Artificial Intelligence (AI) is playing a crucial role in transforming Spectrum Sensing (SS) and Cognitive Radio Networks (CRNs), especially for next-generation wireless communication systems. This study presents a meta-analysis of 13 survey articles, also analyzing a total of 113 primary studies, to synthesize the applications of AI, specifically Machine Learning (ML) and Deep Learning (DL), in spectrum sensing. Key models identified include Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN), among others. The analysis reveals measurable performance improvements and the main metrics to measure it. Despite these advancements, challenges persist, including computational complexity, adaptability to real-time environments, and model generalization. The study also highlights promising future directions like energy-efficient AI architectures, federated learning for decentralized CRNs, and cooperative spectrum sensing methods. Addressing these challenges and pursuing open research areas is critical to fully realize AI-powered CRNs. Such progress is expected to enable autonomous and intelligent spectrum management in beyond-5G and 6G networks, ultimately enhancing system reliability, scalability, and spectrum utilization efficiency.

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.

This paper introduces an innovative Energy-Autonomous Wireless Sensing Node (EAWSN) that addresses power constraints by harnessing ambient light for energy. It combines this energy harvesting capability with the Time Domain to Digital Conversion (TDDC) technique for efficient and accurate measurements of resistive sensors. Bluetooth Low Energy (BLE) communication ensures data can be transmitted wirelessly to a base station, providing a promising solution for various applications, particularly in environments with limited access to wired power sources, enabling long-term, maintenance-free operation by eliminating batteries. Experimental results showed a linear relationship between the test resistance Rmand the measured number of clock pulses Nmwithin the sensor’s operating range.

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.

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.

This article provides a survey on the adoption of LoRa in the agricultural field and reviews state-of-the-art solutions for smart agriculture, analyzing the potential of this technology in different infield applications. In particular, we consider four reference scenarios, namely, irrigation systems, plantation and crop monitoring, tree monitoring, and livestock monitoring, which exhibit heterogeneous requirements in terms of network bandwidth, density, sensors’ complexity, and energy demand, as well as latency in the decision process. We discuss how LoRa-based solutions can work in these scenarios, analyzing their scalability, interoperability, network architecture, and energy efficiency. Finally, we present possible future research directions and point out some open issues which might become the main research trends for the next years.