Energy Consumption Constraints Research Articles

Path optimization of underwater vehicles in multi-obstacle environment based on energy constraint strategy

Purpose To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy. Design/methodology/approach Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment. Findings The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles. Originality/value The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

Optimization of dual-function suspension structures using particle swarm optimization approaches

The suspension system integrating both vibration control and energy harvesting capabilities is denoted as Dual-function Suspension (DFS). The principal objectives for DFS encompass lightweight structure, high output force, extensive adjustability in damping, and minimized energy consumption. In pursuit of optimizing the linear motor and magnetorheological damper (MRD) amalgamated into the DFS, a multi-objective Particle Swarm Optimization (PSO) algorithm is conceived, emphasizing primary and secondary objectives to enhance the holistic performance of the DFS. A comprehensive mathematical model of the DFS is established, and subsequent to this modeling, the structural parameters of DFS are meticulously analyzed. Drawing upon the insights from this analysis, primary and supplementary optimization objectives are delineated, employing PSO for the refinement of the DFS structure. Following this, the Pareto solution set, derived from the optimization process, is judiciously selected utilizing fuzzy theorem principles. The outcomes reveal that, under the constraints of unaltered suspension packaging dimensions and overall energy consumption, the optimized suspension system manifests a 50% augmentation in output force, a 30% expansion in adjustable damping range, and a 39% reduction in thrust ripple compared to its pre-optimized state.

Intelligent multi-agent model for energy-efficient communication in wireless sensor networks

The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Many-objective joint optimization of computation offloading and service caching in mobile edge computing

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained.

Reliability Enhancement Strategies for Workflow Scheduling Under Energy Consumption Constraints in Clouds

Reliability Enhancement Strategies for Workflow Scheduling Under Energy Consumption Constraints in Clouds

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

AbstractThis paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

3D Trajectory Planning for Real-Time Image Acquisition in UAV-Assisted VR

Nowadays, unmanned aerial vehicles (UAVs), empowered with the capability of high-definition image transmission, are used to capture the rapidly changing physical environment by leveraging its high flexibility to reconstruct an immersive realistic virtual environment for metaverse users. In this paper, we consider a novel UAV-assisted image acquisition system where a UAV is dispatched to take off from an initial location to capture real-time images of multiple ground targets and then transfer the captured images back to the ground user for virtual environment reconstruction. We aim to minimize the time for the UAV to complete the image acquisition task by optimizing the three-dimensional UAV trajectory under the constraints of image quality, information causality and energy consumption. To this end, we first formulate the investigated scenario into a mixed integer optimization problem, which, however, is difficult to solve due to the infinite time-varying variables closely coupled with each other. Then, a three-stage progressive algorithm is proposed to obtain an efficient solution to the formulated mixed integer optimization problem, where the constraints of image quality, information causality and energy consumption can be sequentially satisfied. Finally, comprehensive performance evaluation is conducted to verify the effectiveness of the proposed three-stage progressive trajectory design algorithm, and the results show that the proposed algorithm significantly outperforms the benchmark schemes.

Multi-UAV Cooperative Task Offloading and Resource Allocation in 5G Advanced and Beyond

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Monte Carlo‐based service migration under multiple constraints in mobile edge computing

AbstractMobile edge computing as an emerging technique can provide services for mobile terminals, and meanwhile the mobility of users brings new challenges. When a user moves across different areas, the system needs to determine whether to migrate service so as to guarantee quality of experience for the user. However, it is difficult to obtain the optimal migration policy in real time due to the huge state space. Considering delay‐sensitive data‐intensive applications run by mobile terminals with limited battery power, an efficient service migration policy should be able to make a good tradeoff among service cost, service delay and terminal energy consumption. Here, an online Monte Carlo‐based service migration (MCSM) policy is proposed to minimize service cost under constraints of deadline and terminal energy consumption. A penalty mechanism is designed to update reward when partial or all constraints are not meet. State‐action value estimation and policy improvement are triggered only on the completion of each episode. Each episode is traversed reversely to calculate the average cumulative reward so as to improve policy. Experimental results show that the proposed approach can improve service success ratio and reduce average service cost compared to the existing service migration policies.

