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In this paper, it is shown that lower transmission/sampling rates can be used in human activity recognition using channel state information (F1-scores > 85%) and that extremely high sampling rates are unnecessary once the system has been deployed. This is done by analysing the effects of interpolating different sampling rates on Wi-Fi dynamic channel state information for human activity recognition. While current research focuses on training and testing with homogeneous and very high sampling rates (> 100 Hz), this paper outlines some issues with higher sampling rates and explores the impact of training and testing with heterogeneous sampling rates in order to advance more towards joint communication and sensing, where one cannot be certain of the received data rate over time while not knowing the exact training set due to weight sharing in Federated Learning. This paper shows the effect of training and testing with heterogeneous sampling rates (including interpolated datasets) on convolutional neural networks in WiFi sensing.

Unlike motor-vehicle transport, the implementation of lnformation and Communications Technologies (ICT) and Cooperative Intelligent Transport Systems (C-ITS) in cycling has not been comprehensively investigated [1]. Cycling offers several benefits both to society and the environment and is one of the most sustainable and green transportation modes [2]. Many people worldwide have been switching to bicycles during the last decades, and this has increased even more due to the Covid pandemic [3]. Furthermore, the number of people who ride an e-bike is also rising [ 4]. Thus, the number of cyclists is increasing and, in turn, the number of cycling accidents is increasing too. For instance, in the Netherlands, one of the most cycling-friendly countries, 31 % of all road fatalities in 2019 were cyclists (203 fatalities), while in 2020, it was 37% (229 fatalities). One-third of these fatalities were e-bike users [5]. Despite the constantly evolving landscape of cycling and electric bike adoption, applications of new technologies in bicycles are still immature. In recent years, academic research on new technologies related to cyclists' comfort and safety is growing [6, 7, 8]. Furthermore, many studies focus on technologies affecting cyclists' road safety; however, it is unclear what type of technologies are implemented for bicycles. To the best of the authors' knowledge, a comprehensive review of such studies is lacking. Additionally, a clear definition of a 'smart bike'- a concept gaining popularity nowadays, is missing in the literature. To address this gap, the objective of this paper is two fold: 1) to review the state-of-the-art technologies implemented in bicycles to improve cyclists' safety, and 2) to propose an original classification for the levels of smartness of newly emerging 'smart bikes'.

A premature crack is a significant indicator for early failure detection for underground polyvinyl chloride (PVC) water pipes. Using an array of strain gauges mounted on a pipe surface to monitor the strain of adjacent areas of a premature crack is a novel technology that has not been explored. To reduce the risks and upfront investments, we need a testbed to investigate, verify, and validate the innovative technology. However, establishing such a pipe monitoring testbed that covers completely realistic underground situations is challenging. The main reason lies in three main challenges: (i) mimicking the natural changes of water flowing in pipes; (ii) identifying the proper placement of strain gauges and detectable crack sizes; (iii) simulating the crucial underground conditions such as temperature and external stress. To this end, in this paper, we present a testbed to get more insights into the effects of different crack types on the pressure-strain characteristic in realistic conditions. In particular, we use pressure meters and water pumps to control the water flow (for challenge (i)); deploy various strain gauges types and induce cracks with different sizes (for challenge (ii)); fill the pipe with water at various temperatures and underground-like external stress (for challenge (iii)); Analyzing experimental results reveals useful hints for designing a realistic testbed, including but not limited to, the required distance among strain gauges, the influence of temperature and pipe axial stress. The dataset and analytic results of this work would provide more insights into how to design a realistic testbed for underground PVC water pipe crack detection.

Magnetic induction is a prospective candidate for wireless communication in un-derground water pipelines. It can overcome many challenges of electromagnetic and acoustic methods in the underground environment. However, similar to the other methods, the communication system needs to transfer data efficiently and reliably from underground nodes to above-ground base stations because of ener-gy constraints. Furthermore, difficulties in repairing the underground devices re-quire proper hardware setup to minimise the maintenance. However, the optimal hardware setup and energy efficiency have yet to be studied in a real testbed, leaving a question about the influence of the underground environments on this method. This article presents our work in progress to address the above gaps. In particular, we introduce three questions and corresponding approach methodolo-gy in designing a magnetic induction communication, including the procedure to define an optimal hardware setup, the impact of underground conditions and the tradeoff between energy consumption and communication reliability.

Health care and human comfort are intertwined, but proper health care is costly, especially in the case of dementia. Device-free sensing has the potential to increase safety and comfort in activities of daily living in human lives by enabling truly unobtrusive ways to achieve human activity recognition. However, in order to truly enable device-free sensing in real-life scenarios, it should be integrated seamlessly in wireless communication. Currently, transmitting devices employ high (> 100 Hz) constant transmission rates and the designed neural networks cannot deal with realistic wireless networking, including changing receiver rates.

A premature crack and pipe bending are two of the major signals indicating future failures of a PVC water main. Identifying these defectives when they are young can prevent the break consequence. A new PVC pipe concept that can diagnose its prem-ature crack based on changes in pressure strain characteristics has been introduced. However, the bending influence has yet to be fully explored in this pipe concept. Therefore, this research investigates the full impacts of both bending and a premature crack on the pressure strain characteristic. The result reveals different patterns of pressure strain characteristic deviation between bending and a premature crack. It can help future research to design a method to detect and discriminate these pipe defectives