Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
AoI Analysis and AoI-Aware Mechanism for Wireless Sensor Networks
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AoI Analysis and AoI-Aware Mechanism for Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (25 October 2023) | Viewed by 2949

Special Issue Editor

Prof. Dr. Qingchun Chen

E-Mail Website
Guest Editor
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
Interests: wireless communication network; Internet of Things; intelligent sensing and intelligent information processing

Special Issue Information

Dear Colleagues,

Wireless sensor networks play a key role in a wide range of Internet of Things (IoT) scenarios, such as wireless data collection and processing, event detection, target tracking and positioning, and passive wireless perception. Establishing how to better meet the freshness requirements of status update/perception is essential for many IoT applications. In recent years, the age of information (AoI) was proposed to measure the timeliness of the received information from the destination’s perspective. The timeliness of status update/perception is of great importance, especially in real-time monitoring applications, in which the dynamics of the monitored processes need to be tracked accurately for further actions to be taken.

In this Special Issue, the editor aims to present research on AoI-aware wireless sensor networks during the evolution of Internet of Things (IoT) applications to automate business processes and support human efficiency. This Special Issue will cover the implications of the AoI performance metric with existing performance measures of a wireless sensor network, and focus on the basic theory and key technologies of AoI-aware wireless sensor networks. The AoI-related needs/requirements of wireless sensor networks and the services that might address these needs will be a topic of interest. The importance of the AoI metric, the achievable AoI performance limit and the dominating factors, as well as the AoI-guaranteeing mechanism, will also be addressed to shed some light on the AoI-aware wireless sensor network design.

This Special Issue encourages authors from academia and industry to submit new research results related to AoI-aware mechanisms for wireless sensor networks. The topics include, but are not limited to, the following:

  • AoI notation and its applications in wireless sensor networks
    • AoI notation in wireless sensor networks: time-average age, peak age, information theoretic freshness, age violation probability, data obsolescence function and data freshness function, relationship between AoI and other performance measures
  • AoI Analysis and the Achievable AoI Limit
    • AoI analysis method: queuing model-based AoI analysis method, Lyapunov optimization-based AoI analysis method, other AoI analysis methods
    • Achievable AoI performance limit analysis for wireless networks: the minimum achievable AoI in a one-hop/multi-hop wireless sensor network; the minimum achievable AoI in a wireless multiple access network, a wireless multicast network and a wireless powered network
    • Inherent tradeoff analysis: the achievable AoI performance limit of a wireless sensor network and a wireless communication network with the integration of sensing, communication, caching and computing
  • AoI-aware applications
    • AoI-aware wireless sensing and communication in wireless sensor networks
    • AoI-aware signal processing in wireless sensor networks
    • AoI-aware edge computing
    • AoI-aware scheduling design for wireless sensor networks with heterogeneous traffic requirements
    • AoI-aware physical layer security techniques
    • AoI-aware resource allocation scheduling for wireless networks.
  • Technologies to be used
    • Queuing theory
    • Optimization theory
    • Resource allocation and scheduling
    • And many more.

Prof. Dr. Qingchun Chen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • the timeliness of received information
  • timely status update
  • time sensitive applications
  • data freshness
  • queuing model
  • queuing network
  • IoT
  • wireless communication and networks
  • resource allocation
  • optimized scheduling

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 971 KiB  
Article
On the Adaptive Buffer-Aided TDMA Uplink System with AoI-Aware Status-Update Services and Timely Throughput Traffics
by Tianheng Wang, Qingchun Chen, Shuo Wang and Lei Zheng
Sensors 2024, 24(2), 506; https://doi.org/10.3390/s24020506 - 13 Jan 2024
Viewed by 756
Abstract
In this paper, we study a buffer-aided TDMA uplink network, where multiple status-update devices and throughput-demand devices are supposed to upload their data to one information access point (AP), and all devices are assumed to be provisioned with a data buffer to temporarily [...] Read more.
In this paper, we study a buffer-aided TDMA uplink network, where multiple status-update devices and throughput-demand devices are supposed to upload their data to one information access point (AP), and all devices are assumed to be provisioned with a data buffer to temporarily store the randomly generated data from either the installed sensor or upper-layer applications. To fulfill the communication requirements using two types of devices, the average Age of Information (AoI) is utilized to characterize the data freshness of the status-update devices, while the average sum rate is employed to capture the average transmission performance of the throughput-demand devices. On this basis, a joint-optimization problem was formulated to minimize the average AoI for status-update devices and to maximize the average sum rate for the throughput-demand devices. Lyapunov optimization framework was used to solve the problem of obtaining an AoI-aware adaptive TDMA uplink scheme. Numerical results are presented to show that an AoI-aware adaptive TDMA uplink scheme can effectively fulfill the heterogeneous service requirements using status-update devices and throughput-demand devices. Full article
(This article belongs to the Special Issue AoI Analysis and AoI-Aware Mechanism for Wireless Sensor Networks)
►▼ Show Figures

Figure 1

22 pages, 8388 KiB  
Article
An Adaptive Multi-Scale Network Based on Depth Information for Crowd Counting
by Peng Zhang, Weimin Lei, Xinlei Zhao, Lijia Dong and Zhaonan Lin
Sensors 2023, 23(18), 7805; https://doi.org/10.3390/s23187805 - 11 Sep 2023
Cited by 1 | Viewed by 1052
Abstract
Crowd counting, as a basic computer vision task, plays an important role in many fields such as video surveillance, accident prediction, public security, and intelligent transportation. At present, crowd counting tasks face various challenges. Firstly, due to the diversity of crowd distribution and [...] Read more.
Crowd counting, as a basic computer vision task, plays an important role in many fields such as video surveillance, accident prediction, public security, and intelligent transportation. At present, crowd counting tasks face various challenges. Firstly, due to the diversity of crowd distribution and increasing population density, there is a phenomenon of large-scale crowd aggregation in public places, sports stadiums, and stations, resulting in very serious occlusion. Secondly, when annotating large-scale datasets, positioning errors can also easily affect training results. In addition, the size of human head targets in dense images is not consistent, making it difficult to identify both near and far targets using only one network simultaneously. The existing crowd counting methods mainly use density plot regression methods. However, this framework does not distinguish the features between distant and near targets and cannot adaptively respond to scale changes. Therefore, the detection performance in areas with sparse population distribution is not good. To solve such problems, we propose an adaptive multi-scale far and near distance network based on the convolutional neural network (CNN) framework for counting dense populations and achieving a good balance between accuracy, inference speed, and performance. However, on the feature level, in order to enable the model to distinguish the differences between near and far features, we use stacked convolution layers to deepen the depth of the network, allocate different receptive fields according to the distance between the target and the camera, and fuse the features between nearby targets to enhance the feature extraction ability of pedestrians under nearby targets. Secondly, depth information is used to distinguish distant and near targets of different scales and the original image is cut into four different patches to perform pixel-level adaptive modeling on the population. In addition, we add density normalized average precision (nAP) indicators to analyze the accuracy of our method in spatial positioning. This paper validates the effectiveness of NF-Net on three challenging benchmarks in Shanghai Tech Part A and B, UCF_ CC_50, and UCF-QNRF datasets. Compared with SOTA, it has more significant performance in various scenarios. In the UCF-QNRF dataset, it is further validated that our method effectively solves the interference of complex backgrounds. Full article
(This article belongs to the Special Issue AoI Analysis and AoI-Aware Mechanism for Wireless Sensor Networks)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop