Global Positioning Research Articles

Prevalence and distribution of transmissible venereal tumor in dogs from a rural community of Mexico

Objective: The aim of this work was to determine the rates of prevalence and recurrence of TVT infection in owned dogs, as well as the distribution of the disease, in Cuajinicuilapa, Guerrero, Mexico. Methods: This work was an observational, descriptive, longitudinal study. A TVT diagnosis was performed in all dogs in the community based on clinical records, a physical examination, and a cytological evaluation, and further confirmed by histopathology. Tissue samples were surgically obtained for histopathological analysis and, when required, to remove tumors and give treatment with vincristine; a GPS system was used to identify possible spatial groupings. All cases were re-evaluated one year after. Results: A prevalence of 5.15% was found in 1047 dogs (512 females and 535 males; 25 positive females and 29 positive males). TVT infection was more frequent in 2–3-years-old dogs. A spatial aggregation pattern was observed in the local cases. One year after, no lesions were found in the 29 animals treated. Conclusion: TVT is a common disease, and its distribution suggests its aggregation in certain areas of the locality. Further studies on the dynamics of dog populations in small cities are required.

Analysis and performance evaluation of computation models for node localization in deep sea using UWSN

SummaryUnderwater wireless sensor network (UWSN) connects the real world to the hidden resources available deep into the sea using sensor nodes deployed sparsely and are interconnected to gather information for any movement/change in the ocean. The computed information on nodes obtained from concurrently running UWSN's applications will be meaningful, only if the location of this change will be identified. Nevertheless, it is always difficult to obtain the exact location coordinates of any misadventure like tectonic plate's movement but using localization algorithms in UWSN helps to obtain the coordinates. Since localization algorithms for terrestrial networks are not feasible for UWSN because of environmental challenges. Moreover, GPS systems do not work after 15 m of depth and the radio frequency gets suppressed. So, the requirement arises to analyze existing localization approaches in the deep sea where acoustic signals are used for communication and data transfer. In this paper, along with describing the UWSN's applications and challenges, the underwater localization schemes are reviewed to present, summarize, and mention the scope of improvement. Further, classification into range‐based and range‐free categories of these schemes is depicted with implementation in the NS2.30 simulation environment of some of the recent techniques to showcase the reasons for better performance.

Development, Simulation and Visualization of Autonomous Farm Vehicles

Abstract: A Research in Autonomous Agricultural Vehicle Technology is presented. With the coming of the Smart Farming Revolution the development of more complicated machines has begun. Agricultural machines are made with the capability of driving themselves, using GPS maps and electronic sensors, an approach that will lead to the development of futuristic Precise Autonomous Farming Systems. The basic issues to be addressed are autonomous path planning, obstacle detection and avoidance for which various sensors will be employed such as GPS, laser-based sensors, ultrasonic sensors, IMU sensors and Camera sensors whose data has to be constantly monitored using Dead Reckoning algorithm and constant Pose Estimation. In these sensors, Camera, Ultrasonic, Laser-Based Sensors are used as Detection sensors while the IMU and GPS sensors are used as Localization sensors. Global Avoidance Subsystem which examines the whole environment of an autonomous vehicle mission-level path planner that pre-plans paths around all known obstacles. While Local Avoidance Subsystem/Reactive planner consists of an obstacle filter and an obstacle avoidance algorithm and re-plans a short way for the vehicle to navigate. With the ROS framework and all sensors data, a vehicle can guide itself through an unknown environment. This will help the farmer to undercut the labour shortage and improve the precision in farming which in turn improves the output to meet the growing demands

Integrated Fault Detection System for Underground Lines Using ESP32 and LORA Communication Using IOT

Abstract: This paper introduces an advanced fault detection system tailored for underground line infrastructure, featuring the integration of ESP32 microcontroller technology and LORA communication protocols. The primary objective of this system is to enhance the reliability and efficiency of fault detection processes, crucial for maintaining the integrity of underground lines. At the core of this system lies the ESP32 microcontroller, serving as the central processing unit responsible for coordinating various functionalities. The detection mechanism incorporates six toggle switches strategically positioned along the underground lines, each assigned to monitor a specific location segment. In the event of a fault occurrence, the corresponding toggle switch is triggered, promptly initiating the fault detection process. Upon fault detection, the system seamlessly orchestrates the transmission of GPS location data to a LORA communication device. This data transmission mechanism ensures rapid dissemination of critical information regarding the fault's precise location. Additionally, an LED indicator is employed to provide immediate visual feedback, enabling quick identification of fault occurrences. Moreover, a LCD display unit offers realtime updates on the system's operational status, facilitating efficient monitoring and management. On the receiving end, the LORA communication device securely receives the transmitted data and forwards it to the cloud infrastructure using IOT technology. This cloud integration enables seamless data aggregation, storage, and analysis, empowering stakeholders with valuable insights into underground line health and performance.

