Agrotech: Advanced Field Control

Agriculture in most countries is largely dependent on the environmental conditions and climate. Real-time remote monitoring of field conditions allows for early detection of unfavorable conditions developing in the field that is generally a laborious task taken up by the farmers themselves. Additionally, with the rapid shift in climatic conditions and issues pertaining to global warming, the rainfall dependent agricultural yield is promptly affected. In this paper, we present a wireless system using nRF24L01 RF modules to monitor the farm environment conditions and operate the final control element aimed at implementing drip irrigation. The proposed system is designed to be economical and having a capacity for further expansion, allowing the widespread devices across the farm to be controlled. The soil sogginess level is measured by a self-made soil moisture sensor. A ball valve is actuated based on the levels of soil moisture, temperature and humidity checked against a set of predefined levels. The collected data is then pushed onto a cloud platform for future study or use. Keywords: Wireless sensor network, real-time monitoring, nRF24L01 RF module, highly economical and efficient system

A Comparative Study of 4G and VoLTE

This research paper provides an insight into the comparison between VoLTE and 4G. 4G Wireless Systems or Fourth generation wireless system is a packet switched wireless system with wide area coverage and high throughput. It is designed to be cost effective and to provide high spectral efficiency. VoLTE (voice over LTE) supports voice calls over 4G LTE network. With advanced VoLTE technology, the 4G network now provides high-speed data services, high-quality voice and video calls at affordable price. This paper first presents the challenges and benefits of both 4G and VoLTE and then compares them with consideration of different points. Keywords: GSM, LTE, VoLTE, 3G, HSPA, RAN

Batteryless, Miniaturized Implantable Glucose Sensor Using a Fluorescent Hydrogel

We propose a biomedical sensor system for continuous monitoring of glucose concentration. Despite recent advances in implantable biomedical devices, mm sized devices have yet to be developed due to the power limitation of the device in a tissue. We here present a mm sized wireless system with backscattered frequency-modulation communication that enables a low-power operation to read the glucose level from a fluorescent hydrogel sensor. The configuration of the reader structure is optimized for an efficient wireless power transfer and data communication, miniaturizing the entire implantable device to 3 × 6 mm 2 size. The operation distance between the reader and the implantable device reaches 2 mm with a transmission power of 33 dBm. We demonstrate that the frequency of backscattered signals changes according to the light intensity of the fluorescent glucose sensor. We envision that the present wireless interface can be applied to other fluorescence-based biosensors to make them highly comfortable, biocompatible, and stable within a body.

Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning

Spectrum sensing is an important function in radio frequency spectrum management and cognitive radio networks. Spectrum sensing is used by one wireless system (e.g., a secondary user) to detect the presence of a wireless service with higher priority (e.g., a primary user) with which it has to coexist in the radio frequency spectrum. If the wireless signal is detected, the second user system releases the given frequency to maintain the principle of not interfering. This paper proposes a machine learning implementation of spectrum sensing using the entropy measure as a feature vector. In the training phase, the information about the activity of the wireless service with higher priority is gathered, and the model is formed. In the classification phase, the wireless system compares the current sensing report to the created model to calculate the posterior probability and classify the sensing report into either the presence or absence of wireless service with higher priority. This paper proposes the novel application of the FDE measure recently introduced in the research community as a feature vector to build the model and implement the classification. An improved implementation of the FDE (IFDE) is used to enhance the robustness to noise. IFDE is further enhanced with an adaptive method (AIFDE) to automatically select the hyper-parameter introduced in IFDE. Then, this paper combines the machine learning approach with the entropy measure approach, which are both recent developments in spectrum sensing research. The approach is compared to similar approaches in literature and the classical energy detection method using a generated radar signal data set with different conditions of SNR(dB) and fading conditions. The results show that the proposed approach is able to outperform the approaches from literature based on other entropy measures or the ED in a consistent way across different levels of SNR and fading conditions.

Compact 3-bit Frequency Reconfigurable Monopole Antenna Realized with a Switchable Three-Line Section

This work proposes a compact 3-bit frequency-reconfigurable monopole antenna covering a broad reconfigurable range by inserting a switchable three-line section (STLS). The design starts with a conventional quarter-wavelength monopole line antenna, which is then replaced by a novel structure, the STLS. The STLS is composed of three parallel-connected lines with different lengths. Accordingly, three RF p–i–n diodes are introduced in the STLS to achieve binary reconfiguration. After all parameters of the antenna have been optimized, it will eventually output 2N = 8 (N is the number of switches) independent working states with different equivalent lengths and a reconfigurable working frequency. The number of states in a binary reconfigurable antenna is optimally large in relation to the number of switches used, which means that it can be extremely convenient for digital control of switching all the states and capable of decreasing the number of RF p–i–n diodes we used, thereby minimizing the manufacturing cost and loss of diodes. A prototype antenna is fabricated and tested, and the measurement results agree well with the simulation results, validating the good features, such as a large reconfigurable switchable frequency range from 0.95 GHz to 2.45 GHz with considerable working bandwidth varying from 40 MHz to 540 MHz for each state, simple structure, and a compact size of 70 × 40 mm2, which can be appropriately used for a multi-radio wireless system and handheld devices. All the states have a similar monopole radiation pattern with a good maximum efficiency and an acceptable peak gain according to its compact size.

A New Covid-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network

<p class="0abstract">The continuous advancements in wireless network systems have reshaped the healthcare systems towards using emerging communication technologies at different levels. This paper makes two major contributions. Firstly, a new monitoring and tracking wireless system is developed to handle the COVID-19 spread problem. Unmanned aerial vehicles (UAVs), i.e., drones, are used as base stations as well as data collection points from Internet of Things (IoT) devices on the ground. These UAVs are also able to exchange data with other UAVs and cloud servers. Secondly, this paper introduces a new reinforcement learning (RL) framework for learning the optimal signal-aware UAV trajectories under quality of service constraints. The proposed RL algorithm is instrumental in making the UAV movement decisions that maximize the signal power at the receiver and the data collected from the ground agents. Simulation experiments confirm that the system overcomes conventional wireless monitoring systems and demonstrates efficiency especially in terms of flexible continues connectivity, line-of sight visibility, and collision avoidance.</p>

Enabling Device-to-Device Transmission for NOMA-Aided Systems

To utilize the close transmission, we assume that the device-to-device (D2D) link is activated to improve the performance of the far user. We consider two groups of users in the nonorthogonal multiple access- (NOMA)- aided wireless system. These features are necessary for massive connectivity in future wireless systems. The system performance also shows suitable performance at far distance users. To evaluate the performance in detail, we derive novel closed form expressions of outage probability. In practical situations impaired by channel uncertainty, it is necessary to evaluate the impact of channel error levels on outage probability. Our numerical results indicated that the transmit power at the base station and channel error level are the main impacts on system performance. Despite these impacts, our obtained numerical results demonstrated that the proposed scheme can still increase energy efficiency and achieve significant outage performance via many practical challenges.