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Predicting Link Quality in Industrial Wi-Fi Networks: From Classical Models to Lightweight Machine Learning
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Widening the Coverage of Reference Broadcast Infrastructure Synchronization in Wi-Fi Networks
No full textPrecise clock synchronization protocols are increasingly used to ensure that all the nodes in a network share the very same time base. They enable several mechanisms aimed at improving determinism at both the application and communication levels, which makes them highly relevant to industrial environments. Reference Broadcast Infrastructure Synchronization (RBIS) is a solution specifically conceived for Wi-Fi that exploits existing beacons and can run on commercial devices. In this paper, an evolution of RBIS is presented, we call DOMINO, whose coverage area is much larger than the single Wi-Fi infrastructure network, potentially including the whole plant. In particular, wireless stations that can see more than one access point at the same time behave as boundary clocks and propagate the reference time across overlapping networks
Linear Combination of Exponential Moving Averages for Wireless Channel Prediction
No full textThe ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications.In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity
Mixing Neural Networks and Exponential Moving Averages for Predicting Wireless Links Behavior
No full textPredicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation.In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry
Wireless Sensor Networks Based on TSCH/TDMA with Power Consumption and Latency Constraints
Full text linkOne of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique
Compression of Executable QR Codes or sQRy for Industry: an Example for Wi-Fi Access Points
No full textExecutable QR codes, or sQRy, is a technology dated 2022 that permits to include a runnable program inside a QR code, enabling interaction with the user even in the absence of an Internet connection. sQRy are enablers for different practical applications, including network equipment configuration, diagnostics, and enhanced smart manuals in industrial contexts. Many other non-industry-related fields can also benefit from this technology. Regardless of where SQRy are used, text strings are among the most commonly embedded data. However, due to strict limitations on the available payload, the occupancy of strings limits the length of the programs that can be embedded. In this work, we propose a simple yet effective strategy that can reduce the space taken by strings, hence broadening sQRy applicability
A Lightweight Simulation Environment for TSCH-Based Wireless Sensor Networks
Full text linkSimulators are becoming increasingly important in the context of wireless networks. They allow fair comparison of different network configurations and algorithms under the same environmental conditions, and permit proper network sizing without the need for in-field experiments with real devices. Additionally, simulators enable adaptive reconfiguration of network parameters, either at runtime when employed as network digital twins or offline, by improving the training of artificial intelligence and machine learning algorithms through data augmentation. In this paper we present TSCHmodeler, a lightweight discrete-event simulator for the IEEE 802.15.4 TSCH protocol characterized by a streamlined design. Outcomes provided by the simulator were checked against experimental results acquired from a simple, real implementation, achieving a high degree of similarity, despite the simulator's limitations related to design choices favoring simplicity and usability. Then, as a concrete application of the simulator, TSCHmodeler was used to obtain latency and power consumption in a non-trivial multi-hop network topology evaluated under various configurations
Ultralow Power and Green TSCH-Based WSNs With Proactive Reduction of Idle Listening
Full text linkWireless sensor networks are characterized by low power consumption because motes are typically battery-powered. Time slotted channel hopping (TSCH) relies on a fixed transmission schedule, which enables the receiver module of wireless motes to be switched off every time it is not needed. Unfortunately, in many practical contexts most of the reserved slots remain unused, which leads to appreciable energy waste. For periodic traffic, proactive reduction of idle listening (PRIL) techniques have been proven able to mitigate this problem. In this paper, PRIL multi-hop (PRIL-M) is introduced with the aim to improve existing PRIL techniques, by lowering energy waste further in large real-world mesh networks. PRIL-M is advantageous in all those contexts where ultra-low power consumption is more important than end-to-end latency. Applications that can benefit from PRIL-M include, e.g., environmental monitoring, where sensors are deployed over the target area and must operate for years without maintenance. A thorough simulation campaign showed that, in these scenarios, energy consumption of PRIL-M is 75% less than standard TSCH, while the average latency is about 20 times larger
Machine Learning to Predict Slot Usage in TSCH Wireless Sensor Networks
No full textWireless sensor networks (WSNs) are employed across a wide range of industrial applications where ultra-low power consumption is a critical prerequisite. At the same time, these systems must maintain a certain level of determinism to ensure reliable and predictable operation. In this view, time slotted channel hopping (TSCH) is a communication technology that meets both conditions, making it an attractive option for its usage in industrial WSNs.This work proposes the use of machine learning to learn the traffic pattern generated in networks based on the TSCH protocol, in order to turn nodes into a deep sleep state when no transmission is planned and thus to improve the energy efficiency of the WSN. The ability of machine learning models to make good predictions at different network levels in a typical tree network topology was analyzed in depth, showing how their capabilities degrade while approaching the root of the tree. The application of these models on simulated data based on an accurate modeling of wireless sensor nodes indicates that the investigated algorithms can be suitably used to further and substantially reduce the power consumption of a TSCH network
On the Accuracy and Precision of Moving Averages to Estimate Wi-Fi Link Quality
No full textThe radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions
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