Intelligent Reflecting Surface-Assisted Wireless Powered Heterogeneous Networks

In this paper, we introduce an intelligent reflecting surface (IRS)-assisted wireless powered heterogeneous network (WPHN) consisting of two heterogeneous groups of devices. Specifically, one group of devices, i.e., energy-harvesting devices (EHDs), are charged by external energy supplies, while the other group of devices, i.e., non-energy-harvesting devices (NEHDs), are powered by internal energy supplies. An IRS aims to participate in the wireless energy transfer (WET) in downlink and the wireless information transfer (WIT) in the uplink. A sum throughput maximization problem is formulated subject to the constraints of individual energy consumption, transmission time scheduling, and IRS phase shifts. To cope with the non-convexity of the problem, we first derive the optimal IRS phase shifts of the uplink WIT independently. Next, the semi-definite programming (SDP) relaxation is adopted to recast this non-convex problem into the convex one, which can be numerically solved. Then, a novel low-complexity scheme is developed to gain more insights and mitigate the computational complexity induced by the SDP relaxation. In particular, the dual problem and Karush-Kuhn-Tucker conditions are first utilized to obtain the optimal transmission time scheduling. Then, we propose a method based on Riemannian manifold optimization to compute the optimal IRS phase shifts of the downlink WET in closed-form. Finally, simulation results are presented to verify the optimality of our proposed scheme, and highlight the benefits induced by the IRS to coordinate these heterogeneous devices.

Energy Aware Task Allocation for Semi-Asynchronous Mobile Edge Learning

This paper extends the paradigm of "mobile edge learning (MEL)" by designing an energy-aware optimal task allocation scheme for training a machine learning (ML) model in a semi-asynchronous manner across multiple learners connected via the resource-constrained wireless edge network. The tasks are allocated such that the local dataset size selected at each learner ensures completion within a given global delay constraint and a local maximum energy consumption limit. Hence, the designed method is heterogeneity aware (HA) because it offers a trade-off between resource consumption and MEL performance by directly relating the time and energy consumption to the heterogeneous communication/computational capabilities of learners. Because the resulting optimization is an NP-hard quadratically-constrained integer linear program (QCILP), a two-step suggest-and-improve (SAI) solution is proposed. The proposed HA semi-asynchronous (HA-Asyn) approach is compared against the HA synchronous (HA-Sync) scheme and the heterogeneity unaware (HU) synchronous/asynchronous (HU-Sync/Asyn) equal batch allocation schemes. Results from a system of 20 learners tested for various completion time and energy consumption constraints show that the proposed HA-Asyn method works better than the HU-Sync/Asyn approaches and can even provide gains of up-to 25% compared to the HA-Sync scheme.

Optimal formation strategy for interconnected quadrotor drones: Theory and experiment

The formation control for a quadrotor swarm with the energy consumption constraint and/or the regulation performance constraint is challenging, where both the guaranteed-performance formation strategy and the guaranteed-cost formation strategy cannot realize the optimal control. Especially, the flying experiment of the optimal time-varying formation is very difficult to realize. A new control protocol with an optimization index is proposed to realize the optimal time-varying formation and the optimization index is integrated as a Kronecker product form containing the Laplacian matrix of the communication topology of a quadrotor swarm. Moreover, an analytic criterion for optimal time-varying formation achievability is proposed, and explicit expressions of the formation center function and the minimum value of the optimization index are presented, respectively. Furthermore, an optimal formation achievability algorithm is proposed and a formation flying experiment is performed, where the outer-loop position control input is converted into the inner-loop attitude control and an Euler-angle loop controller and an angular velocity loop controller are introduced.

