RF Communication Research Articles

Control and optimization of a smart antenna array by PSO

This paper describes an array of eight Yagi–Uda antennas, a beamforming circuit (BFC) and an antenna control system (ACS), which form a switched smart antenna structure, the principal feature of which is that its main lobe is maximized using the particle swarm optimization (PSO) method, while its side lobes are minimized. The application of the PSO integrated with the 3D electromagnetic simulation software Ansys HFSS has achieved excellent results, and has been validated in practical experiments. The results measured in the open field are in agreement with the simulations and are comparable with the arrangement of the energy distribution concessionaires. As expected, the gains obtained proved to be greater when the main lobes were pointed toward the objective angles. These characteristics meet the needs of supervisory control systems used by electricity distribution companies, which require high-gain RF communication networks in order to monitor electric switches in a distant area covered by the respective base transceiver stations. The entire setup herein described and tested has performed extremely well, and is certainly a promising approach by which to control the outstations of electricity distribution companies without the need to install additional repeaters.

Perception Enhancement and Improving Driving Context Recognition of an Autonomous Vehicle Using UAVs

The safety of various road users and vehicle passengers is very important in our increasingly populated roads and highways. To this end, the correct perception of driving conditions is imperative for a driver to react accordingly to a given driving situation. Various sensors are currently being used in recognizing driving context. To further enhance such driving environment perception, this paper proposes the use of UAVs (unmanned aerial vehicles, also known as drones). In this work, drones are equipped with sensors (radar, lidar, camera, etc.), capable of detecting obstacles, accidents, and the like. Due to their small size and capability to move places, drones can be used collect perception data and transmit them to the vehicle using a secure method, such as an RF, VLC, or hybrid communication protocol. These data obtained from different sources are then combined and processed using a knowledge base and some set of logical rules. The knowledge base is represented by ontology; it contains various logical rules related to the weather, the appropriateness of sensors with respect to the weather, and the activation mechanism of UAVs containing these sensors. Logical rules about which communication protocols to use also exist. Finally, various driving context cognition rules are provided. The result is a more reliable environment perception for the vehicle. When necessary, users are provided with driving assistance information, leading to safe driving and fewer road accidents. As a proof of concept, various use cases were tested in a driving simulator in the laboratory. Experimental results show that the system is an effective tool in improving driving context recognition and in preventing road accidents.

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint

We propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer.

Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks

Integrated with sensors, processors and RF communication modules, intelligent bearing could achieve the autonomous perception and autonomous decision-making, guarantying the safety and reliability during their use. However, because of the resource limitations of the end device, processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network (CNN), which involves a great amount of multiplicative operations. To minimize the computation cost of the conventional CNN, based on the idea of AdderNet, a 1-D adder neural network with a wide first-layer kernel (WAddNN) suitable for bearing fault diagnosis is proposed in this paper. The proposed method uses the l1-norm distance between filters and input features as the output response, thus making the whole network almost free of multiplicative operations. The whole model takes the original signal as the input, uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise, then uses two layers of small kernels for nonlinear mapping. Through experimental comparison with CNN models of the same structure, WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost. The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources.

Reliable Digital Forensics in the Air

As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performance of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.

Bus Tracking System Using RF Communication for Passenger Information over Internet of Things

Abstract: Now-a-days in every bus stations, railway stations, and airports we will get different voice based announcements are given by the authorized transport people in different languages to help public for ease of transportation. The announcements consist’s of some delay’s, occurrence of bus at particular bus station/stop and platform. This project aims for designing a system which is capable of updating the information about the bus location and accident detection of bus. This device is designed to provide with a good advantage of producing the updated information which is available through internet using GSM Communication. This is possible with the help of RF Communication and GSM Communication. RF Transmitter is given with four switches is pressed (i.e) RF Receiver gets particular frequency then the controller will understand the location and updates the same thing about bus location using GSM module connected to the Arduino Uno controller. Here we are using AT89C51 as our micro-controller in RF Receiver kit. This Project uses regulated 5V/500mA power supply 7805 and 7812 are three terminal voltage regulators is used for voltage regulation. A Bridge type full wave rectifier is used to rectify the ac output of another 230/12V step down transformer.

