Radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism by Bei TANG, Qian WANG, Siguang CHEN

“…In order to fit the differentiated energy demands in vertical markets and ensure that internet of things (IoT) devices can hold an efficient and sustainable operation mode, a radio frequency energy harvesting-combined collaborative energy-saving computation offloading mechanism was studied.Specifically, a system energy consumption minimization problem was formulated under the joint optimization consideration of computation offloading decision, uplink bandwidth resource allocation, downlink bandwidth resource allocation and base station power splitting.Meanwhile, by combining the concept of penalty function, a new evaluation index was introduced, and then an adaptive particle swarm optimization-based collaborative energy saving computation offloading (APSO-CESCO) algorithm was proposed to solve the problem.The proposed algorithm constructed dynamic inertia weight and linearly adjusted penalty factor, which could alternate the spatial distribution density of the particle community in real-time during the iterative search process, and the optimal computation offloading policy with tolerable punishment could be well-generated.Furthermore, to prevent particles from exceeding exploration range, the velocity boundary was introduced which could also reduce the generation probability of invalid solutions and improve the actual exploration effectiveness.Finally, the simulation results show that the proposed algorithm can achieve higher convergence efficiency and solution accuracy, and compared with other common benchmark schemes, the system energy consumption can be reduced by 34.09%, 14.72%, and 6.86%, respectively.…”
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Interference Management in UAV-Assisted Multi-Cell Networks by Muchen Jiang, Honglin Ren, Yongxing Qi, Ting Wu

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Green Video Transcoding in Cloud Environments Using Kubernetes: A Framework With Dynamic Renewable Energy Allocation and Priority Scheduling by B. M. Beena, Prashanth Cheluvasai Ranga, A. Vinitha Chowdary, Rohan Gamidi, M. Hemasri, Tejaswi Muppala

“…Two novel scheduling algorithms, Dynamic Renewable Energy Allocation (DREA) and Energy-Aware Priority Scheduling (EAPS), enhance energy efficiency. …”
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Regression-based investigation of latency in automated networks by Urooj Khan, Tariq Soomro, M.N. Brohi

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Resource management in blockchain-enabled heterogeneous edge computing system by Ping ZHANG, Shilin LI, Yiming LIU, Xiaoqi QIN, Xiaodong XU

“…In blockchain-enabled mobile edge computing (BMEC) systems,a new class of blockchain application related computation tasks was introduced to the system.Due to the differences of parallelism among computation tasks,heterogenous computing framework was introduced to suitably split various computation tasks on processors with vastly different processing power to achieve efficient task execution.Under the limited computation and communication resources,a system-wide utility maximization problem by jointly considering heterogeneous processor scheduling,computation and bandwidth resource allocation was formulated as a mixed-integer nonlinear programming problem.To solve the problem efficiently,the formulated problem was transformed into two sub-problems,namely application-driven heterogeneous processor scheduling and joint resource allocation,and a Lagrange-dual based algorithm was proposed.Simulation results show that the proposed scheme can effectively improve the system-wide utility of the BMEC system.…”
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Resource management in blockchain-enabled heterogeneous edge computing system by Ping ZHANG, Shilin LI, Yiming LIU, Xiaoqi QIN, Xiaodong XU

“…In blockchain-enabled mobile edge computing (BMEC) systems,a new class of blockchain application related computation tasks was introduced to the system.Due to the differences of parallelism among computation tasks,heterogenous computing framework was introduced to suitably split various computation tasks on processors with vastly different processing power to achieve efficient task execution.Under the limited computation and communication resources,a system-wide utility maximization problem by jointly considering heterogeneous processor scheduling,computation and bandwidth resource allocation was formulated as a mixed-integer nonlinear programming problem.To solve the problem efficiently,the formulated problem was transformed into two sub-problems,namely application-driven heterogeneous processor scheduling and joint resource allocation,and a Lagrange-dual based algorithm was proposed.Simulation results show that the proposed scheme can effectively improve the system-wide utility of the BMEC system.…”
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Minimization of Resource Consumption with URLLC Constraints for Relay-Assisted IIoT by Yujie Zhao, Tao Peng, Yichen Guo, Yijing Niu, Wenbo Wang

“…At peak rates, the hop with superior channel conditions requires fewer resources. Compared with three baseline algorithms, the proposed algorithm achieves average resource savings of 21.40%, 14.03%, and 17.18%, respectively.…”
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Intelligent resource scheduling scheme for UAV swarm collaborative sensing by ZHAO Pengcheng, LI Tianyang, LENG Supeng, XIONG Kai

“…Adaptive sensing mode selection, stepwise computation offloading, and competitive bandwidth allocation were integrated to achieve heterogeneous resource coordination across communication, sensing, and computation (CSC), thereby enhancing collaborative sensing efficiency. …”
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Resource trading strategies with risk selection in collaborative training market. by Quyuan Wang, Xinyu Ni, Yuping Tu, Ying Wang, Jiadi Liu

“…While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying economic mechanisms of resource trading. …”
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Vehicle Authentication-Based Resilient Routing Algorithm With Dynamic Task Allocation for VANETs by Nagaraju Pacharla, K. Srinivasa Reddy

