RF) and kinetic energy. The nodes can collect and transmit data wirelessly with the harvested energy. However, the transmission between two sensor nodes is successful only when both nodes have enough energy at the same time. While the receiver can be actively listening, it may deplete the energy long before the sender has accumulated enough energy. Thus, given the scarce, unpredictable, and unevenly distributed energy among sensor nodes, it is challenging to ensure efficient data transmission between them. To address this challenge, we propose a sensor node architecture with multiple radios, each with different energy consumption on the sender and receiver. A node can be put into sleep when charged up and wakes up for communication when it infers that both nodes have enough energy based on its observations. What is more, two nodes can cooperatively and dynamically select different radios according to the stored energy and historical information to maximize the data throughput. To achieve cooperative communication adaptively, the communication procedure is modeled as a cooperative Markov game with partial observability on each node, and multi-agent reinforcement learning (MARL) is employed to achieve the best results. Experimental results on hardware prototype and by simulation show that the proposed approaches achieve up to 89.1% of the optimal throughput and significantly outperform other online algorithms.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna’s performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.

A backpack energy harvester converts mechanical energy associated with the oscillation of the backpack during human walking, into electric energy. It can be a very promising solution for supplying portable devices, especially in outdoor activities, disaster relief, and military applications. In this article, a backpack energy harvesting system that is able to self-adapt its operating condition to maximize the extracted power while supplying a dc load is presented and studied, analytically and experimentally. It is based on a mechanical motion rectifier that converts the up-and-down oscillation of the backpack into a unidirectional rotation of a dc generator. The generator output voltage is regulated by a power electronic interface implementing a maximum power point tracking technique to maximize the extracted power. Experimental results confirm the tracking ability of the proposed system and show the advantages with respect to other wearable energy harvesting systems published in the literature.

Continuous advances in ultralow-power electronics are a fundamental driving force of developing far-field wireless power harvesting techniques, which generally harnesses low/ambient energy in free space or dedicated wireless power from a power base station. This article reviews recent developments and technology trends in far-field (radiative) wireless power harvesting, including modeling of rectification process inside nonlinear devices, insights into rectenna system design, and demonstrations of emerging applications. The rectification process inside nonlinear devices will be explored, upon which a device selection guideline is provided to help quickly identify suitable devices. Then, the optimization of rectenna design is discussed to realize efficiency enhancement at a limited input power level. Since far-field wireless power harvesting has become a critical enabling technology for battery-free Internet of Things, a series of promising applications is demonstrated in this article to highlight the challenges and opportunities in the field.

A comprehensive review of metamaterials and metasurfaces for wireless power transfer (WPT) and wireless energy harvesting (WEH) is presented in this article. According to the features of the electromagnetic field from the source to the receiver, WPT is divided into nonradiative near-field technology and radiative (near and far-field) technologies. Many different and important designs are reviewed and compared. It is shown that metamaterials and metasurfaces can significantly improve the power transfer efficiency and operational distance for WPT systems. They can also improve the energy conversion efficiency of wireless energy harvesters by making the reception less sensitive to incident wave angle and polarization. A rectenna is a critical element for both WPT and WEH. It is shown that metamaterial-based rectennas can achieve a higher RF to dc conversion efficiency. Furthermore, metamaterials can also be used as either parasitic elements or loading components to improve WEH performance in terms of circuit size, beamwidth, and conversion efficiency. Future development directions and opportunities of metamaterials and metasurfaces for WPT and WEH are also proposed in this article.

The design of an ambient radio frequency (RF) energy harvesting system is introduced to replace the battery of the mass Internet-of-Things devices. This system can achieve high sensitivity with high efficiency over a wide input power range by optimizing the differential-drive cross-coupled (DDCC) rectifier and the load. The impedance of the dual-band four-port loop antenna array is directly conjugated to match with the rectifier, which reduces insertion loss of the matching network to improve efficiency. At 915 and 945 MHz for ISM and GSM bands, the rectenna can achieve high sensitivity of -30 dBm with 22% and 24% efficiency, high efficiency of 65% and 48% with -20-dBm input power, and a wide input power range from -30 to -18 dBm with 22-65% and 24-48% efficiency. The dual-band design explores the frequency diversity to harvest more RF energy from the environment, while the cross-shaped antenna array utilizes the space diversity to harvest RF energy from different directions with a compact size of 0.15λ x 0.15λ per antenna element (where λ is the free space wavelength at 915 MHz). This new harvesting system can provide output dc power of 21.4 and 2.95 μW in real-world outdoor and indoor ambient environments, respectively.

A quartz oscillator has a stable frequency that can be tuned by applying a voltage across the quartz crystal. This study applied a triboelectric generator (TEG) as mechanical stimuli on the input voltage of a remote transmitter in a transmitter-to-receiver system. The intermodulation (IM) frequencies were chosen instead of the transmitter frequencies because the IM signals are generally significant. This study first examined the rectified electric voltage from contact-separation and single-electrode mode TEGs. The series connection TEG, stored capacitor, and passive voltage regulator (zener diode) determine the quality of the direct current voltage. The increase in the input voltage via mechanical stimuli on the transmitter was proved to deliver a signal where the IM frequencies increase with the stimuli in the system.

Many variable-capacitance energy harvesters employ a large inductor to improve their power efficiency by reducing conduction losses, which is suboptimal in applications requiring a small form-factor, such as in implants. This article describes a variable-capacitance harvester that performs optimally using miniaturized inductors. The impact of scaling the inductor on the generated energy of conventional semisynchronous and fully-synchronous charge-constraint topologies is investigated analytically as well as experimentally. It is shown that the proposed harvester outperforms the semisynchronous and fully-synchronous charge-constraint harvesters while using very small inductance values. Using two reservoir capacitors to generate energy without requiring large inductors, as well as utilizing a different switching scheme are the main factors contributing to this advantage. Since harvesting energy from slow moving mechanical sources, such as body movements, constitutes a major challenge, all three harvesters are implemented and tested with an actuating frequency as low as 0.5 Hz and for inductance values between 1 $\mu$ H to 1 mH. The experimental results for sample designs corroborate the analytical expressions and show that to generate optimal harvested energy of the proposed harvester, the semisynchronous harvester requires a 15.6 times larger inductor.