MagLoc: A magnetic induction based localization scheme for fresh food logistics

An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect food quality and contamination issues and thereby reduce costs and food wastage. This, in turn, involves several challenges including the development of inexpensive quality/contamination sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor level communications, online data management and analytics, and logistics driven by such analytics. In this paper, we study the issues related to the communication among sensing modules deployed in the fresh food boxes and thereby an automated localization of the boxes that may have quality/contamination issues. In this context we study the near-field magnetic induction (NFMI) based communication and localization, as the ubiquitous RF communications suffer high attenuation through the water/mineral rich tissue media. An accurate localization of the sensors inside boxes within the food pallets is very challenging in this environment. In this paper we propose a novel magnetic induction based localization scheme, and show that with a small number of anchor nodes, the localization can be done without any errors for boxes as small as 0.5 meter on the side, and with small errors even for boxes half as big.

A COMPREHENSIVE STUDY ON THE DEVELOPMENT OF AN AUTOMATED RFID-BASED SECURITY SYSTEM FOR RESIDENTIAL AND INDUSTRIAL APPLICATIONS

The project's objective is to develop a fully automated security access system for both household and industrial use. Security is a key concern for both individuals and firms. In response to the demand for security, we have created an automated security access system that is easy for users to navigate. Automation is the most commonly written word in the domain of electronics. The need for automation has sparked numerous technological breakthroughs. RF communications is one of the technologies that has experienced significant advancements. The outcome of this process is the production of RFID cards that transmit a distinct identifying number. The RFID number can be detected using an RF reader. The level of access to the house or industry might be granted in its entirety or restricted based on the RFID cards. The determination of granting either full access or limited access is made by an onboard computer that is connected to the RF reader. The doors of the house/industry serve as the output module and are connected to the onboard computer via a motor. This onboard computer has a variety of input and output ports. The word typically used to refer to the onboard computer is microcontroller. The controller's input and output ports are connected to various input and output modules based on specific needs. Put simply, the microcontroller serves as a means of communication amongst all the modules in the project. The device is equipped with an LCD screen that provides information regarding the status of the doors, whether they are open or closed.

A hybrid acoustic-RF communication framework for networked control of autonomous underwater vehicles: design and cosimulation

Underwater control applications, especially ones using autonomous underwater vehicles (AUVs) have become very popular for industrial and military underwater exploration missions. This has led to the requirement of establishing a high data rate communication link between base stations and AUVs, while underwater systems mostly rely on acoustic communications. However, limited data rate and considerable propagation delay are the major challenges for employing acoustic communication in missions requiring high control gains. In this paper, we propose a hybrid acoustic and RF communication framework for establishing a networked control system, in which, for long distance communication and control the acoustic link is used, and in the short range, the RF link is employed. Our scenario for testing implements a docking maneuver application, where a docking station determines the positions of the AUVs via acoustic or RF communication, and different medium access schemes are used for coordinating the transmission of the nodes according to the communication mode. Considering the full dynamics of the entire system for controlling the AUVs, the real-time behavior of the underwater networked control system is evaluated realistically using our proposed integrated cosimulation environment, which includes different simulators. Our performance results indicate that under calm water conditions, our proposed hybrid system reduces the docking time by 33% compared to the acoustic-only.

