Energy Constraints Research Articles

A cloud-edge framework for energy-efficient event-driven control: an integration of online supervised learning, spiking neural networks and local plasticity rules

Abstract This paper presents a novel cloud-edge framework for addressing energy constraints in complex control systems. Our approach centers around a learning-based controller using Spiking Neural Networks (SNN) on physical plants. By integrating a biologically plausible learning method with local plasticity rules, we harness the energy efficiency, scalability of the newtwork, and low latency of SNNs. This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication. The plant updates weights only when errors surpass predefined thresholds, ensuring efficiency and robustness in various conditions. Applied to linear workbench systems and satellite rendezvous scenarios, including obstacle avoidance, our architecture dramatically lowers normalized tracking error by 96% with increased network size. The event-driven nature of SNNs minimizes energy consumption, utilizing only about 11.1 × 104 pJ (0.3% of conventional computing requirements). The results demonstrate the system’s adjustment to changing work environments and its efficient use of energy resources, with a moderate increase in energy consumption of 37% for dynamic obstacles, compared to non-obstacle scenarios.

Optimal stealthy deception attack strategy under energy constraints

This article explores the issue of devising optimal stealthy deception attack strategy based on remote state estimation in cyber-phsical systens (CPSs) under energy constraints. Taking the energy limitations into consideration, the attacker needs to launch signals under stealth conditions and alters the transmitted data in a limited time span with a limited number of changes. The stealth attack model is proposed, which enables the attack signal to evade the detector without being detected. By analyzing various forms of continuous attacks and discrete attacks, we study five different attack sequences and find the optimal attack sequence by evaluating the long-range remote estimation error. Using the Lagrange multiplier means, the optimal parameters are obtained to maximize the long-range estimation error while guaranteeing the integral stealth of the detector. The calculation process is summarized in Algorithm 1. Simulation instances are given to demonstrate the authenticity of this paper’s work.

Anytime Multi-Task Multi-Agent Pickup and Delivery Under Energy Constraint

Anytime Multi-Task Multi-Agent Pickup and Delivery Under Energy Constraint

Ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

ABSTRACTAs we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low‐overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large‐scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.

Efficient Defect-Driven Cation Exchange beyond the Nanoscale Semiconductors toward Antibacterial Functionalization.

Defect engineering is an exciting tool for customizing semiconductors' structural and optoelectronic properties. Elaborating programmable methodologies to circumvent energy constraints in multievent inversions expands our understanding of the mechanisms governing the functionalization of nanomaterials. Herein, we introduce a novel strategy based on defect incorporation and solution rationalization, which triggers energetically unfavorable cation exchange reactions in extended solids. Using Sb2X3 + Ag (I) → Ag: Sb2X3 (X= S, Se) as a system to model, we demonstrate that incorporating chalcogen vacancies and AgSbVX complex defects into initial thin films (TFs) is crucial for activating long-range solid-state ion diffusion. Additional regulation of the Lewis acidity of auxiliary chemicals provides an exceptional conversion yield of the Ag precursor into a solid-state product up to 90%, simultaneously transforming upper matrix layers into AgSbX2. The proposed strategy enables tailoring radiative recombination processes, offers efficiency to invert TFs at moderate temperatures quickly, and yields structures of large areas with substantial antibacterial activity in visible light for a particular inversion system. Similar customization can be applied to most sulfides/selenides with controlled reaction yields.

An Energy-Efficient Scheme Design for NOMA-Based UAV-Assisted MEC Systems

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system’s energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches.

Enhancing 5G Vehicular Edge Computing Efficiency with the Hungarian Algorithm for Optimal Task Offloading

The rapid advancements in vehicular technologies have enabled modern autonomous vehicles (AVs) to perform complex tasks, such as augmented reality, real-time video surveillance, and automated parking. However, these applications require significant computational resources, which AVs often lack. To address this limitation, Vehicular Edge Computing (VEC) has emerged as a promising solution, allowing AVs to offload computational tasks to nearby vehicles and edge servers. This offloading process, however, is complicated by factors such as high vehicle mobility and intermittent connectivity. In this paper, we propose the Hungarian Algorithm for Task Offloading (HATO), a novel approach designed to optimize the distribution of computational tasks in 5G-enabled VEC systems. HATO leverages 5G’s low-latency, high-bandwidth communication to efficiently allocate tasks across edge servers and nearby vehicles, utilizing the Hungarian algorithm for optimal task assignment. By designating an edge server to gather contextual information from surrounding nodes and compute the best offloading scheme, HATO reduces computational burdens on AVs and minimizes task failures. Through extensive simulations in both urban and highway scenarios, HATO achieved a significant performance improvement, reducing execution time by up to 75.4% compared to existing methods under full 5G coverage in high-density environments. Additionally, HATO demonstrated zero energy constraint violations and achieved the highest task processing reliability, with an offloading success rate of 87.75% in high-density urban areas. These results highlight the potential of HATO to enhance the efficiency and scalability of VEC systems for autonomous vehicles.

