Water Distribution Networks (WDNs) are complex, dynamic systems critical to modern society but increasingly difficult to manage due to urbanization, fluctuating demands, and resource constraints. To address these challenges, Smart Water Distribution Networks (SWDNs) utilize Internet of Things (IoT) devices and protocols like Long Range Wide Area Network (LoRaWAN) for real-time monitoring and analysis, enabling smarter and more efficient water management. This demo presents SWIM (Smart Water Interaction & Monitoring), an innovative application designed to modernize SWDNs. SWIM integrates Digital Twins (DTs), established simulation tools like EPANET, and Machine Learning (ML) to provide predictive analytics, anomaly detection, and real-time control. By employing neural networks, SWIM achieves high-accuracy hydraulic predictions with minimal input data. Built on IoTs and Low Power Wide Area Networks (LPWANs), SWIM delivers scalable, efficient, and user-friendly solutions. It aligns with the principles of Industry 5.0, demonstrating the potential to revolutionize water distribution networks and ensure their sustainability in the face of modern challenges.

The optimization and digitalization of Water Distribution Networks (WDNs) are becoming key objectives in our modern society. Indeed, WDNs are typically old, worn and obsolete. These inadequate conditions of the infrastructures lead to significant water loss due to leakages inside pipes, junctions and nodes. It has been measured that in Europe the average value of lost water is about 26 %. Leakage control in current WDNs is typically passive, repairing leaks only when they are visible. Emerging Low Power Wide Area Network (LPWAN) technologies, and especially IoT ones, can help monitor water consumption and automatically detect leakages. In this context, LoRaWAN can be the right way to deploy a smart monitoring system for WDNs. Moreover, most of the current smart WDNs solutions just collect measurements from the smart metres and send the data to the cloud servers, in order to execute the intended analyses, in centralised way. In this paper, we propose new solutions to improve monitoring, leak management and prediction by exploiting edge processing capabilities inside LoRaWAN networks. Our approach is based on an IoT system of water sensors that are placed at junctions of the WDN to have measurements in correspondence to various smart metres in the network and Machine Learning (ML) algorithms to process the data directly at the edge in order to visualise and predict leakages. We present a numerical simulation tool useful to evaluate the suggested monitoring method. Based on our results, we examine whether it is possible to identify network leaks using the edges without having a complete or accurate overview of the collected measurements of the full WDN. System performance is shown separately at gateways network.

Many Internet-connected appliances are often moved to different environments, such as when they are re-located. And even when they are buried in a given physical environment, their ownership "moves", such as when a home or smart space changes hands. This calls for roaming-friendly IoT authentication devised to circumvent the need to deploy long-term authentication credentials across different visited domains. Noting that this issue has been very extensively addressed since at least three decades in cellular network, in this paper we integrate, within the RIOT IoT Operating system, an authentication and key agreement protocol designed to be as close as possible to the standard one used by 4G/5G cellular systems. Our design accounts for a few technical improvements made possible since, unlike the case of cellular networks, we are here free from back-ward compatibility issues. Our proof-of-concept implementation is built on COAP for the radio interface, and on HTTPS for the core network signaling parts, and can be further configured to use two different types of secret keys: pre-shared or on-demand, (re)generated via a SRAM-PUF API available in RIOT.

Similarly to elsewhere on the Internet, practical security in the Internet of Things (IoT) is achieved by combining an array of mechanisms, at work at all layers of the protocol stack, in system software, and in hardware. Standardized protocols, such as Datagram Transport Layer Security (DTLS 1.2) and Transport Layer Security (TLS 1.2), are often recommended to secure communications to/from IoT devices. Recently, the TLS 1.3 standard was released and DTLS 1.3 is in the final stages of standardization. In this paper, we give an overview of version 1.3 of these protocols, and we provide the first experimental comparative performance analysis of different implementations and various configurations of these protocols, on real IoT devices based on low-power microcontrollers. We show how different implementations lead to different compromises. We measure and compare bytes-over-the-air, memory footprint, and energy consumption. We show that, when DTLS/TLS 1.3 requires more resources than DTLS/TLS 1.2, this additional overhead is quite reasonable. We also observe that, in some configurations, DTLS/TLS 1.3 actually decreases overhead and resource consumption. All in all, our study indicates that there is still room to optimize the existing implementations of these protocols.