Energy Harvesting Sensor Research Articles

Age of Information Minimization for an Energy Harvesting Source With Updating Erasures: Without and With Feedback

Consider an energy harvesting (EH) sensor that continuously monitors a system and sends time-stamped status update to a destination. The sensor harvests energy from nature and uses it to power its updating operations. The destination keeps track of the system status through the successfully received updates. With the recently introduced information freshness metric “Age of Information” (AoI), our objective is to design optimal online status updating policy to minimize the long-term average AoI at the destination, subject to the energy causality constraint at the sensor. Due to the noisy channel between the sensor and the destination, each transmitted update may be erased with a fixed probability, and the AoI at the destination will be reset to zero only when an update is successfully received. We first consider status updating without feedback available to the sensor and show that the Best-effort Uniform updating (BU) policy is optimal in a broadly defined class of online policies. We then investigate status updating with perfect feedback to the sensor and prove similar optimality of the Best-effort Uniform updating with Retransmission (BUR) policy. In order to prove the optimality of the proposed policies, for each case, we first identify a lower bound on the long-term average AoI among a broad class of online policies, and then construct a sequence of virtual policies to approach the lower bound asymptotically. Since those virtual policies are sub-optimal to the original policy, the original policy is thus optimal.

Design of Self-sustainable Wireless Sensor Networks with Energy Harvesting and Wireless Charging

Energy provisioning plays a key role in the sustainable operations of Wireless Sensor Networks (WSNs). Recent efforts deploy multi-source energy harvesting sensors to utilize ambient energy. Meanwhile, wireless charging is a reliable energy source not affected by spatial-temporal ambient dynamics. This article integrates multiple energy provisioning strategies and adaptive adjustment to accomplish self-sustainability under complex weather conditions. We design and optimize a three-tier framework with the first two tiers focusing on the planning problems of sensors with various types and distributed energy storage powered by environmental energy. Then we schedule the Mobile Chargers (MC) between different charging activities and propose an efficient, 4-factor approximation algorithm. Finally, we adaptively adjust the algorithms to capture real-time energy profiles and jointly optimize those correlated modules. Our extensive simulations demonstrate significant improvement of network lifetime ( ), increase of harvested energy (15%), reduction of network cost (30%), and the charging capability of MC by 100%.

Energy-collision-aware Minimum Latency Aggregation Scheduling for Energy-harvesting Sensor Networks

The emerging energy-harvesting technology enables charging sensor batteries with renewable energy sources, which has been effectively integrated into Wireless Sensor Networks (EH-WSNs). Due to the limited energy-harvesting capacities of tiny sensors, the captured energy remains scarce and differs greatly among nodes, which makes the data aggregation scheduling problem more challenging than that in energy-abundant WSNs. In this article, we investigate the Minimum Latency Aggregation Scheduling (MLAS) problem in EH-WSNs. First, we identify a new kind of collision in EH-WSNs, named as energy-collision, and design several special structures to avoid it during data aggregation. To reduce the latency, we try to choose the parent adaptively according to nodes’ transmission tasks and energy-harvesting ability, under the consideration of collisions avoidance. By considering transmitting time, residual energy, and energy-collision, three scheduling algorithms are proposed under protocol interference model. Under physical interference model, several approximate algorithms are also designed by taking account of the interference from the nodes several hops away. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Age of Information: An Introduction and Survey

We summarize recent contributions in the broad area of age of information (AoI). In particular, we describe the current state of the art in the design and optimization of low-latency cyberphysical systems and applications in which sources send time-stamped status updates to interested recipients. These applications desire status updates at the recipients to be as timely as possible; however, this is typically constrained by limited system resources. We describe AoI timeliness metrics and present general methods of AoI evaluation analysis that are applicable to a wide variety of sources and systems. Starting from elementary single-server queues, we apply these AoI methods to a range of increasingly complex systems, including energy harvesting sensors transmitting over noisy channels, parallel server systems, queueing networks, and various single-hop and multi-hop wireless networks. We also explore how update age is related to MMSE methods of sampling, estimation and control of stochastic processes. The paper concludes with a review of efforts to employ age optimization in cyberphysical applications.

3D Printed Long-Range Cavity Structure UHF RFID Tag Antenna with Painting Conductive Ink on Convex Surface.

