This paper proposes a multi-user integrated sensing and communication (ISAC) framework based on coded generalized spatial shift keying (MU-CGSSK) that enables secure, interference-free downlink transmission alongside high-resolution radar sensing. The system adopts a separated deployment model, allowing simultaneous communication and sensing over a shared frequency band using co-located antenna arrays. A systematic linear block code is employed to generate structured antenna activation patterns that ensure low bit error rates and optimal MU separation. A key feature is the proposed two-stage physical layer security (PLS) scheme. In the first stage, a channel state information (CSI)-based precoder eliminates MU and radar-induced interference while generating implicit jamming against eavesdroppers. The second stage enhances confidentiality under partial CSI leakage by applying lightweight scrambling to antenna index mappings. An alternating optimization algorithm jointly designs the precoding vector and power allocation to maximize the achievable secrecy sum-rate. Simulation results demonstrate that the MU-CGSSK ISAC system achieves reliable MU communication, accurate multi-target detection using the MUSIC algorithm, and robust secrecy performance under asymmetric channel conditions.

This paper proposes a precoding-aided generalized space shift keying (GSSK)-based integrated sensing and communication (ISAC) framework for reliable multi-user downlink transmission, high-resolution radar sensing, and enhanced physical-layer security against a passive eavesdropper. Unlike conventional GSSK, where only $N_{a}$ out of $N_{t}$ antennas are active, the proposed design assigns non-zero coefficients to the remaining $(N_{t}-N_{a})$ antennas and transmits communication and sensing components through a unified vector. A precoder is derived to suppress multi-user interference and cancel radar-induced interference, preserving the BER of conventional multi-user GSSK. The resulting transmit covariance remains diagonal and proportional to the identity matrix, enabling ESPRIT-based DoA estimation and evaluation under distributed clutter. To improve confidentiality, the radar coefficients are optimized to maximize the achievable secrecy sum rate under a total power constraint. Simulations results show identical BER and DoA accuracy to conventional GSSK under perfect CSI, while significantly improving the achievable secrecy sum rate.

The increasing demand for secure and timely information delivery in short-packet communication systems highlights the need for metrics that jointly capture data freshness and confidentiality. Existing measures, such as the secrecy rate or secrecy age, fail to characterize the freshness of securely received updates. Building upon recent works, we adopt the age of secure information (AoSI) framework and extend it to multi-eavesdropper Rayleigh fading under a retransmission-based policy. We also introduce performance loss metrics to quantify the trade-off between AoI-optimal and AoSI-optimal designs. Numerical results validate the analytical derivations and reveal the secrecy and freshness trade-off through comparative analysis of AoI, secrecy age, and AoSI.

This paper presents a multi–phase classification framework for identifying applications from encrypted network traffic. The proposed model employs deep learning-–based classifiers organized in a phased ensemble structure guided by majority voting. Each classifier operates on a distinct number of packets within a flow, contributing progressively refined predictions as more packet data become available. The ensemble prediction is iteratively updated by integrating outputs from classifiers trained on fewer packets, enabling accurate early classification with limited input. The model is evaluated on a recent dataset comprising real network flows from diverse applications, with comprehensive comparisons across different classification architectures and voting strategies. Experimental results demonstrate the effectiveness and adaptability of the proposed approach for encrypted traffic classification.

Security is a critical element in the design of communication systems. However, despite its essential role in enabling low-cost implementations and robust theoretical foundations, physical layer security is often overlooked in developing countermeasures against eavesdropping. In this work, we propose practical physical-layer countermeasures that enhance secrecy with low computational overhead. Our approach decomposes the constellation diagram using basis vectors, constructing the original message symbol in the main lobe aimed at the intended receiver, while projecting randomized constellation points in other directions to mislead potential eavesdroppers. When the eavesdropper’s location is known, we use static basis vectors, achieving a higher level of secrecy in that direction. When the location is unknown, we dynamically change the basis vectors to provide information-theoretic security in all directions through basis vector randomization. This design enables the intended receiver to operate without knowledge of the basis vectors, reducing receiver overhead. We evaluate the scheme’s secrecy performance through bit error rate and secrecy rate analyses, demonstrating its potential to improve physical layer security.

This paper addresses the need for enhanced control over signal propagation in wireless communication using reconfigurable intelligent surfaces (RIS). We propose RIS-assisted backscatter communication (BC) systems, introducing a linear pre-processing-based spatial modulation (SM) technique for data transmission. Our work focuses on signaling design, phase shift optimization, and bit error rate (BER) evaluation through Monte-Carlo simulations. Results demonstrate that RIS-assisted pre-processing in BC enhances transmission reliability and energy efficiency, offering a promising solution for next-generation low-power communication systems.

