Energy Harvesting Framework Research Articles

Cross-Layer Framework for Energy Harvesting- LPWAN Resource Management Based on Fuzzy Cognitive Maps and Adaptive Glowworm Swarm Optimization for Smart Forest

Cross-Layer Framework for Energy Harvesting- LPWAN Resource Management Based on Fuzzy Cognitive Maps and Adaptive Glowworm Swarm Optimization for Smart Forest

A hybrid energy harvesting framework and graphene‐based supercapacitor for 6G and smart grid networks

SummaryThis paper proposes a novel hybrid energy harvesting framework integrated with a graphene‐based supercapacitor to address the energy demands of 6G communication systems and smart grid networks. To ensure consistent power generation, the proposed framework combines diverse energy harvesting mechanisms, including acoustic, vibration, and radio frequency (RF) energy sources. These harvested energies are then efficiently managed and stored in a proposed graphene‐based supercapacitor compared with existing Li‐ion batteries, due to its high energy density and rapid charge‐discharge capabilities. Smart grids and 6G networks can utilize the energy that hybrid systems harvest. This paper examines the analysis of RF energy extracted from various antenna configurations. In the acoustic energy harvesting system, energy extraction has been achieved by subjecting it to acoustic pressure and impedance. Notably, the inclusion of acoustic impedance has resulted in a greater amount of harvested energy. The vibration energy harvesting analysis with angular frequencies under various force conditions is employed. The magnitude of force applied to the harvesting system significantly influences the efficiency of the harvested energy. The three‐dimensional structure and voltage levels of the graphene‐based supercapacitor have been examined for various electrodes. For maximizing the harvested hybrid energy, convex optimization is formulated due to its ability to efficiently navigate complex design spaces while ensuring reliable and globally optimal solutions. CVX tool in MATLAB is used for validation. The results improve the energy harvesting efficiency and enhance the power availability of all the harvesting sources. Furthermore, the compatibility of the proposed framework with existing network infrastructure highlights its potential for seamless integration into real‐world communication and energy distribution systems.

Joint Energy and Information Precoding for NOMA-Based WPCNs Aided by Reconfigurable Intelligent Surface

In a wireless powered communication network (WPCN), wireless energy acquisition is weak due to the fading characteristic of wireless link, which makes it hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This article proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. Under the multi user scenario, we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that compared with the non-RIS system, the ASS can increase the throughput of the system under multiuser scenario by 502.49%. For the system under single scenario, the energy precoding and information precoding are determined respectively by using the beamforming and the traditional maximal ratio combining (MRC) algorithm. The optimization problem is transformed into a complex higher order form optimization problem. Finally, a closed-form expression of the channel capacity is derived.

Differential Privacy and IRS Empowered Intelligent Energy Harvesting for 6G Internet of Things

In the era of the sixth generation (6G), the deployment of massive Internet of Things (IoT) generates and processes large amounts of data, resulting in high energy demand and huge challenges to the energy-limited IoT devices. To achieve green and sustainable communication, energy harvesting is a feasible technology to prolong the lifetime of IoT. However, the existing energy harvesting architecture cannot guarantee the privacy of energy users while improving the intelligence and effectiveness of energy transmission. To solve these issues, we propose a differential privacy and intelligent reflecting surface empowered privacy-preserving energy harvesting framework for 6G-enabled IoT. First, a secure and intelligent energy harvesting framework is designed, which includes an intelligent reflecting surface-aided radio frequency power transmission mechanism and a differential privacy-based energy harvesting mechanism. Second, an exponential mechanism-based privacy-preserving energy harvesting scheme is established, where we analyze the adversary mode, propose the differential privacy-enabled location-preserving algorithm, and provide security analysis and proof. Third, we quantify the user satisfaction for energy harvesting and propose a deep reinforcement learning empowered resource allocation scheme to maximize the weighted satisfaction of all system users. Finally, simulation results show the effectiveness of the proposed secure and intelligent energy harvesting architecture for 6G IoT.

Smart Energy Harvesting for Internet of Things Networks.