Intelligent Helmet for Bike Rider’s Safety

Abstract: The intelligent helmet introduced in the paper aims to bolster rider safety, especially in areas like India with high rates of motorcycle accidents. By incorporating features like alcohol detection and accident identification, it addresses common causes of accidents such as driver negligence or impairment. Integrated with the bike's ignition system, it prevents ignition activation if the rider is intoxicated, thus averting potential accidents. In case of an accident, the helmet autonomously alerts registered numbers or authorities, providing precise GPS location data for swift response. Additionally, it monitors real-time vehicle speed and triggers SMS alerts if speed limits are exceeded, aiming to prevent accidents caused by speeding. Through RF technology, the helmet ensures seamless connectivity with the bike's systems, facilitating efficient data transmission and significantly enhancing rider safety.

Scouts vs. usurpers: alternative foraging strategies facilitate coexistence between neotropical Cathartid vultures

Understanding how diverse assemblages of scavengers can coexist on shared ecological resources is a fundamental challenge in community ecology. However, current approaches typically focus on behaviour at carcass provisioning sites, missing how important differences in movement behaviour and foraging strategies can facilitate sympatric species coexistence. Such information is particularly important for vultures – obligate scavengers representing the most endangered avian foraging guild. Their loss from ecosystems can trigger trophic cascades, mesopredator release and disease outbreaks. We provide the first‐ever analyses of GPS location data from wild King Vultures Sarcoramphus papa and Greater Yellow‐headed Vultures Cathartes melambrotus, coupled with trait data (from both wild‐living and museum specimens) and visitation data from camera traps deployed at provisioned carcasses, to characterize vulture flight behaviour and strategies in the Peruvian Amazon. We found marked species differences in several key movement characteristics, including: King Vultures having home‐ranges five times larger, average flight heights four times greater and ground speeds 40% faster than those of Greater Yellow‐headed Vultures. Despite these differences, both species flew similar distances each day (on average), probably due to King Vultures taking 50% fewer flights and spending 40% less time in the air per day. Consistent with these patterns, King Vulture body mass was more than double that of the Greater Yellow‐headed Vulture, with a substantially larger hang wing index (a measure of long‐distance flight efficiency). At carcasses, Greater Yellow‐headed Vultures typically arrived first but were rapidly outnumbered by both King and Black Vultures Coragyps atratus. We find that the movement behaviour of obligate apex scavengers in the western Amazon is linked to their ability to coexist – Greater Yellow‐headed Vultures, a smaller stature ‘scouting’ species adapted to fly low, forage early and arrive first at carcasses, are ultimately displaced by larger‐bodied, wider ranging King Vultures at large ephemeral carrion resources. Expansion of future GPS tracking initiatives could facilitate the exploration of direct facultative interactions from animal movement data and give further insight into how diverse communities assemble and interact.

Wheat height and phenology retrieval using GPS/BDS interferometric reflectometry technology

Monitoring vegetation growth utilizing the Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) technology has obtained widespread attention in recent years. GNSS-IR based approaches demonstrate the advantageous characteristics of high temporal resolution while maintaining cost-effectiveness. Most existing studies generally utilize reflected signals to retrieve the plant height and vegetation water content, while few studies focus on vegetation phenology and yield. In this study, both phenology metric dates and plant height of wheat were extracted using reflected signals data from GPS and BeiDou Navigation Satellite System (BDS) in a wheat farmland, located at northeast of Henan, China. The results showed that there was reasonable consistency between the retrieval heights and in-situ measurements, with the correlation coefficient of 0.836 for GPS and 0.868 for BDS. The damping coefficient calculated by signal-to-noise ratio (SNR) presented obvious characteristics during different wheat growth stages and the trends of the damping coefficient were basically consistent with that of the normalized difference vegetation index (NDVI). When taking a lag between the leaf area index (LAI) and the damping coefficient into consideration, the correlation was greatly increased. Moreover, the phenology metrics dates extracted by the damping coefficient were close to those estimated by NDVI with errors less than 8 days. Hence, this research preliminarily proves the feasibility of obtaining the wheat phenological period by using GNSS-IR technology.

