In hostile and remote environments, such as mountains, forests or suburban areas, traditional communications may not be available, especially after a disaster, such as a flood, a forest fire or an earthquake. In these situations, the wireless networks may become congested or completely disrupted and may not be adequate to support the traffic generated by rescuers. It is also considered as the key tool in Corona Virus (COVID-19) battle. Moreover, the conventional approaches with fixed gateways may not work either, and this might lead to decoding errors due to the large distance between mobile nodes and the gateway. To avoid the decoding errors and improve the reliability of the messages, we propose to use intermediate Unmanned Aerial Vehicles (UAVs) to transfer messages from ground-based Long Range (LoRa) nodes to the remote base station (BS). Specifically, this UAV-enabled LoRa architecture is based on the ad hoc WiFi network, wherein, UAVs act as relays for the traffic generated between LoRa nodes and BS. To make the architecture more efficient, a distributed topology control algorithm is also proposed for UAVs. The algorithm is based on virtual spring forces and movement prediction technique that periodically updates the UAV topology to adapt to the movement of the ground-based LoRa nodes that move on the surface. The simulation results show the feasibility of the proposed approach for packet reception rate and average delay quality of service (QoS) metrics. It is observed that the mechanisms implemented in a UAV-enabled LoRa network effectively help to improve the packet reception rate with nominal buffer delays.

The Internet-of-things (IoT) has been gradually paving the way for the pervasive connectivity of wireless networks. Due to the ability to connect a number of devices to the Internet, many applications of IoT networks have recently been proposed. Though these applications range from industrial automation to smart homes, healthcare applications are the most critical. Providing reliable connectivity among wearables and other monitoring devices is one of the major tasks of such healthcare networks. The main source of power for such low-powered IoT devices is the batteries, which have a limited lifetime and need to be replaced or recharged periodically. In order to improve their lifecycle, one of the most promising proposals is to harvest energy from the ambient resources in the environment. For this purpose, we designed an energy harvesting protocol that harvests energy from two ambient energy sources, namely radio frequency (RF) at 2.4 GHz and thermal energy. A rectenna is used to harvest RF energy, while the thermoelectric generator (TEG) is employed to harvest human thermal energy. To verify the proposed design, extensive simulations are performed in Green Castalia, which is a framework that is used with the Castalia simulator in OMNeT++. The results show significant improvements in terms of the harvested energy and lifecycle improvement of IoT devices.

Non-orthogonal multiple access (NOMA) has become the key technology in the future 5G wireless networks. It can achieve multi-user multiplexing in the transmit power domain by allocating different power, which can effectively improve the system capacity and spectral efficiency. Aiming at the problem of high computational complexity and improving system capacity in non-orthogonal multiple access (NOMA) based on orthogonal frequency division multiple access (OFDMA) for 5G wireless cellular networks, this paper proposes an improved low complexity radio resource allocation algorithm for user grouping and power allocation optimization. The optimization model is established with the goal of maximizing system capacity. Through the step-by-step optimization idea, the complex non-convex optimization problem is decomposed into two sub-problems to be solved separately. Firstly, all users are grouped based on the greedy method, and then the power allocation is performed on the sub-carriers of the fixed group. Simulation results show that the proposed algorithm has better system capacity than the existing state-of-the-art algorithms and reduced complexity performance.

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 the proposed work, we present a secure low-cost speech communication system for long-distance communication. The system utilizes long range (LoRa) technology to transmit speech signals. LoRa technology uses spread-spectrum modulation to enable long-range communication with low power consumption. LoRa modulation allows for data transfer at a slow speed, typically below 22 kbps, which makes it infeasible for transmitting speech. To address this limitation, we suggest a speech coding technique that reduces the overall data rate of speech signals to below 7.5 kbps. This lower rate is more compatible with the LoRa module and ideal for transmitting speech. Moreover, this technique can improve the LoRa transmission range. Additionally, we have developed an encryption-decryption method to ensure the privacy of the messages and prevent unauthorized access by third parties.

In this paper, a novel method for detecting breast cancer utilizing an ultra-wideband (UWB) microstrip patch antenna operating in the UWB frequency range is presented. The promise of non-invasive, radiation-free breast cancer screening is provided by UWB technology. We give a brief overview of the difficulties in detecting breast cancer as well as the benefits of UWB technology in medical applications in this study. This study presents a compact printed microstrip patch antenna used for the bio-medical application at industrial, scientific, and medical (ISM) and UWB frequencies. The proposed antenna consists of a hexagonal-shaped microstrip patch antenna with a slotted ground antenna used for high bandwidth for a wider range of applications. The compact biomedical antenna is printed on Roger 5880 substrate with an overall dimension of 25*25 mm. Due to the high demand for results accuracy, Roger 5880 substrate material is used because it has good dielectric characteristics and loss factors that are appropriate for medical and radio frequency (RF) applications. A 50-ohm impedance line is used to feed the antenna. The proposed antenna has a higher bandwidth of almost 12.5 GHz at such a low frequency, starting from 3.53 to 16 GHz. The antenna has an acceptable gain of 3.38 Db, which is enough for the required application. The UWB microstrip patch antenna has the potential to improve breast cancer detection accuracy and reliability, which would aid in early diagnosis and better cancer patient treatment. This work represents a major advancement in the development of non-invasive, low-cost breast imaging methods for early cancer diagnosis.