Cache-Aware Computation Offloading in IoT Systems

Wei, Hua; Luo, Hong; Sun, Yan; Obaidat, Mohammad S.

doi:10.1109/jsyst.2019.2903293

Cited by 40 publications

(20 citation statements)

References 32 publications

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“…Based on the information in the current time slot, their tasks are dynamically offloaded to the nearby user equipment that has a lot of idle CPU resources. In a large amount of research work on edge computing and D2D offloading, a few research results have taken into account information security issues [31]. Whether it is a centralized or distributed architecture, the focus of most research is how to make offload decisions, how to schedule resources, and how to optimize system performance after base stations or UEs have mastered information about various mobile devices and edge servers.…”

Section: Related Researchmentioning

confidence: 99%

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Ding

Zhang

2021

Mathematical Problems in Engineering

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Due to the limited computing resources of the mobile edge computing (MEC) server, a massive Internet of things device computing unloading strategy using game theory in mobile edge computing is proposed. First of all, in order to make full use of the massive local Internet of things equipment resources, a new MEC system computing an unloading system model based on device-to-device (D2D) communication is designed and modeled, including communication model, task model, and computing model. Then, by using the utility function, the parameters are substituted into it, and the optimization problem with the goal of maximizing the number of CPU cycles and minimizing the energy consumption is constructed with the unloading strategy and power as constraints. Finally, the game theory is used to solve the problem of computing offload. Based on the proposed beneficial task offload theory, combined with the mobile user device computing offload task amount, transmission rate, idle device performance, and other factors, the computing offload scheme suitable for their own situation is selected. The simulation results show that the proposed scheme has better convergence characteristics, and, compared with other schemes, the proposed scheme significantly improves the amount of data transmission and reduces the energy consumption of the task.

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Section: Related Researchmentioning

confidence: 99%

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Ding

Zhang

2021

Mathematical Problems in Engineering

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“…Various Device Limitations. The conventional network system needs upgrades and evolution, so do our own devices, which also have some flaws and need to be dealt with [115]. We pick up our cell phones quite frequently nowadays and hardware limitations such as poor battery life have forced us to depend on our software by optimizing our resource allocation.…”

Section: Factors Influencing Qoementioning

confidence: 99%

A Survey on Caching in Mobile Edge Computing

Zhao

Zhang

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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Boosted by the visions of 5G technology, along with the proliferation of IoT devices, we are lucky to bear witness of a tremendous shift within the Internet, mobile computing, and ubiquitous applications pervading people’s daily life. To catch up with the speed of this evolution, new architectures, which enable us to decentralize and focus more on the edge of the network, have to show up. To effectively address the record-breaking growth of data traffic, we also have to come up with new caching mechanisms in coping with users’ demanding QoE along with other aspects, such as data privacy and energy efficiency. In this paper, we intend to begin with reviews on edge caching. First, we make a detailed overview of mobile edge caching. Then, we move on to the QoS and QoE part, discussing the related literature concerning these topics. We then start to address factors concerning edge caching and QoE. Four cutting-edge applications utilizing these technologies with challenges posed by current network architectures are made clear. Our paper closes with some future research directions.

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“…In particular, due to the integration of powerful sensoring and computing functions, IoT devices are equipped with intelligent identification, behavior tracking and daily management, heart rate monitoring, etc. [6]- [8]. Meanwhile, the application of short-range communication technology enables these IoT devices to form a variety of ad hoc network application scenarios (e.g., content airdrops and Apple Edge Cache Service).…”

Section: Introductionmentioning

confidence: 99%

Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching

Wang

et al. 2020

IEEE Internet Things J.

329

161

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Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet of Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a FederAted Deep reinforcement learning-based cooperative Edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the convergence of the proposed FADE, and it achieves the expectation convergence. Trace-driven simulation results show that the proposed FADE framework reduces 92% of performance loss and average 60% system payment over the centralized deep reinforcement learning (DRL) algorithm, achieves only a 4% performance loss of the desirable omniscient oracle algorithm, and obtains 7%, 11% and 9% network performance improvements compared to some existing schemes, i.e., least recently used (LRU), least frequently

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Cache-Aware Computation Offloading in IoT Systems

Cited by 40 publications

References 32 publications

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing

A Survey on Caching in Mobile Edge Computing

Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching

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