Search

Battery power has been a popular choice of delivering DC power to many portable RF systems including wireless sensor nodes/internet of Everything (IoE). An autonomously powered RF system of the orders of 100’s of µwatts is becoming increasingly popular in recent times because of integrated solutions in energy harvesting. The minimum sensitivity of an RF energy harvester and the targeted deliverable output voltage for a specific load resistance are dictated by threshold voltage of transistors used and reverse leakage of such transistors. A detailed look into the architectures of rectifiers can help tackle design problems in the RF-DC conversion process. So in this work we focused on the design of integrated RF energy harvesting by studying four important architectures of RF-DC converters. To achieve a fair comparison between these architectures, a matching network operating at 0.8 MHz is designed and applied for simulation. All of these converters are tested in LT-SPICE XVII using the same designed matching network and the rectifiers are implemented in 180 nm CMOS technology.

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.

Edge devices are embedded sensing or actuation devices accessible via wireless sensor networks in applications such as monitoring of structural health or environmental conditions in buildings. To avoid retrofitting costs and ease the deployment, these devices are often battery-powered, thus requiring manual battery replacement to maintain their operations over time. Yet, as the sensor network scales up to thousands of sensors, maintenance becomes a time consuming and labor-expensive task. Energy harvesting is often used to extend the lifetime of sensor nodes and avoid battery replacement.In this dissertation, we present techniques that combine hardware, software, and artificial intelligence techniques to extend the lifetime of the sensor nodes devices for decades without sacrificing application performance even in low energy availability environments.As a hardware solution, we present a sensing platform that can be deployed anywhere inside a building and monitor a wide range of parameters without needing periodic battery replacement that is typical of current solutions. Instead of a rechargeable battery, it uses a supercapacitor to store the energy harvested from the environment. To facilitate deployment and integration with existing buildings, the platform uses Bluetooth Low Energy (BLE) to relay data.Since the amount of energy harvested can change between sensor node locations, and the applications can have different energy requirements over time, we present a learning-based method to extend the operating lifetime of network-connected edge devices while increasing the application performance with available energy. We describe design choices that enable an indoor environment sensing device to exploit reinforcement learning for periodic and event-driven sensing with ambient light energy harvesting. Using simulations and real deployments, we show that our sensor nodes adapt to ambient lighting conditions and send measurements and events continuously during nights and weekends without interruptions. We use real-world deployment data to continually adapt sensing to changing environmental patterns and use transfer learning to reduce the training time. To be effective these techniques require prior knowledge of the environment in which the sensor nodes are deployed. In the absence of historical data, the application performance deteriorates. To address this problem, we present an approach that leverages meta reinforcement learning to increase the application performance of newly deployed batteryless sensor nodes without historical data. Our method exploits information from other sensor node locations to expedite the learning of newly deployed sensor nodes and improves the application performance after a few days of deployment.

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.

Optimal energy delay scheduling for capacity allocation issues on interference chan- nel energy harvesting wireless sensor networks (EH-WSN) addresses for static data streams and energy topography. To create the optimizations issue of unique and multi-time interval corre- spondingly. The goal is for reducing overall regression of the network. Though, the optimization issue is not convex, creating it hard to get an optimal solution. The main objective of this manuscript is directly addressing the original non-convex formulation using the enhanced Beetle Antennae Search (BAS) algorithm that efficiently finds the optimal solution. This algorithm is also better for another complex optimization issues on wireless network design. The simulations under delay, delratio, drop, energy, network lifetime, overhead and throughput are carried; the expected outcome displays that improved BAS algorithm executes considerably better than the convex approximation technique. This idea is useful for other complex optimization issues on wireless network design.

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.

