Energy Efficient Transmission Research Articles

Self-trainable and adaptive sensor intelligence for selective data generation

With the increasing integration of machine learning into IoT devices, managing energy consumption and data transmission has become a critical challenge. Many IoT applications depend on complex computations performed on server-side infrastructure, necessitating efficient methods to reduce unnecessary data transmission. One promising solution involves deploying compact machine learning models near sensors, enabling intelligent identification and transmission of only relevant data frames. However, existing near-sensor models lack adaptability, as they require extensive pre-training and are often rigidly configured prior to deployment. This paper proposes a novel framework that fuses online learning, active learning, and knowledge distillation to enable adaptive, resource-efficient near-sensor intelligence. Our approach allows near-sensor models to dynamically fine-tune their parameters post-deployment using online learning, eliminating the need for extensive pre-labeling and training. Through a sequential training and execution process, the framework achieves continuous adaptability without prior knowledge of the deployment environment. To enhance performance while preserving model efficiency, we integrate knowledge distillation, enabling the transfer of critical insights from a larger teacher model to a compact student model. Additionally, active learning reduces the required training data while maintaining competitive performance. We validated our framework on both benchmark data from the MS COCO dataset and in a simulated IoT environment. The results demonstrate significant improvements in energy efficiency and data transmission optimization, highlighting the practical applicability of our method in real-world IoT scenarios.

A Novel Compressive Sensing Method for Secure and Energy Efficient ECG Signal Transmission Applications

A Novel Compressive Sensing Method for Secure and Energy Efficient ECG Signal Transmission Applications

Open Protocols, the new standard for acoustic tracking: results from interoperability and performance tests in European waters

BackgroundThe lack of compatibility between acoustic telemetry equipment from different manufacturers has been a major obstacle to consolidating large collaborative tracking networks. Undisclosed encrypted signal coding protocols limit the use of acoustic telemetry to study animal movements over large spatial scales, reduce competition between manufacturers, and stifle innovation. The European Tracking Network, in collaboration with several acoustic telemetry manufacturers, has worked to develop new transparent protocols for acoustic tracking. The results are energy-efficient transmission protocols accessible to all researchers and manufacturers. Today, the Open Protocols (OP) are already available to manufacturers and developers, and the first transmitters and receivers to implement them are already in the water.ResultsThe main objective of this study was to confirm the compatibility between devices from different manufacturers using OP, characterise the acoustic range of each transmitter–receiver manufacturer combination, compare the detection efficiency to the standard protocols used at present (R64K and encrypted protocols), and assess its robustness against spurious detections. An international collaborative effort was made to conduct acoustic range tests in four main aquatic habitats: a river, a coastal lagoon, a coastal habitat, and the open sea. Receivers and transmitters from different manufacturers were deployed at increasing distances from each other using the same experimental design at each location. The decay of detection probability with distance was modelled for each transmitter–receiver manufacturer combination by applying logistic regression using a Bayesian approach. Furthermore, to thoroughly assess performance differences in an applied research context, we conducted a direct field comparison between groups of smolts tagged with OP and R64K tags, tracking their migration to the sea.ConclusionsOur results confirm full compatibility between the tested devices, with negligible differences in the measured acoustic ranges between OP manufacturers and when compared to encrypted protocols. The OP was also robust against spurious detections, and the field comparison between OP and R64K showed equal performance. We hope these novel insights will encourage international research groups to promote OP-based studies to ensure compatibility and maximise the benefits of acoustic telemetry networks.

Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes to minimize network fragmentation and balance energy consumption. However, placing gateways near the sink node can result in increased communication overhead and higher energy consumption in regions with concentrated data flow. To address these issues, we propose an energy-efficient artificial fish swarm-based clustering cognitive intelligence protocol (EAFSCCIP). EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. The protocol focuses on reducing energy consumption and extending network lifetime by considering real-time energy levels and the proximity of nodes for CH selection. Simulations have been executed in NS3 to validate and compare the performance of the proposed algorithm with the existing clustering protocols. The results indicate that EAFSCCIP significantly enhances the packet delivery ratio (PDR) by an average of 5.33% over existing methods and improves network lifetime by 6.54% compared to traditional protocols. It also reduces energy consumption by 25.6% and decreases packet loss by 50.5%, while achieving 20.4% higher throughput at the initial stage. These improvements make EAFSCCIP a promising solution for applications like acoustic monitoring in UWSNs, providing a balance between energy efficiency and reliable data transmission.