Who is sufficient, and why? A mixed-methods approach to the social determinants of sufficiency lifestyles in the pursuit of decarbonisation

Sufficiency consists of less materially intensive consumption patterns that focus on wellbeing rather than material wealth. Such consumption patterns are commonly referred to as sufficiency lifestyles and are increasingly seen as a key driver for achieving decarbonisation. However, while research finds that sustainable consumption and environmental behaviour are often associated with high social status, lower carbon footprints and lower consumption more broadly are associated in the literature with poverty and deprivation. This article aims to investigate whether a combination of low carbon footprint and high wellbeing exists, what socio-demographic characteristics are associated with it, and to explore the actual experiences of people engaged in sufficiency lifestyles through initiatives and their characteristics. We use a mixed-methods design with data from demographically representative surveys in Denmark, Germany, Italy and Latvia (N=5,080), and in-depth interviews with participants from sufficiency-oriented intentional communities (N=90). We find that sufficiency lifestyles are linked to a stronger inclination towards sufficiency orientation or a heightened stronger environmental identity, accompanied by a negative correlation with social deprivation aspects. Drawing on the interviews, we find that individuals engaged in sufficiency-oriented practices tend to benefit from economic, social and cultural resources that allow them to overcome energy dependency and constraints, and that their motivations are not limited to pro-environmental behaviour. Based on these empirical findings, we explore potential avenues for the diffusion of sufficiency-oriented lifestyles, including the establishment of binding rules through a democratic process that curb overconsumption while providing attractive low-carbon lifestyles for all.

Comparative Analysis of Electric and Conventional Vehicles Performance in the Evacuation Process of Mount Semeru Eruption Victims Based on Geographic Information Systems

The Lumajang Regency is highly vulnerable to various natural disasters, particularly the potential eruption of Mount Semeru. In disaster response efforts, the local government needs to prepare effective and efficient evacuation routes, taking into account the magnitude of the eruption impact in the Semeru disaster-prone area. This research focuses on evacuating vulnerable residents using electric and conventional vehicles. This study is categorized as a vehicle routing problem with energy constraint (VRPEC) because the electric vehicles utilized in this research do not require recharging during their operational process, ensuring rapid evacuation as it is essential. By utilizing Geographic Information Systems (GIS)-based optimization, the best route to evacuate all victims within 12 h is determined. This study involves developing scenarios considering the number of vehicles and their travel distances. There are also evacuation guidelines, including the implementation of priority points and evacuation zone usage. The research results indicate that scenarios EV 5, 8, and 10 are the most optimal for using electric vehicles. Meanwhile, the optimal scenario for conventional vehicles is scenario 5. This analysis shows that implementing electric vehicle scenarios is superior to conventional vehicle scenarios in terms of the total time required to evacuate all victims.

Characterization and utilization of apple peel and grape stems extract constituents as green restraints for aluminum dissolution

Different flavone extracts from apple peel and aldehydes from grape stems were investigated as restraints of the anodic aluminum dissolution procedure in 60% H3PO4:40% H2SO4. The potential-limiting current correlation for Al anode was assessed and associated with regularly improving apple peel and grape stems extract concentration (100 to 1000 ppm range). The limiting current reduces whereas retardation effectiveness (%) increases as the concentrations of apple peel and grape stems extract rise. Apple peel/grape stems mixture extract is pondered to have the most retardation impact. Apple peel and grape stems extract retardation mechanism depends on the adsorption manner at the aluminum metal, which was confirmed via scanning electron microscopy (SEM) and UV-VIS-NIR spectra which reflect that elevated extract concentration (1000 ppm) have a hopeful and positive impact on the Al surface quality. The activation energy and activation constraints (changes in enthalpy, entropy, and Gibbs free energy) were established, and suggestions for powerful interaction between the additives and the aluminum surface were conveyed. The extract items were inspected via Fourier transform infrared spectroscopy and Gc-mass. The apple peel and grape stems extract establish perspective as a natural electro-polishing green restraint. The weight loss data obtained is in excellent conformity with the result obtained by electrochemical measurements. The synergistic influence between apple peel/grape stems (S = 1.64–1.83 ) is noticeable. The lowest Ra and PV estimates are recorded via an apple peel /grape stems mixture, which achieves the greatest reflectance estimate and retardation effectiveness. This is recognized high active site number for apple peel /grape stems mixture extract.

