Angle-of-arrival (AoA) has been utilized as a reliable feature for physical layer authentication (PLA), as impersonation attacks require restrictive spatial and signal conditions. However, in configurations where the legitimate and adversary users are spatially close or aligned, AoA-based authentication may fail. This paper investigates enhanced sensing-based authentication using channel state information (CSI). We develop a phase-correction pipeline to sanitize CSI and propose antenna subarray aggregation across frequencies, drastically improving AoA estimation. Moreover, we show that by leveraging sensing parameters such as time-of-flight (ToF), received signal strength indicator (RSSI), impersonation attacks can be accurately revealed where AoA performs poorly. Our proposed scheme is validated using a real dataset collected on the Nokia campus in Stuttgart, Germany. Experimental results show that, in addition to the highly accurate AoA estimation, proximal impersonation attacks can be detected with an accuracy of 94.4-100%. These findings demonstrate that spatial and frequency-domain processing of CSI enables effective, hardware-friendly, robust PLA through attacks detection.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a key enabler for sixth-generation (6G) wireless systems, offering enhanced spectral efficiency and ubiquitous coverage. In such systems, the association of user equipments (UEs) to access points (APs) is a critical challenge, as it directly impacts scalability, interference suppression, and overall system performance. Conventional user association (UA) methods optimize communication throughput but overlook emerging 6G requirements from sensing and integrated sensing and communication (ISAC) applications. To address this, we propose a scalable user association (SUA) scheme for CF-mMIMO networks that explicitly considers heterogeneous UE service needs, including communication, sensing, and ISAC. The proposed SUA scheme integrates AP masking, link prioritization, and optimization-driven AP selection to balance system load and enhance service quality. Simulation results demonstrate that the proposed approach significantly reduces interference and computational runtime, while improving symbol error rate for communication UEs and probability of detection for sensing UEs.

The rapid growth of the Internet of Things (IoT) has triggered the need for scalable and energy-efficient communication solutions. While LoRaWAN is widely used for long-range wireless access, its Aloha-based MAC protocol struggles with high collision rates in dense networks. Existing solutions such as irregular repetition slotted ALOHA (IRSA) and contention resolution diversity slotted ALOHA (CRDSA) have improved network performance by using packet repetitions and successive interference cancellation. However, they do not fully leverage the unique properties of LoRaWAN Spreading Factors (SFs). To address this gap, we propose a new approach called SF-IRSA, where IoT devices transmit replicas using different SFs, enabling the decoder to apply an SF-IRSA-SIC process that leverages both temporal and spatial dimensions for efficient packet decoding. Our theoretical analysis and simulations show that SF-IRSA outperforms IRSA and CRDSA in terms of throughput and reliability. Specifically, using up to two SFs results in a 16.2% increase in the asymptotic throughput compared to standard IRSA. When extending to three SFs, the throughput gain reaches 116.9%, with a maximum of $\mathbf{2 2 4. 5 2 \%}$ while using $\mathbf{6}$ SFs.

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while pre-serving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy, unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes up-link transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments.

This paper investigates the impact of reactive jamming on LoRaWAN networks, focusing on showing that LoRaWAN communications can be effectively disrupted with minimal jammer exposure time. The susceptibility of LoRa to jamming is assessed through a theoretical study of how the frame success rate is impacted by only a few jamming symbols. Different jamming approaches are studied, among which repeated-symbol jamming appears to be the most disruptive, with sufficient jamming power. A key contribution of this work is the proposal of a software-defined radio (SDR)-based jamming approach implemented on GNU Radio that generates a controlled number of random symbols, independent of the standard LoRa frame structure. This approach enables precise control over jammer exposure time and provides flexibility in studying the effect of jamming symbols on network performance. The theoretical analysis is validated through experimental results, where the implemented jammer is used to assess the impact of jamming under various configurations. Our findings demonstrate that LoRa-based networks can be disrupted with a minimal number of symbols, emphasizing the need for future research on stealthy communication techniques to counter such jamming attacks.

In the current context of massive IoT, the Pure-Aloha scheme used in LoRaWAN is reaching its limit, and Slotted-Aloha is being considered as an alternative, as it offers twice Pure-Aloha’s packet success rate. It however requires synchronization across the nodes. In this paper, we propose a new slot structure adapted to devices with low quality clock, and a duty-cycle efficient synchronization protocol for LoRaWAN class A devices with the lowest overhead to date. We discuss the conditions of its integration into LoRaWAN. The experimental results confirm that it succeeds in tracking each device’s synchronization state, identifying the exact moment they desynchronize and resynchronizing them. The proposed protocol is also proven to be more duty-cycle efficient than existing constant-rate synchronization solutions.