In this paper, we describe a long-range convex cavity-type passive ultra-high-frequency (UHF) radio frequency identification (RFID) tag to use on various metal and non-metal surfaces, for IoT sensor energy harvesting. The tag antenna is built on the 3D printed cavity structure with polylactic acid (PLA) plastic and painted with the conductive ink on the 1 mm protruding area (convex) of inner surface and the side-walls of the cavity structure to form a cavity structure. The tag is designed to operate in the UHF band (840–960 MHz). This long-range cavity tag antenna (CTA) works at both 920 MHz and 915 MHz UHF RFID frequencies. It provides a linear polarized (LP) frontal reading range of 35 m and side reading range above 15 m when mounted on either metal or non-metal objects. We describe the antenna characteristics, structure, modeling, simulation, and experimental results. A mathematical reading range also was calculated and compared with experimental data.

An Energy-Efficient UWB Transmitter with Wireless Injection Locking for RF Energy-Harvesting Sensors.

An ultralow-power ultrawideband (UWB) transmitter with an energy-efficient injection-locked radio frequency (RF) clock harvester that generates a carrier from an RF signal is proposed for RF energy-harvesting Internet-of-Things (IoT) sensor applications. The energy-efficient RF clock harvester based on the injection-locked ring oscillator (ILRO) is proposed to achieve optimal locking range and minimum input sensitivity to obtain an injection-locked 450 MHz clock in ultralow-power operation. A current-starved inverter-based delay stage is adopted that allows delay adjustment by bias voltage to minimize dynamic current consumption while maintaining a constant delay regardless of changes in process, supply voltage, and temperature (PVT). To minimize static current consumption, a UWB transmitter based on a digital-based UWB pulse generator and a pulse-driven switching drive amplifier is proposed. The proposed injection-locked RF clock harvester achieves the best RF input sensitivity of −34 dBm at a power consumption of 2.03 μW, enabling energy-efficient clock harvesting from low RF input power. In ultralow-power operation, a 23.8% locking range is achieved at the RF injection power of −15 dBm to cope with frequency changes due to PVT variations. The proposed UWB transmitter with RF clock harvester achieves the lowest energy consumption per pulse with an average power consumption of 97.03 μW and an energy consumption of 19.41 pJ/pulse, enabling operation with the energy available in RF energy-harvesting applications.

EH-Edge--An Energy Harvesting-Driven Edge IoT Platform for Online Failure Prediction of Rail Transit Vehicles: A case study of a cloud, edge, and end device collaborative computing paradigm

Research about online failure prediction of rail vehicle core components (such as wheels, bearings, and bogies) based on big data and artificial intelligence (AI) has become popular in view of its role of improving rail vehicle operation safety. The recent vibration energy harvesting sensor network relieves sensor nodes' dependence on wired power, which provides a green and low-cost way of collecting data from rail vehicle core components. However, the integration of an energy harvesting sensor network and AI to provide online failure prediction for rail vehicle components still faces several challenges, such as weak energy harvesting power and unstable vehicle-ground communication data rate. In this article, EH-Edge, an energy harvesting-driven cloud-edge-end device collaborative Internet of Things (IoT) platform, is proposed to efficiently integrate energy harvesting and AI to solve these challenges. A two-level collaborative AI failure prediction is proposed and deployed in the EH-Edge platform to reduce energy consumption in terms of sensor node, amount of data upload, and time delay of failure prediction. Detailed software and hardware designs and real-world data sets are also published.

How Can Wake-up Radio Reduce LoRa Downlink Latency for Energy Harvesting Sensor Nodes?

LoRa is popular for internet of things applications as this communication technology offers both a long range and a low power consumption. However, LoRaWAN, the standard MAC protocol that uses LoRa as physical layer, has the bottleneck of a high downlink latency to achieve energy efficiency. To overcome this drawback we explore the use of wake-up radio combined with LoRa, and propose an adequate MAC protocol that takes profit of both these heterogeneous and complementary technologies. This protocol allows an opportunistic selection of a cluster head that forwards commands from the gateway to the nodes in the same cluster. Furthermore, to achieve self-sustainability, sensor nodes might include an energy harvesting sub-system, for instance to scavenge energy from the light, and their quality of service can be tuned, according to their available energy. To have an effective self-sustaining LoRa system, we propose a new energy manager that allows less fluctuations of the quality of service between days and nights. Latency and energy are modeled in a hybrid manner, i.e., leveraging microbenchmarks on real hardware platforms, to explore the influence of the energy harvesting conditions on the quality of service of this heterogeneous network. It is clearly demonstrated that the cooperation of nodes within a cluster drastically reduces the latency of LoRa base station commands, e.g., by almost 90% compared to traditional LoRa scheme for a 10 nodes cluster.

Optimal Transmit Power Allocation for an Energy-Harvesting Sensor in Wireless Cyber-Physical Systems.

