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computational offloading and Resource Allocation for IoT applications using Decision Tree based Reinforcement Learning

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AD HOC NETWORKS 2025年 170卷

作者： Walia, Guneet Kaur Kumar, Mohit Dr BR Ambedkar NIT Dept Informat Technol Jalandhar 144008 Punjab India

The pervasive penetration of IoT devices in various domains such as autonomous vehicles, supply chain management, video surveillance, healthcare, industrial automation etc. necessitates for advanced computing paradigms to achieve real time response delivery. Edge computing offers prompt service response via its competent decentralized platform for catering disseminate workload, hence serving as front-runner for competently handling a wide spectrum of IoT applications. However, optimal distribution of workload in the form of incoming tasks to appropriate destinations remains a challenging issue due to multiple factors such as dynamic offloading decision, optimal resource allocation, heterogeneity of devices, unbalanced workload etc in collaborative Cloud-Edge layered architecture. Employing advanced Artificial Intelligence (AI)-based techniques, provides promising solutions to address the complex task assignment problem. However, existing solutions encounter significant challenges, including prolonged convergence time, extended learning periods for agents and inability to adapt to a stochastic environment. Hence, our work aims to design a unified framework for performing computational offloading and resource allocation in diverse IoT applications using Decision Tree Empowered Reinforcement Learning (DTRL) technique. The proposed work formulates the optimization problem for offloading decisions at runtime and allocates the optimal resources for incoming tasks to improve the Quality-of-Service parameters (QoS). The computational results conducted over a simulation environment proved that the proposed approach has the high convergence ability, exploration and exploitation capability and outperforms the existing state-of-the-art approaches in terms of delay, energy consumption, waiting time, task acceptance ratio and service cost.

关键词： Edge computing IoT computational offloading Resource allocation Delay

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computational offloading mechanism for native and android runtime based mobile applications

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JOURNAL OF SYSTEMS AND SOFTWARE 2016年 121卷 28-39页

作者： Yousafzai, Abdullah Gani, Abdullah Noor, Rafidah Md Naveed, Anjum Ahmad, Raja Wasim Chang, Victor Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Xian Jiaotong Liverppol Univ Int Business Sch Suzhou Suzhou Peoples R China

Mobile cloud computing is a promising approach to augment the computational capabilities of mobile devices for emerging resource-hungry mobile applications. Android-based smartphones have opened real world venues for mobile cloud applications mainly because of the open source nature of Android. computational offloading mechanism enables the augmentation of smartphone capabilities. The problem is majority of existing computational offloading solutions for Android-based smartphones heavily depends on Dalvik VM (an application-level VM). Apart from being a discontinued product, Dalvik VM consumes extra time and energy because of the just-in-time (JIT) compilation of bytecode into machine instructions. With regard to this problem, Google has introduced Android Runtime (ART) featuring ahead-of time (AHOT) compilation to native instructions in place of Dalvik VM. However, current state-of-the-art offloading solutions do not consider AHOT compilations to native binaries in the ART environment. To address the issue in offloading ART-based mobile applications, we propose a computational offloading framework. The proposed framework requires infrastructural support from cloud data centers to provide offloading as a service for heterogeneous mobile devices. Numerical results from proof-of-concept implementation revealed that the proposed framework improves the execution time of the experimental application by 76% and reduces its energy consumption by 70%. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Mobile cloud computing computational offloading Android runtime environment Application partitioning Mobile cloud scheduling

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computational offloading and Delay Minimization for UAV-aided Edge Federated Learning 59

Computational Offloading and Delay Minimization for UAV-aide...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Liaq, Mudassar Ejaz, Waleed Lakehead Univ Dept Elect & Comp Engn Thunder Bay ON Canada

ISBN: (纸本)9781728190549

The Internet of things (IoT) applications are generating large volumes of data, and processing this data securely, reliably, and timely is required for effective decision-making. However, the limited processing capability of IoT devices is a significant bottleneck in processing these datasets. A potential solution to overcome this challenge is federated learning using unmanned aerial vehicle (UAV) as mobile edge computing (MEC) servers. In this paper, we propose a UAV-aided edge federated learning (UAFL) framework where we utilize UAV-MEC's computation capacity to process some portion of the datasets from the straggling devices (devices which are unable to process their dataset in reasonable time and are lagging, increasing delay in the whole system). We formulate an optimization problem to minimize system delay considering UAV-MEC's computation power, computation and communication power of IoT devices, and quality of service constraints. We transform the proposed problem by introducing auxiliary variables and epigraph form and then solve the problem using concurrent deterministic simplex with root relaxation algorithm. Simulation results show that UAFL outperforms the traditional federated learning and edge-based learning system by approximately 5%.

