Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

Recent developments in industrial wireless sensor networks (IWSNs) have revolutionized industrial automation systems. However, harsh industrial environment poses great challenges to a time-critical and reliable wireless communication. For instance, effects of multipath fading, noise and cochannel interference can have unpredictable and time-varying impacts on the propagation channel, leading to the failure of on-time packet delivery. To address this problem, in this paper we propose a channel-based Optimal Back-off Delay Control (OBDC) scheme which can minimize the total time a packet spends in the sensor node (TSN) by assessing the features of a generic wireless channel. Specifically, we first explore the channel impairments by investigating the probability density function (PDF) of the level crossing rate (LCR) of the received signal in the industrial wireless environment. Then, with the obtained channel assessment results, we develop a phase-type semi-Markov model to investigate the probability distribution of the backoff delay of a packet in the sensor node (SN). The probability distribution of the back-off delay can be further substituted with TSN according to the queuing theory. The proposed OBDC scheme examines the Kullback-Leibler (KL) divergence between the obtained distribution of TSN and the packet arrival rate, and reduces the TSN according to an objective function which is constantly renewed in every transmission round with regard to a delay constraint. The simulation results show that the OBDC scheme can reduce TSN and guarantee to keep the TSN in an acceptable range even though the wireless channel is impaired by interference effects. It also shows that the OBDC scheme can reduce the proportion of packets meeting their deadline to the total packets in transmission when the number of SN and LCR changes.

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“…Fig. 16 confirms that LCR, which has been obtained from the simulation, are identical to the analytic expressions describing in (2).…”

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confidence: 81%

Channel-Based Optimal Back-Off Delay Control in Delay-Constrained Industrial WSNs

Zhang

Cheffena

et al. 2020

IEEE Trans. Wireless Commun.

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“…Rather than using single-auction methods, a double-auction model can achieve a higher efficiency with competitive bidding between customers and service suppliers. A double-auction mechanism can model the interaction of two or three parties well, where buyers request SFCs with the bidding price, suppliers provide their services with the asking price, and the broker decides the transaction value [26]. Through competitive bidding and asking, the profit in Fig.…”

Section: Introductionmentioning

confidence: 99%

In Broker We Trust: A Double-Auction Approach for Resource Allocation in NFV Markets

Borjigin

Ota

Dong

2018

IEEE Trans. Netw. Serv. Manage.

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Network function virtualization (NFV) is an emerging scheme to provide virtualized network function (VNF) services for next-generation networks. However, finding an efficient way to distribute different resources to customers is difficult. In this paper, we develop a new double-auction approach named DARA that is used for both service function chain (SFC) routing and NFV price adjustment to maximize the profits of all participants. To the best of our knowledge, this is the first work to adopt a double-auction strategy in this area. The objective of the proposed approach is to maximize the profits of three types of participants: NFV broker, customers and service providers. Moreover, we prove that the approach is a weakly dominant strategy in a given NFV market by finding the Bayesian Nash equilibrium in the double-auction game. Finally, according to the results of the performance evaluation, our approach outperforms the single-auction mechanism with higher profits for the three types of participants in the given NFV market.

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“…To efficiently handle large-scale video datasets and improve the performance of VS systems, researchers have attempted to deploy VS systems in distributed computing, cloud computing, and edge computing environments [12,17,22]. In [12], Kavalionak et al introduced a distributed protocol for a face recognition system, which exploits the computing power of the monitoring devices to perform person recognition.…”

Section: Related Workmentioning

confidence: 99%

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Chen

Deng

et al. 2024

IEEE Trans. Ind. Inf.

184

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In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.

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Double Auction-Based Resource Allocation for Mobile Edge Computing in Industrial Internet of Things

Cited by 206 publications

References 23 publications

Channel-Based Optimal Back-Off Delay Control in Delay-Constrained Industrial WSNs

Channel-Based Optimal Back-Off Delay Control in Delay-Constrained Industrial WSNs

In Broker We Trust: A Double-Auction Approach for Resource Allocation in NFV Markets

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

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