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about $\mathbf{1 7. 1 8 \%}$ in indoor environments and $\mathbf{9. 7 9 \%}$ in outdoor environments.

The advancement of wireless communication systems toward 5G and beyond is spurred by the demand for high data rates, exceedingly dependable low-latency communication, and extensive connectivity that aligns with sensing requisites such as advanced high-resolution sensing and target detection. Consequently, embedding sensing into communication has gained considerable attention. In this work, we propose an alternative approach for optimizing integrated sensing and communication (ISAC) waveform for target detection by concurrently maximizing the power of the communication signal at an intended user and minimizing the multi-user and sensing interference. We formulate the problem asa non-disciplined convex programming (NDCP) optimization and we use a distribution-based approach for interference cancellation. Precisely, we establish the distribution of the communication signal and the multi-user communication interference received by the intended user. After that, we establish that the sensing interference can be distributed as a centralized Chi-squared if the sensing covariance matrix is idempotent. We design such a matrix based on the symmetrical idempotent property. Additionally, we propose a disciplined convex programming (DCP) form of the problem, and using successive convex approximation (SCA), we show that the solutions can reach a stable waveform for efficient target detection. Furthermore, we compare the proposed waveform with state of the art radar-communication waveform designs and demonstrate its superior performance by computer simulations.

Unmanned Aerial Vehicles (UAVs) have become increasingly popular in various applications, especially with the emergence of 6G systems and networks. However, their widespread adoption has also led to concerns regarding security vulnerabilities, making the development of reliable intrusion detection systems (IDS) essential for ensuring UAVs safety and mission success. This paper presents a new IDS for UAV networks. A binary-tuple representation was used for encoding class labels, along with a deep learning-based approach employed for classification. The proposed system enhances the intrusion detection by capturing complex class relationships and temporal network patterns. Moreover, a cross-correlation study between common features of different UAVs was conducted to discard correlated features that might mislead the classification of the proposed IDS. The full study was carried out using the UAV-IDS-2020 dataset, and we assessed the performance of the proposed IDS using different evaluation metrics. The experimental results highlighted the effectiveness of the proposed multiclass classifier model with an accuracy of 95%.

The proliferation of the Internet of Things fosters collaboration among connected devices for tasks like indoor localization. However, existing indoor localization solutions struggle with dynamic and harsh conditions, requiring extensive data collection and environment-specific calibration. These factors impede cooperation, scalability, and the utilization of prior research efforts. To address these challenges, we propose FeMLoc, a federated meta-learning (MTL) framework for localization. FeMLoc operates in two stages: 1) collaborative meta-training, where edge devices contribute diverse localization data to train a global meta-model using a combination of model-agnostic MTL and federated averaging techniques and 2) rapid adaptation for new environments, where the pretrained global meta-model initializes localization models, requiring only minimal fine-tuning with a small amount of new data. In this article, we provide a detailed technical overview of FeMLoc, highlighting its unique approach to privacy-preserving MTL in the context of indoor localization. Our performance evaluations on real-world data sets, including UJIIndoorLoc, demonstrate the superiority of FeMLoc over state-of-the-art methods, enabling swift adaptation to new indoor environments with reduced calibration effort. Specifically, FeMLoc achieves up to 80.95% improvement in localization accuracy compared to the conventional baseline neural network (NN) approach after only 100 gradient steps. Alternatively, for a target accuracy of around 5m, FeMLoc achieves the same level of accuracy up to 82.21% faster than the baseline NN approach. This translates to FeMLoc requiring fewer training iterations, thereby significantly reducing fingerprint data collection and calibration efforts. Moreover, FeMLoc exhibits enhanced scalability, making it well-suited for location-aware massive connectivity driven by emerging wireless communication technologies.

