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.

This survey explores the convergence of Internet of Things (IoT) technologies with Water Distribution Systems (WDSs), focusing on large-scale deployments and the role of edge computing (EC). Effective water management increasingly relies on IoT monitoring, resulting in massive deployments and the generation of Big Data. While previous research has examined these topics individually, this work integrates them into a comprehensive analysis. We systematically reviewed 255 studies on IoT in WDS, identifying key challenges such as interoperability, scalability, energy efficiency, network coverage, and reliability. We also examined technologies like LPWAN and the growing use of EC for real-time data processing. In large-scale WDS scenarios, where vast amounts of data are generated, we highlighted the importance of technologies like NB-IoT, SigFox, and LoRaWAN due to their low power consumption and wide coverage. Based on our findings, we provide guidelines for sustainable, large-scale IoT deployment in WDS, emphasizing the need for edge data processing to reduce cloud dependency, improve scalability, and enable smarter cities and digital twins.

The digitalization of Water Distribution Systems (WDSs) is becoming a key objective in modern society. The increasing complexity of contemporary WDSs, driven by urbanization, fluctuating consumer demand, and limited resources, makes their management particularly challenging, especially in large-scale scenarios. This paper proposes the SWI-FEED framework designed to facilitate the widespread deployment of the Internet of Things (IoT) for enhanced monitoring and optimization of WDSs. The framework aims to investigate the utilization of massive IoT in monitoring and optimizing WDSs in different contexts, with a particular focus on four use cases such as optimal node activation, IoT gateways deployment, distributed leakage detection and water demand disaggregation. SWI-FEED has been tested with predefined network models available in the Open Water Analytics community public repository. Specifically, the four use cases are evaluated using a large network consisting of 4,419 sensor nodes, 3 tanks and 5,066 pipes. Overall, this comprehensive framework provides a holistic approach to address possible challenges of a WDS and optimize the efficiency of large-scale IoT deployments. It reduces the energy consumption of IoT devices within the WDS while enhancing leak detection and localization capabilities in real-world water networks. Our adopted theoretical methodology is based on graph theory, which allows IoT gateways to be strategically positioned to maximize network coverage and minimize infrastructure redundancy. This makes it possible to significantly reduce the number of gateways required and, consequently, the overall system energy consumption.

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on a synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

LoRaWAN is a wireless technology that enables high-density deployments of IoT devices. Designed for Low Power Wide Area Networks (LPWAN), LoRaWAN employs large cells to service a potentially extremely high number of devices. The technology enforces a centralized architecture, directing all data generated by the devices to a single network server for data processing. End-to-end encryption is used to guarantee the confidentiality and security of data. In this demo, we present Edge2LoRa, a system architecture designed to incorporate edge processing in LoRaWAN without compromising the security and confidentiality of data. Edge2LoRa maintains backward compatibility and addresses scalability issues arising from handling large amounts of data sourced from a diverse range of devices. The demo provides evidence of the advantages in terms of reduced latency, lower network bandwidth requirements, higher scalability, and improved security and privacy resulting from the application of the Edge processing paradigm to LoRaWAN.

According to IoT Analytics, NB-IoT, LoRaWAN, and Sigfox are today the most popular technologies for low-power wide-area networks (86% of the market), both in terms of end-user adoption as well as ecosystem support. While NB-IoT utilizes licensed bands, LoRaWAN and Sigfox both employ the sub-GHz ISM bands, potentially interfering with each other.In this paper, we present a thorough coexistence study between LoRaWAN and Sigfox, in realistic urban scenarios, with different duty cycles and traffic conditions. The choice of such scenarios and simulation parameters are supported by an in-depth lit-erature review, and simulations are based on the SEAMCAT simulator. The results offer new insights on the coexistence of LoRaWAN and Sigfox for emerging IoT applications. Finally, as interference mitigation strategy, we analyze the performance obtained applying protection distance mechanisms.

In this article we present SHARP, an original approach for obtaining human activity recognition (HAR) through the use of commercial IEEE 802.11 (Wi-Fi) devices. SHARP grants the possibility to discern the activities of different persons, across different time-spans and environments. To achieve this, we devise a new technique to clean and process the channel frequency response (CFR) phase of the Wi-Fi channel, obtaining an estimate of the Doppler shift at a radio monitor device. The Doppler shift reveals the presence of moving scatterers in the environment, while not being affected by (environment-specific) static objects. SHARP is trained on data collected as a person performs seven different activities in a single environment. It is then tested on different setups, to assess its performance as the person, the day and/or the environment change with respect to those considered at training time. In the worst-case scenario, it reaches an average accuracy higher than$95\%$, validating the effectiveness of the extracted Doppler information, used in conjunction with a learning algorithm based on a neural network, in recognizing human activities in a subject and environment independent way. The collected CFR dataset and the code are publicly available for replicability and benchmarking purposes [1].

