Energy Harvesting Wireless Sensor Nodes Research Articles

A Compact RF Energy Harvesting Wireless Sensor Node with an Energy Intensity Adaptive Management Algorithm.

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of -24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

Multi-Objective Resource Scheduling for IoT Systems Using Reinforcement Learning

IoT embedded systems have multiple objectives that need to be maximized simultaneously. These objectives conflict with each other due to limited resources and tradeoffs that need to be made. This requires multi-objective optimization (MOO) and multiple Pareto-optimal solutions are possible. In such a case, tradeoffs are made w.r.t. a user-defined preference. This work presents a general Multi-objective Reinforcement Learning (MORL) framework for MOO of IoT embedded systems. This framework comprises a general Multi-objective Markov Decision Process (MOMDP) formulation and two novel low-compute MORL algorithms. The algorithms learn policies to tradeoff between multiple objectives using a single preference parameter. We take the energy scheduling problem in general Energy Harvesting Wireless Sensor Nodes (EHWSNs) as a case example in which a sensor node is required to maximize its sensing rate, and transmission performance as well as ensure long-term uninterrupted operation within a very tight energy budget. We simulate single-task and dual-task EHWSN systems to evaluate our framework. The results demonstrate that our MORL algorithms can learn better policies at lower learning costs and successfully tradeoff between multiple objectives at runtime.

Dynamic Energy Management for Perpetual Operation of Energy Harvesting Wireless Sensor Node Using Fuzzy Q-Learning

In an energy harvesting wireless sensor node (EHWSN), balance of energy harvested and consumption using dynamic energy management to achieve the goal of perpetual operation is one of the most important research topics. In this study, a novel fuzzy Q-learning (FQL)-based dynamic energy management (FQLDEM) is proposed in adapting its policy to the time varying environment, regarding both the harvested energy and the energy consumption of the WSN. The FQLDEM applies Q-learning to train, evaluate, and update the fuzzy rule base and then uses the fuzzy inference system (FIS) for determining the working duty cycle of the sensor of the EHWSN. Through the interaction with the energy harvesting environment, the learning agent of the FQL will be able to find the appropriate fuzzy rules in adapting the working duty cycle for the goal of energy neutrality such that the objective of perpetual operation of the EHWSN can be achieved. Experimental results show that the FQLDEM can maintain the battery charge status at a higher level than other existing methods did, such as the reinforcement learning (RL) method and dynamic duty cycle adaption (DDCA), and achieve the perpetual operation of the EHWSN. Furthermore, experimental results for required on-demand sensing measurements exhibit that the FQLDEM method can be slowly upgraded to meet 65% of the service quality control requirements in the early stage, which outperforms the RL-based and DDCA methods.

Uniform thresholding based transmission policy for energy harvesting wireless sensor nodes in fading channel

The work presents a novel computationally efficient transmission policy for throughput maximization over point-to-point sensor links employing harvest-use-store protocol with finite storage capacity battery. In these settings, under finite averaging duration constraint, the stochastic dynamic programming (SDP) technique provides the optimal solution for throughput maximization, but the implementation complexity for SDP is prohibitively large. Thus, there is a need to explore new solutions that can provide near-optimal throughput with lower implementation complexity. The work in this paper presents a adaptive transmission policy based on uniform thresholding that achieves a near-optimal throughput obtainable by SDP. Quantitative comparison with optimal online policies shows that the proposed policy attains performance close to SDP with lower implementation complexity.