Wireless communication systems rely on channel estimation and equalization to ensure reliable and efficient data transmission. However, with the increasing demand for high connectivity in massive IoT networks, these processes are facing significant challenges. The complexity and intensive computation required for channel estimation and equalization results in high communication latency and power consumption, which can ultimately prevent the transceiver from restoring the originally transmitted signal. In this paper, we propose a novel approach to simplify wireless communication systems by using a conditional generative adversarial network (cGAN) model to replace both channel estimation and equalization blocks. We formulate the data recovery task as a translation from received data to the corresponding transmitted signal and introduce the concept of Rx-to-Tx translation based on a cGAN, which was initially developed for image-to-image translation. Our preliminary results demonstrate the feasibility and effectiveness of this approach, particularly for digital modulations. By carefully tuning the model's hyperparameters, we achieve the theoretical symbol error rate (SER) of QAMs in a Rayleigh propagation channel. Our proposed approach has the potential to significantly reduce the computational complexity and overhead typically associated with traditional channel estimation and equalization blocks. This can lead to more efficient and cost-effective wireless communication systems.

In the last few years, there has been a massive increase in Internet of Things (IoT) devices and the data generated from these appliances. Devices involved in IoT networks can be challenging because of their resource-constrained nature, and security integration's on these devices are frequently disregarded. This results in attackers targeting more IoT devices. Thus, as the number of possible attacks on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to deal with these attacks effectively. This paper presents a hybrid deep learning-based approach, a one- dimensional convolutional neural network, and long short-term memory (1D CNN-LSTM) algorithm, for anomaly detection that harnesses the power of the IoT, providing qualities to efficiently examine all traffic across the IoT. The comprehensive study was conducted utilizing the Bot-IoT dataset extracted from real network traffic, consisting of benign and malicious variants. Then, the anomaly detection including binary and multi-decision categories has been performed. The experimental results highlighted the superiority of the proposed model with an accuracy of 99.20% and lower false alarm with 0.80% compared to single CNN-based IDS.

The Internet of Things (IoT) has grown exponentially over the past decade, but this growth has also raised a number of issues for the ongoing operation of IoT applications, including resource limitations, server overload, and the risk of improper use of private data. To address these challenges, Blockchain technology, which initially powered the crypto-currency Bitcoin, is gaining recognition as a solution that can enhance security and privacy. Blockchain (BC) provides several essential features, such as a consensus approach, peer-to-peer communications, trust without the need for a third party, and transactions controlled by conditions and functions through the use of smart contracts. Thus, BC technology is a suitable candidate for building a decentralized, autonomous Internet of Things system that addresses the issues raised by IoT. In this paper, we propose Secure Smart Home Communications based on the Ethereum BC and Smart Contract as a new design to solve the dilemma between the limited resources of IoT devices and the concerns of a centralized architecture. Quantitative and qualitative evaluations of the architecture under common threat models have highlighted its effectiveness in providing security and privacy for IoT applications.

A resource-constrained unmanned aerial vehicle (UAV) can be used as a flying LoRa gateway (GW) to move inside the target area for efficient data collection and LoRa resource management. In this work, we propose deep reinforcement learning (DRL) to optimize the energy efficiency (EE) in wireless LoRa networks composed of LoRa end devices (EDs) and a flying GW to extend the network lifetime. The trained DRL agent can efficiently allocate the spreading factors (SFs) and transmission powers (TPs) to EDs while considering the air-to-ground wireless link and the availability of SFs. In addition, we allow the flying GW to adjust its optimal policy onboard and perform online resource allocation. This is accomplished through retraining the DRL agent using reduced action space. Simulation results demonstrate that our proposed DRL-based online resource allocation scheme can achieve higher EE in LoRa networks over three benchmark schemes.

In this paper, we propose a model for Delay Tolerant Networks (DTNs) based coalition and stable structure of all relay nodes to deliver a complete message from one source to one destination using the Epidemic Forwarding Policy. The message is viewed as a series of uniformly sized chunks that are produced by a fixed source. For a message to be considered successfully delivered, all of its chunks must arrive at the fixed destination within the validated time. The Age of Information (AoI) provides a deadline by which all message chunks must reach the destination to be considered timely. Mobile relays within the network will facilitate the transfer of chunks using an insensitive reward mechanism. We propose a distributed coalition algorithm that aims to establish a state of stability among all participating relays in the game. Through this algorithm, we were able to determine that the formation of stable coalitions results in higher payoffs for relay nodes compared to acting alone, as shown in our performance evaluation results.

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.

