Energy-efficient Resource Allocation Research Articles

Energy-efficient resource allocation for bidirectional wireless power and information transfer over interference channels

Energy efficiency is an important performance metric for the sustainable operation of energy-limited communication networks. This study investigates an energy-efficient resource allocation strategy to maximize the energy efficiency of wireless-powered bidirectional communication over interference channels. The receivers in the system harvest energy and decode information simultaneously using a time switching or power splitting policy from data signals sent by transmitters in the forward link. The harvested energy is subsequently used to send response signals back to the transmitters in the backward link. Our objective is to jointly optimize the transmit power of the transmitters and the energy harvesting ratio of the receivers to maximize the energy efficiency of the entire system while maintaining the minimum required data rates in both the forward and backward links. Considering the non-convexity and NP-hardness of this optimization problem, we first apply nonlinear fractional programming and a dual method to derive an analytical expression for each control parameter and then propose an iterative algorithm to find suboptimal solutions with low computational complexity. The simulation results show that the proposed algorithm adjusts both the transmit power and energy harvesting ratio adaptively considering the co-channel interference and thus achieves near-optimal energy efficiency performance within a short computation time.

Erratum to: “Energy-Efficient Resource Allocation for Heterogeneous Edge–Cloud Computing”

Erratum to: “Energy-Efficient Resource Allocation for Heterogeneous Edge–Cloud Computing”

Energy-Efficient Resource Allocation for Space–Air–Ground Integrated Industrial Power Internet of Things Network

Energy-Efficient Resource Allocation for Space–Air–Ground Integrated Industrial Power Internet of Things Network

Energy-Efficient Resource Allocation for IRS-Assisted SWIPT-Enabled D2D Communications With Imperfect CSI

Energy-Efficient Resource Allocation for IRS-Assisted SWIPT-Enabled D2D Communications With Imperfect CSI

Fast multi-type resource allocation in local-edge-cloud computing for energy-efficient service provision

With the advancement of information technology, the concept of local-edge-cloud computing has gained prominence. Operating on a collaborative model, heterogeneous computing nodes converge to deliver a spectrum of multi-type services, including calculation-intensive, latency-sensitive, and privacy-requiring services. This collaborative approach fosters high-quality development in power economy. However, the proliferation of heterogeneous computing nodes, while beneficial, introduces challenges. The intricate connections and limited energy supply may lead to interruptions in the service processes of nodes. In this study, we present an energy-efficient resource allocation scheme designed for low-latency multi-type service provision within a local-edge-cloud collaboration. Our methodology focuses on optimizing the performance of multi-type service provision in a local-edge-cloud network, taking into account considerations such as latency, resource allocation, and energy consumption. To accomplish this, we employ the Alopex-based Differential Evolution algorithm. Initially, we construct three sub-models to analyze latency and energy aspects across various computing modes. Subsequently, we formulate a constrained optimization problem aimed at minimizing both latency and energy consumption in multi-type service provisioning. These models seek to derive optimal resource allocation decisions for the given scenario. To address this optimization problem, we introduce the hybrid differential evolution algorithm, Alopex-DE. A formal analysis is conducted to showcase its near-optimal performance in comparison to three state-of-the-art algorithms. Additionally, extensive simulations are carried out to validate the superior effectiveness of our proposed approach.

Deep Learning Modified Reinforcement Learning with Virtual Machine Consolidation for Energy-Efficient Resource Allocation in Cloud Computing

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

Joint Incentive Mechanism Design and Energy-Efficient Resource Allocation for Federated Learning in UAV-Assisted Internet of Vehicles

With the increasing demand for application development of task publishers (e.g., automobile enterprises) in the Internet of Vehicles (IoV), federated learning (FL) can be used to enable vehicle users (VUs) to conduct local application training without disclosing data. However, the challenges of VUs’ intermittent connectivity, low proactivity, and limited resources are inevitable issues in the process of FL. In this paper, we propose a UAV-assisted FL framework in the context of the IoV. An incentive stage and a training stage are involved in this framework. UAVs serve as central servers, which assist to incentivize VUs, manage VUs’ contributed resources, and provide model aggregation, making sure communication efficiency and mobility enhancement in FL. The numerical results show that, compared with the baseline algorithms, the proposed algorithm reduces energy consumption by 50.3% and improves model convergence speed by 30.6%.