Nonblocking conditions for Clos fabrics with non-uniform switch radixes

Datacenter networks (DCNs) evolve over years and so comprise switches from different generations. Thus, each stage/layer of the Clos fabric may consist of switches with varying radixes (i.e., different port counts), leading to non-uniform stages. While optical circuit switches are increasingly deployed in DCNs to enhance transmission capacity and energy efficiency, the nonblocking condition, crucial for determining the performance of circuit-switched networks, has been established only for Clos fabrics with uniform stages. This study extends the nonblocking condition to Clos fabrics with non-uniform stages. To facilitate practicality, we formulate the condition using integer linear programming (ILP). Using our novel, to our knowledge, condition, we quantitatively demonstrate how much the nonblocking property is compromised under two practical scenarios, random link failures and network expansion, which would break network uniformity. In particular, we reveal that network expansion, common in DCN evolution, could significantly undermine the nonblocking property. Additionally, we assess the computational efficiency of our ILP formulation, which can successfully evaluate the nonblocking property of a large Clos fabric accommodating 32K terminals/uplinks in just 19 min.

Research on Wireless Charging System Based on Photovoltaic Power Supply

Given the popularity of mobile devices and rapid advances in wireless technology, it is impossible to overlook how important charging methods should be as convenient and environmentally sustainable. This paper investigates the PV power supply integration in their wireless charging systems, which aligns well with developing an environmentally safe and renewable alternative for recharging that would alleviate pressure on conventional harvesting of electricity, while also improving ecological conservation and reducing carbon emissions following earlier interests. Firstly, based on the PV battery model modeling methods and fully considering light intensity conditions at various temperatures throughout a year-long experimental validation of the electrical output characteristics of these PV panels. Second, it adapts and optimizes DC-DC converters to the output of PV panels for higher energy conversion efficiency by stabilizing energy outputs due to weathering issues. Additional area for research includes the choice and tuning of Maximum Power Point Tracking (MPPT) algorithms that will enable this system to follow maximum power point under diverse weather and light conditions in order to maximize energy harvesting. Moreover, the paper also performs an in-depth study of coupling transformer design and optimization of main circuit parameters for better energy transmission efficiency and System stability. The feasibility and effectiveness of the PV-powered wireless charging systems are validated by simulation verification as well as experimental testing in this paper.

UTILIZING MACHINE LEARNING ALGORITHMS TO IMPROVE THE ACCURACY OF SOLAR ENERGY YIELD PREDICTIONS BASED ON VARIOUS INPUT VARIABLES

This paper explores a framework for an ML model for solar yield estimation using random forest, gradient boosting, support vector machines, and neural networks There are various factors affecting solar energy production, some of which are weather conditions, solar intensity, temperature, among others, and characteristics of the panel. The trend for development and usage of renewable energy sources continues to gain momentum. The estimation of production rates for solar energy is crucial for the efficiency of energy generation, transmission, and supply. Hypothesis-driven and list-based models of traditional statistics are sometimes rather low in predictive validity because the relationships between these factors may be non-linear and highly interactive. The complex structure of these dependencies indicates that advanced ML algorithms are an appropriate approach to modeling the yield and increasing the utility of the resulting predictions. This work predictively analyzes historical solar generation data and climatic factors and preprocesses the data through normalization and feature engineering. The models are then adjusted and tested using a cross-validation approach in order to minimize the risk of obtaining high accuracy values only on the training data set. Evaluations of performance with mean absolute error, root mean square error, and coefficient of determination are used to measure the efficiency of each model. Machine learning algorithms widely outperform other models when applied to predict the yield of solar energy in the same or related areas. The random forests seem to perform better for prediction because they operate in an ensemble, which allows for modeling interactions between them. Through enhanced prediction precision, the approach proves to be a helpful resource for solar energy managers to attain better correlation between supply and demand, enhance solar energy production, and encourage the use of sustainable energy resources. Extension of this study in future work would involve the use of real-time data coupled with adaptive learning models to improve the prediction resilience.