Energy-Neutral and QoS-Aware Protocol in Wireless Sensor Networks for Health Monitoring of Hoisting Systems

Hoisting equipment is core to many industrial systems and, therefore, their state of health significantly affects production lines and personnel safety; this is especially important in environments such as coal mines. The health of the hoisting system can be estimated by deploying energy harvesting wireless sensor nodes that monitor the drum surface stress. In this network of sensor devices, it is very costly to send highly sampled data as it causes radio congestion and consumes energy. However, from our experience of sensing hoist systems, we note that the data observed at the upper surface of the hoist are significantly more indicative of the state of health of the whole system, compared with data sensed at the lower surface. Therefore, we need to take advantage of this to optimize the communications of sensor nodes. However, scarce energy can be collected for these devices from the hoist itself, along with the prioritized Quality of Service (QoS) requirements (throughput, delay) of monitoring signals, raises important challenges for energy management. In this article, we use Lyapunov optimization techniques and propose an energy-neutral and QoS-aware protocol (EQP), including duty cycling and network scheduling to solve it. Extensive simulations show that the EQP helps sensor nodes realize consecutive monitoring, and achieve more than 38% utility gain compared with existing strategies.

Prediction of Harvested Energy for Wireless Sensor Node

Energy harvesting wireless sensor nodes are interesting for the Internet of Things, since they can provide continuous operation by adapting workload not only to the current energy reserves, but to the amount of energy that can be harvested in the future also. We present a multistage day ahead hourly solar energy prediction algorithm. The predictor uses cloud cover and precipitation probability predictions from weather forecast obtained once per day for 24 hours in advance. To compensate for short-term weather changes until the next weather forecast data is obtained, forecast errors of humidity and atmospheric pressure are fed to the fuzzy logic filter. The filter adjusts predictions of cloud cover and precipitation probability, which are applied to the clear-sky radiation model in order to obtain prediction of solar energy. The prediction of solar energy is additionally corrected based on the energy prediction error in the preceding time slot. The results show that the proposed predictor outperforms the state-of-the-art predictors in terms of prediction error. Proposed predictor and state-of-the-art predictors were also evaluated using a simulated wireless sensor node with the simple energy management algorithm, where the proposed predictor was the most efficient at maintaining energy neutrality.

Implementation of Energy-Neutral Operation on Vibration Energy Harvesting WSN

This paper presents a method for realizing energy neutral operation on energy harvesting wireless sensor nodes (WSN) and its implementation, regarding that the available environmental energy is unpredictable and changes over time. The method utilizes adaptive duty cycling which provides energy-neutral operation according to the energy available in the environment and the instantaneous energy state of the node through an energy management circuit. The proposed method is implemented using a MicaZ mote as the WSN and two different vibration-based harvesters: piezoelectric and electromagnetic. The node incorporating a piezoelectric harvester, operates for only 130.5 s with a fixed duty-cycle of 0.21%, and requires an inactive time of 93.5 s for charging. On the other hand, with the proposed strategy, the node achieves energy-neutral operation by self-adjusting to 0.17% duty-cycle. Energy-neutral operation is also demonstrated by incorporating an electromagnetic energy harvester attached to the wrist of a runner: When no energy is available for harvesting, the proposed strategy shows about 64% increment in lifetime before going to sleep mode. These demonstrate that the proposed energy management policy proves to achieve energy-neutral operation in an efficient way.

The Design of an Energy Harvesting Wireless Sensor Node for Tracking Pink Iguanas

The design of wireless sensor nodes for animal tracking is a multidisciplinary activity that presents several research challenges both from a technical and a biological point of view. A monitoring device has to be designed accounting for all system requirements including the specific characteristics of animals and environment. In this work we present some aspects of the design of a wireless sensor node to track and monitor the pink iguana of the Galápagos: a recently discovered species living in remote locations at the Galápagos Islands. The few individuals of this species live in a relatively small area that lacks of any available communication infrastructure. We present and discuss the energy harvesting architecture and the related energy management logic. We also discuss the impact of packaging on the sensor performance and the consequences of the limited available energy on the GPS tracking.

Leveraging Energy Harvesting and Wake-Up Receivers for Long-Term Wireless Sensor Networks.

