Sixth-Generation (6G) wireless networks will interconnect billions of resource-constrained devices and time-critical services, where classical, fixed, and heavy cryptography strains latency and energy budgets and struggles against large-scale, pre-computation attacks. Physical-Layer Security (PLS) is therefore pivotal to deliver lightweight, information-theoretic protection, but still requires strong, reconfigurable confusion components that can be diversified per slice, session, or device to blunt large-scale precomputation and side-channel attacks. In order to address the above requirement, we introduce the first-ever chaos-lifted substitution box (S-box) for PLS that couples a $β$-transformation-driven dynamical system with dyadic conditional sampling to generate time-varying, seedable 8-bit permutations on demand. This construction preserves uniformity via ergodicity, yields full 8-bit bijections, and supports on-the-fly diversification across sessions. The resulting S-box attains optimal algebraic degree 7 on every output bit and high average nonlinearity 102.5 (85% of the 8-bit bound), strengthening resistance to algebraic and linear cryptanalysis. Differential and linear profiling report max DDT entry 10 (probability 0.039) and max linear probability 0.648, motivating deployment within a multi-round cipher with a strong diffusion layer, where the security-to-efficiency trade-off is compelling. Our proposed reconfigurable, lightweight S-box directly fulfills key PLS requirements of 6G networks by delivering fast, hardware-amenable confusion components with built-in agility against evolving threats.

This work investigates measurement strategies for link parameter estimation in Quantum Network Tomography (QNT), where network links are modeled as depolarizing quantum channels distributing Werner states. Three distinct measurement schemes are analyzed: local Z-basis measurements (LZM), joint Bell-state measurements (JBM), and pre-shared entanglement-assisted measurements (PEM). For each scheme, we derive the probability distributions of measurement outcomes and examine how noise in the distributed states influences estimation precision. Closed-form expressions for the Quantum Fisher Information Matrix (QFIM) are obtained, and the estimation precision is evaluated through the Quantum Cramer-Rao Bound (QCRB). Numerical analysis reveals that the PEM scheme achieves the lowest QCRB, offering the highest estimation accuracy, while JBM provides a favorable balance between precision and implementation complexity. The LZM method, although experimentally simpler, exhibits higher estimation error relative to the other schemes; however, it outperforms JBM in high-noise regimes for single-link estimation. We further evaluate the estimation performance on a four-node star network by comparing a JBM-only configuration with a hybrid configuration that combines JBM and LZM. When two monitors are used, the JBM-only strategy outperforms the hybrid approach across all noise regimes. However, with three monitors, it achieves a lower QCRB only in low-noise regimes with heterogeneous links. The results establish a practical basis for selecting measurement strategies in experimental quantum networks, enabling more accurate and scalable link parameter estimation under realistic noise conditions.

Massive number of simultaneous Internet of Things (IoT) uplinks strain gateways with interference and energy limits, yet devices often lack neighbors' Channel State Information (CSI) and cannot sustain centralized Mobile Edge Computing (MEC) or heavy Machine Learning (ML) coordination. Classical Bayesian solvers help with uncertainty but become intractable as users and strategies grow, making lightweight, distributed control essential. In this paper, we introduce the first-ever, novel epistemic Bayesian game for uplink power control under incomplete CSI that operates while suppressing interference among multiple uplink channels from distributed IoT devices firing at the same time. Nodes run inter-/intra-epistemic belief updates over opponents' strategies, replacing exhaustive expected-utility tables with conditional belief hierarchies. Using an exponential-Gamma SINR model and higher-order utility moments (variance, skewness, kurtosis), the scheme remains computationally lean with a single-round upper bound of $O\!\left(N^{2} S^{2N}\right)$. Precise power control and stronger coverage amid realistic interference: with channel magnitude equal to $1$ and a signal-to-interference-plus-noise ratio (SINR) threshold of $-18$ dB, coverage reaches approximately $60\%$ at approximately $55\%$ of the maximum transmit power; mid-rate devices with a threshold of $-27$ dB achieve full coverage with less than $0.1\%$ of the maximum transmit power.Under $80\%$ interference, a fourth-moment policy cuts average power from approximately $52\%$ to approximately $20\%$ of the maximum transmit power with comparable outage, outperforming expectation-only baselines. These results highlight a principled, computationally lean path to optimal power allocation and higher network coverage under real-world uncertainty within dense, distributed IoT networks.