Space-based solar power: Unlocking continuous, renewable energy through wireless transmission from space

Space-Based Solar Power (SBSP) is an emerging technology that aims to harness the abundant and uninterrupted solar energy available in space and beam it wirelessly to Earth. This innovative approach addresses the limitations of terrestrial solar energy, such as weather variability and the day-night cycle, by positioning solar power stations in space where sunlight is constant. The stations, composed of large arrays of solar panels, capture the solar energy and convert it into microwaves or laser beams, which are then transmitted to receiving stations on Earth for conversion into usable electricity. Research in SBSP focuses on optimal orbital positions, with geostationary and low Earth orbits being considered for maximum energy capture and efficient transmission. Advancements in materials, wireless power transmission technologies, and space-based assembly techniques are critical to the design and deployment of these stations. Additionally, economic and technical feasibility studies explore the challenges of launching, maintaining, and scaling SBSP systems. These include high initial costs, energy transmission efficiency, safety concerns, and the logistics of long-term maintenance. The potential benefits of SBSP are significant. It offers a clean, renewable energy source that could meet the growing global demand for electricity while reducing reliance on fossil fuels. By providing continuous energy, SBSP could enhance energy security and contribute to global efforts to combat climate change. However, several challenges remain, including addressing space debris risks and ensuring the safety of high-power energy transmission to Earth. As research and technological advancements continue, SBSP holds promise as a transformative solution for future energy needs.

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

This paper investigates the application of high-capacity supercapacitors in railway systems, with a particular focus on their role in energy recovery during braking processes. The study highlights the potential for significant energy savings by capturing and storing energy generated through electrodynamic braking. Experimental measurements conducted on a diesel–electric multiple unit revealed that approximately 28.3% to 30.5% of the energy could be recovered from the traction network, regardless of the type of drive used—whether electric or diesel. This research also explores the integration of starch-based carbon as an electrode material in supercapacitors, offering an innovative, sustainable alternative to traditional graphite or graphene electrodes. The carbon material was obtained through a simple carbonization process, with experimental results demonstrating a material capacity of approximately 130 F/g. To quantify the energy recovery, calculations were made regarding the mass and power requirements of the supercapacitors. For the tested vehicle, it was estimated that around 28.7% of the energy could be recovered during the braking process. To store 15 kWh of energy, the total mass of the capacitors required is approximately 245.1 kg. The study emphasizes the importance of increasing voltage levels in railway systems, which can enhance energy transmission and utilization efficiency. Additionally, the paper discusses the necessity of controlled energy discharge, allowing for the flexible management of energy release to meet the varying power demands of trains. By integrating high-voltage supercapacitors and advanced materials like starch-based carbon, this research paves the way for more sustainable and efficient railway systems, contributing to the industry’s goals of reducing emissions and improving operational performance. The findings underscore the crucial role of these capacitors in modernizing railway infrastructure and promoting environmentally responsible transportation solutions.

Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink’s traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

Dynamic characteristics and transmission efficiency of energy-harvesting shock absorber with low speed and heavy duty capacity

Traditional hydraulic shock absorbers commonly encounter challenges such as cavitation, oil leakage, and the management of energy dissipation. The proposed regenerative shock absorber, which utilizes a ball screw mechanism, offers advantages such as high efficiency and heavy load capacity. This paper focuses on the design, modeling, and testing of a mechatronic shock absorber. Virtual prototype simulation techniques are employed to investigate the dynamic behaviors of the shock absorber. The paper presents a systematic efficiency model of a shock absorber, which is based on the ball screw and gearbox mechanisms. The study comprehensively analyzes the effects of rotation speed, axial load, and structure parameters on transmission efficiency. The results demonstrate that a peak power of 38 W can be achieved with a damping force of only 340 N, and the recovery efficiency can reach up to 28%. The experimental test results validate the effectiveness and reliability of the proposed dynamic and efficiency models. The originality of this study lies in the development of a novel energy-harvesting shock absorber with a ball screw mechanism in series with a gearbox, which shows excellent energy recovery potential and transmission efficiency under low speed and high duty conditions.