Energy-Efficient Resource Allocation in Ultra-Dense Networks With EMBB and URLLC Users Coexistence

Energy-Efficient Resource Allocation in Ultra-Dense Networks With EMBB and URLLC Users Coexistence

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

Energy Efficient Task Offloading and Resource Allocation in Air-Ground Integrated MEC Systems: A Distributed Online Approach

System-centric energy efficient computation offloading and resource allocation in latency-sensitive MEC systems

Mobile edge computing (MEC) enables computation tasks of end users to be offloaded and processed at nearby edge servers, which is a promising technology that can effectively alleviate end users’ computing pressure. However, how to balance two important metrics for MEC computation offloading, i.e., latency and energy efficiency, is still a key challenge. In this paper, we investigate joint computation offloading and resource allocation for latency-sensitive applications in order to maximize system-centric computation energy efficiency (SCEE) in MEC systems. An optimization problem is formulated to maximize the sum computation energy efficiency of all users which contains the energy consumption at both user and edge server sides. Meanwhile, task completion latency constraints for all users are considered. We decompose the original optimization problem into two tractable subproblems. The optimal computation offloading as well as communication and computing resource allocation solutions are obtained by solving these subproblems. Furthermore, a hierarchical system-centric energy efficient computation offloading and resource allocation (SEECR) algorithm is developed. Numerical results show that compared with the benchmark schemes, the proposed algorithm can effectively improve the computation energy efficiency performance while guaranteeing the latency requirements of all users.

Base Station Switching and Resource Allocation for 5G Heterogenous Networks

Enhanced 4th Generation Wireless Network (4G) is called 5th Generation Wireless Network (5G) as it helps in increasing the data rate, capacity and therefore energy efficiency and spectral efficiency of 5G network, in 5G massive MIMO, multiple numbers of antennas are used to transmit the signal with same timefrequency to maximise the number of users, who can communicate with less number of channels. Energy conception is the most dangerous issue in all the generations of wireless networks such as traditional firstgeneration to fifth-generation because of interference, eco signals, fading, packet loss, wastage of bandwidth, remaining energy and security like malicious attacks, blocking whole attacks and wormhole attacks. This efficient research work focused energy-efficient resource allocation scheme based on the shortest job first scheduling algorithm in wireless network (SJF) for the downlink orthogonal frequency division multiple accesses (OFDMA) heterogeneous networks (HetNets) developed. To maximize the spectrum allocation efficiency for the fifth generation (5G) mobile networks, frequency reuse-1 is employed. Thus, advanced inter-cell interference coordination techniques are required to mitigate the inter-cell interference for 5G HetNets. In this paper, the energy-efficient optimization problem based on coordinated scheduling is formulated, which is a shortest path problem and link breakage is intractable to solve directly. The above proposed model was analysed using different parameters energy, bandwidth, Quality of Service (QoS) and interference.

Optimal Energy-efficient Resource Allocation and Fault Tolerance scheme for task offloading in IoT-FoG Computing Networks

Due to technology development in the recent past, it is observed that an exponential growth in the usage of high speed intelligent devices which includes smart objects, smart home, smart vehicles etc. Therefore, for the efficient transmission between different smart objects/devices situated across different regions, an effective communication is required and the corresponding technology is called as ‘Internet of Things (IoT)’. This leads to an issues in various aspects like, increase in complexity, low data rate & latency and security may reduce the performance of the existing technologies. Therefore, there is a huge requirement of advanced technology which may support ultra-low latency transmission for the task received from the devices. Fog computing (FC) is one of an emerging technology that can improve network performance and provide resource-constrained applications for the IoT devices. In this paper, the Optimal Energy-efficient Resource Allocation (OEeRA) algorithm is proposed based on Minimal Cost Resource Allocation (MCRA) and Fault Identification and Rectification (FIR) algorithms for effective task offloading of IoT-FoG computing networks. The MCRA algorithm is proposed to assign at least one FN and Resource Block (RB) for each device, and also it ensures that each FN is connected with one or more RBs and devices. The leftover RBs are collected and stored in the buffer to replace the faulty RBs, as proposed in the FIR algorithm, which achieves better processing and response time with higher fault detection accuracy. The energy efficiency (EE) of the proposed OEeRA algorithm is computed through MCRA and FIR algorithms by varying FN, RB, and IoT devices. The performance analysis shows that the proposed algorithm achieved the maximum EE of 6.12 × 109 bit/J, 5.69 × 1010 bit/J, and 3.019 × 1010 (bit/J) for varying RBs, IoTs, and FNs, respectively.

Energy Efficient Resource Allocation in 5G RAN Slicing with Grey Wolf Optimization

The massive connections and the real time control applications have different requirement on delay, energy, rate and reliability of the system. In order to meet the diversified 5G requirements, network slicing technique guarantees on the wide scale applications. In this paper, we have proposed a dynamic resource allocation system with two time scale. The one time scale is used for the resource allocation in the system and the other is used for optimized use of latency and power. Lyapunov drift function is used for the balance between the power consumption and the user satisfaction. Further, Grey Wolf Optimization (GWO) is used for the resource allocation strategy so as to gain the reliability of the system with heterogeneous requirements. The proposed methodology shows the improvement of 27% in user satisfaction and 17.5% in power consumption. The proposed framework can be utilized for the rate as well as latency sensitive applications in 5G.