Wireless sensor nodes are traditionally powered by individual batteries, and a significant effort has been devoted to maximizing the lifetime of these devices. However, as the batteries can only store a finite amount of energy, the network is still doomed to die, and changing the batteries is not always possible. A promising solution is to enable each node to harvest energy directly in its environment, using individual energy harvesters. Moreover, novel ultra-low power wake-up receivers, which allow continuous listening of the channel with negligible power consumption, are emerging. These devices enable asynchronous communication, further reducing the power consumption related to communication, which is typically one the most energy-consuming tasks in wireless sensor networks. Energy harvesting and wake-up receivers can be combined to significantly increase the energy efficiency of sensor networks. In this paper, we propose an energy manager for energy harvesting wireless sensor nodes and an asynchronous medium access control protocol, which exploits ultra-low power wake-up receivers. The two components are designed to work together and especially to fit the stringent constraints of wireless sensor nodes. The proposed approach has been implemented on a real hardware platform and tested in the field. Experimental results demonstrate the benefits of the proposed approach in terms of energy efficiency, power consumption and throughput, which can be up to more than two-times higher compared to traditional schemes.

Improving energy usage in energy harvesting wireless sensor nodes using weather forecast

Improving energy usage in energy harvesting wireless sensor nodes using weather forecast

Analyzing lifetime of energy harvesting wireless multimedia sensor nodes in industrial environments

Recently, there has been a great demand for multimedia communication using Wireless Multimedia Sensor Networks (WMSNs) in industrial environments thanks to their low cost, flexibility, and rapid deployment. However, WMSNs face a major challenge of limited lifetime due to their limited battery capacity. Compared to regular data transmission, multimedia data transmission causes higher energy consumption because of larger data sizes leading to faster depletion of sensor node's batteries. The objective of this paper is to analytically quantify the impact of different energy harvesting methods based on vibration, indoor solar, and temperature difference as well as Fast-Zonal DCT and BinDCT based image compression methods on the lifetime of Telos and Mica2 sensor nodes deployed in indoor industrial environment. Performance results show that energy harvesting and image compression techniques improve lifetime of Mica2 and Telos motes by 51.8% and 25.8%, respectively when used with proper power management methods.

Energy Management for Energy Harvesting Wireless Sensors With Adaptive Retransmission

This paper analyzes the communication between two energy harvesting wireless sensor nodes. The nodes use automatic repeat request and forward error correction mechanism for the error control. The random nature of available energy and arrivals of harvested energy may induce interruption to the signal sampling and decoding operations. We propose a selective sampling scheme, where the length of the transmitted packet to be sampled depends on the available energy at the receiver. The receiver performs the decoding when complete samples of the packet are available. The selective sampling information bits are piggybacked on the automatic repeat request messages for the transmitter use. This way, the receiver node manages more efficiently its energy use. Besides, we present the partially observable Markov decision process formulation, which minimizes the long-term average pairwise error probability and optimizes the transmit power. Optimal and suboptimal power assignment strategies are introduced for retransmissions, which are adapted to the selective sampling and channel state information. With finite battery size and fixed power assignment policy, an analytical expression for the average packet drop probability is derived. Numerical simulations show the performance gain of the proposed scheme with power assignment strategy over the conventional scheme.

Characterization of commercial thermoelectric modules for application in energy harvesting wireless sensor nodes

Thermal energy harvesting systems commonly exploit thermoelectric generators as power sources. This paper describes characterization of commercial thermoelectric cooling/heating modules, with the aim to select preferable ones for use as generators in energy harvesting wireless sensor nodes. Performed numerical electro–thermal analysis considers generators under three thermally different operating conditions (as standalone devices, with heatsink, and inside the wireless sensor node). Electrical load of the generators is determined by the power management circuitry. Characterization is performed by combining simulation results and current–voltage characteristic of the circuitry. Assessment of the generators is based on voltage and power transferred to the load at various temperature differences. Obtained results are experimentally verified by thermovision and, for the case of standalone devices, compared with an analytical approximation. It is determined that preferable generators can be selected on the basis of the power factor, figure–of–merit, or thermocouple legs length, depending on the imposed thermal operating conditions.