Event‐triggered asymmetrical saturation‐constraint impulsive control of multi‐agent systems

AbstractThe impulsive consensus problem of nonlinear multi‐agent system under energy consumption constraint is considered in the article. For considering the restricted communication channel, asymmetrical input saturation constraint is adopted into impulse control protocol. Then, based on the current national policy of energy conservation and emission reduction, a novel control protocol which combines asymmetrical input saturation constraint impulse protocol with event‐triggered approach is raised and set event decision time at pulse time. Then, the control protocol not only can be able to decrease communication frequency and energy consumption, but also avoid “Zeno behavior”. Furthermore, some theorems and their proofs about the consensus problems with and without leader are provided. Through mathematical derivation, sufficient conditions of problems have been crystallized. In the end, some different simulation instances are presented to verify the research.

Ellipsoidal Trajectory Optimization for Minimizing Latency and Data Transmission Energy in UAV-Assisted MEC Using Deep Reinforcement Learning

Due to their flexible deployment and movement capability, unmanned aerial vehicles (UAVs) are being utilized as flying mobile edge computing (MEC) platforms, offering real-time computational resources and low-latency data processing for a wide range of applications. This article aims to explore a UAV-assisted MEC system where multiple UAVs provide MEC services to mobile devices (MDs) using an ellipsoidal trajectory. Depending on the position, size, and orientation of the ellipsoidal trajectories, the coverage area of the UAV, the energy consumption, and the task transmission latency of MDs change. This has rarely been investigated in the existing works. Furthermore, unlike other studies, we consider that each MD has varying task offloading rates, which, together with varying user densities, makes the problem more challenging. Therefore, we formulate an optimization problem that finds the center position, major radius, minor radius, and rotation angle of the ellipsoidal trajectory of UAV-assisted MEC servers, to minimize the total transmission latency and energy consumption of mobile devices while taking into account the required data transmission rate, task transmission time, and energy consumption constraints. Then, we transform this optimization problem into a Markov decision process and propose a deep Q-learning-based ellipsoidal trajectory optimization (DETO) algorithm, to resolve it. The results from our simulations demonstrate that DETO efficiently computes the optimal position and trajectory for each UAV, and can achieve better performance compared to other baselines, leading to the reduced data transmission latency and energy consumption of mobile devices across a range of simulation scenarios.

Enhancing Electrocardiogram (ECG) Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Implantable Cardiac Monitor (ICM) devices are demonstrating, as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient’s heart rhythm, and when triggered, send it to a secure server where health care professionals (HCPs) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to make alerts for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in a relatively high false-positive rate), and this, combined with the device’s nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing number of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics that make their analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. It carries this out by combining high-frequency noise detection (which often occurs in ICM data) with a semi-supervised learning pipeline that allows for the re-labeling of training episodes and by using segmentation and dimension-reduction techniques that are robust to morphology variations of the sECG signal (which are typical to ICM data). As a result, it performs better than state-of-the-art techniques on such data with, e.g., an F1 score of 0.51 vs. 0.38 of our baseline state-of-the-art technique in correctly calling atrial fibrillation in ICM data. As such, it could be used in numerous ways, such as aiding HCPs in the analysis of ECGs originating from ICMs by, e.g., suggesting a rhythm type.

A Path Planning Method for Logistics Oriented Drone Flight Routes

<p>This article mainly studies the path planning of unmanned aerial vehicle logistics delivery, considering the constraints in the process of unmanned aerial vehicle delivery, and establishes a unmanned aerial vehicle flight environment model based on logistics management. Based on the performance constraints and task requirements of logistics drones, a multi constraint logistics drone path planning model is established from the perspectives of transportation safety, economy, and speed. The established constraints include flight altitude, maximum angle constraints, energy consumption constraints, etc. Then, a hybrid algorithm is used to plan the drone path, and dynamic window algorithm is used for local path planning, Finally, the hybrid algorithm was fused through a smoothing strategy, and simulation experiments confirmed that the drone’s flight range, energy consumption, and planning time were significantly improved during the delivery process.</p> <p> </p>

Low-Latency Federated Learning via Dynamic Model Partitioning for Healthcare IoT.