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Energy efficiency resource allocation for cell-edge users with social-aware based grouping D2D

Energy-Efficient Resource Allocation for Aggregated RF/VLC Systems

Visible light communication (VLC) is envisioned as a core component of future wireless communication networks due to, among others, the huge unlicensed bandwidth it offers and the fact that it does not cause any interference to existing radio frequency (RF) communication systems. In order to take advantage of both RF and VLC, most research on their coexistence has focused on hybrid designs where data transmission to any user could originate from either an RF or a VLC access point (AP). However, hybrid RF/VLC systems fail to exploit the distinct transmission characteristics (e.g., susceptibility of VLC transmissions to blockages, limited field-of-view of VLC APs and receivers, more coverage and better reliability of RF systems, etc.) of RF and VLC systems to fully reap the benefits they can offer. Aggregated RF/VLC systems, in which any user can be served simultaneously by both RF and VLC APs, have recently emerged as a more promising and robust design for the coexistence of RF and VLC systems. To this end, this paper, for the first time, investigates AP assignment, subchannel allocation (SA), and transmit power allocation (PA) to optimize the energy efficiency (EE) of aggregated RF/VLC systems while considering the effects of interference and VLC line-of-sight link blockages. A novel and challenging EE optimization problem is formulated for which an efficient joint solution based on alternating optimization is developed. More particularly, an energy-efficient AP assignment algorithm based on matching theory is proposed. Then, a low-complexity SA scheme that allocates subchannels to users based on their channel conditions is developed. Finally, an effective PA algorithm is presented by utilizing the quadratic transform approach and a multi-objective optimization framework. Extensive simulation results reveal that: 1) the proposed joint AP assignment, SA, and PA solution obtains significant EE, sum-rate, and outage performance gains with low complexity, and 2) the aggregated RF/VLC system provides considerable performance improvement compared to hybrid RF/VLC systems.

Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

In order to reduce power consumption and delay time when mobile devices use wireless networks, this paper proposes dividing groups of mobile devices into clusters, and proposes two cluster architectures, which are dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by 2-hop of priority weighted round robin (DRONEE-PFT2-PWRR) and dual-radio opportunistic networking for energy efficiency combining particle swarm optimization with fuzzy logic control and Taguchi by multi-hop of priority weighted rate control (DRONEE-PFTM-PWRC). Furthermore, our proposed DRONEE-PFT2-PWRR algorithm increases cluster coverage using 2-hop and our proposed DRONEE-PFTM-PWRC algorithm improves power consumption of the system by multi-hop cluster architecture. Internal cluster communication uses Wi-Fi transmission packets, which can reduce energy consumption when a mobile device communicates with a long term evolution advanced (LTE-A) base station, and reduce interference on signals between mobile devices. In the selection of cluster heads, this paper uses particle swarm optimization (PSO) to find the best cluster head position, and the PSO fitness parameter value is adjusted using fuzzy logic control (FLC) combined with the Taguchi method. Priority weighted round robin (PWRR) architecture is proposed for LTE-A base station and cluster network resource allocation, and quality of service is introduced to give priority weight to packets according to data type. Furthermore, priority weighted rate control (PWRC) is proposed. This method determines whether the network resources of the base station meet the demands of the cluster, and then assigns weights according to the requirements of the cluster and its priorities, and finally allocates network resources according to those weights. This increases the uplink throughput of network resource allocation. Simulation results show that our proposed methods significantly outperform the DRONEE-weighted (DRONEE-W) algorithm method in terms of power consumption, network throughput, and transmission delay time.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network.

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks.

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems.

MSEC-D based energy consumption optimization strategy in satellite communication systems

In this paper, we consider a satellite communication system that consists of a source low earth orbit (SLEO) satellite, several relay low earth orbit (RLEO) satellites, and a destination ground station (GS). Under the assumption that the data can be split into different sizes of data flow while transmitted through the system in different time slots, a joint route selection and time-slot allocation problem are formulated. Considering the constraints on the availability of transmission links, conservation of data flow, and system resources, an optimization problem that minimizes the total system energy consumption is formulated. As the original optimization problem is NP-hard, which cannot be solved conveniently, we transform the problem to the maximum transmission rate route selection subproblem and energy-efficient resource allocation subproblem. To solve these subproblems, we propose two algorithms, namely the multi-time-expanding graph-based maximum transmission rate route selection (MTEG-R) algorithm and the minimum system energy consumption - Dijkstra (MSEC-D) algorithm. The proposed algorithms enable the determination of routes selection and time slots allocation for data transmission. Simulation results demonstrate the effectiveness of the proposed scheme.

Energy efficient resource allocation for re-configurable intelligent surface-assisted wireless networks

This paper focuses on energy-efficient resource allocation in reconfigurable intelligent surface (RIS)-assisted multiple-input-single-output (MISO) communication systems. Specifically, it revisits the solution to the energy efficiency (EE) problem using the alternating optimization (AO) approach. In each AO iteration, the RIS phase optimization is achieved using the gradient descent method, which unfortunately does not guarantee convergence. To overcome this limitation, we propose two alternatives: the Wolfe-based gradient-descent (GAW) EE maximization Algorithm and the trust region (TR)-based EE maximization algorithm. Additionally, we use Dinkelbach’s algorithm to obtain the optimal transmit power allocation. Our results demonstrate that the proposed methods outperform the existing approach that uses sequential fractional programming (SFP) for phase optimization and the traditional relay-based method.