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This paper introduces a novel compact ultralightweight multiband RF energy harvester fabricated on a paper substrate. The proposed rectenna is designed to operate in all recently released LTE bands (range 0.79–0.96 GHz; 1.71–2.17 GHz; and 2.5–2.69 GHz). High compactness and ease of integration between antenna and rectifier are achieved by using a topology of nested annular slots. The proposed rectifier features an RF-to-dc conversion efficiency in the range of 5%–16% for an available input power of −20 dBm in all bands of interest, which increases up to 11%–30% at −15 dBm. The rectenna has been finally tested both in laboratory and in realistic scenarios featuring a superior performance to other state-of-the-art RF harvesters on flexible substrates. [ABSTRACT FROM PUBLISHER]

This paper demonstrates the design of an 800-MHz solar-powered active wireless beacon composed of an antenna and an integrated oscillator on a low-cost paper substrate. Inkjet printing is used to fabricate the conductive circuit traces and the folded slot antenna, while the oscillator circuit is designed using off-the-shelf components mounted on the paper substrate. Flexible, low-cost, amorphous silicon (a-Si) solar cells are placed on top of the slot ground and provide autonomous operation of the active circuit eliminating the use of a battery. A prototype is built and characterized in terms of phase noise, radiation patterns, and the effect of solar irradiance. Such low-cost flexible circuits can find significant applications as beacon generator circuits for real-time identification and position purposes, wearable biomonitoring as well as solar-to-wireless power transfer topologies. The measured phase noise is -\116 dBc/\Hz at 1-MHz offset, while drain current is 4 mA and supply voltage is 1.8 V. [ABSTRACT FROM AUTHOR]

The papers in this special section focuses on signal processing advances in wireless transmission of power and information. Wireless power transfer (WPT) and wireless information and power transfer (WIPT) have received growing attention in the research community in the past few years. In this special issue, a total of fourteen papers present state-of-the-art results in the broad area of wireless transmission of information and power with a special emphasis on signal processing advances. The special issue starts with a guest editor-authored tutorial overview paper that reviews the signal processing, machine learning, sensing, and computing techniques, challenges and opportunities in future networks based on WPT and WIPT. [ABSTRACT FROM AUTHOR]

In this paper, we propose and study an energy-efficient trajectory optimization scheme for unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks. In such networks, a single UAV is powered by both solar energy and charging stations (CSs), resulting in sustainable communication services, while avoiding energy outage. In particular, we optimize the trajectory design of UAV by jointly considering the average data rate, the total energy consumption, and the fairness of coverage for the IoT terminals. A dynamic spatial-temporal configuration scheme is operated for terminals working in the discontinuous reception (DRX) mode. The module-free, action-confined on-policy and off-policy reinforcement learning (RL) approaches are proposed and jointly applied to solve the formulated optimization problem in this paper. We evaluate the effectiveness of the proposed strategy by comparing it with other dynamic benchmark algorithms. The extensive simulation results provided in this paper reveal that the proposed scheme outperforms the benchmarks in terms of data transmission, energy efficiency and adaptivity of avoiding battery depletion. By deploying the proposed trajectory scheme, the UAV is able to adapt itself according to the temporal and dynamic conditions of communication networks. [ABSTRACT FROM AUTHOR]