As wearable microelectronics become more ubiquitous, and the size and power requirements of such devices decrease, there is increasing research interest in harnessing power from ambient environmental sources through embedded systems as alternative to battery replacements. Accordingly, a multimodal hybrid piezo-electromagnetic insole energy harvester (PEM-IEH) has been presented in this paper as a means to reclaim the biomechanical energy wasted in the surroundings during daily walking. The hybrid device consists of two piezo-ceramic wafer plates, two magnets, and two wound coils. The designed harvester has been simulated, fabricated and experimentally validated. From the frequency response, the hybrid harvester exhibits four resonant frequencies concentrated around 8, 25, 50, and 51 Hz. A maximum combined power of 1400 µW is generated across the optimal load resistances of upper and lower electromagnetic generators at first resonant frequency (8 Hz) under 0.5 g base acceleration, and a 269 µW peak power is obtained across the optimum load resistances of the upper and lower piezoelectric generators at 4th resonance (51 Hz) under 0.5 g. The miniature (46.8 cm3) and lightweight (43.3 g) harvester was incorporated into the sole of an adult-sized commercial shoe, and has been shown to be able to charge a 100 µF capacitor, up to 2.4 Volt within approximately 10 min of slow jogging. The capacitor charging performance presents a remarkable potential application of the harvester in development of micro-power monitoring sensors and wearable microelectronic gadgets where batteries pose a practical bottleneck.

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.

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 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.

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.

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.

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.

In this paper, we consider an energy-harvesting (EH) relay network composing of multiple sources, a destination and multiple EH decode-and-forward (DF) relays. The EH relays all equip with a power splitter to divide the received signal power into two parts, one for information decoding and the remaining for signal relaying. The power splitting ratio (PSR) depicts the trade-off between the relaying energy and decoding energy. We propose an optimal power splitting and joint source-relay selection (OPS-JSRS) scheme where the optimal power-splitting ratio is obtained and the best source-relay pair is selected to transmit the message. For the purpose of comparison, we examine the optimal power splitting and round-robin (OPS-RR) scheme and the traditional power splitting and joint source-relay selection (TPS-JSRS) scheme. The exact and asymptotic closed-form expressions of outage probability for OPS-RR, TPS-JSRS and OPS-JSRS schemes are derived. Numerical results show that OPS-JSRS scheme is better than OPS-RR and TPS-JSRS schemes in terms of outage probability, explaining the superiority of the proposed OPS-JSRS scheme. Additionally, outage probability performance of OPS-JSRS scheme can be improved by increasing the number of sources and relays.

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.

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.

Electronic devices, with low power demand, can be energized using vibration energy harvesters which gather and transform energy from mechanical vibrations. This investigation looks at the feasibility of a method of energy harvesting from mechanical vibrations using the naturally occurring charging phenomenon within a system of two bodies which possess different work functions. In this work, a brief review of similar technologies, namely piezoelectric, electromagnetic and electrostatic energy harvesters is first given. This is followed by the development of a theoretical model and an investigation of different Work Function Energy Harvester (WFEH) operation modes, with conclusions on a possible optimum mode of operation. The design of an experiment to test the developed theory is then presented followed by some preliminary results. Generally it is found that a WFEH has potential for use in energy harvesting applications with the possibility of giving equal or better output power when compared to traditional electrostatic harvesters.

The rapid development of wearable sensing and interfacing electronics is facing challenges in sustainability and energy independence. The reliable sustainable operation of such autonomous wearable electronics hinges on the rational integration of energy harvesting and storage modules, as well as their corresponding control and regulation circuitries. Such emerging self-powered wearable systems still lack systematic system-level energy management considerations and are thus limited in efficiency, reliability, and practicality for their targeted use cases. Viewing the scattered wearable energy technologies through the concept of independent microgrids allows us to reassess the goal of establishing a reliable, practical, and energy-economical wearable system. Powered by distributed on-body energy harvesting modules, the continuous operation of wearable devices can thus be realized with their strategic integration with energy storage managed by the decentralized, hierarchical control system. This Perspective discusses the vision of a wearable microgrid, based on a judicious scenario-specific selection of harvesting and storage modules, with commensurate performance, towards the rational design of practical wearable electronic systems with high energy autonomy and reliability. The energy supply via different sources and power demands of various wearable applications are examined for determining appropriate energy reserve, attainable functionalities, and suitable operation modes, along with key considerations in device form-factors for maximizing system efficiency. By applying the concept of a microgrid on miniaturized self-powered systems for wearables, we propose three system-level design guidelines - commensurate energy rating, complimentary device characteristics, and compatible form factors - towards the future development of reliable, self-sustainable on-body systems and autonomous implantable, ingestible, or small mobile devices. We conclude by discussing the prospects for developing more efficient and sustainable wearable microgrids for higher power applications, through accurate and smart energy budgeting and regulation involving artificial intelligence and advanced algorithms towards dynamic data-driven prediction of rapidly changing power supply and demands.