Most of the developed localization solutions rely on RSSI fingerprinting. However, in the LoRa networks, due to the spreading factor (SF) in the network setting, traditional fingerprinting may lack representativeness of the radio map, leading to inaccurate position estimates. As such, in this work, we propose a novel LoRa RSSI fingerprinting approach that takes into account the SF. The performance evaluation shows the prominence of our proposed approach since we achieved an improvement in localization accuracy by up to 6.67% compared to the state-of-the-art methods. The evaluation has been done using a fully connected deep neural network (DNN) set as the baseline. To further improve the localization accuracy, we propose a deep reinforcement learning model that captures the ever-growing complexity of LoRa networks and copes with their scalability. The obtained results show an improvement of 48.10% in the localization accuracy compared to the baseline DNN model.

The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training.

During the last decade, the integration of artificial intelligence (AI) and the use of intrusion detection systems (IDSs) in the Internet of Things(IoT) networks have brought a new dimension to technological progress. Deep learning (DL) and machine learning (ML)-based IDS are vulnerable to adversarial perturbations. However, anomaly detection methods suffer from unbalanced and missing sample data, thus causing IDS training to be complicated. In this paper, we propose using conditional generative adversarial networks (cGANs) to enhance the training process by handling the unbalanced data and coping with the lack of specifics class samples, which may succeed in evading our Convolutional Neural Network-Long Short-Term Memory (CNNLSTM) based-IDS model. We evaluated our proposed IDS model before and after applying the adversarial training using the Bot-IoT dataset. Promising results showed that the accuracy of detecting Theft attacks could be increased by 40%. To the best of our knowledge, we are the first to suggest the combination of cGAN and CNNLSTM based-IDS system to enhance its performance.

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause signal attenuation and crosstalk which degrades the system capacity and increases the error probability. In this paper, we propose to enhance the OAM FSO communications in terms of bit error rate and spectral efficiency, for different levels of AT regimes. The performance gain is achieved by introducing orthogonal frequency division multiplexing (OFDM) with index modulation technique to the OAM FSO system.

The accurate localization of wireless devices plays an important role in several real-time Internet of Things (IoT) applications. In a network composed of many IoT sensors, a distributed collaborative localization approach can give more accurate localization performance based on a decentralized and low-complexity processing. However, the presence of Non-Line of Sight links between IoT devices detrimentally impacts the localization accuracy. In this paper, we propose a distributed localization algorithm based on a convex relaxation of the Huber loss function. Moreover, to reduce the algorithm convergence time, an iterative stochastic gradient descent algorithm is proposed. Through numerical simulations, we show that the proposed algorithm when used with optimal relaxation parameters of the Huber loss function achieves very low root mean square error and outperforms existing algorithms in the literature. Finally, we validate our proposed scheme using real experimental data.

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause intermodal crosstalk and power disparities between OAM modes. In this paper, we are interested in multiple-input multiple-output (MIMO) coherent FSO communication systems using OAM multiplexing. We propose space-time (ST) coding at the transmitter to enhance the bit error rate (BER) performance against atmospheric turbulence. Through numerical simulations, we show performance improvement thanks to ST coded schemes for different MIMO dimensions. Furthermore, we derive an analytical expression for the error probability upper bound of the ST coded OAM FSO channel affected by atmospheric turbulence. The theoretical error probability is compared with Monte Carlo simulations and a good agreement is observed.

This paper proposes a unified statistical channel model to characterize the atmospheric turbulence induced distortions faced by orbital angular momentum (OAM) in free space optical (FSO) communication systems. In this channel model, the self-channel irradiance of OAM modes as well as crosstalk irradiances between different OAM modes are characterized by a Generalized Gamma distribution (GGD). The latter distribution is shown to provide an excellent match with simulated data for all regimes of atmospheric turbulence. Therefore, it can be used to overcome the computationally complex numerical simulations to model the propagation of OAM modes through atmospheric turbulent FSO channels. The GGD allows obtaining very simple tractable closed-form expressions for a variety of performance metrics. Indeed, the average capacity, the bit-error rate, and the outage probability are derived for FSO systems using single OAM mode transmission with direct detection. Furthermore, we extend our study to FSO systems using OAM mode diversity to improve the performance. By using a maximum ratio combining (MRC) at the receiver, the GGD is also shown to fit the simulated combined received optical powers. Finally, space-time (ST) coding is proposed to provide diversity and multiplexing gains, and the error probability is theoretically derived under the newly proposed generic model.