“…This research proposes a Vehicle Authentication based Resilient Routing Algorithm with Dynamic Task Allocation (VARRA-DTA) Scheme for selecting the optimal and secured route in VANET with effective task allocation strategy that reduces the latency in the network. …”
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Outage probability based power allocation and relay selection algorithm in cooperative communication by Li-yue SUN, Xiao-hui ZHAO, Ming GUO

“…The relay selection and power optimization of multi-relay cooperative communication network under a joint sum power constraint was considered.A low complexity lay selection and power allocation algorithm was also pro-posed in amplify-and-forward cooperative network aiming at minimizing the probability of system outage.In this scheme,optimal power allocation among source and relay nodes was conducted.According to the SNR,an optimal relay node set was selected and a power allocation factor leading to lower system outage probability was obtained by steepest descent method.This algorithm did not need large quantity of l statistical information and equal power condition.It can obtain the best set of relay nodes under optimal power allocation only by solving the arranged matrix on the basis of Sig-nal to Noise Ratio.Simulation results show that the proposed relay select and power allocation algorithm,under the same conditions,compared in the outage probability in erent relay node set among several algorithms,achieves better performance in outage probability and power efficiency.…”
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AI-based online VoI-aware healthcare and medical monitoring task computing by Akbar Asgharzadeh-Bonab, Jalil Mazloum, Ali Nouruzi

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MODIFIED ROMANOVSKY ALGORITHM FOR QUICK FINDING OF APPROXIMATE SOLUTION TO HOMOGENEOUS ALLOCATION PROBLEM by Rudolf A. Neydorf, Artem A. Zhikulin

“…The modification of Romanovsky algorithm for the approximate solution to the homogeneous allocation problem is developed. …”
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Self-adapted task allocation algorithm with complicated coalition in wireless sensor network by Wen-zhong GUO, Jin-shu SU, Cheng-yu CHEN, Guo-long CHEN

“…Considering the real-time requirement and some specific limitations (e.g.insufficient computing resource,energy constraint,etc) in task scheduling of wireless sensor networks,different priorities were assigned to tasks according to their deadline,and an adaptive task allocation algorithm with complicated coalition was designed through analyzing historical information.Moreover,a discrete particle swarm optimization algorithm was designed via employing binary matrix coding form.The proposed optimization algorithm generates coalitions in parallel and then performs subtask allocation algorithm based on load and energy balance.Finally,the experimental results show that the proposed algorithm strikes a good balance between local solution and global exploration,and achieves a satisfactory result within a short period of time.…”
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Self-adapted task allocation algorithm with complicated coalition in wireless sensor network by Wen-zhong GUO, Jin-shu SU, Cheng-yu CHEN, Guo-long CHEN

“…Considering the real-time requirement and some specific limitations (e.g.insufficient computing resource,energy constraint,etc) in task scheduling of wireless sensor networks,different priorities were assigned to tasks according to their deadline,and an adaptive task allocation algorithm with complicated coalition was designed through analyzing historical information.Moreover,a discrete particle swarm optimization algorithm was designed via employing binary matrix coding form.The proposed optimization algorithm generates coalitions in parallel and then performs subtask allocation algorithm based on load and energy balance.Finally,the experimental results show that the proposed algorithm strikes a good balance between local solution and global exploration,and achieves a satisfactory result within a short period of time.…”
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Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning by Abimbola Efunogbon, Enjie Liu, Renxie Qiu, Taiwo Efunogbon

“…We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. …”
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Automatic generation of control solution for resource allocation using Petri net model Geração automática da solução de controle para alocação de recursos utilizando redes de Petri... by Francisco Yastami Nakamoto, Paulo Eigi Miyagi, Diolino José dos Santos Filho

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Actor–Critic Reinforcement Learning for Throughput-Optimized Power Allocation in Energy Harvesting NOMA Relay-Assisted Networks by Faeik T. Al Rabee, Ala'Eddin Masadeh, Sharief Abdel-Razeq, Haythem Bany Salameh

“…To address these challenges, this paper proposes a novel power-allocation framework that integrates an EH-capable source node, a relay, and multiple power-domain NOMA-based users. …”
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Reconfigurable intelligent surface assisted resource optimization method for heterogeneous network by Xianhao SHEN, Ziling ZENG, Shaohua NIU

“…For reconfigurable intelligent surface (RIS)-assisted heterogeneous network slicing, a resource optimization method with joint resource allocation and phase shift optimization was proposed.A joint optimization problem with different objectives was constructed for different services in heterogeneous networks.For enhanced mobile broadband (eMBB) services, the resource block allocation, power allocation and RIS phase shift matrix were jointly optimized based on the alternating optimization algorithm to maximize the total traversal capacity of eMBB users.For ultra-reliable low-latency communication (URLLC) services, a pre-configured RIS-based heuristic URLLC allocation algorithm was proposed with the objectives of maximizing the URLLC packet reception rate and minimizing the amount of eMBB rate loss.Simulation results demonstrate that the proposed algorithm achieves about 99.99% URLLC packet reception rate using only 80 RISs compared to 95.95% URLLC packet reception rate when no RISs are deployed, while the total eMBB rate is increased by 86.24%.…”
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Deep reinforcement learning based resource provisioning for federated edge learning by Xingyun Chen, Junjie Pang, Tonghui Sun

“…The MFLD algorithm leverages Deep Reinforcement Learning (DRL) techniques to automatically select UEs and allocate the computation resources according to the task requirement. …”
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