Offloading Mechanism Research Articles

Design and Evaluation of a Patellar Tendon-Bearing Brace with Off-Loading Mechanism on Tibia.

The current study aimed to design a patellar-tendon-bearing (PTB) brace capable of measuring and quantifying weight offloading on the tibia. The PTB brace was designed with off-loading mechanism on the tibia with features, including ankle joint, vertical sliding adaptor, vertical sliding piece, and upper connector of load cells to PTB brace. Also, the present study investigated the effect of brace on 20 healthy individuals under 8 different off-loading conditions, based on measuring the vertical distance between the calf shells and foot plate through a sliding adapter at 0.5, 1, 1.5, 2, 2.5, 3, and 3.5 cm. The Pedar device and load cells embedded in PTB brace were used to determine the extent of offloading and assess the reliability and validity of brace. Increasing the vertical distance between the calf shells and the footplate can lead to a greater amount of offloading. Accordingly, off-loading ranged from a minimum of 16.5% at 0 cm position to a maximum of 60.48% at 3.5 cm position of sliding adapter. Percentage values of tibia off-loading in 8 conditions were not significantly different in Padar devices and PTB brace. Therefore, PTB brace load cells, as a valid method, can measure off-loading levels. When fabricating a PTB brace, a monitoring system with load cells is essential to measure the amount of tibial offloading, leading to readjustment if limb slides down inside the brace. Additionally, a component is needed to correctly position limb in off-loading condition. In the current study, sliding adapter of brace can provide that capability.

UAV‐aided distribution line inspection using double‐layer offloading mechanism

AbstractWith the continuous growth of electricity demand, the safe and stable operation of distribution lines is crucial for power transportation. Unmanned aerial vehicle (UAV) inspection has been widely used for the maintenance and repair of distribution lines. Due to the limitations of computational power and endurance, it is difficult for UAVs to independently complete data processing. Combined with mobile edge computing (MEC), this paper proposes a computing offloading strategy based on multi‐agent reinforcement learning and double‐layer offloading mechanism, which can further utilize the computing power of non‐task devices and edge servers. Firstly, three‐layer system architecture, named MEC‐U‐NTDC (MEC‐UAV‐Non‐task Device Cloud), is built. Secondly, double‐layer offloading mechanism is designed to comprehensively utilize the computing power of edge servers and neighbouring non‐task devices. Finally, a multi‐agent algorithm DLMQMIX is proposed to minimize the total cost for UAV inspection. Simulation experiments show that the proposed algorithm can effectively solve the task offloading problem of UAV‐aided distribution line inspection, and compared with algorithms such as PSO, GA, and QMIX, it performs better in terms of average delay, system cost, and load balancing, achieving a smaller total system cost.

Game theory-based optimization for efficient IoT task offloading in 6G network base stations

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.

MADRLOM: A Computation offloading mechanism for software-defined cloud-edge computing power network

Cloud-edge computing power network often exhibits complex and heterogeneous structures, posing several challenges to computation offloading that significantly impact network performance and the efficient utilization of computation resources. In this paper, we propose a cloud-edge computing power network architecture that efficiently integrates cloud and edge computing resources into a single network system using software-defined networking technology to support upper-layer business applications. In this context, existing computation offloading methods struggle with issues like users' personalized requirements for latency and energy consumption, as well as the inability to adapt to dynamically complex environments. To overcome these challenges, we introduce a computation offloading mechanism, MADRLOM, focusing on the network's heterogeneity and modeling the computation offloading problem at the cloud-edge end. We formalize the offloading problem as a Markov process and employ a multi-agent deep reinforcement learning algorithm based on priority experience replay sampling to address the planning problem of computation offloading, allowing user equipment to continuously optimize offloading strategies in response to environmental changes and achieve rational resource allocation. Through dynamic offloading strategies, computation tasks can be intelligently allocated to appropriate computing nodes, thereby achieving optimal resource utilization. We utilized the Mininet simulation platform to construct the experimental environment for the software-defined cloud-edge computing power network and compared it with several representative computation offloading strategies. The experimental results demonstrate that the MADRLOM significantly reduces the total system overhead in the software-defined cloud-edge computing network and shows excellent adaptability to environmental changes.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

A framework for offloading and migration of serverless functions in the Edge–Cloud Continuum