This paper studies the secrecy performance of a wireless network (primary network) overlaid with an ambient RF energy harvesting Internet of Things (IoT) network (secondary network). The nodes in the secondary network are assumed to be solely powered by ambient RF energy harvested from the transmissions of the primary network. We assume that the secondary nodes can eavesdrop on the primary transmissions due to which the primary network uses secrecy guard zones. The primary transmitter goes silent if any secondary receiver is detected within its guard zone. Using tools from stochastic geometry, we derive the probability of successful connection of the primary network as well as the probability of secure communication. Two conditions must be jointly satisfied in order to ensure successful connection: 1) the signal-to-interference-plus-noise ratio (SINR) at the primary receiver is above a predefined threshold, and 2) the primary transmitter is not silent. In order to ensure secure communication, the SINR value at each of the secondary nodes should be less than a predefined threshold. Clearly, when more secondary nodes are deployed, more primary transmitters will remain silent for a given guard zone radius, which will in turn impact the amount of energy harvested by the secondary network. Our results concretely show the existence of an optimal deployment density for the secondary network that maximizes the density of nodes that are able to harvest sufficient amount of energy. Furthermore, we show the dependence of this optimal deployment density on the guard zone radius of the primary network. In addition, we show that the optimal guard zone radius selected by the primary network is a function of the deployment density of the secondary network. This interesting coupling between the performance of the two networks is studied using tools from game theory. We propose an algorithm that can assist the two networks to converge to Nash equilibrium. The convergence of this algorithm is verified using simulations. Overall, this paper is one of the few concrete works that symbiotically merge tools from stochastic geometry and game theory. [ABSTRACT FROM PUBLISHER]

In the real-world, it is virtually impossible to have non-causal knowledge of future events. Research in energy harvesting (EH) systems that assumes knowledge of future energy arrivals falls short in terms of practical utility, pointing to the need for online strategies. In addition, the modeling and analysis for EH transmitter and receiver are inherently different. Compared with EH transmitter, EH receiver has received less attention. In this paper, we formulate Markov decision process problems and perform online optimization to maximize the number of bits decoded for an EH receiver with a time-switching architecture, which harvests energy from both a dedicated transmitter and other sources. We consider both infinite and finite horizon scenarios. For the infinite horizon, we provide an upper bound on the average expected reward. Then, we find an optimal policy which can achieve performance arbitrarily close to this bound. For the finite horizon, we first provide a policy obtained from standard backward induction with space quantization. Its performance can be close to optimal online performance as the number of quantization intervals increases, at the cost of relatively high computational complexity. Then, by carefully restricting the state space, we present a computationally efficient policy, which achieves comparatively good performance. [ABSTRACT FROM AUTHOR]

Energy consumption has become one of the main parameters in evaluating sensor systems in the IoT market. Long range sensor systems are not an exception. Their application in the outdoor environment makes them an ideal candidate to combine energy harvesting mechanisms with the low power features. Using just low power electric components is not enough to ensure energy efficiency. Techniques and mechanisms for harvesting and saving energy are presented in this paper and in its core, a low power, energy efficient communication protocol is described. The proposed protocol optimizes the way in which information is collected from the environment, packed and transmitted over long distances with minimum energy. It is specially designed for energy constrained sensor modules which rely on energy harvesting. The gathered information is transmitted in a form of two different packet types called Teach-in and Data telegrams, respectively. A detailed structure of these packets and the energy required for transmission is explained in this paper, along with a deeper protocol physical layer analysis. In addition, this paper demonstrates the huge potential for mass market, as a result of the many proposed solutions that have been tested and implemented in a fully developed functioning prototype. [ABSTRACT FROM AUTHOR]

This paper formulates and solves optimization problems whose objective is to maximize the sum-throughput of wireless sensor networks with radio frequency (RF) energy harvesting (EH) and backscatter communication. The paper proposes and analyzes three protocols for maximizing the sum-throughput; namely, the time-division with downlink data decoding protocol, hybrid power splitting/data decoding protocol, and backscatter-enabled combination protocol. The time-division protocol optimizes a wireless powered communication network (WPCN) with a two-way communication link between the sensors and a hybrid access point. The hybrid protocol optimizes a WPCN with a power splitting downlink data decoding phase. The backscattered-enabled combination protocol optimizes a WPCN with a power splitting downlink data decoding phase and backscatter communication enabled sensors. Numerical results show that the hybrid and combination protocols deliver up to 14.5% and 30.5% increase in sum-throughput, respectively, compared with the reference time-switching RF EH protocol in a dynamic environment. The findings are significant for the future widespread adoption of WPCN systems powered by RF energy sources in the real world by increasing the amount of harvested energy and system achievable data rate. [ABSTRACT FROM AUTHOR]