关键词： computational offloading constraint IoT devices and UAVs resource optimization system delay minimization UAV-aided edge federated learning

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computational offloading for Non-Time-Critical Applications 42

Computational Offloading for Non-Time-Critical Applications

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42nd IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Patsch, Richard Univ Appl Sci Tech Wien Vienna Austria

ISBN: (数字)9781665471770

ISBN: (纸本)9781665471770

The increasing demand for computational resources keeps outpacing available User Equipment (UE). To overcome intrinsic hardware limitations of UEs, computational offloading was proposed. The combination of UE and seemingly endless computational capacity in the cloud aims to cope with those limitations. Numerous frameworks leverage Edge Computing (EC) but a significant drawback of this is the required infrastructure. Some use cases however, do not benefit from lower response time and can remain in the cloud, where more potent resources are at one's disposal. Main contributions are to determine computational demands, allocate serverless resources, partition code and integrate computational offloading into a modern software deployment process. By focusing on non-time-critical use cases, drawbacks of EC can be neglected to create a more developerfriendly approach. Originality lies in the resource allocation of serverless resources for such endeavours, appropriate deployment of partitions and integration into CI/CD pipelines. Methodology used will be Design Science Research. Thus, many iterations and proof-of-concept implementations yield knowledge and artefacts.

关键词： computational offloading mobile cloud computing partitioning serverless

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computational offloading for CNN-based Toxic Comment Detection on a Smartwatch 5

Computational Offloading for CNN-based Toxic Comment Detecti...

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5th International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Zualkernan, Imran A. Towheed, Mohammed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah U Arab Emirates

ISBN: (纸本)9781728172163

Smartwatches are an important enabler of the Social Internet of Things (SIoT). However, a successful transition to SIoT will require negotiating challenges specific to social networks. One current challenge for social networks is the detection and removal of toxic comments like insults, threats, or sexually explicit language. Many proposed techniques for detecting toxic comments use deep neural networks. Like Siri, a smartwatch can use a remote service to detect toxic comments, or alternatively run the neural network on the edge to detect such comments. This paper presents the results of an experiment comparing the tradeoffs in memory consumption, CPU load and response time between running a toxic text detection CNN on a Samsung S3 smartwatch, or running the CNN remotely using computational offloading. Sentences were processed either periodically or by using a Poisson distribution with periods of between 0.25 and 4 minutes. The results were that there was little difference in battery depletion between running the CNN locally on the watch or remotely running the CNN. However, using WIFI for offloading resulted in much better (< 1 second) response time than running the CNN on the watch (1-2 seconds). This suggests that computational offloading is a preferred solution in this instance.

关键词： CNN toxic comment detection computational offloading social internet of things SIoT smartwatch

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Outage Analysis of Optimal UAV Cooperation with IRS via Energy Harvesting Enhancement Assisted computational offloading

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1885-1905页

作者： Baofeng Ji Ying Wang Weixing Wang Shahid Mumtaz Charalampos Tsimenidis School of Information Engineering Henan University of Science and TechnologyLuoyang471000China Research and Innovation Center of Intelligent System Longmen LaboratoryLuoyang471000China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System(CEMEE) Luoyang471000China Sustainable Digital Communications and Energy Systems(DIGCOM)Group Nottingham Trent UniversityNottinghamUK

The utilization of mobile edge computing(MEC)for unmanned aerial vehicle(UAV)communication presents a viable solution for achieving high reliability and low latency *** study explores the potential of employing intelligent reflective surfaces(IRS)andUAVs as relay nodes to efficiently offload user computing tasks to theMEC server system ***,the user node accesses the primary user spectrum,while adhering to the constraint of satisfying the primary user peak interference ***,the UAV acquires energy without interrupting the primary user’s regular communication by employing two energy harvesting schemes,namely time switching(TS)and power splitting(PS).The selection of the optimal UAV is based on the maximization of the instantaneous signal-to-noise ***,the analytical expression for the outage probability of the system in Rayleigh channels is derived and *** study investigates the impact of various system parameters,including the number of UAVs,peak interference power,TS,and PS factors,on the system’s outage performance through *** proposed system is also compared to two conventional benchmark schemes:the optimal UAV link transmission and the IRS link *** simulation results validate the theoretical derivation and demonstrate the superiority of the proposed scheme over the benchmark schemes.