Function-as-a-Service (FaaS) has emerged as an evolution of traditional Cloud service models, allowing users to define and execute pieces of codes (i.e., functions) in a serverless manner, with the provider taking care of most operational issues. With the unending growth of resource availability in the Edge-to-Cloud Continuum, there is increasing interest in adopting FaaS near the Edge as well, to better support geo-distributed and pervasive applications. However, as the existing FaaS frameworks have mostly been designed with Cloud in mind, new architectures are necessary to cope with the additional challenges of the Continuum, such as higher heterogeneity, network latencies, limited computing capacity.In this paper, we present an extended version of Serverledge, a FaaS framework designed to span Edge and Cloud computing landscapes. Serverledge relies on a decentralized architecture, where each FaaS node is able to autonomously schedule and execute functions. To take advantage of the computational capacity of the infrastructure, Serverledge nodes also rely on horizontal and vertical function offloading mechanisms. In this work we particularly focus on the design of mechanisms for function offloading and live function migration across nodes. We implement these mechanisms in Serverledge and evaluate their impact and performance considering different scenarios and functions.

Design and Simulation of Autonomous Up-and-down System for Transmission Line Live Operation Robot with the UAV Assistance

Aiming at the current bottleneck problem that restricts the practical application of robot system engineering in transmission line live operation, in order to improve the on-line and off-loading efficiency of live working robots on transmission lines and improve the practical level of the robot system as a whole, this paper proposes a design and operation method of autonomous on-line and off-loading mechanism of double-arm live working robots based on UAV assistance. Firstly, the realization process of autonomous up-and-down of the robot was analyzed, based on which the structure of the auxiliary hook of the up-and-down system and the lifting winch were designed. Then, the force analysis was carried out on the up-and-down process of the robot, and the appropriate winch drive motor was selected through theoretical calculation. In order to ensure the rationality of the up-and-down process, the operation motion planning was carried out on the up-and-down of the robot. Stress analysis and simulation were carried out on the maximum load bearing point of the auxiliary hook and the walking wheel mechanical arm under different states, and the results showed that the hook and the robot arm could meet the requirements of the robot loading and unloading, and the whole robot loading and unloading system was designed reasonably. Compared with the traditional loading and unloading system designed in this paper, the loading and unloading efficiency could be improved. It has important theoretical significance for the development of the physical system of robot uplink and down.

Retracted: Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

Retracted: Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture Within the Fog-Cloud Environment

A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three-Layer Architecture Within the Fog-Cloud Environment

Prioritized Task Offloading Mechanism in Cloud-Fog Computing Using Improved Asynchronous Advantage Actor Critic Algorithm

Prioritized Task Offloading Mechanism in Cloud-Fog Computing Using Improved Asynchronous Advantage Actor Critic Algorithm

Metaheuristics Method for Computation Offloading In Mobile Edge Computing: Survey

In recent years, edge computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited devices. The main feature of edge computing is to push computation, networking, and storage facilities closer to the network edge. This enables user equipment (UE) to profit from the edge computing paradigm by mainly offloading their intensive computation tasks to edge resources. Because of some rising problems such as inherent software and hardware heterogeneity, restrictions, dynamism, and stochastic behavior of the ecosystem, the computation offloading issues consider as the essential challenging problems in the MEC environment. However, to the best of the author's knowledge, in spite of its significance, in Metaheuristic -based computation offloading mechanisms, there is not any systematic, comprehensive, and detailed survey in the MEC environment. In this paper, we provide a review on the Metaheuristic -based computation offloading mechanisms in the MEC environment in the form of a classical taxonomy to identify the contemporary mechanisms on this crucial topic and to offer open issues as well. The proposed taxonomy is classified into three main fields: genetic algorithm (GA)-based mechanisms, Partical Swarm Optimization(PSO)-based mechanisms, and Hybrid GA with PSO -based mechanisms. Next, these classes are compared with each other based on the essential features such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and weaknesses are discussed, as well. Finally, open issues and uncovered or inadequately covered future research challenges are argued, and the survey is concluded.