Recently, Energy Harvesting-supported Device-to-Device (EH-D2D) communication receives extensive concerns due to its excellent properties in Energy Efficiency (EE), offloading capability, etc. Researches on EH-D2D mainly focus on resource allocation schemes of available energy. With the popularity of mobile devices and applications, content sharing becomes anytime, anywhere. Multicast is the foundation to realize the content sharing and can reduce transmission consumption because of multi-user service feature. Thus, Mutlicast can further improve EE of EH-D2D in communication mode rather than in resource allocation. However, the heterogeneity and imbalance of available energy, content requests and social ties on the user side will bring great challenges to the deployment of EH-D2D Multicast (EH-D2MD) communication. This paper aims at EE optimization and establishes a two-layer cluster formation model, which solves grouping, cluster head selection and power control jointly. When facing the non-convex of the sum of fractional functions and the product of binary variables, this paper specifically designs a two-step Convex Approximation Algorithm (CDA). CDA skillfully transforms the modeled non-convex Mixed Integer Non-Linear Programming (MINLP) problem into a convex MINLP one, which is easier to solve. Simulation results reveal that CDA can obtain the joint cluster formation results with approximate optimal EE and lower complexity. [ABSTRACT FROM AUTHOR]

This paper presents a reconfigurable dc–dc converter for maximum thermoelectric generator (TEG) energy harvesting in a battery-powered duty-cycling wireless sensor node. The proposed dc–dc converter adopts discontinuous energy harvesting, which operates in single-input dual-output (SIDO) boost, battery-TEG pile-up buck (BTPB), dual-phase buck–boost (DPBB), and battery supplied buck modes with a single shared inductor. Fabricated in a 65-nm CMOS process, the converter adopts an adaptive dead-time controller for zero-voltage switching (ZVS) and an adaptive switch size (ASS) with maximum power point tracking (MPPT) to maximize efficiency in a wide TEG voltage range. The SIDO boost mode achieves 88.5% and 81.1% peak end-to-end efficiency with maximum and no-load conditions (only charging the battery), respectively. The combination of BTPB and DPBB modes to maximize the TEG power extraction during the battery-powered operation results in up to 44% saving in battery power. The battery-supplied buck mode achieves 93.3% peak efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cooperative simultaneous wireless information and power transfer (SWIPT) network with one source–destination pair and multiple energy harvesting relays. How to efficiently use these relays has been addressed in conventional cooperative networks, and this paper investigates the impact of SWIPT on the performance of such multirelay cooperative networks. In particular, the main contribution of this paper is that a closed-form expression of the outage probability achieved by the multirelay cooperative protocol is obtained, as well as its approximation at high SNR. In addition, the performance of the cooperative network with the SWIPT relays is compared with that in conventional systems with self-powered relays. [ABSTRACT FROM PUBLISHER]

Wireless powered communication (WPC) has been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we study a wireless powered time-division duplex (TDD) multiuser multiple-input multiple-output (MU-MIMO) system, where the base station (BS) has its own power supply and all users can harvest radio frequency (RF) energy from the BS. We aim to maximize the users’ information rates by jointly optimizing the duration of users’ time slots and the signal covariance matrices of the BS and users. Different to the commonly used sum rate and max-min rate criteria, the proportional fairness of users’ rates is considered in the objective function. We first study the ideal case with the perfect channel state information (CSI), and show that the non-convex proportionally fair rate optimization problem can be transformed into an equivalent convex optimization problem. Then we consider practical systems with imperfect CSI, where the CSI mismatch follows a Gaussian distribution. A chance-constrained robust system design is proposed for this scenario, where the Bernstein inequality is applied to convert the chance constraints into the convex constraints. Finally, we consider a more general case where only partial knowledge of the CSI mismatch is available. In this case, the conditional value-at-risk (CVaR) method is applied to solve the distributionally robust system rate optimization problem. Simulation results are presented to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates intelligent reflecting surface based backscatter communication (IRS-BackCom), in order to realize computational task offloading of energy-constrained mobile edge computing network in a self-sustainable manner. Specifically, the system operation is divided into two phases. In the first one, the ambient signal energy from a power beacon (PB) either provides the energy supply of local computing and energy harvesting circuits, or flows into the energy storage, when reaching the IRS. In the second one, the stored energy is used to enable IRS-BackCom for partial computational data offloading and energize local computing circuit. Based on this, the maximization problem of sum computational bits is formulated. By jointly optimizing the beamforming vector at the PB, the backscatter matrix at the IRS, the time scheduling of two-phase process, as well as the time of local computing, sum computational bits are maximized. In addition, this paper proposes element clustering to realize BackCom, so as to reduce the control and computation complexity of IRS. According to different operating mechanisms, two cluster operation modes are considered, namely independent cluster operation mode and joint cluster operation mode. Simulation results demonstrate the achievable sum computational bits by the proposed IRS-BackCom schemes. [ABSTRACT FROM AUTHOR]