Efficient and reliable resource allocation within densely-deployed massive IoT networks remains a key challenge due to resource constraints among low size, weight and power (SWaP) IoT devices and within the network and limitations of conventional centralized methods under incomplete information. We propose a novel auction-based framework for adaptive resource allocation, combining space-time-frequency spreading (STFS) techniques with Bayesian Game approaches. We introduce novel modified Simultaneous Ascending Auction (mSAA) mechanism tailored to densely-deployed and low-complexity IoT networks, enabling distributed computation and reduced power consumption. By incorporating Bayesian game-based bidding strategies and optimizing dispersion matrices for signal transmission, the proposed approach ensures enhanced channel throughput and energy efficiency. Comparative analysis against traditional auction types, including First-Price and Second-Price Sealed-Bid Auctions, as well as the Vickrey–Clarke–Groves (VCG) mechanism, demonstrates the superiority of mSAA in terms of surplus maximization, revenue efficiency, and robustness in risk-prone bidding environments. Simulation results validate the model’s adaptability to heterogeneous IoT nodes and its potential for dense deployment across different environments and verticals.

The dairy industry is experiencing a surge in data from Edge devices, using spectroscopic techniques for milk quality assessment. Milk spectral data can help understand the species of milk producer and detect inter-species adulteration. Transmitting raw milk spectral data to the cloud for processing faces challenges due to limited network resources such as bandwidth, computational memory, and energy availability. Edge processing offers a solution by training data closer to the source, enhancing efficiency and real-time analysis by providing reduced latency, improved accuracy, resource-aware computation, and real-time customization. However, traditional Deep Learning (DL) methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) struggle on resource-constrained Edge devices due to complexity. To address this, we propose an Edge-Centric Application-Adaptive Light-Weight DL approach (AppAdapt-LWDL) for milk species identification and adulteration detection. Our method optimizes DL models via double model optimization, involving low-magnitude pruning and post-training quantization. Our novel application-adaptive algorithm balances speed and accuracy by determining the pruning ratio automatically for the specific application. The chosen model is then quantized for smaller databases, ideal for embedded devices. The AppAdapt-LWDL framework significantly accelerates training, speeds up inferencing, enhances energy efficiency, and maintains accuracy based on application needs.

Efficient resource allocation (RA) strategies within massive and dense Internet of Things (IoT) networks is one of the major challenges in deployment of IoT-network based smart ecosystems involving heterogeneous power-constrained IoT devices operating in varied radio and environmental conditions. In this paper, we focus on the transmit power minimization problem for IoT devices while maintaining a threshold channel throughput. The established optimization literature is not robust against the fast-fading channel and the interaction among different transmit signals in each instance. Besides, realistically, each IoT node possesses incomplete channel state information (CSI) on its neighbors, such as the channel gain being private information for the node itself. In this work, we resort to Bayesian game theoretic strategies for solving the transmit power optimization problem exploiting incomplete CSIs within massive IoT networks. We provide a steady discussion on the rationale for selecting the game theory, particularly the Bayesian scheme, with a graphical visualization of our formulated problem. We take advantage of the property of the existence and uniqueness of the Bayesian Nash equilibrium (BNE), which exhibits reduced computational complexity while optimizing transmit power and maintaining target throughput within networks comprised of heterogeneous devices.

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system’s perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.