DRL-driven zero-RIS assisted energy-efficient task offloading in vehicular edge computing networks

The increasing complexity of modern automotive applications presents difficulties when running them on the on-board units (OBUs) of vehicles. While 5G/6G vehicular edge computing networks (VECNs) offer potential solutions through computation task offloading, ensuring prompt, energy-efficient access to these networks remains a significant challenge. To overcome these challenges, reconfigurable intelligent surfaces (RIS) can play an important role in 6G vehicular networks. With RIS, networks can provide better connectivity, increased data rate and energy efficient access, and communication channel security. In this paper, we utilize zero-energy RIS (ze-RIS) to aid vehicular computation offloading while maximizing the energy and time savings while meeting the task and environmental constraints. A joint power and offloading mechanism controlling DRL-driven RIS-assisted energy efficient task offloading (DREEO) scheme is proposed. DREEO utilizes a hybrid approach that combines binary and partial offloading mechanisms, complemented by an intelligent communication link switching mechanism. This strategy helps in saving both energy and time effectively. An efficiency factor, serving as both a performance indicator and a reward function, is introduced for the DRL agent, considering both saved energy and time. Through extensive evaluations, DREEO scheme shown an increase in task success rate from 2.13% to 7.36% and has improved the efficiency factor from 21.97 to 51.27. Furthermore, compared to other evaluated schemes, the DREEO scheme consistently outperforms them in terms of reward and the TFPS ratio, the DRL properties.

Biosynthesis of Octacosamicin A: Uncommon Starter/extender Units and Product Releasing via Intermolecular Amidation.

Octacosamicin A is an antifungal metabolite featuring a linear polyene-polyol chain flanked by N-hydroxyguanidine and glycine moieties. We report here that sub-inhibitory concentrations of streptomycin elicited the production of octacosamicin A in Amycolatopsis azurea DSM 43854T . We identified the biosynthetic gene cluster (oca BGC) that encodes a modular polyketide synthase (PKS) system for assembling the polyene-polyol chain of octacosamicin A. Our analysis suggested that the N-hydroxyguanidine unit originates from a 4-guanidinobutyryl-CoA starter unit, while the PKS incorporates an α-hydroxyketone moiety using a (2R)-hydroxymalonyl-CoA extender unit. The modular PKS system contains a non-canonical terminal module that lacks thioesterase (TE) and acyl carrier protein (ACP) domains, indicating the biosynthesis is likely to employ an unconventional and cryptic off-loading mechanism that attaches glycine to the polyene-polyol chain via an intermolecular amidation reaction.

Edge-Cloud Offloading: Knapsack Potential Game in 5G Multi-Access Edge Computing

In 5G, multi-access edge computing enables the applications to be offloaded to near-end edge servers for faster response. According to the 3GPP standards, users in 5G are separated into many types, e.g., vehicles, AR/VR, IoT devices, etc. Specifically, the high-priority traffic can preempt edge resources to guarantee the service quality. However, even if a traffic is transmitted with low priority, its latency requirement in 5G is much lower than that in 4G. Too strict latency requirement and priority-based service make resource configuration difficult on the edge side. Therefore, we propose the edge-cloud offloading mechanism, in which each edge server can offload tasks to back-end cloud server to ensure service quality of both high- and low-priority traffic. In this paper, we establish a priority-based queuing system to model the edge-cloud offloading behaviors. Based on the formulation of our system model, we propose Knapsack Potential Game (KPG) to derive an optimal offloading ratio for each edge server to balance the cost-effectiveness of the overall system. We demonstrate that KPG has low computational complexity and outperforms two baseline algorithms. The results indicate that KPG’s performance is optimal and provides a theoretical guideline to operators while designing their edge-cloud offloading strategies without large-scale implementation.

Design and evaluation of an offloading orthosis for medial knee osteoarthritis

Knee osteoarthritis is an incurable degenerative joint disease that affects millions of people. Characterized by stiffness and knee pain in the early stages, it can cause loss of function and mobility. Most treatment options are either not sustainable, invasive, or costly. Alternatively, offloading knee orthoses are a cost-effective option that provides immediate pain relief when worn. Offloading knee orthoses, however, have low patient compliance rates. To improve patient compliance and optimize patient benefit, current orthosis designs must be enhanced to improve comfort, increase the perceived effect, and be adjustable to the patient. Consequently, this research presents the design, fabrication, and testing of a new offloading knee orthosis joint. The novel modular orthosis features an offloading mechanism intended to relieve the load on the joint solely during stance phase and reduce the moment during swing phase when offloading is not needed. Three-point bending tests of the experimental prototype demonstrated an offloading moment of 3.36 Nm, creating a noticeable offloading effect during stance, and reduced the moment to less than 0.5 Nm after 35° of knee flexion, thus, potentially increasing comfort during swing phase and sitting when offloading forces are not needed.

