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Efficient Sensing Networks of AI via Edge Computing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2107

Special Issue Editors

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Guest Editor
Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: neural network; memristor system; deep learning; intelligent control
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Guest Editor
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: reservoir operation; hydrological forecasting; water resources management; artificial intelligence; engineering optimization
Special Issues, Collections and Topics in MDPI journals
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Interests: computer vision; pattern recognition; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of the Internet of Things (IoT) and mobile communication technology, huge data can be obtained from various intelligent devices, e.g., smartphones, smart cameras, wearable devices, environmental sensors, household appliances, and vehicles. The massive amount of data has promoted the development of artificial intelligence, especially the performance of deep neural networks, which has been significantly improved. Meanwhile, artificial intelligence helps IoT/mobile devices to make decisions and make them more intelligently.

Edge computing is a feasible and promising technique to meet the challenges. It places a large number of computing nodes near the terminal device to meet the high computing and low latency requirements of deep learning applications. It also provides additional benefits in terms of bandwidth efficiency, privacy, and scalability. However, the edge computing system is much more resource-sensitive than the cloud side, and thus, a more efficient sensing network model is necessary.

Considering the distribution and heterogeneity of edge computing systems, it brings great challenges to the design of efficient sensing networks for edge computing. The current network design considers less the scenarios and frameworks where the model is to be deployed and does not regard the design of efficient models for edge computing systems as a specific research topic. Therefore, efficient sensing network designs should be deeply investigated on edge computing scenarios. This Special Issue will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to the efficient sensing network design for convergence of AI and edge computing.

Combining AI and edge computing empowers the Internet of Things (IoT) and mobile devices with intelligence and brings a tremendous impact on people’s lives. For the characteristics of edge computing, the AI learning models to be deployed should be high-performance and efficient, but recent efficient sensing network design research hardly considers the particularity of edge computing. From our perspective, much more work should concentrate on this issue. This Special Issue will encourage academic and industrial researchers to focus on research on efficient sensing network theory and methods at the intersection of AI and edge computing. Due to its uniqueness and timeliness, we expect a large number of submissions to this Special Issue.

Topics of interest include but are not limited to the following:

  • Compact and high-performing sensing network design for edge computing;
  • Inference efficiency improvement for edge computing;
  • Automatic machine learning methods for sensing networks on edge computing;
  • Reasonable evaluation methods of efficiency of sensing network algorithms and architectures on edge computing platforms;
  • Sensing network training method for edge computing;
  • Efficient sensing network software infrastructure for edge computing;
  • Hardware design for inference and training of sensing networks on edge computing systems;
  • Other topics for efficient network design for AI and edge computing.

Prof. Dr. Shiping Wen
Prof. Dr. Zhongkai Feng
Prof. Dr. Cihui Yang
Guest Editors

Manuscript Submission Information

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  • edge computing
  • artificial intelligence
  • sensing network
  • IoT
  • deep learning

Published Papers (1 paper)

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27 pages, 15047 KiB  
A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices
by Ruiheng Yang, Zhikun Chen, Bin’an Wang, Yunfei Guo and Lingtong Hu
Sensors 2023, 23(14), 6497; - 18 Jul 2023
Cited by 1 | Viewed by 995
Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and [...] Read more.
Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1–2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices. Full article
(This article belongs to the Special Issue Efficient Sensing Networks of AI via Edge Computing)
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