The widespread adoption of the Internet of Things (IoT) has led to a notable rise in the number of interconnected smart devices. Within this IoT environment, we perceive an immense diversity of IoT devices originating from numerous manufacturing entities. They differ in size, form, storage, and processing capability, as well as in their set of features, capabilities, and limitations. In this heterogeneous environment, one of the most arduous tasks is to guarantee security and privacy, especially for resource-constrained devices that possess limited power and low processing capability. Due to these limitations, these devices often lack robust security functions, rendering them prone to malevolent actors or unauthorized users who can access private information or alter device functions. In this context, one of the most critical security challenges in this domain is to accurately identify each device in an IoT network. The efficient management of the IoT ecosystem hinges on the ability to uniquely identify each connected device. This, in turn, simplifies the process of monitoring, updating, and troubleshooting these devices for administrators, contributing to the establishment of a trustworthy and secure IoT network. To cope with this challenge, surveying the existing approaches within the IoT identification literature is of paramount importance to provide the IoT research community with relevant insights into the latest achievements. Furthermore, we aim to provide a thorough and comprehensive study of the key literature related to IoT identification research by elaborating proposals that rely on various techniques covering the physical, network, and application layers of the IoT ecosystem. We conclude this survey by discussing the persistent challenges and unresolved issues that may impede the mainstream adoption of IoT, prohibiting several entities from fully leveraging the potential of this evolving technology.

The rapid growth of low power wide area networks (LPWANs) in unlicensed industrial, scientific, and medical (ISM) bands has significantly increased spectrum congestion, raising new challenges for coexistence monitoring and regulatory compliance. Existing datasets and simulators are largely designed for communication performance evaluation and lack the temporal continuity, regulatory context, and non-compliant behaviors required for spectrum enforcement tasks. This paper introduces a regulatory-aware, cross-layer spectrum simulator for the coexistence of long range (LoRa) and IEEE 802.15.4g systems in the 868 MHz band. The proposed framework generates continuous-time complex baseband I/Q sequences driven by stochastic traffic models and constrained by ETSI regulations. It combines PHY-accurate waveform synthesis with realistic channel effects and explicitly injects regulatory violations, including bandwidth, duty cycle, out-of-band emissions, and power exceedance. Our proposed simulator, SynSpec-EU868, produces labeled spectrum snapshots suitable for training data-driven spectrum monitoring and compliance detection models, providing a practical tool for next-generation regulatory-aware sensing systems.

In this paper, we provide a software -defined radio implementation for our transmission scheme proposed in [2] to secure the wireless transmission on a wiretap channel with a passive eavesdropper. We show that a message modulated with our BICM-IPM scheme can be correctly decoded at the legitimate receiver. However, the decoding of this message at the eavesdropper results in a near random hit stream.

Low Power Wide Area Networks (LPWANs) enable cost-effective, low power consumption, and long-range IoT applications but often operate under low Signal-to-Noise Ratio (SNR) conditions. Conventional deep learning models for signal recognition struggle to generalize in such dynamic environments. We propose a lightweight framework combining ensemble learning, a Mixture of Experts (MoE) with uncertainty-aware soft gating, and meta-adaptation using the Almost No Inner Loop (ANIL) method. Expert models trained at distinct lowSNR levels share frozen base weights, while only the classifier head is adapted during few-shot learning for rapid specialization. The uncertainty-aware gating mechanism produces sparse, temperature-controlled expert weights, enhancing robustness and decision reliability. Experiments demonstrate improved accuracy and generalization across both seen and unseen SNR levels, highlighting the framework's effectiveness for real-world wireless signal technology recognition in spectrum monitoring systems.

In this paper, we propose a new PHY layer trans-mission scheme for the uplink of a Single Input Multiple Output (SIMO) massive Machine Type Communication (mMTC). Our scheme is based on a Chirp Spread Spectrum (CSS) modulation at the transmitter side and a joint Maximum-Ratio Combiner (MRC) and Successive Interference Cancellation (SIC) decoder. One of the key characteristics of CSS signals is the linear variation of the frequency carrier over time, with symbol-dependent cutoff points. In the frequency domain, this temporal behavior leads to a peak power with magnitude and carrier-frequency position that are symbol-dependent. Indeed, adjacent carriers to the peak-carrier also hold a substantial amount of power. In a multi-user context, the non-homogeneous power-peak distribution across the different symbols biases the in-terpretation of the spectrum at the receiver side. Indeed, in a noisy environment, the high-power on the neighboring carriers of the peak-one induces erroneous decision. These two issues are further exacerbated by the SIC decoder as errors propagate from one device to another, creating a bottleneck. To address these challenges, we propose to (i) balance the power at the peak-carriers regardless of the symbol; (ii) refine the position of the peak-power index using a trellis-coded modulation (TCM). Our results show that the power balance combined with the TCM refinement of the peak-power index enhances the error performance rate of the SIC decoder.