Industrial Internet-of-Things (IIoT) involve multiple groups of sensors, each group sending its observations on a particular phenomenon to a central computing platform over a multiple access channel (MAC). The central platform incorporates a decision fusion center (DFC) that arrives at global decisions regarding each set of phenomena by combining the received local sensor decisions. Owing to the diverse nature of the sensors and heterogeneous nature of the information they report, it becomes extremely challenging for the DFC to denoise the signals and arrive at multiple reliable global decisions regarding multiple phenomena. The industrial environment represents a specific indoor scenario devoid of windows and filled with different noisy electrical and measuring units. In that case, the MAC is modelled as a large-scale shadowed and slowly-faded channel corrupted with a combination of Gaussian and impulsive noise. The primary contribution of this paper is to propose a flexible, robust and highly noise-resilient multi-signal transmission framework based on Wavelet packet division multiplexing (WPDM). The local sensor observations from each group of sensors are waveform coded onto wavelet packet basis functions before reporting them over the MAC. We assume a multi-antenna DFC where the waveform-coded sensor observations can be separated by a bank of linear filters or a correlator receiver, owing to the orthogonality of the received waveforms. At the DFC we formulate and compare fusion rules for fusing received multiple sensor decisions, to arrive at reliable conclusions regarding multiple phenomena. Simulation results show that WPDM-aided wireless sensor network (WSN) for IIoT environments offer higher immunity to noise by more than 10 times over performance without WPDM in terms of probability of false detection.

A Bayesian game theoretic model is developed to dynamically select channel sensing intervals in a massively dense network of Internet of Things. In such networks, the core objective is to minimize every node's energy consumption while having incomplete information about other nodes actively communicating in the network. Selecting channel sensing intervals in a medium access control (MAC) protocol is absolutely crucial, especially in massively dense networks, and selecting intelligently these intervals can optimize the overall network energy consumption while also minimizing latency during the information transfer. In the proposed model, a sensing interval chosen by a node is dynamically derived using current and previous incoming traffic patterns at other nodes in the vicinity. This paper shows that formulating the problem of channel sensing intervals as a Bayesian game model can extensively improve the performance of a MAC protocol when incorporating information from other nodes within the network.

The ubiquity of sensor technology and the Internet of Things prompted us to propose to develop a real-time digital dashboard to visualize the anxiety risks of populations during a pandemic, as in the case of COVID-19. To this end, here we provide an end-to-end communication architecture to detect physiological data related to heart rate, blood pressure, and SPO2, using wearable sensors and communicate them to remote servers. Based on this collected data, the centralized dashboard will classify in real time the patients of each geographic region involved according to a specific attribute, i.e., normal, mild, moderate, high, severe, or extreme. In addition, we also propose to incorporate the emerging technologies of Space Time Frequency Spreading (STFS) and Space-Time Spreading-Aided Indexed Modulation (STS-IM) for the design of the communication links. It has been found that the integration of STFS and STS-IM promises to reduce the likelihood of data disruption for the proposed architecture.

COVID-19 affects the mental health of many people around the world. In particular, isolation situations due to lockdown have become more challenging for elderly people as they have limited access to technology. At the same time, technologies of remote systems using Internet of Things (IoT) have emerged as a pivotal role for healthcare management and therefore could assist the elderly in managing and improving their mental health and quality of life. In this paper, we suggest the use of wearable devices with health sensors, therapeutic music playback devices, and a cloud-based data collection system as an integrated Internet of Things architecture capable of assessing the level of anxiety and depression of elderly people. Through an accurate monitoring and reporting of the measured temperature, pulse rate, and SpO2, the system is capable of assessing the level of anxiety and depression and triggers the playback of therapeutic music to reduce the level of stress and anxiety. The implementation of the system, using NodeMCU platform, and space time spreading (STS)-aided communication links, emerges as a promising solution to help the healthcare sector and families to manage and reduce the anxiety and depression risks among elderly population.

Today, various mobile applications and wearable devices support the management of diabetes by offering early and remote monitoring facilities. However, most of the available products recommend the activity/exercise level for patients based on standard data about the impact of exercise on calories burnt and blood Glucose levels. There is a risk associated with such products due to lack of customization to the individual patients. In this paper, we propose to use an Internet of Medical Things (IoMT) architecture to predict the level of activity required each day by the patient to maintain the recommended level of blood Glucose. We compare the performance of Artificial Neural Network (ANN) and Support Vector Machine (SVM) for their prediction accuracy. The proposed model takes pre-exercise Glucose level as input parameter and recommends the duration and intensity of the physical activity required by the patient each day. ANN has been observed to perform better for its classification accuracy.

