This research addresses the accuracy issues in IoT-based human activity recognition (HAR) applications, essential for health monitoring, elderly care, gait analysis, security, and Industry 5.0. This study uses 12 machine learning approaches, split equally between support vector machine (SVM) and k-nearest neighbor (k-NN) models. Data from 102 individuals, aged 18–43, were used to train and test these models. The researchers aimed to detect twelve daily activities, such as sitting, walking, and cycling. Results showed k-NN models achieved slightly higher accuracy (97.08%) compared to SVM models (95.88%), though SVM had faster processing times. The improved machine learning approaches proved effective in accurately classifying daily activities, with k-NN models outperforming SVM models marginally. The paper provides significant contributions to the field of HAR by enhancing the performance of SVM and k-NN classifiers, optimizing them for higher accuracy and faster processing. Through robust testing with samples of real-world data, the study provides a detailed comparative analysis that highlights strengths and weaknesses of each classifier model, specifically within IoT-based systems. This work not only advances the theoretical understanding and practical applications of HAR systems in areas, such as healthcare and industrial automation, but also sets the stage for future research that could explore hybrid models or further enhancements, consequently improving the efficiency and functionality of IoT devices based on activity recognition.

For two-way wireless relay networks (TWRNs), the simultaneous bidirectional transmission has been shown to outperform other strategies using decode-and-forward (DF) distributed space-time coding (DSTC), thanks to its high spectral efficiency. However, it has a rather high relay decoding complexity and cannot use the direct link between the communicating terminals. In this letter, we propose a simple DSTC transmission scheme for TWRNs that avoids the latter disadvantages at the same symbol rate and with a performance advantage at high powers. Our strategy allows the communicating terminals to use the direct link between them to achieve a higher diversity gain. An extension of the proposed strategy to the differential case is also discussed.

The intraoral Tongue Drive System (iTDS) is a wireless assistive technology (AT) that may be operated using a variety of user-defined voluntary tongue motions. In this study, we introduce a novel arch-shaped iTDS that takes up the buccal shelf space in the mouth without constricting tongue motions. Damage to the central nervous system that causes upper limb paralysis. For instance, severe spinal cord injuries, certain strokes or multiple amputations of the upper limbs can make it very difficult for a lot of individuals to go about their everyday lives since they cause them to lose function, control, and independence. Because its muscles are strong and dexterous and do not quickly tire when not necessary to exert power, the tongue is an excellent choice for controlling an AT. Additionally, through cranial nerves, which are unaffected by a spinal cord damage, the tongue is directly attached to the brain. Furthermore, since the tongue movements may be totally hidden from view, TDS-based AT safeguard users’ privacy. In order to provide an effective solution, this paper proposed a novel antenna design with dual-band and implantability. The antenna is modelled in single-layer human muscle considering the feasibility and computational time. Then, the performance is evaluated through multi-layered human oral cavity model. A protective layer is established to prevent direct contact between the antenna and human body. Simulation results show that the proposed antenna has better reflection coefficient, impedance, and radiation pattern as compared with existing antennas.

In this paper, a low-complexity suboptimal decoder for coherent and non-coherent quasi-orthogonal space time block codes with three and four transmit antennas is proposed. Our decoder enjoys a nearly linear complexity and approximately the same performance as the optimal maximum-likelihood (ML) decoder. Simulations show the advantages of the proposed decoder with respect to several other popular approaches to the coherent and non-coherent decoding.

In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a botnet of daemons residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as “TCP SYN-Flood” is studied. This type of attack uses spoofed Internet Protocol (IP) addresses for SYN packets by exploiting the weakness in Transmission Control Protocol (TCP) 3-Way handshake used by the TCP/IP suite of protocols, which make the web servers unreachable for legitimate users or even worse, it might lead to server crash. In this paper, a resilient, efficient, lightweight, and robust IP traceback algorithm is proposed using an IP tracing packet for each attack path. The proposed algorithm suggests that edge routers—where the attack starts from—observe the traffic pattern passing through, and if the observed traffic carries the signature of TCP SYN-Flood DDoS attack and a high percentage of it is destined to a particular web server(s), it starts the tracing process by generating an IP trace packet, which accompanies the attack path recording the routers’ IP addresses on the path between the attacker/daemon and the victim, which can extract the path and react properly upon receiving it by discarding any SYN packets originating from that attacker/daemon. To our knowledge, this is the first research that efficiently traces these kinds of attacks while they are running. The proposed solution has low computation and message overhead, efficient detection and tracing time, and converges in near optimal time. The results are validated using extensive simulation runs.

In wireless communications, the fundamental challenge facing mobile operators nowadays is the scarcity of the spectrum and the license fees. Because of the increased traffic, critical performance analysis becomes important to overcome such issues. To tackle these issues and improve the network performance and capacity, a coexistence model of LTE and Wi-Fi with two virtual zones is suggested. The inner zone is the secondary zone, which represents Wi-Fi with an unlicensed spectrum to serve the best effort user data and the outer zone is the primary zone, which represents LTE with licensed spectrum to serve all data connections. The numerical solution of the model is presented using MOSEL-2 simulation and the mathematical solution is derived to validate the model. A threshold minimum bit rate established the user admission. Based on a priority level of the service, most of the traffic is served by the primary LTE zone, however, low priority traffic is moved from the primary LTE zone to the secondary Wi-Fi zone so that the traffic of the entire network is balanced. Although this coexistence sometimes harms LTE performance, the simulation results demonstrate the efficiency of the proposed model in balancing the load over the network, and, consequently, the network performance is improved.