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme. [ABSTRACT FROM AUTHOR]

In this paper, we study the average throughput performance of energy beamforming in a multi-antenna wireless-powered communication network (WPCN). The considered network consists of one hybrid access point (AP) with multiple antennas and a single-antenna user. The user has no constant power supply and, thus, needs to harvest energy from the signals broadcast by the AP in the downlink (DL) before sending its data back to the AP with the harvested energy in the uplink (UL). We derive closed-form expressions of the average throughput and their asymptotic expressions at a high SNR for both delay-limited and delay-tolerant transmission modes. The optimal DL energy harvesting time, which maximizes the system throughput, is then obtained for high SNR. All analytical expressions are validated by numerical simulations. The impact of various parameters, such as the AP transmit power, the energy harvesting time, and the number of antennas, on the system throughput is also investigated. [ABSTRACT FROM AUTHOR]

Wireless nodes in future communication systems need to overcome three barriers when compared to their transitional counterparts, namely, to support significantly higher data rates, have long-lasting energy supplies, and remain fully operational in interference-limited heterogeneous networks. This could be partially achieved by providing three promising features, which are radio frequency (RF) energy harvesting, improper Gaussian signaling, and operating in full-duplex communication mode, i.e., transmit and receive at the same time within the same frequency band. In this paper, we consider these aspects jointly in a multi-antenna heterogeneous two-tier network. In this network, the users in the femtocell share the scarce resources with the cellular users in the macro-cell and have to cope with the interference from the macro-cell base station as well as the transmitter noise and residual self-interference due to imperfect full-duplex operation. Interestingly enough, while these impairments are detrimental from the achievable rate perspective, they are beneficial from the energy harvesting aspect, as they carry RF energy. In this paper, we consider this natural tradeoff jointly and propose appropriate optimization problems for beamforming and optimal resource allocation. Moreover, various receiver structures are employed for both information detection and energy harvesting (EH) capabilities. This paper aims at characterizing the tradeoff between the achievable rates and harvested energies. Rate and energy maximization problems are thoroughly investigated. Interestingly, with sufficiently high EH demands, we observe the convergence of the rate region obtained by non-linear precoding to the rate region achieved by widely linear precoding at the base station. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the capacity of a point-to-point, full-duplex (FD) wirelessly powered communication system impaired by self-interference. This system is comprised of an energy transmitter (ET) and an energy harvesting user (EHU), which operates in the FD mode. The ET transmits energy toward the EHU. The EHU harvests this energy and uses it to transmit information back to the ET. As a result of the FD mode, both nodes are affected by self-interference. The self-interference has different effects at the two nodes. In particular, the self-interference impairs the decoding of the received information signal at the ET, whereas it serves as an additional source of energy at the EHU. In this paper, we derive the capacity of the adopted system model assuming a processing cost at the EHU and an additive white Gaussian noise channel with block fading. Thereby, we show that the capacity achieving scheme is relatively simple and therefore applicable to devices with limited resources. Moreover, our numerical results show significant improvements in terms of data rate when the capacity achieving strategy is employed compared to half-duplex transmission, even for very high self-interference at the ET. Moreover, we show the positive effects of the self-interference at the EHU, as well as the crippling effect of the processing cost. [ABSTRACT FROM AUTHOR]

