Special Issue "Artificial Intelligence on the Edge"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 28 February 2023 | Viewed by 5330

Special Issue Editors

Dr. Lorenzo Carnevale
E-Mail Website
Guest Editor
Department of Mathematics, Computer Science, Physics and Hearth Sciences (MIFT), University of Messina, 98166 Messina, Italy
Interests: Artificial Intelligence; Cloud Computing; Edge Computing; Big Data
Special Issues, Collections and Topics in MDPI journals
Dr. Massimo Villari
E-Mail Website
Guest Editor
Universita degli Studi di Messina, Messina, Italy
Interests: Osmotic Computing; Cloud Computing; Fog-Edge Computing; IoT; IT Security

Special Issue Information

Dear Colleagues,

As the demand for Internet of Things (IoT) solutions has grown over time, edge computing has gained momentum, because of the need to move computation close to data sources. Indeed, the rise of connected devices, which Gartner estimates number more than 20 quadrillion in 2020, is growing the need for computation in scenarios where prompt responses are crucial. On the other hand, the upcoming deployment of 5G networks will bring drastic performance improvements, traffic optimization and new ultra-low-latency services in locations where cloud connectivity is too low, such as in oil platforms or cruise ships.

On the other hand, the increasing number of connected devices is also generating a huge number of raw data directly on the edge. For example, Cisco estimates that nearly 850 ZB will be generated by people, machines and things at the network edge by 2021 [1]. That is where artificial intelligence (AI) steps in, leading data transformation in real-time extracted business value. Therefore, the migration of machine learning and deep learning techniques over to the edge enables a new field of research, where the intelligence is distributed over devices. In this context, TensorFlow has already released a tool that enables AI on the edge, but many challenges remain.

The benefits of AI on the edge are typically visible over several application fields, such as wearable technologies, smart homes, smart cities, Industry 4.0, agriculture, autonomous driving, video surveillance, social and industrial robotics, etc.

This Special Issue aims to promote high-quality research on all the aspects related to the training, inference and migration to the edge of artificial intelligence services. Topics of interest include, but are not limited to:

  • Machine learning services on the edge;
  • Deep learning services on the edge;
  • The migration of AI-based services from the cloud into the edge;
  • The optimization of real-time, AI-based solutions on the edge;
  • Edge-centric distributed intelligent services;
  • Edge-centric collaborative intelligent services;
  • Edge-centric federated intelligent services;
  • The security of data distribution over AI-based edge systems;
  • Trust and privacy management in AI-based edge systems;
  • The quality of services and energy efficiency for AI-based edge systems;
  • AI for the IoT;
  • AI for microcontroller and microprocessor.

Dr. Lorenzo Carnevale
Dr. Massimo Villari
Guest Editors

References:

  1. https://www.cisco.com/c/en/us/solutions/executive-perspectives/annual-internet-report/index.html

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Edge computing
  • Internet of Things

Published Papers (4 papers)

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Research

Article
Earthquake Detection at the Edge: IoT Crowdsensing Network
Information 2022, 13(4), 195; https://doi.org/10.3390/info13040195 - 13 Apr 2022
Viewed by 665
Abstract
State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with [...] Read more.
State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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Article
Shrink and Eliminate: A Study of Post-Training Quantization and Repeated Operations Elimination in RNN Models
Information 2022, 13(4), 176; https://doi.org/10.3390/info13040176 - 31 Mar 2022
Cited by 1 | Viewed by 818
Abstract
Recurrent neural networks (RNNs) are neural networks (NN) designed for time-series applications. There is a growing interest in running RNNs to support these applications on edge devices. However, RNNs have large memory and computational demands that make them challenging to implement on edge [...] Read more.
Recurrent neural networks (RNNs) are neural networks (NN) designed for time-series applications. There is a growing interest in running RNNs to support these applications on edge devices. However, RNNs have large memory and computational demands that make them challenging to implement on edge devices. Quantization is used to shrink the size and the computational needs of such models by decreasing weights and activation precision. Further, the delta networks method increases the sparsity in activation vectors by relying on the temporal relationship between successive input sequences to eliminate repeated computations and memory accesses. In this paper, we study the effect of quantization on LSTM-, GRU-, LiGRU-, and SRU-based RNN models for speech recognition on the TIMIT dataset. We show how to apply post-training quantization on these models with a minimal increase in the error by skipping quantization of selected paths. In addition, we show that the quantization of activation vectors in RNNs to integer precision leads to considerable sparsity if the delta networks method is applied. Then, we propose a method for increasing the sparsity in the activation vectors while minimizing the error and maximizing the percentage of eliminated computations. The proposed quantization method managed to compress the four models more than 85%, with an error increase of 0.6, 0, 2.1, and 0.2 percentage points, respectively. By applying the delta networks method to the quantized models, more than 50% of the operations can be eliminated, in most cases with only a minor increase in the error. Comparing the four models to each other under the quantization and delta networks method, we found that compressed LSTM-based models are the most-optimum solutions at low-error-rates constraints. The compressed SRU-based models are the smallest in size, suitable when higher error rates are acceptable, and the compressed LiGRU-based models have the highest number of eliminated operations. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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Article
File System Support for Privacy-Preserving Analysis and Forensics in Low-Bandwidth Edge Environments
Information 2021, 12(10), 430; https://doi.org/10.3390/info12100430 - 18 Oct 2021
Cited by 1 | Viewed by 878
Abstract
In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of [...] Read more.
In this paper, we present initial results from our distributed edge systems research in the domain of sustainable harvesting of common good resources in the Arctic Ocean. Specifically, we are developing a digital platform for real-time privacy-preserving sustainability management in the domain of commercial fishery surveillance operations. This is in response to potentially privacy-infringing mandates from some governments to combat overfishing and other sustainability challenges. Our approach is to deploy sensory devices and distributed artificial intelligence algorithms on mobile, offshore fishing vessels and at mainland central control centers. To facilitate this, we need a novel data plane supporting efficient, available, secure, tamper-proof, and compliant data management in this weakly connected offshore environment. We have built our first prototype of Dorvu, a novel distributed file system in this context. Our devised architecture, the design trade-offs among conflicting properties, and our initial experiences are further detailed in this paper. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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Article
Combine-Net: An Improved Filter Pruning Algorithm
Information 2021, 12(7), 264; https://doi.org/10.3390/info12070264 - 29 Jun 2021
Cited by 2 | Viewed by 956
Abstract
The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. [...] Read more.
The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation. Full article
(This article belongs to the Special Issue Artificial Intelligence on the Edge)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Context-Preserving AI-Based Edge Systems: A Survey and Outlook
Authors: Catherine Inibhunu
Affiliation: Faculty of Business and Information Technology
Abstract: The potential to derive knowledge from vast data sources and settings can have significant impact on multiple applications from smart healthcare, smart buildings, smart energy consumption to smart cities whereby data driven recommendations on provision of services is enabled. This can be facilitated through deployment of end to end data services that effectively allows collection, transmission, processing, streaming, and knowledge discovery through integrated machine learning (ML) algorithms to provision of data driven services. However, the content and context of the data captured at the edge can be quite invasive from physiology about humans from wearables to sensitive building details including their location. To this respect, services that seek to harness such data must incorporate means for which the privacy of data sources and context is preserved. Preserving data privacy at the edge is an area still in infancy. In this paper, a review of recent methods proposed for preserving data privacy is presented. This includes a discussion on how the existing methods have been applied, highlighting the current limitations and pointing out the principles that need to be integrated within ML services at the edge in order to ensure the privacy of data sources and context are well preserved while still providing data driven recommendations in provision of critical services.

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