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system.

In this paper, we investigate the practical implication of employing virtual massive multiple-input-multiple output (MIMO) based distributed decision fusion (DF) for collaborative wideband spectrum sensing (WSS) in a cognitive radio (CR)-like network. Towards that end, an indoor-only measurement campaign has been conducted to capture the propagation statistics of a 4 × 64 massive MIMO system with one authorized primary user (PU) and 4 unauthorized secondary users (SUs) transmitting simultaneously over a 20 MHz band divided into 1200 subcarriers. The frequency subcarriers belong to an Orthogonal-frequency-division-multiplexing (OFDM)-like set-up without the addition of cyclic prefix (CP) to the transmit symbols. Measurements are accumulated for different relative positions of the SUs which are analyzed to extract fading, shadowing, noise and interference power statistics. Log-likelihood ratio (LLR) based fusion rule and three different sets of sub-optimum fusion rules along with their time-reversed versions are formulated for combining decisions on the availability of each subcarrier transmitted by the SUs. The extracted channel characteristics are incorporated in both analytical and simulated performance analysis of the devised fusion rules for comparison and testing the validity of distributed DF in realistic collaborative WSS scenario.

In this paper, in order to tackle major challenges of spectrum exploration & allocation in Cognitive Radio (CR) networks, we apply the general framework of Decision Fusion (DF) to wideband collaborative spectrum sensing based on Orthogonal Frequency Division Multiplexing (OFDM) reporting. At the transmitter side, we employ OFDM without Cyclic Prefix (CP) in order to improve overall bandwidth efficiency of the reporting phase in networks with high user density. On the other hand, at the receiver side (of the reporting channel) we device the Time-Reversal Widely Linear (TR-WL), Time-Reversal Maximal Ratio Combining (TR-MRC) and modified TR-MRC (TR-mMRC) rules for DF. The DF Center (DFC) is assumed to be equipped with a large antenna array, serving a number of unauthorized users competing for the spectrum, thereby resulting in a “virtual” massive Multiple-Input Multiple-Output (MIMO) channel. The effectiveness of the proposed TR-based rules in combating (a) inter-symbol and (b) inter-carrier interference over conventional (non-TR) counterparts is then examined, as a function of the Signal-to-Interference-plus-Noise Ratio (SINR). Closed-form performance, in terms of system false-alarm and detection probabilities, is derived for the formulated fusion rules. Finally, the impact of large-scale channel effects on the proposed fusion rules is also investigated, via Monte-Carlo simulations.

This paper presents a detailed study for indoor wireless environments, where transmit power, rate and target bit error rate (BER) are varied to increase spectral efficiency. The study is conducted for the recently proposed joint fading and two-path shadowing (JFTS) channel model, which is shown to be accurate for modeling non-Gaussian indoor WLAN environments. Analysis is done for both average and instantaneous BER constraints without channel coding, where only a discrete finite set of constellations is available. Numerical results show that, for a JFTS channel i) varying only the transmission rate (modulation constellation size) achieves more improvement in spectral efficiency compared to varying transmit power only, and ii) varying rate and/or power subject to instantaneous BER (I-BER) constraint offers better performance than when subject to average BER (A-BER) constraint.

This paper presents a compact circuit design for implementing full-duplex relays serving network architectures envisioned for 5G applications. The proposed design prevents the transmit signal from interfering with the received signal through signal inversion. Signal inversion is accomplished through a compact design based on parametric amplifier circuit which operates simultaneously in two different modes, re-transmit and demodulate. This design also has an added advantage of being capable of generating phase difference between the transmitted and received signals by controlling the local oscillator signal. The validity of the design is evaluated against an example Smart Grid architecture, where it is employed to function as amplify-and-forward full-duplex relays/repeaters for serving several communication links. Simulation results indicate that full-duplex mode outperforms half-duplex one in terms of average channel capacity as well as bit error rate irrespective of the position of the relays with respect to distance from source/destination.

