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.

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 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, requires 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 article 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 article 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.

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.

The applications of the unmanned aerial vehicles (UAVs) increase rapidly in everyday life, thus detecting the UAVs and/or its pilot is a crucial task. Many UAVs adopt frequency hopping spread spectrum (FHSS) technology to efficiently and securely communicate with their radio controllers (RCs) where the signal follows a hopping pattern to prevent harmful interference. In order to realistically distinguish the frequency hopping (FH) RC signals, one should consider the real-world radio propagation environment since many UAVs communicate with RCs from a far distance in which signal faces both slow and fast fading phenomenons. Therefore, in this study different from the literature, we consider a system that works under real- conditions by capturing over-the-air signals at hilly terrain suburban environments in the presence of foliages. We adopt the short-time Fourier transform (STFT) approach to capture the hopping sequence of each signal. Furthermore, time guards associated with each hopping sequence are calculated using the autocorrelation function (ACF) of the STFT which results in differentiating the each UAV RC signal accurately. In order to validate the performance of the proposed method, the results of normalized mean square error (MSE) respect to different signal-to-noise ratio (SNR), window size and Tx-Rx separation values are given.