Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Wang, Xiaofei; Han, Yiwen; Leung, Victor C. M.; Niyato, Dusit; Yan, Xueqiang; Xu, Chen

doi:10.1109/comst.2020.2970550

Cited by 913 publications

(426 citation statements)

References 172 publications

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“…In addition to other features, these approaches can be beneficial for facing the problems deriving from changes in data distribution over time that, particularly in health applications, could negatively affect the performance of the whole system. Covariate shift, for example, can be efficiently faced when segmentation of deep networks on the edge [54] is introduced. In this case, in fact, only the device containing the first layers of the network has to be modified in order to correct the shift.…”

Section: Discussion and Trendsmentioning

confidence: 99%

Trends in IoT based solutions for health care: Moving AI to the edge

Greco

Percannella

Ritrovato

et al. 2020

Pattern Recognition Letters

217

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In recent times, we assist to an ever growing diffusion of smart medical sensors and Internet of things devices that are heavily changing the way healthcare is approached worldwide. In this context, a combination of Cloud and IoT architectures is often exploited to make smart healthcare systems capable of supporting near realtime applications when processing and performing Artificial Intelligence on the huge amount of data produced by wearable sensor networks. Anyway, the response time and the availability of cloud based systems, together with security and privacy, still represent critical issues that prevents Internet of Medical Things (IoMT) devices and architectures from being a reliable and effective solution to the aim. Lately, there is a growing interest towards architectures and approaches that exploit Edge and Fog computing as an answer to compensate the weaknesses of the cloud. In this paper, we propose a short review about the general use of IoT solutions in health care, starting from early health monitoring solutions from wearable sensors up to a discussion about the latest trends in fog/edge computing for smart health.

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Section: Discussion and Trendsmentioning

confidence: 99%

Trends in IoT based solutions for health care: Moving AI to the edge

Greco

Percannella

Ritrovato

et al. 2020

Pattern Recognition Letters

217

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“…Key notations used in this paper are listed in Table III. As categorized in [43], we define edge devices present in the network into two types: the mobile edge "nodes", i.e., vehicles and "edge computing servers", i.e., RSUs. Before joining the blockchain network, a node needs to register itself and acquire a wallet address and a pair of public and private keys for privacy-preserving communications.…”

Section: System Modeling and The Proposed Blockchain Designmentioning

confidence: 99%

A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Ayaz

Sheng

Tian

et al. 2021

IEEE Internet Things J.

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Blockchain applications in vehicular networks can offer many advantages including decentralization and improved security. However, most of consensus algorithms in blockchain are difficult to be implemented in a Vehicular Ad-Hoc Networks (VANET) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time consuming consensus algorithms, e.g., Proof-of-Work, running on mobile edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g. Proof-of-Stake (PoS) is biased towards nodes with higher amount of stakes and Proof-of-Elapsed-Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on Proof-of-Quality-Factor (PoQF) consensus algorithm, where threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on probabilistic prediction of channel quality between transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. The paper also analyzes the throughput of block generation, as well as the asymptotic latency, security and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against collusion of nodes is proved using game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.

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“…Another approach is Edge Intelligence (EdgeAI) where the AI model is distributed across network edges. Several works have discussed the convergence of edge and AI [ 30 , 31 , 32 , 33 , 34 , 35 ]. AI model can be pre-trained then modified and optimized to be appropriate to run in the resource-constrained edges.…”

Section: Background and Related Workmentioning

confidence: 99%

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

Janbi

Katib

Albeshri

et al. 2020

Sensors

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Artificial intelligence (AI) has taken us by storm, helping us to make decisions in everything we do, even in finding our “true love” and the “significant other”. While 5G promises us high-speed mobile internet, 6G pledges to support ubiquitous AI services through next-generation softwarization, heterogeneity, and configurability of networks. The work on 6G is in its infancy and requires the community to conceptualize and develop its design, implementation, deployment, and use cases. Towards this end, this paper proposes a framework for Distributed AI as a Service (DAIaaS) provisioning for Internet of Everything (IoE) and 6G environments. The AI service is “distributed” because the actual training and inference computations are divided into smaller, concurrent, computations suited to the level and capacity of resources available with cloud, fog, and edge layers. Multiple DAIaaS provisioning configurations for distributed training and inference are proposed to investigate the design choices and performance bottlenecks of DAIaaS. Specifically, we have developed three case studies (e.g., smart airport) with eight scenarios (e.g., federated learning) comprising nine applications and AI delivery models (smart surveillance, etc.) and 50 distinct sensor and software modules (e.g., object tracker). The evaluation of the case studies and the DAIaaS framework is reported in terms of end-to-end delay, network usage, energy consumption, and financial savings with recommendations to achieve higher performance. DAIaaS will facilitate standardization of distributed AI provisioning, allow developers to focus on the domain-specific details without worrying about distributed training and inference, and help systemize the mass-production of technologies for smarter environments.

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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Cited by 913 publications

References 172 publications

Trends in IoT based solutions for health care: Moving AI to the edge

Trends in IoT based solutions for health care: Moving AI to the edge

A Proof-of-Quality-Factor (PoQF)-Based Blockchain and Edge Computing for Vehicular Message Dissemination

Distributed Artificial Intelligence-as-a-Service (DAIaaS) for Smarter IoE and 6G Environments

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