Query Timing Analysis for Content-Based Wake-Up Realizing Informative IoT Data Collection

Shiraishi, Junya; Kalør, Anders E.; Chiariotti, Federico; Leyva-Mayorga, Israel; Popovski, Petar; Yomo, Hiroyuki

doi:10.1109/lwc.2022.3225333

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Iterating on that approach, the creators of ROD-SAN found a method for aggregating data in the WuS [46,47] to benefit from the possible active state of the destination, thus reducing the energy consumption and latency even more. Embedding data in the WuS also allows content-based waking up [48], using the length of the WuS to prioritize the freshest data and avoid unnecessary wake ups [49].…”

Section: Wur-based Data Collection Protocolsmentioning

confidence: 99%

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

Rup,

Bajic

2024

Sustainability

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The industrial Internet of things (IIoT) is a major lever in Industry 4.0 development, where reducing the carbon footprint and energy consumption has become crucial for modern companies. Today’s IIoT device infrastructure wastes large amounts of energy on wireless communication, limiting device lifetime and increasing power consumption and battery requirements. Communication capabilities seriously affect the responsiveness and availability of autonomous IoT devices when collecting data and retrieving commands to/from higher-level applications. Thus, the objective of optimizing communication remains paramount; in addition to typical optimization methods, such as algorithms and protocols, a new concept is emerging, known as wake-up radio (WuR). WuR provides novel on-demand radio communication schemes that can increase device efficiency. By expanding the lifespan of IoT devices while maintaining high reactivity and communication performance, the WuR approach paves the way for a “place-and-forget” IoT device deployment methodology that combines a small carbon footprint with an extended lifetime and highly responsive functionality. WuR technology, when applied to IoT devices, facilitates green IIoT, thereby enabling the emergence of a novel on-demand IoT (OD-IoT) concept. This article presents an analysis of the state-of-the-art WuR technology within the green IoT paradigm and details the OD-IoT concept. Furthermore, this review provides an overview of WuR applications and their impact on the IIoT, including relevant industry use cases. Finally, we describe our experimental performance evaluation of a WuR-enabled device that is commercially available off the shelf. Specifically, we focused on the communication range and energy consumption, successfully demonstrating the applicability of WuR and the strong potential that it has and the benefits that it offers for sustainable IIoT systems.

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Section: Wur-based Data Collection Protocolsmentioning

confidence: 99%

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

Rup,

Bajic

2024

Sustainability

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“…In real world scenarios, the information coming from sensors can be multi-structured and data can have different importance levels for the end user [ 19 , 27 ]. The application of ML offers a powerful tool to integrate this aspect and extend the concept of AoI in the more general concept of ”value of information” where the semantic aspects of the data become important to decide whether to transmit them, or not.…”

Section: Ml-based Sensor Transmission Optimization Using Aoimentioning

confidence: 99%

“…Finally, AoI assumes underlying binary information. In reality, information coming from sensors, especially tracking smart living applications, can be multi-structured [ 19 , 20 ], and an interpretation is often required.…”

Section: Introductionmentioning

confidence: 99%

Tackling Age of Information in Access Policies for Sensing Ecosystems

Zancanaro

Cisotto

Badia

2023

Sensors

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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.

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Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks

et al. 2023

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Query Timing Analysis for Content-Based Wake-Up Realizing Informative IoT Data Collection

Cited by 4 publications

References 16 publications

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

Green and Sustainable Industrial Internet of Things Systems Leveraging Wake-Up Radio to Enable On-Demand IoT Communication

Tackling Age of Information in Access Policies for Sensing Ecosystems

Machine Learning Prediction Based Adaptive Duty Cycle MAC Protocol for Solar Energy Harvesting Wireless Sensor Networks

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