A Sub-cm3 Energy-Harvesting Stacked Wireless Sensor Node Featuring a Near-Threshold Voltage IA-32 Microcontroller in 14-nm Tri-Gate CMOS for Always-ON Always-Sensing Applications

An energy-harvesting wireless sensor node (WSN) integrates a 14-nm, 0.79-mm2, 32-b Intel Architecture core-based near-threshold voltage (NTV) microcontroller (MCU) that provides 17- $\mu \text{W}$ /MHz always-ON, always-sensing (AOAS) capability. The MCU implements four independent voltage-frequency islands, managed by an integrated power management unit and features a subthreshold voltage capable on-die oscillator and 42-nm fin-pitch, 8.3-pA leakage-per-bit SRAM. The MCU operates across a wide frequency (voltage) range from 297 MHz (1 V) to 0.5 MHz (308 mV), dissipating 23.5 mW to 21 $\mu \text{W}$ , and achieves $4.8\times $ better energy efficiency at an optimum supply voltage ( $V_{\text {OPT}}$ ) of 370 mV, 3.5 MHz, and 17 pJ/cycle. A functional AOAS WSN incorporating the NTV MCU shows promise for sustained $\mu \text{W}$ operation.

Energy management algorithm for solar‐powered energy harvesting wireless sensor node for Internet of Things

The solar powered energy harvesting sensor node is a key technology for Internet of Things (IoT), but currently it offers only a small amount of energy storage and is capable of harvesting only a trivial amount of energy. Therefore, new technology for managing the energy associated with this sensor node is required. In particular, it is important to manage the transmission interval because the level of energy consumption during data transmission is the highest in the sensor node. If the proper transmission interval is calculated, the sensor node can be used semi-permanently. In this study, the authors propose an energy prediction algorithm that uses the light intensity of fluorescent lamps in an indoor environment. The proposed algorithm can be used to accurately estimate the amount of energy that will be harvested by a solar panel using a weighted average for light intensity. Then, the optimal transmission interval is calculated using the amount of predicted harvested energy and residual energy. The results from the authors' experimental testbeds show that their algorithm's performance is better than the existing approaches. The energy prediction error of their algorithm is approximately 0.5%.

Low-Cost Wake-Up Receiver for RF Energy Harvesting Wireless Sensor Networks

The existing passive wake-up receivers (WuRxs) are radio frequency identification (RFID) tag based, which incur high cost and complexity. In this paper, we study cost-effective and long-range WuRx solutions for range-based wake-up (RW) as well as directed wake-up (DW). In particular, we consider the case of an RF energy harvesting wireless sensor node and investigate how a low-cost WuRx can be built using an RF energy harvester available at the node. Experimental results show that our developed prototype can achieve a wake-up range of 1.16 m with +13 dBm transmit power. Furthermore, our empirical study shows that at +30 dBm transmit power the wake-up distance of our developed RW module is >9 m. High accuracy of DW is demonstrated by sending a 5-bit ID from a transmitter at a bit rate up to 33.33 kbps. Finally, we present optimized WuRx designs for RW and DW using Agilent advanced design system, which offer up to 5.69 times higher wake-up range for RW and energy savings per bit of about 0.41 mJ and 21.40 nJ, respectively, at the transmitter and the sensor node in DW.

Photovoltaic Energy Harvesting Wireless Sensor Node for Telemetry Applications Optimized for Low Illumination Levels

This paper reports the design of a photovoltaic energy harvesting device used as a telemetry node in wireless sensor networks. The device draws power from the small solar cell, stores it into the primary energy buffer and backup supercapacitor, collects measured data from various sensors and transmits them over low power radio link at 868 MHz. Its design ensures reliable cold booting under very poor illumination conditions (down to 20 lx). The solar cell also enables indirect illumination level detection for the subcircuit that manages stored energy (day/night detector). The device is allowed to draw power from the backup supercapacitor only when it is not possible to gather enough energy from the solar cell during the sleep period. Short lasting and sudden drops of the illumination do not activate the backup power supply. A wireless sensor node design is adjusted to the proposed photovoltaic harvesting circuitry, so the overall power consumption in the sleep mode is less than 25 μW. Also, due to adaptive power consumption, proposed device topology ensures its autonomy time in the total darkness of 81 h. The device has been produced using commercially available components enabling versatile telemetric functionality by the implementation of different sensors.