In this article, we address the problem of prolonging the battery life of Internet of Things (IoT) nodes by introducing a smart energy harvesting framework for IoT networks supported by femtocell access points (FAPs) based on the principles of Contract Theory and Reinforcement Learning. Initially, the IoT nodes’ social and physical characteristics are identified and captured through the concept of IoT node types. Then, Contract Theory is adopted to capture the interactions among the FAPs, who provide personalized rewards, i.e., charging power, to the IoT nodes to incentivize them to invest their effort, i.e., transmission power, to report their data to the FAPs. The IoT nodes’ and FAPs’ contract-theoretic utility functions are formulated, following the network economic concept of the involved entities’ personalized profit. A contract-theoretic optimization problem is introduced to determine the optimal personalized contracts among each IoT node connected to a FAP, i.e., a pair of transmission and charging power, aiming to jointly guarantee the optimal satisfaction of all the involved entities in the examined IoT system. An artificial intelligent framework based on reinforcement learning is introduced to support the IoT nodes’ autonomous association to the most beneficial FAP in terms of long-term gained rewards. Finally, a detailed simulation and comparative results are presented to show the pure operation performance of the proposed framework, as well as its drawbacks and benefits, compared to other approaches. Our findings show that the personalized contracts offered to the IoT nodes outperform by a factor of four compared to an agnostic type approach in terms of the achieved IoT system’s social welfare.

Mechanically and electrically nonlinear non-ideal piezoelectric energy harvesting framework with experimental validations

In the literature of vibration energy harvesting, mechanically nonlinear frameworks have mostly employed linear electrical circuitry to formulate AC input–AC output problems, while the existing efforts on nonlinear power conditioning circuits have assumed linear mechanical behavior. However, even for the simplest case of a stiff (geometrically linear) piezoelectric cantilever, material softening and dissipative nonlinearities in the mechanical domain have to be accomodated to accurately predict the response for moderate to high excitation levels, and likewise a stable DC signal must be obtained to charge a storage component in realistic energy harvesting applications. Furthermore, often times the voltage output is not large enough to assume ideal diode behavior to reduce diodes to switches in AC–DC conversion modeling. Therefore, a relatively complete representation of piezoelectric energy harvesting requires accounting for the mechanical (e.g., material and dissipative) nonlinearities as well as the nonlinear process of AC–DC conversion with non-ideal circuit elements, such as real diodes. To this end, we present and experimentally validate a multiphysics framework and harmonic balance analysis that combines these mechanical and electrical nonlinear non-ideal effects to predict the DC electrical output (DC voltage across the load) in terms of the AC mechanical input (base vibration) for arbitrary vibration and voltage levels. The focus is placed on a bimorph cantilever with piezoelectric laminates under base excitation. The terminals of the piezoelectric layers are combined in series and connected to a bridge rectifier with non-ideal diodes and a filter capacitor. The multi-term harmonic balance framework can capture the ripple in the DC signal as well as amplitude-dependent nonlinear dynamics accounting for realistic diode behavior. In addition to quantitative comparisons and validations by comparing the experimental data and model simulations, important qualitative trends are unveiled for mechanically and electrically nonlinear non-ideal dynamics of piezoelectric energy harvesting.

A Joint Optimization Framework for Energy Harvesting Based Cooperative CR Networks

The energy harvesting cognitive radio networks (EH-CRNs) are expected to play a key role in satisfying the demands of next generation wireless communications. In the underlay CRN, the secondary base station (SBS) is prohibited to transmit at high power to keep the interference temperature within acceptable limits. Thus, the transmission to a secondary user (SU) located far away from SBS may not be possible. In this paper, we investigate cooperative EH-CRNs where an SU close to the SBS harvests the energy and helps in relaying the data of a distant SU. The aim is to maximize the sum rate of the system subject to the limited power budget at the SBS and to the interference constraint of the primary network. We seek a joint optimization framework to distribute power among users at the SBS, power allocation for cooperative transmission, the time slot optimization for transmission of each user, and the power splitting (PS) ratio optimization for energy harvesting (EH) and information decoding (ID). The joint problem under various system constraints results in a non-convex optimization. The problem is first transformed to a convex optimization and then a joint solution is obtained from the duality theory and the simplex method. Further, two solutions, one under uniform power allocation with optimized time and EH while the other with uniform time and optimized power and EH, are also presented. Simulations are provided to validate the proposed optimization model.

Queue stability and low energy for energy harvesting cognitive radio networks

Cognitive radio network explores the framework for energy harvesting and derives effective algorithms such as Nash Equilibrium re-Optimization, Greedy one-slot Game, and Bi Layer re Game. The stability of data queue for energy harvesting cognitive radio network was analyzed by optimization of primary users and noncooperative game of secondary users with Nash equilibrium of stochastic Stackelberg game in a bilevel manner. The proposed work considers the effective data queue stability for secondary user in a cognitive radio network with the necessary conditions such as data arrival, energy arrival, transmission success probability, and expected transmission steady-state delay to make the data queue stable. The queue needs to be stabilized for online algorithm Greedy one-slot Game and offline algorithm Bi Layer re Game. The simulations show the relevance of queue stability by finding the average queue delay and average queue size by comparing both the algorithms.