Node Localization Based on Unsupervised Approach and Secure Routing for Healthcare Management in Delay‐Tolerant Network

ABSTRACTThe ability of the current global healthcare system to continue providing high‐quality medical care has been questioned. By using delay‐tolerant networks (DTNs) in healthcare management, people that spend a lot of time treating illnesses are exposed to more people while also putting their lives in danger. Unsupervised approach‐based node localization and secure routing in DTNs are developed in the proposed method. The proposed adaptive density‐based localization algorithm is constructed in two modules. The entire network is clustered depending on density utilizing density‐based spatial clustering of applications with noise (DBSCAN). An RSA‐based location estimation algorithm is employed to identify the unlocalized node within each cluster. After finding the location of each node, routing mechanism is carried out. An adaptive spray and wait routing strategy is utilized in the proposed method. When a node only has one copy of a message, the spray and wait, routing mechanism moves the relay node while it waits for the destination to be countered. Then modified DNA encryption algorithm is applied for secure data transmission. It is used for encrypting the information before storing it in the network. The trusted center will generate the private key needed for encrypting the data based on the sensitivity level of the information. This proposed algorithm is tested with several metrics which attain better performance, like 94% localization accuracy, 6% localization error, average residual energy of 93%, 5.7 Mbps throughput, 18% encryption time, and 14% decryption time. Thus, the techniques used in the proposed approach are the best choice for node localization, energy efficiency, and secure data transmission in DTN.

Robust and energy-efficient RPL optimization algorithm with scalable deep reinforcement learning for IIoT

The increasing complexity and quantity of the Industrial Internet of Things (IIoT) pose new challenges to the traditional routing protocol for low-power and lossy networks (RPL) in terms of dynamic management, data transmission reliability, and energy efficiency optimization. This paper proposes a scalable deep reinforcement learning (DRL) algorithm with a multi-attention actor double critic model for routing optimization (MADC) to meet the requirements of IIoT for efficient and intelligent routing decisions while improving data transmission reliability and energy efficiency. Specifically, MADC employs the centralized training and decentralized execution (CTDE) learning paradigm to decouple the model’s training and inference tasks, which reduces the difficulty and computational cost of model learning and improves the training efficiency. In addition, a lightweight actor network based on multi-scale convolutional attention mechanism is designed in MADC, which can provide intelligent and real-time decision-making capabilities for resource-constrained nodes with low computational and storage complexities. Moreover, a scalable critic network utilizing multiple attention mechanisms is proposed. It is not only suitable for dynamic and changing network environments but also can more comprehensively and accurately evaluate local observation states, providing more accurate and efficient guidance for model optimization. Furthermore, MADC incorporates a double critic network architecture to mitigate potential overestimation issues during training, thereby ensuring the model’s robustness and reliability. Simulation results demonstrate that MADC outperforms existing RPL optimization algorithms in terms of energy efficiency, data transmission reliability, and adaptability.

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

ABSTRACT Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques.

Optimizing deep-space DTN congestion control via deep reinforcement learning

This paper introduces an innovative congestion control mechanism for delay/disruption-tolerant networking (DTN) within deep-space communication systems, leveraging the nuanced capabilities of deep reinforcement learning (DRL). This approach significantly departs from traditional methods, addressing the unique challenges of deep-space data transmissions. The proposed DRL-based strategy demonstrates a superior balance of critical factors, including transmission delay, energy efficiency, and data reception integrity. We assess our approach through meticulous simulation and comparison with established benchmark schemes. The findings underscore the mechanism’s enhanced performance metrics, positing it as an appealing solution in the evolving landscape of non-terrestrial networking.