This paper proposes a joint communication and sensing (JCS) system that integrates multiple-input multipleoutput (MIMO) communication and radar functionalities within a shared spectrum. A novel precoder design incorporating Maximal Ratio Combining (MRC) is proposed to eliminate radar-induced and multi-user interference (MUI), ensuring robust communication while maintaining radar sensing accuracy. The communication subsystem leverages spatial shift keying (SSK) to enhance spectral efficiency, while the radar employs a co-located MIMO configuration for precise target detection. Simulation results show that the proposed system achieves a bit error rate (BER) below 10(-2) at 20 dB signal-to-noise ratio (SNR) and a radar detection probability exceeding 90% at 5 dB SNR, validating its effectiveness in interference management. This approach enables seamless integration of communication and sensing, making it a promising solution for autonomous driving, smart cities, and next-generation wireless networks.

In this paper, we explore the optimal use of network coding in multi-source multi-hop Internet of things (IoT) networks, focusing on minimizing the average age of information (AoI) under various system parameters. Specifically, we determine the optimal number of packets for performing network coding at intermediate nodes (servers) to optimize the average AoI performance. Through extensive simulations, we demonstrate how frequency of network coding impacts data freshness across a range of scenarios, including different numbers of sources, transmission success probabilities, and computational capacities. Simulation results provide practical insights into the deployment of network coding in real-world IoT applications, shedding light on the design process of how to implement network coding and highlighting its potential to significantly improve timeliness in data delivery.

In this work, a novel method based on matrix profile and machine learning is proposed for anomaly and attack detection in network traffic. Traditional network security systems are often based on signature-based methods, which are inadequate in detecting unknown attacks. The proposed approach utilizes matrix profile, a powerful tool for time series analysis, to identify anomalies in network traffic and classify these anomalies using machine learning models. In the proposed method, the matrix profile is applied to the selected feature set to highlight anomalies. The proposed information-theoretic features play a crucial role in distinguishing attacks. The results obtained on a publicly available dataset demonstrate that the proposed method achieves high accuracy.

Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and ethics. There is an urgent need for action in cities, particularly to (i) enhance the health and wellbeing of urban residents while ensuring inclusivity in urban development (e.g., through the intelligent design of public spaces, mobility, and transportation) and (ii) improve resilience and sustainability (e.g., through better disaster management, planning of city logistics, and waste management). This paper aims to explore how neuroscientific and neurotechnological solutions can contribute to the development of smart cities, as experts in various fields underline that real-time sensing designs and control algorithms inspired by the brain could help build and plan urban systems that are healthy, safe, inclusive, and resilient. Motivated by the potential interplay between societal challenges and these emerging technologies, we provide an overview of state-of-the-art research through a bibliometric analysis of neurochallenges within the context of smart cities using terms and data extracted from the Scopus database between 2018 and 2022. The results indicate that smart city research remains fragmented and technology-driven, relying heavily on internet of things (IoT) and artificial intelligence (AI)-based technologies. Mostly, it also lacks careful integration and adoption tailored to societal goals and human-centric concerns. In this context, the article explores key research streams and discusses how to create new synergies and complementarities in the challenge-technology intersection. We conclude that realizing the vision of smart cities at the nexus of neuroscience, technology, urban space, and society requires more than just technological progress. Integrating the human dimension alongside various technological tools and systems is crucial. This necessitates better interdisciplinary collaboration and co-production of knowledge toward a hybrid intelligence, where synergies of education and research, technological innovation, and societal innovation are genuinely built. We hope the insights from this analysis will help orient neurotechnological interventions on urban living and ensure they are more responsive to societal and environmental challenges as well as to legal and ethical concerns.

We consider the timeliness in delivering an update consisting of multiple message packets to multiple users via multicast transmissions with or without employing network coding where Age of Information (AoI) is adopted to quantify the timeliness of packets. The expressions for peak and average expected AoIs are analytically derived for both uncoded and network coded transmissions for both 2-user and generalized k-users scenarios where the computational burden stemming from network coding is taken into account. The behavioral analyses of a number of network parameters are investigated, and the effect of data rate and computational capacity of nodes is analyzed. Simulations are performed for various Internet of Things (IoT) deployments, and the analyses suggest that the use of network coding for multicast transmissions can result in substantial AoI improvements, with the exception of scenarios in which sensors have extremely limited computational capabilities.

In recent years, the increasing demand to see the status of objects over the internet leads to an increase in the number of Internet of things (IoT) applications. The unique nature of IoT, which involves potentially millions of interconnected devices with different data rate, power, bandwidth and range specifications, require different performance metrics than those conventionally employed in other communication applications. In conventional wireless communication systems such as cellular networks, performance indicators including data rate and spectral efficiency have become decisive, whereas in energy-constrained real-time IoT applications which require low data rate, the freshness of information has become a more prominent characteristic. Age of information (AoI), which is the elapsed time after the last received packet update was created at the source, has emerged as a fundamental metric for determining the freshness of information and has attracted substantial research interest. In this regard, this paper is dedicated to provide an overview of the current state-of-the-art on the use of AoI for the design and optimization of a large variety IoT applications. After a brief introduction of the IoT and AoI fundamentals, this paper presents a survey of the research works on common design issues such as AoI based optimization, scheduling for IoT networks, application of learning methods in large scale IoT systems, real life applications and experimental results together with a synopsis of potential future applications and research challenges.