In this article, we investigate optimal transmission power allocation at a sensor equipped with the energy-harvesting technology for remote state estimation in wireless cyber-physical systems. The sensor has access to an energy harvester, which can collect energy from the external environment and is an everlasting but unreliable energy source compared with conventional batteries. For the wireless dropping communication channel, the packet dropout rates depend on both the signal-to-noise ratio and the transmission power used by the sensor. We formulate the problem of the optimal transmission power allocation to minimize the remote estimation error covariances as a Markov decision processes (MDPs) subject to energy constraint of the sensor. By analyzing the MDP algorithm, we show that an optimal deterministic and stationary transmission power policy exists. Moreover, we show that the optimal policy has a threshold-type structure. A numerical simulation is provided to illustrate the performance of the transmission power allocation algorithm.

Improvement on Age of Information for Information Update Systems With HARQ Chase Combining and Sensor Harvesting-Transmitting Diversities

The age of information (AoI) has attracted increasing attentions as a new metric and tool to capture the timeliness of reception and freshness of data, which is different from the conventional metric such as outage, delay, and throughput. While most existing works focused on AoI analysis and optimizing method, the information update system design by utilizing diversity to reduce AoI is not fully exploited so far. Inspired by this fact, a novel information status update system is designed by jointing wireless energy harvesting diversity, status update transmitting diversity, and hybrid automatic repeat request (HARQ). Specially, the update system consists of two sensors that can be seen as an unified entity from the destination's perspective, and one destination. The two sensors, which have finite-size batteries and synchronously operate in half-duplex mode, harvest energy from a Bernoulli process source, and cooperatively deliver the status update to destination by sharing a common information source. To exploit the information of the unsuccessfully decoding update packet, the HARQ and chase combining (HARQ-CC) are jointly used at destination so that the HARQ-CC receiving diversity is also exploited. By using Bernoulli process energy arrival probability and sensor transmission probability, we first propose one novel sensor energy harvesting and update delivering model that can be represented by Markov Chain (MC) and develop one novel construction method of the transition matrix of sensor battery energy states. Considering the developed energy state transmission matrix column stochastic, irreducible, and aperiodic, we obtain the stationary distribution of sensor battery energy states so that the steady transmitting power is derived. Secondly, the average AoI of the dual-sensor status update system is derived in terms of a closed-form expression by accurately calculating the statistical descriptions of the maximum retransmission number (time) in HARQ-CC and the service time of one packet. The analytical results are validated by using the comparison from simulations. Moreover, compared with traditional single sensor system, the dual-sensor system not only harvests more energy, but also has smaller average AoI because of the joint exploitation of multi-diversity. Besides, the impact of the system parameters on harvested energy and average AoI is provided, which provides insights in terms of the optimal design of Internet of things system.

Optimizing Information Freshness in a Multiple Access Channel With Heterogeneous Devices

In this work, we study age-optimal scheduling with stability constraints in a multiple access channel with two heterogeneous source nodes transmitting to a common destination. The first node is connected to a power grid and it has randomly arriving data packets. Another energy harvesting (EH) sensor monitors a stochastic process and sends status updates to the destination. We formulate an optimization problem that aims at minimizing the average age of information (AoI) of the EH node subject to the queue stability condition of the grid-connected node. First, we consider a Probabilistic Random Access (PRA) policy where both nodes make independent transmission decisions based on some fixed probability distributions. We show that with this policy, the average AoI is equal to the average peak AoI, if the EH node only sends freshly generated samples. In addition, we derive the optimal solution in closed form, which reveals some interesting properties of the considered system. Furthermore, we consider a Drift-Plus-Penalty (DPP) policy and develop AoI-optimal and peak-AoI-optimal scheduling algorithms using the Lyapunov optimization theory. Simulation results show that the DPP policy outperforms the PRA policy in various scenarios, especially when the destination node has low multi-packet reception capabilities.

Dynamic Power Splitting Simultaneous Wireless Information and Power Transfer Split Receiver for Wireless Sensor Networks

Simultaneous wireless information and power transfer (SWIPT) presents a genuine opportunity toward achieving sustainable low-energy wireless devices. This paper introduces a receiver design for an energy harvesting sensor node (SN). The receiver is equipped with multiple radio frequency (RF) inputs. Furthermore, the receiver contains separate circuitries for information decoding (ID) and energy harvesting (EH) with dynamic power splitting (PS). The ID and EH circuits are connected to a power splitter that first combines all the RF inputs and then splits the power between the circuitries. Moreover, dynamic PS allows regulating the power split ratio between EH and ID circuitries. Consequently, the output of the EH circuit can be monitored and increased by adjusting the PS ratio. The proposed receiver design increases the harvestable energy by moving the ID into a separate circuitry. A voltage multiplier arranged in a Dickson scheme is used to level up the received voltage. At the transmitter side, the energy signal (ES) is sent through high power unmodulated continuous wave centered at the carrier frequency, while the information signal (IS) is sent via low power subcarriers around the carrier frequency. Such power allocation increases the harvestable power while reducing interference to external wireless networks. Moreover, different system measurements, including harvested energy from the received signal and data rates, are presented, where the split receiver scheme showed a significant improvement compared to the combined scheme in terms of harvestable power.

Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: 1) the accuracy of the recovery procedure and 2) the stability of the batteries around a prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet-of-Things scenarios.

Particle Filtering for Nonlinear/Non-Gaussian Systems With Energy Harvesting Sensors Subject to Randomly Occurring Sensor Saturations

In this paper, the particle filtering problem is investigated for a class of nonlinear/non-Gaussian systems with energy harvesting sensors subject to randomly occurring sensor saturations (ROSSs). The random occurrences of the sensor saturations are characterized by a series of Bernoulli distributed stochastic variables with known probability distributions. The energy harvesting sensor transmits its measurement output to the remote filter only when the current energy level is sufficient, where the transmission probability of the measurement is recursively calculated by using the probability distribution of the sensor energy level. The effects of the ROSSs and the possible measurement losses induced by insufficient energies are fully considered in the design of filtering scheme, and an explicit expression of the likelihood function is derived. Finally, the numerical simulation examples (including a benchmark example for nonlinear filtering and the applications in moving target tracking problem) are provided to demonstrate the feasibility and effectiveness of the proposed particle filtering algorithm.

A Coordinated Ambient/Dedicated Radio Frequency Energy Harvesting Scheme Using Machine Learning

This paper proposes a coordinated ambient/ dedicated (CA/D) protocol where backscatter-enabled combination sensors are optimized to harvest energy from intended radio frequency (RF) sources when available and fall back to harvesting energy from unintended sources when only unintended sources are available. The CA/D protocol can use either of the two new machine learning techniques proposed, the linear forecaster with near-time linear regression-based enhancer (LFNTLRE) algorithm and an artificial neural network (ANN), to determine the optimum energy harvesting (EH) schedule. These machine learning algorithms can reliably operate in environments where there is unpredictable availability of unintended sources and ongoing changes in channel conditions between the sensors and the unintended sources. Numerical results show that sensors using the ANN and LFNTLRE algorithm can achieve up to 99.5% and 99.6% hit count relative to that of ideal sensors, respectively, compared to 100% by an ideal sensor. Sensors using the ANN and LFNTLRE algorithm have an accuracy percentage of up to 99% and 100%, respectively, as well. Ensuring reliable operation of RF EH sensors in all environments will enable its future widespread adoption in the real world.

Analysis of the Interdelivery Time in IoT Energy Harvesting Wireless Sensor Networks

In this article, we investigate an energy harvesting (EH) wireless sensor network for the Internet of Things (IoT) where monitoring applications require a continuous update of sensing information. The considered system consists of independent EH sensor nodes equipped with capacitors and providing, through unreliable channels, status updates to a non EH sink. The distribution of the interdelivery time, i.e., the time elapsed between two successive and successful status update deliveries, is derived in the closed-form expression considering a random EH arrival process. Moreover, the interdelivery violation probability metric, defined as the probability to exceed a predetermined interdelivery threshold, is analyzed. Our analysis reveals that the violation probability is highly dependent on the size of the capacitor. Both analytical and simulation results demonstrate the existence of an optimal capacitor size that achieves the minimum violation probability. Moreover, our findings reveal an interesting tradeoff in the system design. On one hand, a small capacitor charges quickly and thus status updates are sent more frequently but with lower transmit power and thus a high error rate. On the other hand, a large capacitor increases the transmit power and boosts the successful data transmission probability, at the expense of a higher waiting time before filling the capacitor and transmitting sensed data.

Online Transmission Policy for Energy Harvesting Sensor Node With Energy Loss

This letter considers a point-to-point data transmission scheme over a fixed number of slots where the energy harvesting transmitter operates in harvest-use-store (HUS) mode and an imperfect supercapacitor with storage loss and energy leakage loss is used as an energy storage device. For these settings, the dynamic programming (DP) method is known to achieve the optimal throughput, but the ` curse of dimensionality' necessitates the search of schemes that allow for an improved performance-complexity trade-off. The work focuses on deriving a computationally efficient online transmission policy that achieves the optimal or near-optimal throughput. This letter presents a novel computationally efficient uniform thresolding based online transmission policy that achieves near-optimal throughput. The work also presents numerical results which demonstrate that the proposed policy with supercapacitor-based energy storage achieves better or the same performance as compared to the battery-based energy storage, thus allowing for a longer lifetime of sensor networks.