关键词： Unmanned aerial vehicle(UAV) intelligent reflective surface(IRS) energy harvesting computational offloading outage probability

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Delay and Cost Optimization in computational offloading Systems with Unknown Task Processing Times

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2021年第4期9卷 1422-1438页

作者： Champati, Jaya Prakash Liang, Ben KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Informat Sci & Engn S-11428 Stockholm Sweden Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

computational offloading systems, where computational tasks can be processed locally or offloaded to a remote cloud, have become prevalent since the advent of cloud computing. The task scheduler in a computational offloading system decides both the selection of tasks to be offloaded to the remote cloud and the scheduling of tasks on the local processors. In this work, we consider the problem of minimizing a weighted sum of the makespan of the tasks and the offloading cost at the remote cloud. In contrast to prior works, we do not assume that the task processing times are known a priori. We show that the original problem can be solved by algorithms designed toward minimizing the maximum between the makespan and the weighted offloading cost, only with doubling of the competitive ratio. Furthermore, when the remote cloud is much faster than the local processors, the latter problem can be equivalently transformed into a makespan minimization problem with unrelated processors. For this case, we propose a Greedy-One-Restart (GOR) algorithm based on online estimation of the unknown processing times, and one-time cancellation and rescheduling of tasks that turn out to require long processing times. Given m local processors, we show that GOR has O(root m) competitive ratio, which is a substantial improvement over the best known algorithms in the literature. For the general case of arbitrary speed at the remote cloud, we extend GOR to a Greedy-Two-Restart (GTR) algorithm and show that it is O(root m)-competitive. Furthermore, where tasks arrive dynamically with unknown arrival times, we extend GOR and GTR to Dynamic-GOR (DGOR) and Dynamic-GTR (DGTR), respectively, and find their competitive ratios. Finally, we discuss how GOR can be extended to accommodate multiple remote processors. In addition to performance bounding by competitive ratios, our simulation results demonstrate that the proposed algorithms are favorable also in terms of average performance, in comparison with t

关键词： computational offloading edge computing mobile cloud computing hybrid cloud offloading cost semi-online algorithms

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Semi-Online computational offloading by Dueling Deep-Q Network for User Behavior Prediction

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IEEE ACCESS 2020年 8卷 118192-118204页

作者： Song, Shinan Fang, Zhiyi Zhang, Zhanyang Chen, Chin-Ling Sun, Hongyu Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China CUNY Comp Sci Dept New York NY 10017 USA Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 413 Taiwan Changchun Sci Tech Univ Sch Informat Engn Changchun 130022 Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Jilin Normal Univ Dept Comp Sci Jilin 136000 Jilin Peoples R China State Key Lab Numer Simulat Jilin 130022 Jilin Peoples R China

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online computational offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.

关键词： Edge computing computational offloading user behavior prediction dueling deep-Q network

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Intelligent computational offloading for mobile-edge server computing and hybrid optimal resource allocation

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第27期83卷 69947-69972页

作者： Muralidhar, K. Shankar, S. Siva Unhelkar, Bhuvan Chakrabarti, Tulika Chakrabarti, Prasun Univ S Florida Muma Coll Business Tampa FL 33620 USA KG Reddy Coll Engn & Technol Dept Comp Sci & Engn Hyderabad Telangana India Sir Padampat Singhania Univ Dept Bas Sci Udaipur Rajasthan India Sir Padampat Singhania Univ Dept Comp Sci & Engn Udaipur Rajasthan India

To get beyond the limitations of mobile devices, the mobile cloud is an emerging technology. offloading resource-intensive applications to distant data centres enabled by the cloud is helping us achieve that. Real-time mobile user applications suffer while using a remote computing solution since MDs encounter increased network response times and delays. In this paper, we suggest an intelligent computational offloading model for MEC. The suggested model aims to apply the DL technique, which automatically chooses the computing source depending on performance, energy consumption, and workload. These factors are used to select the best edge server. For this, a Modified LSTM is proposed in this work. Additionally, TS on the edge cloud infrastructure are directly impacted by VM availability;as a consequence, VM availability is calculated while managing TS. When allocating resources, VM calculations like Make span, task completion times, resource consumption, and migration costs are considered. The capacity to deliver functioning services in the required amount of time after the task is offloaded to the VM is considered while allocating VM resources. Given that this is an optimization problem, the NBUJ hybrid optimization algorithm is developed to solve this issue. At last, the performance of the developed model is validated with the existing models in terms of fitness, migration cost, makespan, resource utilization and so on. And, it is noted that the developed model attains 97.4% of accuracy and 0.0274% of FNR which validates the performance of the proposed model.

关键词： MCC computational offloading Resource allocation NBUJ LSTM

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Cooperative computational offloading in Mobile Edge Computing for Vehicles: A Model-Based DNN Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3376-3391页

作者： Munawar, Suleman Ali, Zaiwar Waqas, Muhammad Tu, Shanshan Hassan, Syed Ali Abbas, Ghulam Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23460 Pakistan Univ Bahrain Coll Informat Technol Dept Comp Engn Sakhir 32038 Bahrain Edith Cowan Univ Sch Engn Perth WA 6027 Australia Beijing Univ Technol Fac Informat Technol Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100021 Peoples R China Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

关键词： Task analysis Servers Delay effects computational modeling Routing Delays Cloud computing Deep neural network (DNN) computational offloading vehicular networks smart task division routing policy mobile edge computing

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