Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing

Guo, Songtao; Xiao, Bin; Yang, Yuanyuan; Yang, Yang

doi:10.1109/infocom.2016.7524497

Cited by 315 publications

(135 citation statements)

References 23 publications

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“…Communication Model: Similar to previous studies [3], [5], the wireless channel from the robots to the access point follows quasi-static block fading. Let L denote the set of offloaded tasks.…”

Section: System Model and Problem Formulationmentioning

confidence: 94%

“…According to [4], [5], the energy consumption of robot n is given by E l n = κL n f 2 n , where κ is the effective switched capacitance depending on the chip architecture.…”

Section: ) Local Computingmentioning

confidence: 99%

“…2) Cloud computing: The execution of offloaded task n includes three phases in sequence: (i) transmitting the input data of task n to the cloud; (ii) executing the task on the cloud; (iii) returning the output data to robot n. Similar to some of existing studies on computation offloading [3], [5], we choose to ignore the output data receiving phase, because the output data size is often considerably smaller than the input data size. Taking an object pose estimation task [10] as an example, the input data includes a set of images and the total size of it could be hundreds of kilobytes at least, while the output data is just the object location and pose, and takes a few dozens of bytes at most.…”

Section: ) Local Computingmentioning

confidence: 99%

“…The differences among them are that some works only jointly optimize offloading selection and communication interference management [3], [5], and some works only aim to make optimal resource allocation and offloading selection decisions [6]- [8], while the others [9] jointly consider the three issues.…”

Section: Introductionmentioning

confidence: 99%

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Cost Aware Offloading Selection and Resource Allocation for Cloud Based Multi-Robot Systems

Sun

Zhou

Yang

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

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Section: System Model and Problem Formulationmentioning

confidence: 94%

“…According to [4], [5], the energy consumption of robot n is given by E l n = κL n f 2 n , where κ is the effective switched capacitance depending on the chip architecture.…”

Section: ) Local Computingmentioning

confidence: 99%

Section: ) Local Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cost Aware Offloading Selection and Resource Allocation for Cloud Based Multi-Robot Systems

Sun

Zhou

Yang

2017

IEICE Trans. Inf. & Syst.

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“…An algorithm "GreenSpot" is proposed that considers application features and energy-performance trade-off to determine whether cloud or local execution will be more preferable. In [7], the authors provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. The algorithm consists of sub-algorithms of computation offloading selection, clock frequency control and transmission power allocation.…”

Section: Related Workmentioning

confidence: 99%

Achieving Mobile Cloud Computing through Heterogeneous Wireless Networks

Alnezari¹,

Rikli²

2017

IJCNS

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A Mobile Cloud Computing environment with remote computing and vertical handover capabilities will be considered. A fuzzy logic model will be suggested for both the offloading and handover functions using energy, delay and computation complexity as criteria. Based on the application requirements, some rules will be proposed and evaluated when deciding on the offloading then the selection of the network that keeps the traffic carried to the cloud at an acceptable level. All this ought to be achieved in real time while the application is still running. The proposed engine will be implemented and run on an Android mobile device connected to Google's App Engine servers. Results will be presented and analyzed, and conclusions will be provided.

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Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

Chen

Wang

Guo

et al. 2022

Concurrency and Computation

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As mobile communications, the Internet, databases, distributed computing, and other technologies continue to develop, the Internet of Things (IoT) has emerged as prevalent technique. However, attacks on security and sensitive data in IoT occur frequently, and these attacks often evade intrusion detection systems strategically by mutating their traffic. To prevent security threats and sensitive data leakage, we propose a game approach based on adversarial deep learning to optimize a dynamic security threshold strategy. We introduce a mobile edge computing framework and utilize a game model to describe the adversarial interaction between the two participants. To solve the complexity of the game problem to gain dynamically randomized adversarial attacks, we present a column generation (CG) framework, which uses a feedforward neural network to quantify data flowing through IoT devices. Considering the limited resources of IoT devices, we calculate an optimal response to cyberattacks via a particle swarm optimization algorithm, aiming to reduce the false alarm rate. The adversarial dynamic threshold (ADT)‐based column generation (CG‐ADT) algorithm generates the set of detection threshold and the probability. Finally, we present the results of experiments conducted to demonstrate the effectiveness and robustness of the proposed dynamic threshold scheme for sensitive data security protection in IoT and its suitability for implementation in production systems.

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Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing

Cited by 315 publications

References 23 publications

Cost Aware Offloading Selection and Resource Allocation for Cloud Based Multi-Robot Systems

Cost Aware Offloading Selection and Resource Allocation for Cloud Based Multi-Robot Systems

Achieving Mobile Cloud Computing through Heterogeneous Wireless Networks

Dynamic threshold strategy optimization for security protection in Internet of Things: An adversarial deep learning‐based game‐theoretical approach

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