Federated learning (FL) is receiving much attention in the Healthcare Internet of Things (H-IoT) to support various instantaneous E-health services. Today, the deployment of FL suffers from several challenges, such as high training latency and data privacy leakage risks, especially for resource-constrained medical devices. In this article, we develop a three-layer FL architecture to decrease training latency by introducing split learning into FL. We formulate a long-term optimization problem to minimize the local model training latency while preserving the privacy of the original medical data in H-IoT. Specially, a Privacy-ware Model Partitioning Algorithm (PMPA) is proposed to solve the formulated problem based on the Lyapunov optimization theory. In PMPA, the local model is partitioned properly between a resource-constrained medical end device and an edge server, which meets privacy requirements and energy consumption constraints. The proposed PMPA is separated into two phases. In the first phase, a partition point set is obtained using Kullback-Leibler (KL) divergence to meet the privacy requirement. In the second phase, we employ the model partitioning function, derived through Lyapunov optimization, to select the partition point from the partition point set that that satisfies the energy consumption constraints. Simulation results show that compared with traditional FL, the proposed algorithm can significantly reduce the local training latency. Moreover, the proposed algorithm improves the efficiency of medical image classification while ensuring medical data security.

Research on human capital and energy development caused by decarbonization

This study inputs human capital, an important parameter, into the model of carbon emission intensity (CI) that integrates the logarithmic mean divisia index (LMDI) and the production-theoretical decomposition analysis (PDA) while focusing on China's decarbonization, to understand the role of human capital on CO2 emissions under the constraint of energy consumption. The human capital is decomposed into education and “learning by doing” for the analysis of the heterogeneous effects of the two on CO2 emissions. The analysis of China and 30 of its provinces from 2000 to 2019 shows that the human capital substitution effect promoted CO2 emissions reduction owing to an increase in the human capital–energy ratio. Regional decomposition reveals the significance of this finding in the eastern, central, and western regions, which shows that the rapid growth of human capital in these three regions has a substitution effect on energy consumption, thereby reducing CO2 emissions. Further results show that education and “learning by doing” had different effects on CO2 emissions, with education mainly causing CO2 emission reductions and “learning by doing” mainly causing CO2 emission increases in the eastern and western regions; both education and “learning by doing” reduced CO2 emission in the central region. This finding indicates that higher levels of education result in the effective accumulation of human capital, thereby facilitating CO2 emissions reduction through energy substitution.

Prediction method of peak carbon emission in power grid based on energy consumption elasticity coefficient

With the implementation of China’s “two-carbon” target strategy, the status of renewable energy in the power system is gradually improving. In order to improve the carbon emission control level of power grid, it is necessary to predict the peak carbon emission of power grid. A prediction method of grid carbon emission peak based on energy elasticity coefficient is proposed. A time series model of grid carbon emission samples was constructed, and a combination of combinatorial sorting and machine learning was used to reconstruct the time series of grid carbon emission peaks. Based on the results of time series reconstruction, feature extraction and classification training of grid carbon emission peak are carried out, and the measurement error correction of grid carbon emission under the constraint of energy consumption elasticity is realized. Combined with the association rule mining method, the fusion processing of grid carbon emission peak samples under the elastic constraint of energy consumption is realized. Through characteristic detection and fusion processing of energy consumption elasticity coefficient, the reconstituted carbon emission samples of power grid constrained by energy consumption elasticity were reconstructed, and the peak carbon emission of power grid was predicted. The simulation results show that this method has high accuracy and convergence in predicting the peak carbon emission of power grid, and improves the error correction ability in the prediction process.