In this paper, we propose a noisy and dynamic-index partitioned modulation (IPM) to secure a quadrature amplitude modulation (QAM) constellation transmission over a non-degraded wiretap channel. Unlike approaches that rely on multi-antenna systems or tight amplitude-phase tracking, IPM partitions QAM into multiple disjoint subsets that are separately indexed by a dynamic key, known at the transmitter (Alice) and the legitimate receiver (Bob), but not at the eavesdropper (Eve). The proposed IPM maps the information bits into multiple sequences, each one lying in a different partition of the QAM constellation space. This mapping is performed through a cross-bit labeling that we define to increase the confusion at the eavesdropper, while minimizing the bit error rate (BER) at the legitimate receiver. As the eavesdropper is not aware of the dynamic index pointing to the different partitions, the IPM creates larger Vorono & iuml; detection zones around Bob's symbol compared to Eve. To induce further confusion at Eve, we inject random uniform noise into IPM symbols. The noise varies in a domain that is fully included in Bob's larger detection zone, but it exceeds Eve's detection zone. The performances of the noisy IPM scheme in terms of error rate and secrecy rate are analytically and numerically evaluated. Our results show that the IPM scheme creates on the Eavesdropper's link an error floor, independently of its signal-to-noise ratio (SNR). However, the IPM scheme preserves the legitimate link, for which the BER decreases as the SNR increases. Indeed, the secrecy rate remains positive for all SNR values and achieves an asymptotical constant plateau value.

The use of Low-Power Wide Area Network (LPWAN) technologies, such as Long Range (LoRa), Sigfox, and IEEE 802.15.4g (ZigBee), has grown significantly, addressing a wide range of applications including smart metering, agriculture, smart homes, and healthcare. These technologies are valued for their simplicity, flexible connectivity, low power consumption, efficient modulation techniques, and moderate data rates. As a result, they can coexist within the same environment, serving either similar or distinct applications. However, the increasing deployment of devices and technologies has amplified the likelihood of interference between them, leading to performance degradation, particularly in real-world scenarios under challenging conditions where noise power surpasses signal power. The rapid proliferation of these technologies, especially within unlicensed Industrial, Scientific, and Medical (ISM) frequency bands, underscores the need for effective techniques to ensure seamless coexistence without disrupting communication. To address this challenge, we investigate the role of data representation and propose a Channel Attention-based Denoising Autoencoder U-Net and Classifier (UNA-DAEC). This model is designed to denoise multi-label LPWAN signals affected by white Gaussian noise and accurately classify overlapping transmissions, specifically IEEE 802.15.4g, Sigfox, and LoRa signals, within the same environment. The primary objective of UNA-DAEC is to achieve reliable signal classification in low Signal-to-Noise Ratio (SNR) conditions. This is achieved by first denoising the noisy signals to obtain optimal representations, enabling high classification accuracy with a single forward and backward propagation. Our results further demonstrate that data representation plays a critical role in identifying and classifying LPWAN signals, particularly in challenging low-SNR environments, with a significant performance of 44%, 7%, and 26% over CNN-based IQ, CNN-based FFT and DAE+Classifier methods, respectively, at -10 dB SNR.

Due to the rapidly increasing number of Internet-connected objects, a huge amount of data is created, stored, and shared. Depending on the use case, this data is visualized, cleaned, checked, visualized, and processed for various purposes. However, this data may encounter many problems such as inaccuracy, duplication, absence, etc. Such issues can be regarded as anomalies that deviate from a referential point, which can be caused by malicious attackers, abnormal behavior of systems, and a failure of devices, transmission channels, or data processing units. Anomaly detection is still one of the most important issues in cybersecurity, especially when it comes to system monitoring, automated forensics, and post-mortem analysis, which require anomaly detection mechanisms. In the literature, different approaches have been developed to detect anomalies, which can be classified as statistic-based, semantic-based, clustering-based, classification-based, and deep learning-based, depending on the algorithms used. This survey focuses on knowledge-based approaches, a sub-category of semantic-based approaches, as opposed to statistical/learning approaches. We provide a detailed comparison of the recent work in knowledge-based subcategories, namely, rule-based, score-based, and hybrid. We described the components of a knowledge-based system and the steps required to process raw data for anomaly detection. Furthermore, we have collected for each approach, when available, information about its semantic expressiveness, computational complexity, and application domain. Finally, we identify the challenges and discuss some future research directions in knowledge-based anomaly detection. Identifying such approaches and challenges can help cybersecurity engineers design better models that meet their application requirements.

