Power Wireless Sensor Networks Research Articles

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

High Output, Biocompatible, Fully Flexible Fiber‐Based Magneto‐Mechano‐Electric Generator for Standalone‐Powered Electronics

AbstractHarvesting magnetic noise fields around power cables emerges as an attractive approach due to its potential as a renewable and ubiquitous energy source for powering wireless sensor networks (WSNs) in IoT applications, miniature electronics, and implantable medical devices. Flexible polymer‐based magneto‐mechano‐electric (MME) generators gain attention for their effectiveness in magnetic energy harvesting owing to their durability and flexibility. In this study, a lead‐free, flexible MME generator is developed by using Polyvinylidene fluoride (PVDF)‐Aluminium nitride (AlN)‐nanofiber composites fabricated via electrospinning with different AlN compositions and integrated with a magnetostrictive Metglas layer that offers self‐bias characteristics. The MME generator is modeled using COMSOL Multiphysics to analyze the magnetic flux density distribution over the Metglas surface and the piezoelectric effect of the nanofiber composites, with the simulation results aligning well with the experimental data. The optimized, flexible MME generator, incorporating 15 wt.% of AlN in the PVDF/Metglas composite, achieves an open‐circuit voltage of 18.5 V and a power density of 0.93 mW‐cm−3 when exposed to an Alternating Current (AC) magnetic noise field of 6 Oe at a resonance frequency of 50 Hz. The generated power is sufficient to operate LEDs and sensor. This newly developed lead‐free, flexible MME generator shows significant promise for advanced applications in self‐powered WSNs.

Influences of bluff body geometries upon performances of piezoelectric wind energy harvesters

Piezoelectric wind-induced vibration energy harvesters (PWVEHs) offer a promising solution for powering wireless sensor networks by converting wind energy into electrical energy. While conventional PWVEHs rely on vortex-induced vibration (VIV) and galloping to work, little has been done to investigate the coupling mechanisms between VIV and galloping and their influences upon energy harvesting performances of flow-induced vibration (FIV). To address this, we propose 36 bluff bodies (BBs) with various frontbody and afterbody shape combinations and conduct FIV experiments in a wind tunnel. The voltage output of each BB is recorded and analyzed based on whether pure VIV, pure galloping vibration, or concurrent VIV-galloping vibrations are involved. Hysteresis analyses are performed for BBs exhibiting concurrent VIV-galloping vibrations. The energy harvesting performances of all BBs are compared to circular and square cross-section BBs, revealing a BB shape that achieved 23.18% and 16. 57% higher maximum output than circular and square BBs, respectively. Computational analysis is conducted to investigate the flow field behind the given BBs. These findings provide valuable insights for developing efficient energy harvesters utilizing VIV and galloping by optimizing the involved BBs.

Average Age of Sensing in Wireless Powered Sensor Networks

Average Age of Sensing in Wireless Powered Sensor Networks

An Analog Beamforming Strategy Based on Statistical Channel Information for Recharging Wireless Powered Sensor Networks

An Analog Beamforming Strategy Based on Statistical Channel Information for Recharging Wireless Powered Sensor Networks

Piezoelectric MEMS Energy Harvester for Low-Power Applications

With the global market value of sensors on the rise, this paper focuses on the fabrication and testing of a proof-of-concept piezoelectric energy harvester which is able to harvest mechanical energy from the ambient environment and convert it into electrical energy in order to power wireless sensor networks. We focused on obtaining a new device structure based on a comb-type array of piezoelectric MEMS cantilevers (2 × 10) for a resonant frequency in the environmental application domain (a few hundred Hz) and a chip area of only 1 cm2. The configuration of the lead-free piezoelectric cantilever consists of a Si substrate, a pair of Ti-Pt electrodes and a sputtered piezoelectric layer of 12% Sc-doped AlN with a thickness of 1000 nm, a dielectric constant of ~13 and e31,f = 1.3 C/m2. At a resonant frequency of 465.2 Hz and an acceleration of 1 g, the maximum value for the collected power was 2.53 µW for an optimal load resistance of 1 MΩ resulting in a power density of 60.2 nW/mm3 for the unpacked device, without taking into account the vibration volume. By increasing the excitation acceleration to 2 g RMS and using LTC3588-1 for the power circuitry we were able to obtain a stabilized output voltage of 1.8 V.

