Energy Efficient Edge Computing Enabled by Satisfaction Games and Approximate Computing

Irtija, Nafis; Anagnostopoulos, Iraklis; Zervakis, Georgios; Tsiropoulou, Eirini Eleni; Amrouch, Hussam; Henkel, Jörg

doi:10.1109/tgcn.2021.3122911

Cited by 26 publications

(8 citation statements)

References 31 publications

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“…A non-cooperative game among the users is formulated and a distributed low-complexity algorithm is proposed to obtain the corresponding Pure Nash Equilibrium (PNE), i.e., optimal data offloading strategy. By adopting the satisfaction games and approximate computing, the authors of [16] introduced an energy efficient solution in MEC-enabled fully autonomous aerial systems (FAAS) to obtain the optimal partial offloading decisions under minimum QoS prerequisites. The authors in [17] modeled joint cost and energy-efficient task offloading in the MEC-enabled healthcare system by Stackelberg game.…”

Section: A Related Workmentioning

confidence: 99%

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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In the traditional mobile edge computing (MEC) system, the availability of MEC services is greatly limited for the edge users of the cell due to serious signal attenuation and inter-cell interference. User-centric MEC (UC-MEC) can be seen as a promising solution to address this issue. In UC-MEC, each user is served by a dedicated access point (AP) cluster enabled with MEC capability instead of a single MEC server, however, at the expense of more energy consumption and greater privacy risks. To achieve efficient and reliable resource utilization with user-centric services, we propose an energy efficient blockchainenabled UC-MEC system where blockchain operations and resource optimization are jointly performed. Firstly, we design a resource-aware, reliable, replicated, redundant, and faulttolerant (R-RAFT) consensus mechanism to implement secure and reliable resource trading. Then, an optimization framework based on alternating direction method of multipliers (ADMM) is proposed to minimize the total energy consumed by wireless transmission, consensus and task computing, where APs clustering, computing resource allocation and bandwidth allocation are jointly considered. Simulation results show superiority of the proposed UC-MEC system over reference schemes, at most 33.96% reduction in the total delay and 48.77% reduction in the total energy consumption.

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Section: A Related Workmentioning

confidence: 99%

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Qin

2023

IEEE Commun. Mag.

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“…Irtija et al [18] designed an energy-efficient edge computing system for multi-access edge computing. The proposed method is also used for a fully autonomous aerial system (FAAS) by using a deep neural network (DNN).…”

Section: Related Workmentioning

confidence: 99%

“…The methods discussed above rely on assisted networks as in [20,24,28] for energy conservation by incorporating the conventional WSN strategies. Independent processes such as in [18,19,23] provide optimization alongside learning paradigms that increase the complexity during iterated training. The proposed energy management scheme steers between the offloading and scheduling decisions using different state models.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Kaliappan¹,

Ranganathan

Selvaraju³

et al. 2022

Energies

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Edge devices and their associated computing techniques require energy efficiency to improve sustainability over time. The operating edge devices are timed to swap between different states to achieve stabilized energy efficiency. This article introduces a Cognitive Energy Management Scheme (CEMS) by considering the offloading and computational states for energy efficacy. The proposed scheme employs state learning for swapping the computing intervals for scheduling or offloading depending on the load. The edge devices are distributed at the time of scheduling and organized for first come, first serve for offloading features. In state learning, the reward is allocated for successful scheduling over offloading to prevent device exhaustion. The computation is therefore swapped for energy-reserved scheduling or offloading based on the previous computed reward. This cognitive management induces device allocation based on energy availability and computing time to prevent energy convergence. Cognitive management is limited in recent works due to non-linear swapping and missing features. The proposed CEMS addresses this issue through precise scheduling and earlier device exhaustion identification. The convergence issue is addressed using rewards assigned to post the state transitions. In the transition process, multiple device energy levels are considered. This consideration prevents early detection of exhaustive devices, unlike conventional wireless networks. The proposed scheme’s performance is compared using the metrics computing rate and time, energy efficacy, offloading ratio, and scheduling failures. The experimental results show that this scheme improves the computing rate and energy efficacy by 7.2% and 9.32%, respectively, for the varying edge devices. It reduces the offloading ratio, scheduling failures, and computing time by 14.97%, 7.27%, and 14.48%, respectively.

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“…Deploying UAVs in critical applications involves several challenges, despite the apparent advantages of UAV‐assisted networks in the IoTs. UAV‐assisted networks are used for complex real‐time operations, and they can further raise significant challenges in terms of computational power, delay, offloading of data, and energy preservation 4 . The limited onboard energy of a UAV is one of the main constraints in a UAV‐assisted system.…”

Section: Introductionmentioning

confidence: 99%

“…UAV-assisted networks are used for complex real-time operations, and they can further raise significant challenges in terms of computational power, delay, offloading of data, and energy preservation. 4 The limited onboard energy of a UAV is one of the main constraints in a UAV-assisted system. The energy of UAVs requires backup and the conservation of energy and this is possible if some of the nonparticipating UAVs go to sleep for some time and wake up when required.…”

mentioning

confidence: 99%

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

Gupta,

Gupta

2024

Int J Communication

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SummaryIn this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS.

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Energy Efficient Edge Computing Enabled by Satisfaction Games and Approximate Computing

Cited by 26 publications

References 31 publications

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

When the User-Centric Network Meets Mobile Edge Computing: Challenges and Optimization

Energy-Efficient Offloading Based on Efficient Cognitive Energy Management Scheme in Edge Computing Device with Energy Optimization

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

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