Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

High capacity and ultra-reliable vehicular communication are going to be important aspects of beyond 5G communication networks. However, the vehicular communication problem becomes complex at a large scale when vehicles are roaming on the road, while simultaneously communicating with each other. Moreover, at higher frequencies (like 28 GHz), the dynamics of vehicular communication completely shift towards unpredictability and low-reliability. These factors may result in high packet error and a large amount of interference, resulting in regular disruptions in communications. A thorough understanding of performance variations is the key to moving towards the next generation of vehicular networks. With this intent, this article aims to provide a comprehensive interference analysis, wherein the closed-form expressions of packet error probability (PEP) and ergodic capacity are derived. Using the expression of the PEP, diversity analysis is provided which unveils the impact of channel nonlinearities on the performance of interference-constrained vehicular networks. The insights provided here are expected to pave the way for reliable and high capacity vehicular communication networks.

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.

Heterogeneous networks are rapidly emerging as one of the key enablers of beyond fifth-generation (5G) wireless networks. It is gradually becoming clear to the network operators that existing cellular networks may not be able to support the traffic demands of the future. Thus, there is an upsurge in the interest of efficiently deploying small-cell networks for accommodating a growing number of user equipment (UEs). This work further extends the state-of-the-art by proposing an optimization framework for reducing the power consumption of small-cell base stations (BSs). Specifically, a novel algorithm has been proposed which dynamically switches off the redundant small-cell BSs based on the traffic demands of the network. Due to the dynamicity of the formulated problem, a new UE admission control policy has been presented when the problem becomes infeasible to solve. To validate the effectiveness of the proposed solution, the simulation results are compared with conventional techniques. It is shown that the proposed power control solution outperforms the conventional approaches both in terms of accommodating more UEs and reducing power consumption.

This paper investigates the feasibility of harvesting Radio Frequency (RF) energy from the Wi-Fi frequency band to power low-power Internet-of-Things (IoT) devices. With the increasing prevalence of IoT applications and wireless sensor networks (WSNs), there is a critical need for sustainable energy sources that can extend the operational lifespan of these devices, particularly in remote locations, where access to reliable power supplies is limited. The paper describes the design, simulation, and fabrication of a dual-band antenna capable of operating at 2.4 GHz and 5 GHz, the frequencies used by Wi-Fi. The simulation and experimental results show that the proposed design is efficient based on the reflection coefficient. Using a high-frequency simulator, we developed two C-shaped and an F-shaped microstrip antenna design, optimized for impedance matching and efficient RF–DC conversion.The captured RF energy is converted into usable electrical power that can be directly utilized by low-power IoT devices or stored in batteries for later use. The paper introduces an efficient design for dual-band antennas to maximize the reception of Wi-Fi signals. It also explains the construction of an impedance-matching network to reduce signal reflection and improve power transfer efficiency. The results indicate that the proposed antennas can effectively harvest Wi-Fi energy, providing a sustainable power source for IoT devices. The practical implementation of this system offers a promising solution to the energy supply challenges faced by remote and low-power IoT applications, paving the way for more efficient and longer-lasting wireless sensor networks.

WiMAX, as a wireless broadband access technology, aims at providing the end users with broadband services identical to that provided by DSL. The data rate requirements of VoIP connections over WiMAX links greatly depend on the choice and parameterization of the employed codec, the uplink scheduling algorithm, the header overhead of all network layers as well as applied header compression techniques. The knowledge of the exact data rate requirements of key services is very important for network dimensioning and planning. In this paper we provide a study of the bandwidth and delay requirements of VoIP services in WiMAX networks. This includes an analysis of the effects of robust header compression, payload header suppression, voice activity detection with discontinuous transmission and other bandwidth affecting factors. Finally, the overhead and induced delay by each one of the WiMAX uplink scheduling algorithms for real time services is analyzed in this paper.