Judicious data management for sustaining an energy harvesting sensor node

SummaryPerpetual lifetimes and low maintenance are few of the attractive aspects of sensor nodes that harvest ambient energy. However, their operation depends heavily on the energy profiles of their harvesting source(s). In this work, we study the suitability of energy harvesting sensor (EHS) Nodes, powered using indoor lighting and vibrations, for a simple temperature monitoring application. To help these nodes sustain, we have proposed schemes that allow the nodes to sample and transmit data judiciously. Along with an adaptive sampling based on autoregressive (AR) model, we have proposed a regulating function based transmission scheme that regulates the amount of transmitted data based on the energy available at the node and the characteristics of the data so that even with limited transmissions the fidelity of the data is not lost. With the help of thorough evaluations, we can conclude that an EHS node fares quite well. Results show that adaptive sampling and transmissions based on regulating function not only save energy at the sensor nodes, but they also reduce the amount of data generated and accumulated in a network.

A Control Flow for Transiently Powered Energy Harvesting Sensor Systems

Transient computing enables application execution to be performed despite power outages. Although it handles the non-deterministic nature of energy harvesting (EH), sensor systems envisioned by the IoT seek more cost- and volume-effective solutions, which are better tailored to application requirements. Additionally, a major drawback of transient computing, keeping track of time, hinders its widespread adoption in the IoT. To overcome these challenges, this paper proposes a control flow for sensor systems by combining two state-of-the-art transient computing schemes in an energy-aware manner, underpinned by a strategy for timekeeping. It enables application execution to be reliably performed even under the most severe EH conditions, with an improved cost and volume efficiency, i.e., smaller energy storage. Benefiting from the combination of the two schemes, dynamic adjustment of system performance is achieved, while the time is accurately tracked. To illustrate the applicability of this flow to actual sensor systems, two case studies: a bicycle trip computer and a step counter, are presented. Empirical results reveal that, even with a tiny amount of energy harvested ( $\simeq$ tens of $\mu \text{J}$ ), our proposed approach can meet application requirements with smaller storage, i.e., 40% and 66% reduction in required capacitance for the presented case studies.

Multistep Prediction-Based Adaptive Dynamic Programming Sensor Scheduling Approach for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

Sensor scheduling for energy-efficient collaborative target tracking in wireless sensor networks (WSNs) is an important problem to deal with the limited network resources. With the recent development and emerging applications of energy acquisition technologies, it has become possible to overcome the bottleneck of battery energy in WSNs using the energy harvesting devices, where theoretically the lifetime of the network could be extended to the infinite. However, the energy harvesting WSN also poses new challenges for sensor scheduling algorithm over the infinite horizon under the limited sensor energy harvesting capabilities. In this article, a novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking in energy harvesting WSNs to schedule sensors over an infinite horizon, according to the ADP mechanism. The “action” module of MSPADP is designed to obtain the sensor scheduling for multiple steps starting from the current step, and implemented by the minimal-cost first search (MCFS) decision tree scheme, and the “critic network” module of MSPADP is iteratively performed to optimize the performance for the remaining infinite steps using neural network. Extended Kalman filter (EKF) is adopted to predict and estimate the target state. The performance index is defined by the tracking accuracy derived from EKF and the energy consumption predicted by the candidate sensor schedule. Theoretical analysis shows the optimality of MSPADP, and simulation results demonstrate its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) sensor scheduling approaches. Note to Practitioners-Collaborative target tracking is a typical problem in wireless sensor networks (WSNs) where the sensors need to be scheduled to address the constraints of the limited network resources, such as sensor energy usually supplied by the battery. In the recent years, energy harvesting device has been developed and applied to WSNs to overcome the energy restriction. As the energy harvesting capabilities of the sensors are limited, sensor scheduling remains as a challenging problem and is studied in this article. A novel multistep prediction-based adaptive dynamic programming (MSPADP) approach is proposed for collaborative target tracking, by scheduling sensors for the current time step based on the predictions of the subsequent steps over an infinite horizon. It runs iteratively in two modules: obtaining the previous optimal multistep sensor scheduling and updating the remaining infinite-step performance. Simulation results show its superior tracking performance compared with single-step prediction-based ADP (SSPADP), multistep prediction-based dynamic programming (MSPDP), and multistep prediction-based pruning (MSPP) approaches, and lay a good foundation for the practical applications.