Design and Development of an RF Energy Harvesting Wireless Sensor Node (EH-WSN) for Aerospace Applications

Numerous applications of wireless sensor networks are constrained by the limited battery power of the sensors. The power consumption of processors and microcontrollers could be scaled down dramatically with the new advances in microelectronics. This reduction gives rise to the possibility of energy harvesting sources to power wireless sensor nodes. In this paper a summary is given of our ongoing research work on RF Energy Harvesting Wireless Sensor Node (EH-WSN) which can plug-in to the already developed Wireless Instrumentation System (WIS) for aerospace applications. Present WSN's which are powered from battery have limited operational lifetime. While energy harvesting has the potential to enable near-perpetual system operation, design of which is a complex trade-off due to the interaction of numerous factors such as the characteristics of the energy source, power supply requirements, power management futures, WSN application behaviour, chemistry and capacity of batteries used etc. In this work, we have identified a suitable power harvesting cum battery management scheme which harvests power consistently and deterministically from a secondary RF source which can be used even in harsh real-time applications. Using a RF power harvesting receiver IC and a compact power management cum storage circuit, we establish the test bed and conduct a series of experiments to verify the effectiveness of the proposed scheme. We have demonstrated continuous operation of the sensor node at an operating distance of 2 meters from the RF power source for a data rate of 240 sps. This is achieved by using special synchronized MAC protocol, low power techniques, usage of low leakage components and systematic coding of the micro controller firmware. This paper provides an insight into how various power reduction techniques can be used and orchestrated such that satisfactory performance can be achieved for a given energy budget.

A Reinforcement Learning-Based ToD Provisioning Dynamic Power Management for Sustainable Operation of Energy Harvesting Wireless Sensor Node

In this paper, a reinforcement learning-based throughput on demand (ToD) provisioning dynamic power management method (RLTDPM) is proposed for sustaining perpetual operation and satisfying the ToD requirements for today's energy harvesting wireless sensor node (EHWSN). The RLTDPM monitors the environmental state of the EHWS and adjusts their operational duty cycle under criteria of energy neutrality to meet the demanded throughput. Outcomes of these observation-adjustment interactions are then evaluated by feedback/reward that represents how well the ToD requests are met; subsequently, the observation-adjustment-evaluation process, so-called reinforcement learning, continues. After the learning process, the RLTDPM is able to autonomously adjust the duty cycle for satisfying the ToD requirement, and in doing so, sustain the perpetual operation of the EHWSN. Simulations of the proposed RLTDPM on a wireless sensor node powered by a battery and solar cell for image sensing tasks were performed. Experimental results demonstrate that the achieved demanded throughput is improved 10.7% for the most stringent ToD requirement, while the residual battery energy of the RLTDPM is improved 7.4% compared with an existing DPM algorithm for EHWSN with image sensing purpose.

Prediction free energy neutral power management for energy harvesting wireless sensor nodes

Current power management mechanisms for energy harvesting wireless sensors typically rely on predicted information about the amount of energy that can be harvested in the future. However, such mechanisms suffer from inevitable prediction errors, which in turn degrade the overall performance in real implementations. To circumvent such problems, we propose a fundamental framework to efficiently manage the harvested energy in a prediction free manner. In particular, we theoretically derive a set of Budget Assigning Principles (BAPs) to maximize the amount of harvested energy that can be utilized by a sensor in the presence of battery energy storage inefficiencies, which in turn maximize the sensor’s performance level in terms of the sensor’s average duty cycle. A Prediction FREE Energy Neutral (P-FREEN) power management mechanism is then proposed to implement the BAPs based solely on current observed energy harvesting rate and battery residual energy level. The performance of P-FREEN is verified via theoretical analysis and extensive computer simulations using real life energy harvesting data sets.