Numerical computation of cut off wave number in polygonal wave guide by eight node finite element mesh generation approach

This study proposes a two-dimensional, eight-noded automated mesh generator for precise and efficient finite element analysis (FEA) in microwave applications. The suggested method for solving the Helmholtz problem employs an optimal domain discretization procedure. MAPLE-13 software's advanced automatic mesh generator was developed specifically for this work. To demonstrate the effectiveness of the approach, three distinct waveguide structures are analyzed, with the results compared to the best available analytical or numerical solutions. The findings indicate that the proposed method yields highly accurate and efficient finite element results, particularly for waveguide structures containing singularities. In microwave applications, this method can significantly enhance energy transmission efficiency.

An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.

Design of waveguide with double layer diffractive optical elements for augmented reality displays

We investigated a diffraction optical waveguide structure with a double layer coupling configuration. This double layer-coupled diffraction optical waveguide structure modulates light information through wavefront modulation for propagation within the optical waveguide and then reproduces the light information through further wavefront modulation, thus achieving optical information transmission. Analysis indicates that the theoretical maximum field of view can reach 90° × 90°. With an actual field of view set to 53° × 53°, an entrance pupil size of 3.2 × 3.2 mm², an eye relief of 10 mm, and 50 pupil expansion, the system’s total energy transmission efficiency is 37%, with field of view uniformity at 91% and uniformity within the eye movement range reaching 97%.

Summary of Wireless Charging Technology for Electric Vehicles

The rapid popularity of electric vehicles has promoted the rapid development of wireless charging technology. This paper reviews the current research progress and application status of wireless charging technology for electric vehicles. Inductive coupling, magnetic resonance coupling and non-directional radiofrequency radiation, three main wireless power transmission methods, including their principles and advantages, are introduced. In addition, the practical application of the technique, such as near field application and far field application, is also discussed. Finally, the development direction of wireless charging technology for electric vehicles in the future is looked forward, including the key technical paths of optimizing system design, improving energy transmission efficiency and reducing system cost. In summary, this paper aims to provide comprehensive theoretical support and practical guidance for the research and practice of wireless charging technology for electric vehicles.

Energy-Efficient Adaptive Bidirectional Transmission Strategy in Simultaneous Wireless Information and Power Transfer (SWIPT)-Enabled Cognitive Relay Network.

Introducing collaborative relay and simultaneous wireless information and power transfer (SWIPT) techniques into a cognitive wireless network, named the SWIPT-enabled cognitive relay network (CRN), is considered a promising approach to deal with insufficiency and the low utilization of spectrum resources, as well as the node's energy-constrained issues in wireless networks. In this paper, to improve the network spectrum efficiency (SE) and energy efficiency (EE) of the SWIPT-enabled CRN, we design an energy-efficient adaptive bidirectional transmission strategy. To be specific, we first select an energy-constrained best relay node with the consideration of signal-to-noise ratio and global channel gain to achieve a better bidirectional relay transmission (BRT). At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT technique, which can solve the node's energy-constrained issue and improve the network EE. Then, with the selected energy-constrained best relay node, we design a total transmit power threshold (TTPT) determining algorithm to find the TTPT, which lets the total transmission rate of the BRT be equal to the bidirectional direct transmission (BDT). Based on this TTPT, we further design an adaptive bidirectional transmission strategy and let the network achieve adaptive transmission between the BRT and BDT to obtain a higher network SE. Furthermore, to further achieve the energy-efficient transmission of the adaptive bidirectional transmission strategy, we optimize the nodes' power under the requirement of primary users' interference threshold and obtain the analytical expressions of the optimal power. Simulation results show that the transmission rate, the outage probability, and the EE of the designed energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN are, respectively, 3.01, 0.07, and 3.10 times that of the non-collaborative transmission, which show the effectiveness of our designed transmission strategy.