Tackling Age of Information in Access Policies for Sensing Ecosystems
Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/7/3456 |
| _version_ | 1827746476697059328 |
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| author | Alberto Zancanaro Giulia Cisotto Leonardo Badia |
| author_facet | Alberto Zancanaro Giulia Cisotto Leonardo Badia |
| author_sort | Alberto Zancanaro |
| collection | DOAJ |
| description | Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system. |
| first_indexed | 2024-03-11T05:25:56Z |
| format | Article |
| id | doaj.art-86b7b2544c734ad5ae9e928ffc5691d1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:25:56Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86b7b2544c734ad5ae9e928ffc5691d12023-11-17T17:32:48ZengMDPI AGSensors1424-82202023-03-01237345610.3390/s23073456Tackling Age of Information in Access Policies for Sensing EcosystemsAlberto Zancanaro0Giulia Cisotto1Leonardo Badia2Department Information Engineering, University of Padova, Via Gradenigo, 6/b, 35121 Padova, ItalyDepartment Information Engineering, University of Padova, Via Gradenigo, 6/b, 35121 Padova, ItalyDepartment Information Engineering, University of Padova, Via Gradenigo, 6/b, 35121 Padova, ItalyRecent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system.https://www.mdpi.com/1424-8220/23/7/3456age of informationInternet of Thingsdata acquisitionnetworksmachine learning |
| spellingShingle | Alberto Zancanaro Giulia Cisotto Leonardo Badia Tackling Age of Information in Access Policies for Sensing Ecosystems Sensors age of information Internet of Things data acquisition networks machine learning |
| title | Tackling Age of Information in Access Policies for Sensing Ecosystems |
| title_full | Tackling Age of Information in Access Policies for Sensing Ecosystems |
| title_fullStr | Tackling Age of Information in Access Policies for Sensing Ecosystems |
| title_full_unstemmed | Tackling Age of Information in Access Policies for Sensing Ecosystems |
| title_short | Tackling Age of Information in Access Policies for Sensing Ecosystems |
| title_sort | tackling age of information in access policies for sensing ecosystems |
| topic | age of information Internet of Things data acquisition networks machine learning |
| url | https://www.mdpi.com/1424-8220/23/7/3456 |
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