GNSS spoofing is a growing threat to UAV operations, especially in decentralized drone swarms. In this paper, we present both a practical spoofing attack and a lightweight detection algorithm based on Wi-Fi RSSI and GNSS coordinates. The attack selectively compromises drones by transmitting a subset of satellite signals, causing drones to calculate wrong positions. Our detection method leverages geometric consistency checks to validate GNSS-based positions without requiring any infrastructure or additional sensors. Simulation results show that the proposed attack successfully misleads victim drones, and that the detection method reliably helps drones identify such attacks.

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.

The growing demand for wireless connectivity has heightened the need for efficient spectrum utilization. Dynamic Spectrum Sensing (DSS), a fundamental capability of Cognitive Radio Networks (CRNs), enables real-time identification of available spectrum without interfering with licensed users. While DSS has evolved significantly, a comprehensive overview capturing its full operational context remains lacking. This survey bridges that gap by systematically analyzing 62 peer-reviewed studies published between 2005 and 2024, selected based on explicit or implicit engagement with DSS and rigorous peer-review criteria. Employing a PRISMA-based methodology, we examine major sensing strategies, including energy detection, cooperative spectrum sensing, and machine learning, based methods, alongside application domains, technologies, and evaluation methods. Special attention is given to how DSS systems address dynamic environments, including time-varying channels and real-time decision-making. Key contributions include a comprehensive literature analysis covering research trends, experimental and implementation practices, as well as environmental variability. The survey also identifies critical open challenges, including security vulnerabilities in cooperative sensing, energy constraints in IoT deployments, and limited adaptability to dynamic and mobile environments. It offers a consolidated foundation for advancing DSS research and practice, guiding future efforts toward resilient, energy-aware, and adaptive sensing solutions for emerging contexts such as 6G networks, IoT, and satellite communications.

The advent of pervasive wireless systems faces several challenges due to the massive data traffic growth resulting from the interconnection of billions of new devices. This makes it essential to provide smart decision-making in identifying available spectrum resources by sensing the radio frequency environment. In this study, we aim to improve the spectrum sensing process and enhance the detection efficiency of secondary users (sensing devices) in identifying primary users (transmitting devices). We consider a scenario in which secondary users are affected by noise and fading, and employ distributed detection and data fusion to combine data from geographically distributed sensors. The results show that collaborative spectrum sensing, where multiple SUs share their sensing data, significantly enhances detection performance. By applying optimization techniques to assign optimal weight vectors to the sensors, we further increase the detection performance of the primary user, where each one is affected by different noise factors. The study reveals that detection performance improves as more users collaborate, and this improvement is validated through scenarios with varying SNR values.

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.

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.

This article proposes a new architecture for privacy-preserving data mining based on Multi Party Computation (MPC) and secure sums. While traditional MPC approaches rely on a small number of aggregation peers replacing a centralized trusted entity, the current study puts forth a distributed solution that involves all data sources in the aggregation process, with the help of a single server for storing intermediate results. A large-scale scenario is examined and the possibility that data become inaccessible during the aggregation process is considered, a possibility that traditional schemes often neglect. Here, it is explicitly examined, as it might be provoked by intermittent network connectivity or sudden user departures. For increasing system reliability, data sources are organized in multiple sets, called rings, which independently work on the aggregation process. Two different protocol schemes are proposed and their failure probability, i.e., the probability that the data mining output cannot guarantee the desired level of accuracy, is analytically modeled. The privacy degree, the communication cost and the computational complexity that the schemes exhibit are also characterized. Finally, the new protocols are applied to some specific use cases, demonstrating their feasibility and attractiveness.

In recent years, underwater communications have seen a growing interest pushed by marine research, oceanography, marine commercial operations, offshore oil industry and defense applications. Generally, underwater communications employ audio signals which can propagate relatively far but are also significantly affected by Doppler distortions. In fact, physical properties of the water and spatial changes due to tides, currents and waves can cause channel variations or unwanted movements of the transmitter or receiver. This study shows how to compensate for the Doppler effect in transmission employing the JANUS standard, a popular modulation scheme for underwater communication. Differently form previous work, we use the pseudo-random symbols of the JANUS preamble to measure and compensate for Doppler distortions without changing the standard. The proposed method has been tested both on the Watermark simulator and real in-field experiments. Results show that the proposed technique allows to correctly receive over 90 % of the packets even with severe Doppler, compensating relative speeds up to 5 m/s.