Fog-Assisted Blockchain Radio Access Network for Web3

Recently, Web3 has received extensive attention as the next generation Internet and is in rapid development, which can highly integrate the physical world with the digital world. The blockchain that can build trust and fog computing with distributed computing mode can fit well with the decentralization idea of Web3. The combination of the two can provide an effective solution for the implementation of Web3. This paper proposes a new network architecture, fog-assisted blockchain radio access network (FogBC-RAN), which supports stable and mutual trust connections on a global scale, and can smoothly coordinate multi-dimensional resources to support new services such as holographic communication, digital twin, and sensory interconnection. With the interconnection of ten thousand chains in the future Web3, the cross-chain information transfer process based on Polkadot for charging sharing is presented, which can realize flexible cross-chain transactions. Meanwhile, for the mainstream computing-intensive applications of Web3, considering the latency, energy consumption and transaction cost, a computing offloading strategy based on matching game is proposed to minimize the system cost. Finally, the simulation results demonstrate the effectiveness of the offloading mechanism proposed in FogBC-RAN.

A deep reinforcement learning based research for optimal offloading decision

Currently, a concern about power resource constraints in the distribution environment is being voiced increasingly, where the increase of power consumption devices overwhelms the terminal load unaffordable and the quality of power consumption cannot be guaranteed. How to acquire the optimal offloading decision of power resources has become a problem that needs to be addressed urgently. To tackle this challenge, a novel reinforcement learning algorithm named Deep Q Network with a partial offloading strategy (DQNP) is proposed to optimize power resource allocation for high computational demands. In the DQNP, a coupled coordination degree model and Lyapunov algorithm are introduced, which trade-offs and decouples the relationships between local-edge and latency–energy consumption. To derive the optimal offloading decision, the resource computation utility function is selected as the objective function. In addition, model pruning is availed to further improve the training time and inference results. Results show that the proposed offloading mechanism can significantly decrease the function value and decline the weighted sum of latency and energy consumption by an average of 3.61%–7.31% relative to other state-of-the-art algorithms. Additionally, the energy loss in the power distribution process is successfully mitigated; furthermore, the effectiveness of the proposed algorithm is also verified.

A Game-Based Incentive-Driven Offloading Framework for Dispersed Computing

The popularization of smart Internet of Things (IoT) devices has facilitated the development of fog/edge computing. However, these infrastructure-based service paradigms may fail to complete tasks successfully due to computation and communication overload, or damage in challenging scenarios such as disasters or traffic jams. Noticing that a crowd of devices with considerable idle resources could be available, we investigate the problems of addressing the computation and communication unavailability with peer assistance in this work. To this end, we propose a dispersed service framework for resource-exhausted scenarios that adaptively offloads users’ data to available network computation points. However, the users may not be able to achieve the offloading due to geographical hindrances. Consequently, the relay is introduced as a bridge for data offloading between the users and the network computation points. Furthermore, a game-based incentive-driven offloading mechanism is designed by analyzing and balancing the cost and gain factors of three main entities (users, relays, and network computation points). Considering the interactions among the entities, a two-level Stackelberg game is established for efficiently allocating potential computation resource, as well as balancing the utility conflicts due to the data offloading. Given the hierarchical interaction structure, the upper level game involves network computation points as followers and the relay as a leader, while the lower level game includes the relay as a follower and users as leaders. Moreover, to facilitate applicability in large-scale scenarios with multiple relays, we decompose multiple relays into multiple single relay problems using a tripartite matching strategy that assigns appropriate relays to users and network computation points. The simulation results demonstrate the effectiveness of the proposed game-based incentive-driven mechanism and show that it outperforms the baselines in terms of the overall utilities of the involved entities and the average energy consumption of users.

Task Computation Offloading for Multi-Access Edge Computing via Attention Communication Deep Reinforcement Learning

This paper investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this paper, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Secondly, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.