Bouncing universe scenarios in an extended gravitational framework involving curvature-matter coupling

Exploration of bouncing cosmic models in modified theories has gained much popularity in modern cosmology. This paper explores the Lagrangian function of a new theory namely F(R,Lm,T) framework by taking four renowned cosmic bouncing models, i.e., the exponential bounce, oscillatory bounce scenario, power law, and matter bouncing. Our primary objective is to fix the form of F(R,Lm,T) function for each model and investigate which kinds of reconstructed Lagrangian function have potential of regenerating bouncing scenario in terms of analytical form. It is seen that except power law model, the analytical solutions are conceivable only for certain cases of these bouncing models. For power law bounce, different cases of Lagrangian function may be rebuilt analytically while for some other bouncing scenarios, it is found that particular solutions are not always attainable and hence only the complimentary solutions can be explored. Further, we examine the behavior of energy constraints and stability of these analytically formed bouncing solutions. Additionally, we determine that the dark energy phase in F(R,Lm,T) gravity is compatible with the experimental data of BAO+Sne-Ia+CMB+H(z) and it is shown that cosmic bounce can be produced with dark energy eras in this gravity. We also present some constraints on the model parameters with Hubble parameter values and ΛCDM to determine the best-fit values of model via least square and reduced chi-squares methods. It is concluded that matter bounce model is the best fitted with the observational data set as well as ΛCDM model because it has least value of χm2.

Advancing greener LNG-fueled vessels: Compact simultaneous reduction system for CH4, NOX and CO2 emissions

Stringent ship regulations necessitate simultaneous emission reductions, challenged by cargo loss and energy constraints. This study proposes a compact reduction system that simultaneously mitigate CH4, NOX, and CO2 emissions onboard LNG-fueled vessels. The proposed system integrates two pivotal technologies: a metal support-based compact methane oxidation catalyst (MOC)-selective catalytic reduction (SCR) system to capture CH4 and NOX, and a cryogenic CO2 capture (CCC) system to capture and store CO2 in its solid phase utilizing LNG's cold energy. This innovative approach addresses two challenges. First, the compact MOC-SCR system employs metal support with an enhanced surface area-to-volume ratio surpassing the capabilities of ceramic support materials, achieving a volume reduction of 80.02% and 79.82% respectively. Secondly, the CCC system significantly lowers specific energy consumption by 61.54% by harnessing LNG's cold energy compared to the absorption-based CO2 capture system. As a result, the proposed system not only reduces CH4, NOX, and CO2 emissions simultaneously with a high capture rate but also reduces volume and weight. Furthermore, it achieves a high CO2 capture rate of 92.12%, while diminishing cargo losses by 25.30%. These findings provide valuable guidance for developing environmentally sustainable vessels, aligning with the continuously increasing stringency of regulations.

Robust Multi-UAV Cooperative Trajectory Planning and Power Control for Reliable Communication in the Presence of Uncertain Jammers

Unmanned aerial vehicles (UAVs) have become a promising application for future communication and spectrum awareness due to their favorable features such as low cost, high mobility, and ease of deployment. Nevertheless, the jamming resistance appears to be a new challenge in multi-UAV cooperative communication scenarios. This paper focuses on designing trajectory planning and power allocation for efficient control and reliable communication in a ground control unit (GCU)-controlled UAV network, where the GCU coordinates multi-UAV systems to execute tasks amidst multiple jammers with imperfect location and power information. Specifically, this paper formulates a nonconvex semi-infinite optimization problem to maximize the average worst-case signal-to-interference-plus-noise ratio (SINR) among multiple UAVs by designing robust flight paths and power control strategy under stringent energy and mobility constraints. To efficiently address this issue, this paper proposes a powerful iterative algorithm utilizing the S-procedure and the successive convex approximation (SCA) method. Extensive simulations validate the effectiveness of the proposed strategy.

A Novel Energy-Aware Path Planning by Autonomous Underwater Vehicle in Underwater Wireless Sensor Networks

Wireless sensor networks can monitor the environment to detect anomalies and reduce the risk of maritime traffic. Energy is necessary for low-power conditions where wireless sensor networks are used. Ensuring the lifespan of energy constraints and providing continuous environmental observation, data collecting, and communication requires management. Battery replacement and energy consumption issues can be resolved with path planning and energy-efficient autonomous underwater vehicle charging for sensor nodes. The nearest neighbour technique is used in this study to solve the energy-aware path planning problem of an autonomous underwater vehicle. Path planning simulations show that the nearest neighbour strategy converges faster and produces a better result than the genetic algorithm. We develop robust and energy-efficient path-planning algorithms that efficiently acquire sensor data while consuming less energy, allowing the monitoring system to respond to anomalies more rapidly. Increased sensor connectivity lowers energy usage and increases network longevity. This study also considers the situation when it is recommended to avoid taking direct travel paths between particular node pairs for a variety of reasons. This recommendation is considered in this study. We present a strategy based on a modified Nearest Neighbour-based Approach from the Nearest Neighbour method to address this more challenging scenario. The direct pathways between such nodes are constrained within the context of this technique. The modified version of Nearest Neighbor-based approach performs well even in that particular situation.