Accident Detection and Ambulance Rescue System using IOT

Abstract: Road accidents and traffic congestion are the major problems in urban areas. Currently there is no technology for accident detection. Also due to the delay in reaching of the ambulance to the accident location and the traffic congestion in between accident location and hospital increases the chances of the death of victim. There is a need of introducing a system to reduce the loss of life due to accidents and the time taken by the ambulance to reach the hospital. To overcome the drawback of existing system we will implement the new system in which there is an automatic detection of accident through sensors provided in the vehicle. A main server unit houses the database of all hospitals in the city. A GPS and Wi-Fi module in the concerned vehicle will send the location of the accident to the main server which will rush an ambulance from a nearest hospital to the accident spot. Along with this there would be control of traffic light signals in the path of the ambulance using RF communication. This will minimize the time of ambulance to reach the hospital. A patient monitoring system in the ambulance will send the vital parameters of the patient to the concerned hospital. This system is fully automated, thus it finds the accident spot and helping to reach the hospital in time. Keywords: IOT, Ambulance Rescue System, Arduino, GPS, Wi-Fi Module

Electronic System for the Remote Monitoring of Solar Power Plant Parameters and Environmental Conditions

An electronic monitoring system was developed to monitor and analyze operating and environmental parameters of solar power plants. The electronic monitoring system consisted of two stages: the first stage was designed to receive data from temperature, illumination, voltage, electric current, and power sensors and modules; the second stage is for data collection, analysis, and display. Microcontrollers PIC18F25K22 (for data collection) and PIC24FV32KA302 (for data analysis and display) were used to implement the electronic monitoring system. RF module EBTYE OEM/ODM E32-433T20DC SX1278 433 MHz UART was used to transmit data to the central server. For the microcontrollers of both stages, original control program algorithms were developed, according to which program codes were written using the C programming language. Data were collected using the MySQL database. The virtual interface and application for displaying instant data were created using programming language Delphi. Data monitoring systems in the market have a number of shortcomings due to the difficulty of ensuring a centralized data management process. In addition, sensors used in most systems require a Wi-Fi Internet connection to transmit information, which is difficult to secure in remote solar parks. The aim of this work is to show that information transmission from individually deployed sensors to the central server can be ensured using the principle of RF communication, and data collection and analysis in a centralized way without additional costs for Wi-Fi networks in remote areas.

A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems

It is crucial to design new communication technologies to surmount the setbacks in RF communication systems. A suitable energy-efficiency scheme helps evade needless energy consumption in wireless communication. Appropriate choice of the most suitable energy-efficiency scheme aids in selecting the most energy-efficient equipment to minimize the expense of energy towards decreasing individual network element energy consumption without affecting their unique features. This review presents the energy efficiency challenges in wireless communication by employing different technologies. The emergence of visible light communication (VLC) provides an energy-efficient wireless communication system despite the various challenges inherent in its adoption that limit its physical realization. This work seeks to harness the potential of the transmission capabilities of VLC while providing an insight into novel practical implementation techniques. The work also addresses the energy consumption problem of low-active components and idle period of active components of base stations by using sleep modes for their systematic turning off and on. The high cost of power supply and the environmental emission of gases from base stations are also addressed by integrating a renewable energy resource into the conventional standalone diesel generators. Overall, the work provides an overview of information necessary for foundational research in energy-efficient resources applied to emerging wireless communication systems.

Statistical Analysis Based Feature Selection Enhanced RF-PUF With >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various spoofing attacks which can allow a rogue device to get access to a large network. The vulnerability of the traditional digital signature-based authentication system lies in the fact that it uses only a key/pin, ignoring the device fingerprint. To circumvent the inherent weakness of the traditional system, various physical signature-based RF fingerprinting methods have been proposed in literature and RF-PUF is a promising choice among them. RF-PUF utilizes the inherent nonidealities of the traditional RF communication system as features at the receiver to uniquely identify a transmitter. It is resilient to key-hacking methods due to the absence of secret key requirements and does not require any additional circuitry on the transmitter end (no additional power, area, and computational burden). However, the concept of RF-PUF was proposed using MATLAB-generated data, which cannot ensure the presence of device entropy mapped to the system-level nonidealities. Hence, an experimental validation using commercial devices is necessary to prove its efficacy. In this work, for the first time, we analyze the effectiveness of RF-PUF on commodity devices, purchased off-the-shelf, without any modifications whatsoever. We have collected data from 30 Xbee S2C modules used as transmitters and released as a public dataset. A new feature has been engineered through PCA and statistical property analysis. With a new and robust feature set, it has been shown that 95% accuracy can be achieved using only ∼1.8 ms of test data fed into a neural network of 10 neurons in 1 layer, reaching >99.8% accuracy with a network of higher model capacity, for the first time in literature without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel has been investigated. The performance of some popular machine learning algorithms has been tested and compared with the neural network approach. A thorough investigation of various PUF properties has been done. With extensive testing of 41238000 cases, the detection probability for RF-PUF for our data is found to be 0.9987, which, for the first time, experimentally establishes RF-PUF as a strong authentication method. Finally, the potential attack models and the robustness of RF-PUF against them have been discussed.