This work explores the age of information (AoI) impact of block coding with maximum-likelihood decoding (MLD), along with the consideration of polar coding with successive-cancellation (SC) decoding as a realizable subcase by employing an Internet of things (IoT) network, where sensors transmit data over discrete memoryless binary symmetric or erasure channels. A general lower bound for block coding is derived along with the upper bounds depending on the coding schemes. Simulation results indicate that block coding can theoretically outperform no coding up to 20 - 25% in terms of AoI depending on the block-length of the code. Suitable conditions in polar coding to converge to theoretical MLD performance are discussed.

Backscatter communications (BCs) have gained significant attention in recent years, especially within the realm of Internet of Things (IoT), due to their ability to operate in a battery-free manner, maintaining the low-power and low-cost structure. The utilization of multiple antennas in BC is becoming increasingly recognized as a promising strategy to deliver robust communication performance, particularly in applications with high data demands. In this work, an innovative BC system is introduced which is based on tag-side preprocessing-aided receive spatial modulation (PSM). It includes the design of both zero-forcing (ZF) and minimum mean-squared-error (MMSE) precoders for the transmitter tags, which enables the activation of a single receive antenna at the reader node (RN). Transmitting information via receive antenna indices presents several advantages when compared to other spatial modulation (SM)-based benchmark approaches employed for BC, such as reduced complexity, enhanced energy efficiency, superior bit error rate (BER) performance, and robustness to channel estimation errors. These advantages are shown via both theoretical analysis and also simulation results in this article.

We propose a novel scheduling strategy for prioritization-critical time-sensitive Internet of things (IoT) networks. The goal is to decrease the number of sensors with outdated information and minimize the difference between their age of information (AoI) and the specified limit, within a resource-constrained environment. The proposed approach relies on reformulating the original problem as a knapsack problem. This novel method is shown to outperform benchmarks to satisfy the needs of priority-aware IoT networks.

We investigate the timeliness in delivering updates within a multisource multihop Internet of Things (IoT) network via multicast transmissions with or without employing network coding, using a completely probabilistic model. Age of Information (AoI) is adopted to quantify the timeliness of packets. Extensive simulation results, which corroborate the theoretical findings, demonstrate that in scenarios where the number of sources is high, the number of intermediate nodes relaying to monitors is low, there are multiple monitors, the transmission success probability is low, and computational resources are sufficient, the utilization of network coding has a great potential to improve the data-freshness in multisource multihop IoT networks which closely represent the spine of the real-life scenarios.

Non-orthogonal multiple access (NOMA) technique is a promising alternative for enhancing network sum-rate while increasing the interference experienced by the users. In heterogeneous networks, the signal of a NOMA user is affected by three types of interference; intra-cell, inter-cell, and inter-tier. In an environment with high interference, using orthogonal multiple access (OMA) along with NOMA is beneficial to improve the edge user performance. This work proposes a hybrid model, where NOMA and OMA are deployed together in the same spectrum band. The performance of hybrid model is compared to NOMA and OMA for heterogeneous networks by stochastic geometry analysis. The analysis includes pico- and femto-cell alternatives to elaborate varying interference conditions. Using the capacity analysis formulation of NOMA, a power level optimization framework is presented for maximum sum-rate and fairness objectives. Besides, two pairing methods, near-to-near and near-to-far pairing, are included from the literature to reveal pairing effect on user-specific and system-level performance. The numerical results indicate NOMA trade-offs for increasing edge user capacity by power optimization and resistance of hybrid model to interference under different conditions.

Controlling the events occurring in the network traffic and detecting malicious activities are of great importance for the security and sustainability of the system. For this reason, it is necessary to accurately detect different types of attacks that may occur in network traffic. On the other hand, it is very important from the security aspect to be able to distinguish the types of attacks that have not been seen before. In this paper, a two-step procedure is proposed that can both correctly classify known attack types and distinguish unknown attack types. In the first stage, incoming traffic is classified by supervised learning with an autoencoder. In the second stage, the reliability of this classification is checked with the help of the autoencoder and the Extreme Value Theorem (EVT). According to the reliability value, incoming traffic is classified as unknown class. The simulation results were obtained with the IDS 2017 data set, which is widely used in the literature.

A precoding approach is proposed in this paper, for quadrature spatial modulation (QSM) over correlated and uncorrelated fading channels. This method is based on phase-rotation and/or amplitude scaling of the transmitted symbols according to the active transmit antenna(s) and is done in order to provide robustness against both the Rayleigh/Rician fading effects and also spatial correlations among transmit antennas. Results indicate that optimal precoding compensates for the vulnerability of the antenna index bits against direct line-of-sight (LOS) channel components and/or heavy inter-antenna correlations at the transmitter and improves the performance compared to conventional QSM case.