Piezo stack energy harvesters with protection components for railway applications

Piezo stack energy harvesting from railway vibrations shows substantial potential for powering wireless sensor networks responsible for monitoring railway infrastructure. Nevertheless, assuring the safety and durability of these energy harvesters in the condition of the dynamically fluctuating railway vibrations remains a considerable challenge. In this paper, we present two innovative protection methods designed for piezo stack energy harvesters with a frequency up-conversion mechanism. These methods involve the incorporation of ring-type and circular stoppers within the resonant system and the utilisation of proposed impact protection components within the impact system. Two distinct harvesters incorporating the proposed protection components, Harvester Types I and II, are designed to align with their respective operating acceleration targets, which are determined based on the amplitudes of railway vibration acceleration. Finite element modelling is used to guide the design process, involving the evaluation and selection of various design parameters aligned with the acceleration target and stress limits. The protection mechanisms’ effectiveness and impact on energy harvesting performance have been validated through experimental testing under measured rail vibration signals, and their performance has been further assessed through stress analyses. Harvester Type I operates seamlessly within a 5 g threshold, effectively mitigating the increase in maximum output acceleration and stress beyond this limit. In contrast, Harvester Type II efficiently dissipates excess energy, enabling the harvester to operate at a 15 g acceleration while reducing the rate of acceleration and stress growth. The results demonstrate that both proposed harvesters provide effective protection from unexpected railway vibration overloads. This research makes a significant impact on the practical, real-world implementation of piezo stack energy harvesters operating within the dynamic environment of railway vibrations.

A Novel Approach for Gender Prediction through Fingerprint Analysis using Fuzzy C-Means Algorithm and Artificial Neural Network

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm for Secure Cluster Head and Routing Selection in WSN

Wireless Sensor Networks (WSN) is a group of Sensor Nodes (SNs) which are performed to intellect a normal singularity from an environment. The minimum resources and computational power of WSN creates vulnerable to number of security attacks. To addresses this problem, this paper proposes a Multi-objective Trust-aware Dynamic Weight Pelican Optimization Algorithm (M-TDWPOA)-based secure and energy aware multi-hop routing in WSN with the Base Station (BS). The proposed method comprises of two significant steps: Initially, dynamic energy-efficient Cluster Head (CH) selection by utilizing the multi-objective functions such as Distance between neighbor nodes, Distance between BS to CH, Energy, Centrality and Trust Threshold. Then, dynamic secure multi-hop routing is selected by the multi-objective functions such as Distance between BS to CH, Energy, Trust Threshold which is dynamically reducing the network overhead. Furthermore, a security aware multi-hop routing is employed through the different trust classes like direct, indirect, recent and authentication. The proposed M-TDWPOA approach is estimated with various performance metrics like alive nodes, dead nodes, energy consumption, Throughput and so on. The proposed M-TDWPOA achieves the minimum energy consumption of 0.4927J, 0.4851J, 0.4771J, 0.4693J and 0.4617J when compared to the existing methods like Fractional Artificial Lion (FAL).