Station keeping control method based on deep reinforcement learning for stratospheric aerostat in dynamic wind field

Aiming at the problem of long-endurance station keeping of stratospheric aerostats under energy constraints in dynamic wind environment, a stratospheric aerostat station keeping controller was designed using the D3QN (Dueling Double Deep Q Network) algorithm of deep reinforcement learning in this article. The performance of the station keeping controllers are evaluated by the average station keeping radius and station keeping effective time ratio. Meanwhile, the effects of different reward functions on the performance of station keeping controller are studied. The simulation results of typical scenarios with European Centre for Medium-Range Weather Forecasts (ECMWF) data show that: under the task constraints of three days of station keeping time and 50 km of station keeping radius, the average station keeping radius of the stratospheric aerostat without power propulsion is 28.25 km, the station keeping time ratio is 90.56%, and the State of Charge (SOC) of the battery is more than 50%. Under the condition of propulsion, the control effect of the average station keeping radius of stratospheric aerostat is significantly improved. The control effect of adding dynamic propulsion in the north-south direction is better than that in the east-west direction, which is close to the dual-channel control effect. At the same time, this study verifies that the station keeping controller based on deep reinforcement learning has strong robustness, and demonstrates that the station keeping controller can be designed with different reward functions for the requirements of different station keeping task.

Quality of Experience Optimization for Real-Time XR Video Transmission With Energy Constraints

Quality of Experience Optimization for Real-Time XR Video Transmission With Energy Constraints

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Optimal Intermittent Deception Attacks With Energy Constraints for Cyber-Physical Systems

Optimal Intermittent Deception Attacks With Energy Constraints for Cyber-Physical Systems

A data-and-knowledge-driven WNN modeling approach for the absorption refrigeration system

Given that the cooling capacity of the absorption refrigeration system depends on both the heat supply and the operating conditions, a data-and-knowledge-driven Wavelet Neural Network (WNN) modeling approach is proposed based on the analysis of its working principle and the coupling relationship between variables. This approach utilizes the Simple Particle Swarm Optimization (SPSO) algorithm instead of the gradient descent method to search the network parameters of WNN, which makes up for the defects of the gradient descent method that is easy to fall into the local optimum. Furthermore, the inequality constraints of energy and temperature are integrated into the loss function of model training as prior knowledge, and the model’s parameters are trained using experimental data, which reduces the training burden of the model and the model’s dependence on the training data, and increases the network’s interpretability and the model’s robustness. The experimental results show that the prediction accuracy and generalization of the WNN model are improved after replacing the optimization algorithm and incorporating the physical knowledge under the same working conditions, and its RMSE and MAE are reduced by 2.08% and 1.63%, respectively, compared with the original WNN model.

Energy Efficient Secured PSO Optimized Clustering and Data Aggregation Routing Protocol for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), sensor nodes are placed to sense and collect data. Due to the energy constraint nature of WSN, optimising the energy during the data dissemination is a major concern. To solve this problem, data aggregation may be used to bring down the redundant transmission of packets in WSN. In most of the previously available techniques, security is also a major concern during data aggregation and routing process with optimized energy. Many data aggregation-based routing systems are subject to security attacks during the data transfer from sensors to source to Clustered Heads (CHs) and then to sink with data aggregation process. Moreover, the existing data aggregation-based routing protocols suffer from data redundancy with less accuracy in aggregated data. For handling such issues order to overcome these issues, an Energy Efficient Secured Clustered Particle Swarm Optimization (PSO) oriented Data Aggregation Routing Protocol (EESCPSO-DARP) that can provide efficient authentication during data aggregation-based routing is introduced in this paper. Moreover, the proposed protocol enhances the rate of the data transmission by efficient prevention of false data injection and other attacks through node authentication and data encryption. This proposed protocol minimizes the energy usage by minimizing the retransmissions by eliminating the possible redundant transmissions of data during data aggregation-based routing. Moreover, the introduced protocol minimizes both communication and computational overhead through optimal clustering and routing with PSO and provides and efficient routing system. This proposed EESCPSO-DARP protocol has been developed by using the NS3 simulator. The results of this protocol showed improved security, higher packet delivery ratio and enhanced network throughput with reduced energy and delay.