RETRACTED: Smart health monitoring and management system: Toward autonomous wearable sensing for Internet of Things using big data analytics

Abstract The current development and growth in the arena of Internet of Things (IoT) are providing a great potential in the route of the novel epoch of healthcare. The vision of the healthcare is expansively favored, as it advances the excellence of life and health of humans, involving several health regulations. The incessant increase of the multifaceted IoT devices in health is broadly tested by challenges such as powering the IoT terminal nodes used for health monitoring, real-time data processing and smart decision and event management. In this paper, we propose a healthcare architecture which is based analysis of energy harvesting for health monitoring sensors and the realization of Big Data analytics in healthcare. The rationale of proposed architecture is twofold: (1) comprehensive conceptual framework for energy harvesting for health monitoring sensors, and (2) data processing and decision management for healthcare. The proposed architecture is three-layered architecture, that comprised (1) energy harvesting and data generation, data pre-processing, and data processing and application. We also verified the consistent data sets on Hadoop server to validate the proposed architecture based on threshold limit value (TLV). The study reveals that the proposed architecture offer valuable imminent into the field of smart health.

Energy Harvesting for Water Quality Monitoring using Floating Sensor Networks: A Generic Framework.

Water is a constrained asset and basic requirement for animals, cultivation and industry that exist on this Earth including mankind. Subsequently, to measure the characteristics like physical, biological and chemical attributes of water becomes indispensable. Floating sensor networks (FSN) with in-situ sensors are now widely used to gather the water quality data. The main goal that deals with FSNs is data reliability, congestion control, optimal node placement and energy. So far, we proposed a generic framework for energy harvesting, reliable data transform congestion detection and deployment strategy for FSN to collect water quality data. Energy harvesting subsystem model will be designed to fulfill the needs for the desired application scenarios in sensor networks and extend the network lifetime. Therefore, this model will help in scheduling the sending/receiving, sleeping and idle time of the node. By incorporating this framework, network lifetime can be prolonged. For reliable data transform and congestion detection; a model will be developed to enhance the data delivery. The objectives of optimal node deployment are full coverage, network connectivity, improve the data fidelity and enhance the network lifetime.

Energy-harvesting based on internet of things and big data analytics for smart health monitoring

Current advancements and growth in the arena of the Internet of Things (IoT) is providing great potential in the novel epoch of healthcare. The future of healthcare is expansively promising, as it advances the excellence of life and health of humans, involving several health regulations. Continual increases of multifaceted IoT devices in healthcare is beset by challenges, such as powering IoT terminal nodes used for health monitoring, data processing, smart decisions, and event management. In this paper, we propose a healthcare architecture which is based on an analysis of energy harvesting for health monitoring sensors and the realization of Big Data analytics in healthcare. The rationale of the proposed architecture is two-fold: (1) comprehensive conceptual framework for energy harvesting for health monitoring sensors; and (2) data processing and decision management for healthcare. The proposed architecture is a three-layered architecture that comprises: (1) energy harvesting and data generation; (2) data pre-processing; and (3) data processing and application. The proposed scheme highlights the effectiveness of energy-harvesting based IoT in healthcare. In addition, it also proposes a solution for smart health monitoring and planning. We also utilized consistent datasets on the Hadoop server to validate the proposed architecture based on threshold limit values (TLVs). The study demonstrates that the proposed architecture offers substantial and immediate value to the field of smart health.

An energy management framework for energy harvesting embedded systems

Energy harvesting (also known as energy scavenging) is the process of generating electrical energy from environmental energy sources. There exists a variety of different energy sources such as solar energy, kinetic energy, or thermal energy. In recent years, this term has been frequently applied in the context of small autonomous devices such as wireless sensor nodes. In this article, a framework for energy management in energy harvesting embedded systems is presented. As a possible scenario, we focus on wireless sensor nodes that are powered by solar cells. We demonstrate that classical power management solutions have to be reconceived and/or new problems arise if perpetual operation of the system is required. In particular, we provide a set of algorithms and methods for various application scenarios, including real-time scheduling, application rate control, as well as reward maximization. The goal is to optimize the performance of the application subject to given energy constraints. Our methods optimize the system performance which, for example, allows the usage of smaller solar cells and smaller batteries. Furthermore, we show how to dimension important system parameters like the minimum battery capacity or a sufficient prediction horizon. Our theoretical results are supported by simulations using long-term measurements of solar energy in an outdoor environment. In contrast to previous works, we present a formal framework which is able to capture the performance, the parameters, and the energy model of various energy harvesting systems. We combine different viewpoints, include corresponding simulation results, and provide a thorough discussion of implementation aspects.