On Directly Modulated Reflective Semiconductor Optical Amplifier with Assistance of Birefringent Fiber Loop

Reflective Semiconductor Optical Amplifiers (RSOAs) are essential devices for the development of new generation networks that rely on the convergence of optical and RF communications. Despite their proven potential for direct modulation, RSOAs’ electro-optic response is limited by their finite bandwidth, which hinders their employment both for signal amplification and modulation at the data rates envisioned by the target applications. In this paper, we elaborate on exploiting a Birefringent Fiber Loop (BFL) to enhance the operation of RSOAs as intensity modulators. We apply a mathematically and computationally reduced model to simulate the RSOA response in the time domain, and correlate it with that of the BFL in the frequency domain. We validate the model’s predictions by an extensive comparison of the simulation against experimental results. The reasonable theoretical findings allow us to establish the employed model as an efficient tool for describing electrically driven RSOA operation and its improvement by means of optical notch filtering. Furthermore, we evaluate and quantify the performance of the scheme and the potential range of RSOA direct modulation capability extension enabled by the BFL, which complies with the experimentally observed trends. The outcomes of this thorough study highlight the BFL supportive role in rendering feasible RSOAs’ direct modulation at data rates beyond those deemed possible by their nominal modulation bandwidth.

Wireless Communication Test on 868 MHz and 2.4 GHz from inside the 18650 Li-Ion Enclosed Metal Shell.

As the RF communication on 18650 Li-ion cell level has not been reported due to its challenges and constrains, in this work, a valid wireless data link is demonstrated in an enclosed empty metal shell at 868 MHz and 2.4 GHz based on the IEEE 802.15.4 standard. The experimental tests are carried out using two generic unturned radiative structures, a wire loop fitted inside a cell shell, and an open terminal sub miniature version A (SMA), subsequently oriented vertically and horizontally relative to the ground plane. Based on signal strength indicator, bit error rate, and packet error rate, the test characterized a payload of 120 bytes at the highest speed of 150 kbps and 250 kbps supported by the IEEE 802.15.4 for the two communication frequencies. A MATLAB simulation is used in parallel to determine the three-dimensional radiative pattern of the two structures, whereas a three-ray model for multipath range propagation is implemented to complete the empirical experiments. It was demonstrated through testing communication of up to 10 m for both operating frequencies, proving the concept of wireless cell communication within short ranges, an essential feature for monitoring the health of each cell inside future electric vehicles (EVs).

Self-powered wireless sensing system driven by daily ambient temperature energy harvesting

This work demonstrates a self-powered wireless IoT sensing system driven by daily ambient temperature energy harvesting. A novel approach using a thermoelectric generator (TEG) which harvests thermal energy from daily ambient temperature fluctuations into electricity as a power source for wireless IoT devices is proposed and investigated. A harvested temperature difference across the TEG is created by employing a phase change material (PCM) which allows the temperature value to be maintained near the melting point at the phase transition during the ambient temperature fluctuations. Experimental results indicated that the harvested temperature differences are 10 °C and 5 °C for 300 ml and 30 ml of PCM, respectively, during the ambient temperature variations from 5 °C to 25 °C. By utilizing the ambient temperature fluctuations between day and night times, an average maximum output power of TEG measured for 3 days is 340 µW and DC-DC electronic conversion efficiency is 28.3%. The total storable energy of approximately 1.46 J for a day is estimated. A capacitor as an energy storage unit could be fully charged and its usable energy is calculated at 0.686 J. The current consumption of 2.1 µA for every transmit cycle is estimated. A self-powered wireless IoT sensing system driven by realistic ambient temperature variations able to sense the ambient temperature as well as the ambient humidity and transmit to a computer via RF communication is demonstrated successfully.