The increase in the number of IoT devices invading both private and professional spaces opens the way for different cyberattacks. Facing such threats requires new approaches capable of reading the context of a given situation and acting accordingly to protect the users’ data and applications. Logical-based approaches can be harnessed in this case because of the semantic dimension they model and implement. In this paper, we make use of an existential rule-based knowledge base to model an IoT environment and detect anomalies as inconsistencies inside the obtained logical system. Accordingly, we first introduce an algorithm for knowledge base rewriting into a context-based knowledge base. Then, we detail our contextualized derivation algorithm for inconsistency detection in such a logical system.

In this paper, we propose a new transmission scheme, referred as Bit Interleaved Coded Modulation with Indexed Partitions (BICM-IPM), to secure the transmission on a wiretap channel with a passive eavesdropper. The BICM-IPM is designed to degrade the quality of QAM symbols transmission on a wiretap channel, while preserving the quality at the legitimate receiver. To achieve this, the QAM constellation is divided into several disjoint subsets, each one indexed by a channel-dependent dynamic secret key, known at the sender (Alice) and the legitimate receiver (Bob), but not at the eavesdropper (Eve). The symbols are then decoded at the legitimate receiver considering only the alphabet containing the symbols relative to the partition. However, in the absence of the index-partition knowledge at the eavesdropper, all the constellation symbols will be considered. Thus, the detection zones around Bob’s symbols will be larger than those at Eve. To further degrade the wiretap channel, we propose to inject a uniformly distributed random noise in a domain space totally included in Bob’s detection zone, but exceeding Eve’s one. Our analytical and numerical results show that the BICM-IPM creates a Bit Error Rate (BER) floor of 50% independent of the Signal-to-Noise Ratio (SNR) on the Eavesdropper’s link, while preserving the legitimate link for which the BER decreases with the SNR. This creates a maximal entropy at the eavesdropper side that is close to decoding a random binary sequence.

Due to mobility constraints, visually impaired people suffer from a poor social integration. To address this challenge, the objective of this project is to develop an autonomous and safe navigation solution for visually impaired individuals, guiding them to specific destinations, particularly in unfamiliar environments both indoor and outdoor. The initial focus is on establishing a precise and reliable localization strategy, which serves as a fundamental component for building such a solution. This project aims to address this critical aspect by integrating expertise in the needs of visually impaired individuals and assistive products, localization techniques, signal processing, AI-based algorithms, electronics, and knowledge of mobile network standards.

Channel adaptation physical layer security (PLS) schemes are degraded when the channel state information (CSI) is imperfect. Imperfect CSI is due to factors such as noisy feedback, outdated CSI, etc. In this paper, we propose a low-complexity noisy CSI denoising scheme based on the autoencoder architecture of deep neural networks referred to as DenoiseSec-Net. To further reduce complexity, we then propose a hybrid version (HybDenoiseSecNet) that combines a legacy denoising scheme and a shallow neural network to achieve a similar performance as DenoiseSecNet. Simulation results, in terms of bit error rate (BER), secrecy capacity, and normalized mean squared error (NMSE), show the performance improvement of our proposed scheme compared to conventional denoising schemes. Finally, we study the significant reduction in computational complexity of the proposed scheme compared to another neural network scheme.

In this paper, we study the secrecy energy efficiency (SEE) in an artificial noise (AN)-aided secure massive multiple-input multiple-output (MIMO) scheme. The scheme uses instantaneous information to design a peak-to-average power (PAPR)aware AN that simultaneously improves secrecy and reduces PAPR. High PAPR leads to non-linear in-band signal distortion and out-of-band radiation causing adjacent channel interference. To ensure optimal secrecy performance, high power amplifiers (HPAs) at the base station (BS) are backed off to operate in the linear region only. The amount of back-off needed to ensure linearity of the HPA has a direct impact on the energy efficiency of the system and by extension the SEE. For our scheme, the magnitude of this back-off is determined by the power allocation ratio between the data and AN. Hence, we propose an optimal power allocation ratio for the scheme. This is to ensure a good trade-off between the energy efficiency, security, and reliability of the system. Simulation results show a better SEE performance for our scheme compared to legacy massive MIMO schemes with or without random AN injection. Finally, we study the impact of spatially correlated Rayleigh fading on the proposed scheme.

This paper introduces a new approach to providing secure physical-layer massive multiple-input multiple-output (MIMO) based communications that can improve the energy efficiency of the system. This is achieved by synthesizing orthogonal artificial noise (AN) that has to be constrained to lie in the null space of the legitimate users’ channels while it should lie in the range space of the eavesdropper’s channel. In addition, this AN reduces the peak-to-average power ratio (PAPR) of the transmit signal. Indeed, low PAPR signals are preferable and more efficient for low-cost hardware, thus improving the energy consumption of massive MIMO systems. In this paper, we propose a new PAPR-aware precoding scheme based on the use of AN to enhance the secrecy performance of massive MIMO while reducing the PAPR of the transmit signal and guaranteeing excellent transmission quality for legitimate users. The scheme is formulated as a convex optimization problem that can be resolved via steepest gradient descent (GD). Accordingly, we developed a new iterative algorithm, referred to as PAPR-Aware-Secure-mMIMO, that makes use of instantaneous information to solve the optimization problem. Simulation results show the efficiency of our proposed algorithm in terms of PAPR reduction and secrecy, which is also studied with respect to power distribution between useful signal and AN, PAPR targets and the number of BS antennas.