This paper treats the problem of optimal resource allocation over time in a finite-horizon setting, in which the resource become available only sequentially and in incremental values, and the utility function is concave and can freely vary over time. Such resource allocation problems have direct applications in data communication networks (e.g., energy harvesting systems). This problem is studied extensively for special choices of the concave utility function (time invariant and logarithmic) in which case the optimal resource allocation policies are well-understood. This paper treats this problem in its general form and analytically characterizes the structure of the optimal resource allocation policy and devises an algorithm for computing the exact solutions analytically. An observation instrumental to devising the provided algorithm is that there exist time instances at which the available resources are exhausted, with no carryover to future. This algorithm identifies all such instances, which in turn, facilitates breaking the original problem into multiple problems with significantly reduced dimensions. Furthermore, some widely used special cases in which the algorithm takes simpler structures are characterized, and the application to the energy harvesting systems is discussed. Numerical evaluations are provided to assess the key properties of the optimal resource allocation structure and to compare the performance with the generic convex optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper considers a green cognitive radio network wherein each secondary user (SU) is solely powered by an energy harvester which extracts energy from RF signals of a primary user transmitter. Each SU operates in a harvesting–transmitting fashion periodically. In each period, all SUs first harvest energy for a fixed duration and then transmit data using the harvested energy to one access point for the rest of the period in the code-division multiple access method. Considering the intrinsic harvesting-throughput tradeoff, we jointly optimize the harvesting time and transmit powers of SUs to maximize the sum throughput of the network, subject to the primary interference constraint and the energy causality constraint. Despite of the nonconvex nature of the formulated optimization problem, we find an equivalent but convex substitution to the original problem. Then, we propose a dual-decomposition-based solution method to solve the problem. Simulations finally demonstrate the efficiency of this paper. [ABSTRACT FROM AUTHOR]

The diagnostic machinery monitoring system presented in this paper coordinates the acquisition of vibration and speed estimates for an electric machine. It functions even when the machine is “off” or spinning down. This effectively turns the machine into its own mechanical network analyzer. Data from this system can be used to correlate electrical and mechanical data in order to distinguish a variety of pathologies in the machine and its mechanical mounts. The entire sensor system powers itself from surrounding magnetic fields, requiring no ohmic contact or special power wiring. The system presented in this paper presents a unique approach for fusing data from multiple sensors to create a coordinated picture of machine operation on a self-powered platform. [ABSTRACT FROM AUTHOR]

The ongoing growth of Internet of Things (IoT) traffic increasesthe emergence of Multi-Access Edge Computing (MEC), which can serve IoT devices to process edge missions properly. The power supply for IoT devices is addressed by energy harvesting (EH) technology. However, the energy arrival model obeys the unknown energy arrival process. In addition, due to the dynamical changes of network state, it is crucial to study the access control in the MEC-based green IoT system. In this paper, we propose an MEC-based access control strategy to maximize the system utility for the green IoT system, considering the spectral efficiency and the successful access ratio from the perspective of operators and users respectively. Specifically, we model the access control problem as a Constrained Markov Decision Process (CMDP), and develop a novel centralized reward-based experience replay deep convolutional Q network algorithm (RCQN) to attain the optimal access control for EH IoT devices. Additionally, to enrich the prior knowledge of access control strategy, we design a Long Short-Term Memory (LSTM) based energy prediction module to predict the energy state of IoT devices by learning the historical energy arrival information, whose output is taken as the state information of RCQN. Simulation results demonstrate that the proposed LSTM-based RCQN access control algorithm significantly improves the system utility with a proper reward design and training mechanism. [ABSTRACT FROM AUTHOR]