Acoustic Dual-Function Communication and Echo-Location in Inaudible Band.

Acoustic communications are experiencing renewed interest as alternative solutions to traditional RF communications, not only in RF-denied environments (such as underwater) but also in areas where the electromagnetic (EM) spectrum is heavily shared among several wireless systems. By introducing additional dedicated channels, independent from the EM ones, acoustic systems can be used to ensure the continuity of some critical services such as communication, localization, detection, and sensing. In this paper, we design and implement a novel acoustic system that uses only low-cost off-the-shelf hardware and the transmission of a single, suitably designed signal in the inaudible band (18–22 kHz) to perform integrated sensing (ranging) and communication. The experimental testbed consists of a common home speaker transmitting acoustic signals to a smartphone, which receives them through the integrated microphone, and of an additional receiver exploiting the same signals to estimate distance information from a physical obstacle in the environment. The performance of the proposed dual-function system in terms of noise, data rate, and accuracy in distance estimation is experimentally evaluated in a real operational environment.

Optimization-Driven Hierarchical Learning Framework for Wireless Powered Backscatter-Aided Relay Communications

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the transmit beamforming and the relays’ radio modes and operating parameters. Due to the non-convex and combinatorial problem structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -network (DQN) algorithm and then optimizes the continuous beamforming and relaying strategies by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the inner-loop DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning performance and achieve a higher reward, up to 20%, compared to the conventional model-free DDPG approach.

Effect of Pointing Error on the Performance of Improved Modified Switching Scheme of Hybrid FSO/RF System under Mixture Gamma Atmospheric Turbulence

In this paper, the performance of improved modified switching (IMS) scheme for the hybrid FSO/RF system is evaluated under the mixture composite irradiance channel fading model with pointing errors. Here, the combined effect of atmospheric turbulence and pointing error in optical domain is modelled by the mixture gamma (MG) distribution, which is a more accurate model in comparison to “K” and “Gamma” distributions. Further, in RF communication the fading channel is modeled as Nakagami “m” distribution. The average data rate, outage probability and error performances of the hybrid FSO/RF system under consideration are evaluated and compared with those of existing hard switching scheme (HS). For a meaningful comparison, IMS and HS schemes are considered in two different scenarios. Firstly, the average data rate of both the switching schemes are made identical by an appropriate choice of RF/FSO SNR thresholds. The two schemes are then compared on the basis of the other two performance metrics i.e. outage and BER. Secondly, the outage probabilities are made identical, and comparison is made on the basis of BER and average data rate.

A Network Communication Frequency Routing Protocol of Coal Mine Safety Monitoring System Based on Wireless Narrowband Data Communication Network

In recent years, the coal industry has been expanding the scale and output of coal mining in China, yet the safety in production is faced with great challenge. Communication system in coal mines plays a significant role in production safety, and wireless narrowband data communication network technology is a new-type advantageous measure to construct wireless network security monitoring system for coal mining. Here, a coal mine monitoring signal transmission method based on bus technology and wireless data transmission technology suitable for coal mines is proposed. The corresponding monitoring system is designed, and the system is able to collect the gas concentration and mechanical operation status of the mines. Wireless sensor network nodes are used for the RF communication module. The communication performance of this system in coal and rock medium and the roadway along the goaf is simulated, and the quantitative relationship between the attenuation coefficient of different communication frequencies and the effective communication distance is obtained. Hence, the results show that the protocol has good performance in terms of route establishment, maintenance, and data transmission hop count for the positioning of coal mine employees.