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.

Modern standards for IoT communications support fast deployment, large coverage in the order of kilometers, and physical layer adaptations to increase link robustness under time-varying propagation and interference conditions. A possible use of such IoT technologies is in case of emergency scenarios where first responders (FRs) arrive after a disastrous event. Indeed, an important challenge for emergency management is the need to (re)establish real-time communication capabilities and to offer integrated decision making facilities based on information gathered by FRs acting on the crisis site. In this paper, we present a system architecture based on LoRaWAN technology for connecting emergency operators in real-time and reliably communicating environmental information, audio streams/messages, and vital signs received from the first responders' sensors. In particular, based on LoRa modulation parameters, we propose an adaptation algorithm which adjusts user's voice messages and the resolution of the data flows to keep alive communications also when link quality is critically low, thus avoiding delay and saturation problems. Opportunistically, audio signals can be processed locally by the first responder's equipment with a speech-to-text conversion, thus significantly reducing traffic requirements. We demonstrate that the adaptation scheme can be performed real-time, even on a per-packet basis. Thanks this innovative system, FRs can communicate from the crisis site in an efficient and cost-effective way.

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LoRaWAN (Long 1 Range Wide Area Network) is an attractive network infrastructure and protocol suite for ultra low power Internet of Things devices. Even if the technology itself is quite mature and specified, the currently deployed wireless resource allocation strategies are still coarse and based on rough heuristics. This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices. Our design relies on three complementary approaches: i) equalize the Time-on-Air of packets transmitted by the system's End Devices in each spreading factor's group; ii) balance the spreading factors across multiple gateways and iii) keep into account the channel capture, which our experimental results show to be very substantial in LoRa. While retaining an extremely simple and scalable implementation, this strategy yields a significant improvement (up to 38%) in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech, and appears to be extremely robust to different operating/load conditions and network topology configurations.

Recent advances on WLAN technology have been focused mostly on boosting network capacity by means of a more efficient and flexible physical layer. A new concept is required at MAC level to exploit fully the new capabilities of the PHY layer. In this article, we propose a contention mechanism based on Repeated Contentions (ReCo) in frequency domain. It provides a simple-to-configure, robust and short-term fair algorithm for the random contention component of the MAC protocol. The throughput efficiency of ReCo is not sensitive to the number of contending stations, so that ReCo does not require adaptive tuning of the access parameters for performance optimization. Efficiency and robustness is gained through the power of repeated contention rounds. We also apply the ReCo concept to the emerging IEEE 802.11ax standard, showing how it can boost performance of random access with respect to the current version of IEEE 802.11ax OFDMA Back-Off (OBO). Our proposal is supported by an experimental test-bed that realizes ReCo by means of simultaneous transmission and reception of short tones, which is feasible on top of programmable OFDM PHY layers.

In this paper we show that inter-technology interference can be recognized by commodity WiFi devices by monitoring the statistics of receiver errors. Indeed, while for WiFi standard frames the error probability varies during the frame reception in different frame fields (PHY, MAC headers, payloads) protected with heterogeneous coding, errors may appear randomly at any point during the time the demodulator is trying to receive an exogenous interfering signal. We thus detect and identify cross-technology interference on off-the-shelf WiFi cards by monitoring the sequence of receiver errors (bad PLCP, bad PCS, invalid headers, etc.) and develop an Artificial Neural Network (ANN) to recognize the source of interference. The result is quite impressive, reaching an average accuracy of almost 99% in recognizing ZigBee, Microwave and LTE (in unlicensed spectrum) interference.

In this paper we propose ReCo, a robust contention scheme for emerging wireless technologies, whose efficiency is not sensitive to the number of contending stations and to the settings of the contention parameters (such as the contention windows and retry limits). The idea is iterating a basic contention mechanism, devised to select a sub-set of stations among the contending ones, in consecutive elimination rounds, before performing a transmission attempt. Elimination rounds can be performed in the time or frequency domain, with different overheads, according to the physical capabilities of the nodes. Closed analytical formulas are given to dimension the number of contention rounds in order to achieve an arbitrary low collision probability. Simulation results and a real implementation for the time-domain solution demonstrate the effectiveness and robustness of this approach in comparison to IEEE 802.11 DCF.

