Several components in the design of next-generation networks, including user profiling and network slicing, rely on accurate models of traffic load. In this context, recent studies have focused on various video traffic categories, while traffic associated with extended reality (XR) services has received limited attention. This paper introduces a novel empirical model for 3D XR traffic, developed by encoding real Point Clouds using a standard-compliant codec, and able to account for the dynamic of service sessions and user behaviors over an entire session. Our methodology encompasses multiple temporal scales, ranging from milliseconds to minutes, to account for different phenomena related to both user behaviour and encoder settings. Initially, we investigate the packet size distribution at the time scale of a semantic unit, corresponding to the encoding of a single point cloud. We verify that it can be effectively represented by a heavy-tailed Gamma distribution. Then, we illustrate how this insight can be leveraged to model application-layer phenomena. Specifically, we demonstrate the applicability of a general semi-hidden Markov model to capture both the temporal dynamics of service sessions and user behaviors. We provide results in terms of comparison of the empirical and fitting traffic distributions, based on quantile to quantile analysis and statistical tests. We also show how the model can be trained on real data and we provide a pseudo-code demonstrating the model application within a network simulator.

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.

Real-world networks are typically described in terms of nodes, links, and communities, having signal values often associated with them. The aim of this letter is to introduce a novel Compound Markov random field model (Compound MRF, or CMRF) for signals defined over graphs, encompassing jointly signal values at nodes, edge weights, and community labels. The proposed CMRF generalizes Markovian models previously proposed in the literature, since it accounts for different kinds of interactions between communities and signal smoothness constraints. Finally, the proposed approach is applied to (joint) graph learning and signal recovery. Numerical results on synthetic and real data illustrate the competitive performance of our method with respect to other state-of-the-art approaches.