Power Optimization in Wireless Sensor Network Using VLSI Technique on FPGA Platform

Nowadays, the demand for high-performance wireless sensor networks (WSN) is increasing, and its power requirement has threatened the survival of WSN. The routing methods cannot optimize power consumption. To improve the power consumption, VLSI based power optimization technology is proposed in this article. Different elements in WSN, such as sensor nodes, modulation schemes, and package data transmission, influence energy usage. Following a WSN power study, it was discovered that lowering the energy usage of sensor networks is critical in WSN. In this manuscript, a power optimization model for wireless sensor networks (POM-WSN) is proposed. The proposed system shows how to build and execute a power-saving strategy for WSNs using a customized collaborative unit with parallel processing capabilities on FPGA (Field Programmable Gate Array) and a smart power component. The customizable cooperation unit focuses on applying specialized hardware to customize Operating System speed and transfer it to a soft intel core. This device decreases the OS (Operating System) central processing unit (CPU) overhead associated with installing processor-based IoT (Internet of Things) devices. The smart power unit controls the soft CPU’s clock and physical peripherals, putting them in the right state depending on the hardware requirements of the program (tasks) being executed. Furthermore, by taking the command signal from a collaborative custom unit, it is necessary to adjust the amplitude and current. The efficiency and energy usage of the FPGA-based energy saver approach for sensor nodes are compared to the energy usage of processor-based WSN nodes implementations. Using FPGA programmable architecture, the research seeks to build effective power-saving approaches for WSNs.

Designs of Energy Harvester for Wireless Sensor Networks

An essential part of the IoT architecture are wireless sensor networks. There has been extensive study done to identify alternative methods of powering wireless sensor networks because the nodes of wireless sensor networks are mostly power constrained. This study mainly introduces three energy collector solutions that can power wireless sensors. They are an electromagnetic vibration energy harvesting system that powers the light source of the fiber Bragg grating sensor, a solar energy collection system that uses ambient photovoltaic energy, and a wind energy harvester that uses gas elastic vibration to convert wind energy into electrical energy. The solar energy collection system is based on maximum power point tracking, in which the solar panels can provide 3W of power with a high efficiency of up to 96%; the wind energy harvester is composed of a wind generator and a power management unit, capable of operating at wind speeds of 2-9m/s, providing 70mW of power. Besides, the electromagnetic vibration energy harvesting system provides an average power output of approximately 40mW when subjected to acceleration levels greater than 0.05g. These results can serve as solutions for sustainable power output.

Cooperative Jamming-Aided Secure Communication in Wireless Powered Sensor Networks

Cooperative Jamming-Aided Secure Communication in Wireless Powered Sensor Networks

Theoretical and experimental research on a Quasi-Zero-Stiffness-Enabled nonlinear piezoelectric energy harvester

Piezoelectric material provides a convenient and efficient avenue to convert ambient vibration energy into electrical energy to power wireless sensor network nodes of Internet of Things. However, the design of effective ultralow-frequency and low-amplitude harvesters using piezoelectric materials remains a challenge. To address this challenging problem, this paper devises a quasi-zero-stiffness-enabled (QZSE) piezoelectric energy harvester based on a geometrical nonlinear structure. The basic configuration and operation principles of the QZSE piezoelectric energy harvester ware elaborated firstly, then the nonlinear electromechanical coupling equation is derived, whose solution informs on both the dynamic and electrical responses of the harvester. The electromechanical-coupled equations are solved with the aid of the fourth-order Runge-Kutta algorithm. The energy harvesting performance is then investigated with respect to the parameter effects of excitation, damping and proof mass. For validating numerical results and demonstrating the advantages of the proposed design, a prototype is fabricated and experimentally tested. The results confirm the ability of the QZSE piezoelectric energy harvester in producing effective vibration-to-electricity energy conversion in ultralow frequency range. This study provides new impetus to energy harvesting from ultralow frequency ambient vibration using piezoelectric materials.