In this demo we present and validate a LoRa cell traffic generator, able to emulate the behavior of thousands of low-rate sensor nodes deployed in the same cell, by using a single Software Defined Radio (SDR) platform. Differently from traditional generators, whose goal is creating packet flows which emulate specific applications and protocols, our focus is generating a combined radio signal, as seen by a gateway, given by the super-position of the signals transmitted by multiple sensors simultaneously active on the same channel. We argue that such a generator can be of interest for testing different network planning solutions for LoRa networks.

In this demo we demonstrate the functionalities of a novel experimentation framework, called WiSHFUL, that facilitates the prototyping and experimental validation of innovative solutions for heterogeneous wireless networks, including cross-technology coordination mechanisms. The framework supports a clean separation between the definition of the logic for optimizing the behaviors of wireless devices and the underlying device capabilities, by means of a unifying platform-independent control interface and programming model. The use of the framework is demonstrated through two representative use cases, where medium access is coordinated between IEEE-802.11 and IEEE-802.15.4 networks.

In this paper we describe how a novel backoff mechanism called Moderated Backoff (MB), recently proposed as a standard extension for 802.11 networks, has been prototyped and experimentally validated on a commercial 802.11 card before being ratified. Indeed, for performance reasons, the time critical operations of MAC protocols, such as the backoff mechanism, are implemented into the card hardware/firmware and cannot be arbitrarily changed by third parties or by manufacturers only for experimental reasons. Our validation has been possible thanks to the availability of the so called Wireless MAC Processor (WMP), a prototype of a novel wireless card architecture in which MAC protocols can be programmed by using proper abstractions and a state-machine formal language, which enable easy modifications of legacy operations. Experimental results are in agreement with simulations and prove the effectiveness of Moderated Backoff, as well as the potentialities of the WMP platform.

Recent years have witnessed the increasing adoption of heterogeneous wireless networks working in unlicensed ISM bands, thus creating serious problems of spectrum overcrowding. Although ZigBee, Bluetooth and WiFi networks have been natively designed for working in presence of interference, it has been observed that several performance impairments may occur because of heterogeneous sensitivity to detect or react to the presence of other technologies. In this paper we focus on the WiFi capability to detect interfering ZigBee links. Despite of the narrowband transmissions performed by ZigBee, in emerging scenarios ZigBee interference can have a significant impact on WiFi performance. Therefore, interference detection is essential for improving coexistence strategies in heterogeneous networks. In our work we show how such a detection can be performed on commodity cards working on time and frequency domain and also analysing data in the error domain. Errors are monitored and classified into error patterns observed in the network in terms of occurrence probability and temporal clustering of different error events. Through statistical analysis we are able to detect the presence of ZigBee transmissions measuring the errors raised by the WiFi card.

We introduce MAClets, software programs uploaded and executed on-demand over wireless cards, and devised to change the card's real-time medium access control operation. MAClets permit seamless reconfiguration of the MAC stack, so as to adapt it to mutated context and spectrum conditions and perform tailored performance optimizations hardly accountable by an once-for-all protocol stack design. Following traditional active networking principles, MAClets can be directly conveyed within data packets and executed on hard-coded devices acting as virtual MAC machines. Indeed, rather than executing a pre-defined protocol, we envision a new architecture for wireless cards based on a protocol interpreter (enabling code portability) and a powerful API. Experiments involving the distribution of MAClets within data packets, and their execution over commodity WLAN cards, show the flexibility and viability of the proposed concept.

Programmable wireless platforms aim at responding to the quest for wireless access flexibility and adaptability. This paper introduces the notion of wireless MAC processors. Instead of implementing a specific MAC protocol stack, Wireless MAC processors do support a set of Medium Access Control “commands” which can be run-time composed (programmed) through software-defined state machines, thus providing the desired MAC protocol operation. We clearly distinguish from related work in this area as, unlike other works which rely on dedicated DSPs or programmable hardware platforms, we experimentally prove the feasibility of the wireless MAC processor concept over ultra-cheap commodity WLAN hardware cards. Specifically, we reflash the firmware of the commercial Broadcom AirForce54G off-the-shelf chipset, replacing its 802.11 WLAN MAC protocol implementation with our proposed extended state machine execution engine. We prove the flexibility of the proposed approach through three use-case implementation examples.