Performance of common digital modulation techniques is analyzed over indoor wireless environments modeled through the recently proposed joint fading and two-path shadowing (JFTS) channel model. Mathematically tractable expressions for the instantaneous signal-to-noise ratio statistics, average bit error rates, and achievable channel cutoff rates are derived. Analytical results are used to: 1) investigate the impact of different JFTS model parameters and different modulation techniques on bit error rates and cutoff rates and 2) demonstrate how the JFTS channel model affects system performance in comparison with conventional empirical channel models. Finally simulation results are used to corroborate this analysis and evaluate the usefulness of such an analysis.

The performance of rate adaptive trellis-coded and uncoded M-ary quadrature amplitude modulation (M-QAM) over large open office indoor wireless environments is studied in this paper. An appropriate composite fading/shadowing channel model termed the joint fading and two-path shadowing (JFTS) model is adopted for such an indoor wireless environment, where mobility of users remains constrained within a small space. Mathematically tractable expressions for the spectral efficiency and average bit error rate (ABER) of adaptive coded and uncoded M-QAM over the JFTS channel are derived. Numerical results demonstrate that in contrast to conventional fading models like Rayleigh and Nakagami-m distributions, the error probability performance of rate adaptive M-QAM over a JFTS faded/shadowed link never approaches zero for lower channel signal-to-noise ratios (CSNRs).

The performance of rate adaptive M-ary quadra- ture amplitude modulation (M-QAM) and adaptive trellis-coded M-QAM in large open office indoor wireless environments is studied in this paper. An appropriate composite fading/shadowing channel model termed the Joint Fading and Two-path Shadowing (JFTS) model is adopted for the indoor wireless environment. Mathematically tractable expressions for the spectral efficiency and average bit error rate (ABER) of adaptive coded and uncoded M-QAM over the JFTS channel are derived. Analytical results demonstrate the performance of adaptive M-QAM over different JFTS channel configurations, and simulation results corroborate the derived analytical ABER expressions.

The performance of Turbo coded Binary Phase Shift Keying (BPSK) over the Joint Fading and Two-path Shadowing (JFTS) environment is analyzed. Exact analytical expression for the Pairwise Error Probability (PEP) of Turbo codes over fully interleaved JFTS channels is presented using the probability distribution of the squared independent and identically distributed JFTS random variables. Finally the Turbo coded Average Bit Error Rate (ABER) of a wireless communication system is simulated over JFTS fading / shadowing channels and compared with the derived analytical result.

A new expression for the Cumulative Distribution Function (CDF) of the Joint Fading and Two-path Shadowing (JFTS) distribution is derived in this paper. The derived theoretical CDF expression is shown to agree with the experimental results. The CDF expression is used to derive an outage expression for non-diversity transmission which is then used to illustrate the performance of adaptive transmission over the JFTS link.

The performance of Binary Phase Shift Keying (BPSK) in a Joint fading and Two-path Shadowing (JFTS)environment is analyzed. Closed form expression for the exact Average Bit Error Rate (ABER) of coherent BPSK scheme is presented using the Moment Generating Function (MGF) of the JFTS distribution. Finally the coherent BPSK ABER of a wireless communication system is simulated over JFTS fading / shadowing channels with and without non- iterative Forward Error Correction (FEC) coding and compared with the analytical ABER expression.

We develop a semi-blind adaptive space-time shift keying (STSK) based multiple-input multiple-output system using a low-complexity iterative channel estimation and data detection scheme. We first employ the minimum number of STSK training blocks, which is related to the number of transmitter antennas, to obtain a rough least square channel estimate (LSCE). Low-complexity single-stream maximum likelihood (ML) data detection is then carried out based on the initial LSCE and the detected data are utilised to refine the decision-directed LSCE. We show that a few iterations are sufficient to approach the optimal ML detection performance obtained with the aid of perfect channel state information.