This paper provides radio frequency (RF) energy harvesting and coverage probability analysis using stochastic geometry in a two-hop communication network. In the first hop, terrestrial base stations (TBSs) transmit RF energy and data to the aerial base stations (ABSs) and in the second hop, ABSs forward the data to the user. Numerical results show that the energy harvesting and coverage probabilities can be maximized at almost the same altitude of the ABSs with respect to the TBSs height for low TBSs densities. However, as the density of the backhaul nodes and the energy harvesting sub-timeslot increases, energy harvesting probability improves while coverage probability monotonically decreases by increasing the ABSs height. [ABSTRACT FROM AUTHOR]

The human-centered body sensor network (BSN) becomes one of the Internet of Things (IoT) development directions in the future due to interdisciplinary advantages in the fields of wireless communication technology, embedded microelectronic technology, and mobile Internet technology. Only depending on the terminal perception and transmission technology is not enough to make up the weak data storage, computing, and analysis capacity. Thus, in combination with green sensors, smart clouds computing, artificial intelligence technology, and BSN, this paper proposes a concept of green cognitive BSN (Green-CBSN). Starting from the three aspects including green active sensor, energy harvesting and efficient data collection, and health big data recognition and interaction, this paper introduces the architecture and thought of Green-CBSN in detail. Then, we invite some volunteers equipped with wearable devices, such as smart clothing and smartphone, and carry out the physiological signal collection, heart rate monitoring, and physiological data emotion analysis experiments of electrocardiograph and photoplethysmography. At last, this paper summarizes the open issues and research directions about IoT security, health big data recognition algorithm optimization, and energy saving and green energy harvesting, to provide the reference for Green-CBSN. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

This paper applies machine learning to optimize the transmission policies of cognitive radio inspired non-orthogonal multiple access (CR-NOMA) networks, where time-division multiple access (TDMA) is used to serve multiple primary users and an energy-constrained secondary user is admitted to the primary users’ time slots via NOMA. During each time slot, the secondary user performs the two tasks: data transmission and energy harvesting based on the signals received from the primary users. The goal of the paper is to maximize the secondary user’s long-term throughput, by optimizing its transmit power and the time-sharing coefficient for its two tasks. The long-term throughput maximization problem is challenging due to the need for making decisions that yield long-term gains but might result in short-term losses. For example, when in a given time slot, a primary user with large channel gains transmits, intuition suggests that the secondary user should not carry out data transmission due to the strong interference from the primary user but perform energy harvesting only, which results in zero data rate for this time slot but yields potential long-term benefits. In this paper, a deep reinforcement learning (DRL) approach is applied to emulate this intuition, where the deep deterministic policy gradient (DDPG) algorithm is employed together with convex optimization. Our simulation results demonstrate that the proposed DRL assisted NOMA transmission scheme can yield significant performance gains over two benchmark schemes. [ABSTRACT FROM AUTHOR]

A call for papers on topics including antennas and propagation, power harvesting and localization is presented.

This paper deals with measurement methodologies for establishing indicators and metrics in order to assess the performance of vibration energy scavengers. The measurement method proposed here is based on the analysis of the flow of energy through several sections of the harvester, which sometime can include complex architectures and nonlinear phenomena. It is worth noting that the proposed solution is applicable not only to linear transducers in presence of periodic vibration source, as mainly proposed in the literature, but also to nonlinear harvester with its conditioning architectures/circuits and in presence of both stochastic and periodic sources. In this context, all the features have been studied from a generalized point of view which can be applied to study a wide range of Lead Zirconate Titanate-based architectures also exploring the effect of parameters variation, transducer architecture, and its consequence in terms of output performance as function of the kinetic input energy parameters. In particular, we outline a new metric addressing the limits of current solutions which the focus on a specific prototype composed of a single transduction element. This paper develops, from theory, a metric for analyzing the implementation of a performance measurement strategy and, in order to experimentally validate this methodology, considers four case studies, which represent the most popular architectures of scavengers proposed in the literature. The metric is first introduced, than the principle of the transducers under analysis is presented, analytical models have been developed, then, numerical analyses have been performed and the obtained behaviors have been validated from an experimental point of view, including characterization and performance evaluation; a comparison as respect the current figure of merits, applied in a specific device, has also been accomplished highlighting the suitability of the proposed methodology. [ABSTRACT FROM AUTHOR]