International audience; This paper presents an ultra-flexible piezoelectric air flow energy harvester capable of powering a wireless sensor. The method to easily adapt the aero-electric generator to the wind is presented. In the wind tunnel, different configurations have been tested to determine the best one for energy harvesting at low wind speed. In particular, the galloping configuration, with the addition of a bluff body at the free end of the cantilever which allows to improve the performance of the micro-generator by coupling the vibrations induced by the vortices and the galloping phenomena. In this study, we also present a method to optimize the energy harvesting without increasing the volume of the device. The effects of mechanical and electrical coupling of several generators on the performance of energy harvesting are presented. Thus, with the electrical parallel coupling of four generators, we obtained a maximum power of 60 mW instead of 30 mW with two generators for a wind speed of about 6 m/s. The mechanical coupling of the micro-generators allowed the device to keep the same volume (z540 cm3), however the threshold wind speed to increase (>6 m/s). The harvested energy was then used to operate a wireless sensor.

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.

Micro-aerial vehicles (MAVs) lack the endurance times demanded by typical mission applications, and previous work to minimize flight path disturbances has also quantified their high atmospheric sen...

In an energy harvesting sensor actor network, a node recharges its battery from harvestable sources, such as solar, wind and vibrations. Sustainability of the network till next recharge time is one of the most important challenges in harvesting sensor networks. In this paper, a fuzzy based adaptive duty cycling algorithm has been proposed to achieve the network sustainability in harvesting sensor actor networks. In this work, current residual energy, predicted harvesting energy (for a futuristic time slot) and predicted residual energy parameters are considered as fuzzy input variables to estimate duty cycle for a sensor node. In this work, a harvesting model has been adopted to predict the harvesting energy. Further, residual energy has been estimated for future time slot using predicted harvesting energy, energy consumption model and current residual energy. Simulation results are presented to show the efficacy of the proposed mechanism by considering network sustainability metrics, such as number of rounds in which network is connected, the round at which first node dies, maximum number of dead nodes and average number of received packets at the actor node.

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.

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.

The performance of an unmanned aerial vehicle (UAV) assisted cooperative cognitive radio (CR) network is studied in this paper. The study is carried out for line-of-sight (LOS) links between UAV and a ground user and UAV aerodynamic power consumption of UAV. The UAV consists of an RF energy harvester and it is also a CR enabled device. It harvests energy from RF signal of source nodes. It rotates periodically over a circular region of interest (CROI) for finding the vacant primary user (PU) band. It uses a PU band to set a link between ground nodes, while the direct links between them suffer from severe fading. The decision about the status of a PU band is made cooperatively. In cooperative sensing, all the sources along with the UAV take sensing decisions and send them to fusion centre (FC). A two stage fusion scheme is proposed to take the overall decision. The considered network model uses non-orthogonal multiple access (NOMA) scheme to forward the information of a source to its corresponding destination. Analytical expressions of harvested energy, overall sensing decision and throughput are developed. The analytical expressions capture the effect of fading as well as change in distances (source-UAV and UAV-destination) due to the circular flight of the UAV. Impact of imperfect successive interference cancellation (SIC) on the system performance is also studied. Power allocation in the second hop link is optimized to maximize the network throughput. Energy-efficiency of the considered network is evaluated. For a given UAV flight period, radius of the circular trajectory, and the velocity of the UAV is optimized to maximize the energy-efficiency. Sensing performance of the considered system model is studied in terms of false alarm. Network throughput is investigated for several network parameters such as fading parameter, the number of sources, the number of UAV's sensing slots, the velocity of UAV, the radius of the UAV trajectory etc. Sensing angle and harvesting angle are optimized to maximize the throughput. Results show that the value of fading parameter, radius of UAV trajectory, and UAV velocity has significant impact on throughput. The performance of NOMA is compared with orthogonal multiple access (OMA).