Resource Allocation for IRS Assisted mmWave Wireless Powered Sensor Networks with User Cooperation

Resource Allocation for IRS Assisted mmWave Wireless Powered Sensor Networks with User Cooperation

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Efficient data acquisition with wireless powered sensor networks for smart healthcare in hospitals

Efficient Data Acquisition with Wireless Powered Sensor Networks for Smart Healthcare in Hospitals

Efficient Data Acquisition with Wireless Powered Sensor Networks for Smart Healthcare in Hospitals

Recyclability of novel energy harvesting and storage technologies for IoT and wireless sensor networks

This paper aims to present a set of separation and recycling methods designed to recover valuable materials and components from innovative high-performance piezoelectric (PEG) and thermoelectric (TEG) generators and monolithic supercapacitors (SC) developed within the InComEss project. This project, a part of the European Horizon H2020 research program, focuses on creating environmentally friendly, cost-effective, and highly efficient Energy Harvesting Systems (EHSs) for powering wireless sensor networks. It combines advanced polymer-based composite materials for piezoelectric, thermoelectric, and supercapacitors, capable of harvesting and storing electrical energy from mechanical or waste heat sources. The authors initially identified key recoverable components. In PEG, these includes Polyvinylidene fluoride (PVDF), silver electrodes, polyimide, fiberglass/resin composite and copper. In TEG the highest-value components are single walled carbon nanotubes (SWCNTs) and polymeric matrices, along with the SWCNTs, while in SC, aluminium, polyethylene terephthalate (PET) and carbon particles are targeted. This paper proposes a range of separation and recycling techniques involving mechanical, thermal, and chemical processes. These methods include microwave-assisted and pyrolysis thermal processes, various mechanical fragmentation and separation processes, hydrometallurgical recovery processes, and solvent-based dissolution methods. However, a comprehensive understanding of the end-of-life waste stream of these devices is necessary for further advancements. The authors are currently conducting a thorough laboratory assessment of the recovery methods to pinpoint the most effective recyclable options. The outcomes of this assessment will be shared in a subsequent article.

Development and characterization of acoustic energy harvesters for low power wireless sensor network

Wireless Sensor Nodes (WSNs) have developed significantly over the years and have a significant potential in diverse applications in the fields of science and technology. The inadequate energy accompanying with WSNs is a key constraint of WSN skills. To overwhelm this main restraint, the development and expansion of effective and reliable energy harvesting systems for WSN atmospheres are being discovered. In this research, low power acoustic affordable and clean energy harvesters are designed and developed by applying different techniques of energy transduction from the sound available in the surroundings. Three acoustic energy harvesters were developed based on piezoelectric phenomenon, electromagnetic transduction & Hybrid respectively. The CAD modelling, lumped modelling and Finite Element Analysis of the harvesters were carried out. The voltages were obtained using FEA for each Acoustic Harvester. Characterization of all the three harvesters were carried out and the power generated by piezoelectric harvester, electromagnetic harvester and Hybrid Acoustic Energy harvester are 2.25x10-9W, 0.0533W and 0.0232W respectively.

AoI-Energy-Aware Collaborative Data Collection in UAV-Enabled Wireless Powered Sensor Networks

AoI-Energy-Aware Collaborative Data Collection in UAV-Enabled Wireless Powered Sensor Networks

Multilevel Adaptive Near-Lossless Compression in Edge Collaborative Wireless Sensor Networks for Mechanical Vibration Monitoring

To address the difficulties of severe lack of storage and computational resources and high delay of transmitting massive vibration data in wireless sensor networks (WSN) for mechanical vibration monitoring (MVM), this paper proposes a novel multilevel adaptive near-lossless compression in edge collaborative WSN (MANLC-ECWSN) for MVM, which could effectively solve the above problems. On the one hand, the sparse pattern of mechanical fault signals is analyzed. A multi-level adaptive near-lossless compression (MANLC) method is proposed to characterize mechanical fault feature information with high accuracy in low storage space, and the proposed method is implemented on the self-developed acquisition node (AN), which effectively improves the storage space and transmission efficiency. On the other hand, the edge computing (EC) technique is integrated into WSN, and data reconstruction and high-precision feature detection are efficiently implemented on the self-developed edge computing node (ECN), which effectively reduces the storage and computing pressure of the data center server. The comprehensive experiments demonstrate that high precision data reconstruction and feature detection could be achieved in the proposed approach from measurements that occupy a little storage space of the AN. The computational power and transmission efficiency of WSN are significantly improved, which provides a potential solution for practical engineering applications.