A Mobile-assisted Edge Computing Framework for Emerging IoT Applications

Guo, Deke; Gu, Siyuan; Xie, Junjie; Luo, Lailong; Xue-shan, Luo; Chen, Yingwen

doi:10.1145/3461841

Cited by 15 publications

(6 citation statements)

References 38 publications

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“…Note that the task start time and the node communication costs (energy and time) are unknown. To take real-time constraint (16) into account, we divide the tasks into several layers; tasks in different layers are dependent, while the tasks in the same layer are independent. Each task can identify its parent tasks based on the dependency between the tasks.…”

Section: Frequency Assignment Problemmentioning

confidence: 99%

“…On the other hand, taking the task real-time constraint into account, we can choose the proper voltage/frequency to execute the tasks to avoid missing the task deadlines. Based on the objective function and the constraints mentioned above, the Frequency Assignment Problem (FAP) is formulated as 13), (16).…”

Section: Frequency Assignment Problemmentioning

confidence: 99%

“…In-network processing is typically used to reduce network traffic [35]. Following the "Fog/Edgecomputing" model [1,16,27], instead of collecting and sending all data to a remote Base Station (BS), a part of the data processing is done on the wireless nodes, reducing the amount of data sent to the BS. Hence, sensing, processing, and controlling tasks, and data transmissions should be executed in a real-time and energy-efficient manner, meeting the time and the energy constraints of the system application, in order to improve the overall system performance (e.g., enhancing federated learning in mobile edge computing) [40,43].…”

Section: Introductionmentioning

confidence: 99%

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Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

Zhou

Kritikakou

et al. 2023

ACM Trans. Sen. Netw.

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Energy efficiency, real-time response, and data transmission reliability are important objectives during networked systems design. This paper aims to develop an efficient task mapping scheme to balance these important but conflicting objectives. To achieve this goal, tasks are triplicated to enhance reliability and mapped on the wireless nodes of the networked systems with Dynamic Voltage and Frequency Scaling (DVFS) capabilities to reduce energy consumption while still meeting real-time constraints. Our contributions include the mathematical formulation of this task mapping problem as mixed-integer programming that balances node energy consumption, enhancing data reliability, under real-time and energy constraints. Compared with the State-of-the-Art (SoA), a joint-design problem is considered in this paper, where DVFS, task triplication, task allocation, and task scheduling are optimized concurrently. To find the optimal solution, the original problem is linearized, and a decomposition-based method is proposed. The optimality of the proposed method is proved rigorously. Furthermore, a heuristic based on the greedy algorithm is designed to reduce the computation time. The proposed methods are evaluated and compared through a series of simulations. The results show that the proposed triplication-based task mapping method on average achieves 24.84% runtime reduction and 28.62% energy saving compared to the SoA methods.

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Section: Frequency Assignment Problemmentioning

confidence: 99%

Section: Frequency Assignment Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

Zhou

Kritikakou

et al. 2023

ACM Trans. Sen. Netw.

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“…Based on an optimal routing search at the dispatching stage, tasks will be migrated from the server in which the burst load occurs to other servers as soon as possible. Guo et al [8] proposed the mobile-assisted edge computing framework to improve the QoS of fixed edge nodes by exploiting mobile edge nodes. They devised a Credible, Reciprocal and Incentive (CRI) auction mechanism to stimulate mobile edge nodes to participate in the services for user requests.…”

Section: Related Workmentioning

confidence: 99%

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

Huang

Deng

Kang

et al. 2021

Entropy

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Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

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“…Edge computing offers computational capabilities close to (or at) the source of data, i.e., sensors or mobile devices [7], opening up a new range of interactive and scalable applications [8]. We are witnessing a rapid evolution of specialized hardware (e.g., GPU, TPU) during the last decade, which enables more complex image processing workloads at the edge.…”

Section: Introductionmentioning

confidence: 99%

Flood Detection Using Real-Time Image Segmentation from Unmanned Aerial Vehicles on Edge-Computing Platform

et al. 2022

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With the proliferation of unmanned aerial vehicles (UAVs) in different contexts and application areas, efforts are being made to endow these devices with enough intelligence so as to allow them to perform complex tasks with full autonomy. In particular, covering scenarios such as disaster areas may become particularly difficult due to infrastructure shortage in some areas, often impeding a cloud-based analysis of the data in near-real time. Enabling AI techniques at the edge is therefore fundamental so that UAVs themselves can both capture and process information to gain an understanding of their context, and determine the appropriate course of action in an independent manner. Towards this goal, in this paper, we take determined steps towards UAV autonomy in a disaster scenario such as a flood. In particular, we use a dataset of UAV images relative to different floods taking place in Spain, and then use an AI-based approach that relies on three widely used deep neural networks (DNNs) for semantic segmentation of images, to automatically determine the regions more affected by rains (flooded areas). The targeted algorithms are optimized for GPU-based edge computing platforms, so that the classification can be carried out on the UAVs themselves, and only the algorithm output is uploaded to the cloud for real-time tracking of the flooded areas. This way, we are able to reduce dependency on infrastructure, and to reduce network resource consumption, making the overall process greener and more robust to connection disruptions. Experimental results using different types of hardware and different architectures show that it is feasible to perform advanced real-time processing of UAV images using sophisticated DNN-based solutions.

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A Mobile-assisted Edge Computing Framework for Emerging IoT Applications

Cited by 15 publications

References 38 publications

Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

Energy Optimized Task Mapping for Reliable and Real-Time Networked Systems

Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

Flood Detection Using Real-Time Image Segmentation from Unmanned Aerial Vehicles on Edge-Computing Platform

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