Physical layer security (PLS) is an emerging paradigm that makes use of wireless channel characteristics to provide security. Many PLS schemes require knowledge of the channel state information (CSI). However, CSI is usually imperfect due to factors such as noisy feedback channels, channel estimation errors and outdated CSI. In this paper, we investigate the impact of imperfect CSI on the secrecy and error rate performances of a PLS scheme that combines adaptive matched-filter (MF) precoding and diversity in orthogonal frequency division multiplexing (OFDM). Particularly, we derive the secrecy capacity and error rate expressions for the legitimate and eavesdropper's channels under imperfect CSI assumption. The impact of the imperfect CSI is studied via theoretical as well as numerical techniques in frequency-selective Rayleigh fading wiretap channel. The analysis is done in both frequency division duplex (FDD) and time division duplex (TDD) modes.

In this paper, we focus on the radio resource planning in the uplink of licensed Orthogonal Frequency Division Multiple Access (OFDMA) based Internet of Things (IoT) networks. The average behavior of the network is considered by assuming that active sensors and collectors are distributed according to independent random Poisson Point Process (PPP) marked by channel randomness. Our objective is to statistically determine the optimal total number of Radio Resources (RRs) required for a typical cell. On one hand, the allocated bandwidth should be sufficiently large to support the traffic of the devices and to guarantee a low access delay. On the other hand, the over-dimensioning is costly from an operator point of view and induces spectrum wastage. For this sake, we propose statistical tools derived from stochastic geometry to evaluate, adjust and adapt the allocated bandwidth according to the network parameters, namely the required Quality of Service (QoS) in terms of rate and access delay, the density of the active sensors, the collector intensities, the antenna configurations and the transmission modes. The optimal total number of RRs required for a typical cell is then calculated by jointly considering the constraints of low access delay, limited power per RR, target data rate and network outage probability. Different types of networks are considered including Single Input Single Output (SISO) systems, Single Input Multiple Output (SIMO) systems using antenna selection or Maximum Ratio Combiner (MRC), and Multiuser Multiple Input Multiple Output (MU-MIMO) systems using Zero-Forcing decoder.

In this paper, we consider an IoT dedicated network corresponding to a non licensed LoRa Low Power Wide Area Network. The LoRa network operates in the unlicensed 868 MHz band within a total bandwidth of 1 MHz divided into 8 orthogonal channels of 125 kHz each. Despite the high level of interference, this network offers long range communications in the order of 2 to 5 km in urban areas and 10 to 30 km in rural areas. To efficiently mitigate this high level of interference, LoRa network essentially relies on a Chirp Spread Spectrum (CSS) modulation and on repetition diversity mechanisms. The LoRa CSS modulation spreads the signal within a band of 125 kHz using 6 possible spreading factors (from 7 to 12) to target data rates (starting from 5 kbps for the closest node to 300 bps for the furthest ones). The repetition diversity mechanisms enable the data recovery when the transmission is subject to bad channel conditions or/and high interference levels. Although the CSS modulation protects edge-cell's devices from the high level of interference induced by nodes in the proximity of the gateway, it fails to protect nodes at the edge of a given SF region and several trials are required to recover the packet. In this paper, we propose an adaptive multi-channels allocation policy that attributes multiple adjacent channels of 125 kHz for nodes situated at the edge of SF zones. We study the impact of this adaptive sub-band allocation on the gateways' intensities, the rate distribution and the power consumption. Our results are based on a statistical characterization of the interference in the network as well as the outage probability in a typical cell.

In this paper, we consider a Low Power Wide Area Network (LPWAN) operating in a licensed-exempt band. The LoRa network provides long-range, wide-area communications for a large amount of objects with limited power consumption. In terms of link budget, nodes that are far from the collector suffer collisions caused by nodes that are close to the collector during the data transmissions. Chirp Spread Spectrum (CSS) modulation is adopted by assigning different Spreading Factors (SF) to active sensors to help reduce destructive collisions in LoRa network. In order to improve the energy efficiency and communication reliability, we propose an application of multi-agent Q-learning algorithm in the dynamic allocation of power and SF to the active nodes for uplink communications in LoRa. The main objective of this paper is to reduce power consumption of the uplink transmissions and to improve the network reliability.