In this paper we study the capability of LoRa technology in rejecting different interfering LoRa signals and the impact on the cell capacity. First, we analyze experimentally the link-level performance of LoRa and show that collisions between packets modulated with the same Spreading Factor (SF) usually lead to channel captures, while different spreading factors can indeed cause packet loss if the interference power is strong enough. Second, we model the effect of such findings to quantify the achievable capacity in a typical LoRa cell: we show that high SFs, generally seen as more robust, can be severely affected by inter-SF interference and that different criteria for deciding SF allocations within the cell may lead to significantly different results. Moreover, the use of power control and packet fragmentation can be detrimental more than beneficial in many deployment scenarios. Finally, we discuss the capacity improvements that can be achieved by increasing the density of LoRa gateways. Our results have important implications for the design of LoRa networks: for example, allocating high SFs to faraway end devices might not improve the experienced performance in case of congested networks because of the increased transmission time and vulnerability period.

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.

We present an innovative smartphone-centric tracking system for indoor and outdoor environments, based on the joint utilization of dead-reckoning and computer vision (CV) techniques. The system is explicitly designed for visually impaired people (although it could be easily generalized to other users) and it is built under the assumption that special reference signals, such as painted lines, colored tapes or tactile pavings are deployed in the environment for guiding visually impaired users along pre-defined paths. Thanks to highly optimized software, we are able to execute the CV and sensor-fusion algorithms in run-time on low power hardware such as a normal smartphone, precisely tracking the users movements.

In this paper, we present Overgrid, a fully distributed peer-to-peer (P2P) architecture designed to automatically control and implement distributed demand response (DR) schemes in a community of smart buildings with energy generation and storage capabilities. As overlay networks in communications establish logical links between peers regardless of the physical topology of the network, the Overgrid is able to apply some power balance criteria to its system of buildings, as they belong to a virtual microgrid, regardless of their physical location. We exploit an innovative distributed algorithm, called flow updating, for monitoring the power consumption of the buildings and the number of nodes in the network, proving its applicability in an Overgrid scenario with realistic power profiles and networks of up to 10 000 buildings. To quantify the energy balance capability of Overgrid, we first study the energy characteristics of several types of buildings in our university campus and in an industrial site to accurately provide some reference buildings models. Then, we classify the amount of “flexible” energy consumption, i.e., the quota that could be potentially exploited for DR programs. Finally, we validate Overgrid emulating a real P2P network of smart buildings behaving according to our reference models. The experimental results prove the feasibility of our approach.

New pervasive technologies often reveal many sensitive information about users' habits, seriously compromising the privacy and sometimes even the personal security of people. To cope with this problem, researchers have developed the idea of privacy-preserving data mining which refers to the possibility of releasing aggregate information about the data provided by multiple users, without any information leakage about individual data. These techniques have different privacy levels and communication costs, but all of them can suffer when some users' data becomes inaccessible during the operation of the privacy preserving protocols. It is thus interesting to validate the applicability of such architectures in real-world scenarios. In this paper we experimentally evaluate two promising privac-preserving techniques on PlanetLab, analyzing the execution time and the failure rate that each scheme exhibits.

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.

The end-to-end available bandwidth of an Internet path is a desirable information that can be exploited to optimize system performance. Several tools have been proposed in the past to estimate it. However, existing measurement techniques were not designed for large-scale deployments. In this paper we show that current tools do not properly work where multiple probing processes share a portion of a path. We provide experimental evidence to quantify the impact of mutual interference between measurements. We further analyze the characteristics of popular tools, quantifying (i) the impact of mutual interference, (ii) the total overhead imposed to the network and (iii) the intrusiveness of the measurement process in a large-scale scenario. Our goal is to effectively quantify the impact of concurrent measurements on current estimation techniques and to offer some simple guidelines for dimensioning a large-scale measurement system.

Most tools designed to estimate the capacity of an Internet path require access on both end hosts of the path, which makes them difficult to deploy and use. In this paper we present a single-sided technique for measuring the capacity without the active cooperation of the destination host, focusing particularly on ADSL links. Compared to current methods used on broadband hosts, our approach generates two orders of magnitude less traffic and is much less intrusive. Our tool, DSLprobe, exploits the typical characteristics of ADSL, namely its bandwidth asymmetry and the relatively low absolute bandwidth, in order to measure both downlink and uplink capacities and to mitigate the impact of cross-traffic. To further improve the accuracy, we study different ways to detect and filter cross-traffic packets and we show how to recognize and overcome limited uplink capacities. We validate our tool both on controlled hosts and on a wide variety of Internet hosts. Finally, we present a case study of two large ADSL providers.