Female Deer Movements Relative to Firearms Hunting in Northern Georgia, USA.

Perceived risk associated with hunters can cause white-tailed deer (Odocoileus virginianus) to shift their activity away from key foraging areas or alter normal movements, which are important considerations in managing hunting and its effects on a population. We studied the effects of seven firearms hunts on the movements of 20 female deer in two Wildlife Management Areas within the Chattahoochee National Forest of northern Georgia, USA, during the 2018-2019 and 2019-2020 hunting seasons. Deer populations and the number of hunters in our study area have declined significantly since the 1980s. In response, hunting regulations for the 2019-2020 hunting season eliminated opportunities for harvesting female deer. To evaluate the indirect effects of antlered deer hunting on non-target female deer, we calculated 90% utilization distributions (UDs), 50% UDs, and step lengths for pre-hunt, hunt, and post-hunt periods using the dynamic Brownian bridge movement model. Data included 30 min GPS locations for 44 deer-hunt combinations. Pre-hunt 50% UDs (x- = 7.0 ha, SE = 0.4 ha) were slightly greater than both hunt (x- = 6.0 ha, SE = 0.3 ha) and post-hunt (x- = 6.0 ha, SE = 0.2 ha) 50% UDs (F = 3.84, p = 0.03). We did not detect differences in step length, nor did we detect differences in size or composition of 90% UDs, among the periods. Overall, our results suggest that the low level of hunting pressure in our study area and lack of exposure to hunters led to no biologically significant changes in female deer movements. To the extent of the findings presented in this paper, adjustments to the management of hunting in our study area do not appear to be necessary to minimize hunting-related disturbances for female deer. However, managers should continue to consider female deer behavior when evaluating future changes to hunting regulations.

Africa's overlooked top predator: Towards a better understanding of martial eagle feeding ecology in the Maasai Mara, Kenya

Raptors exert top‐down influences on ecosystems via their effects on prey population dynamics and community composition. Most raptors are sympatric with other predators, thus complicating our understanding of their relative influence in these systems. Estimates of kill rates and prey biomass recycling have been used as predation metrics that allow quantitative comparison among species and assessment of the relative role of single species within complex food webs. Few studies have produced findings of kill rates or prey biomass recycling for raptors. We used a supervised machine learning algorithm to behaviourally classify high resolution accelerometer informed GPS locations of tagged adult non‐breeding martial eagles Polemaetus bellicosus in the Maasai Mara region of Kenya to estimate kill rates and prey biomass recycling. Eagle locations classified as feeding were clustered using distance and time thresholds to identify kills and calculate kill rates. Identified kill sites were quickly ground‐truthed to confirm kills and identify prey species. We estimated kill rates for martial eagles at 0.59 kills day‐1 for males and 0.38 kills day‐1 for females, and we estimated biomass recycling per ground‐truthed kill at 1796 g for males and 3860 g for females. From our sample of identified ground‐truthed kills, ‘gamebirds' was the most frequently recorded prey category for male eagles and ‘small ungulates' was the most frequently recorded prey category for female eagles. These results position martial eagles close to sympatric mammalian top predators in trophic pyramids and provide evidence for their classification as a top predator.

Edge Computing Based Cardiac Monitoring and Detection System in the Internet of Medical Things

Currently, cardiac disease becomes one of the major health issues in the world. The death due to cardiac arrests has been increasing day by day. The survival rate for the sudden cardiac arrest is low. It is possible to save 60% human life by continuously monitoring the fitness of the person. In recent times, the Internet of Things is emerging promising technologies. If the doctor cannot monitor the patient face to face, there Internet of Things plays important role in remotely monitoring the patient’s condition. With the rise in the use of smart wearable gadgets, the contribution of IoT will be more. In this paper, about five predictor model are analyzed for a particular dataset and chosen the best as Random forest algorithm. The proposed system integrates oxygen saturation level and heart rate sensor data from MAX30100 sensor for monitoring the patient’s vital signs. The remaining parametric data of the patient (AGE, GENDER, BLOOD SUGAR LEVEL, BLOOD PRESSURE LEVEL, GENETIC ISSUE and CHLOESTEROL LEVEL) are fed through the matrix keypad. The designed system monitors the online patient’s vitals continuously. The patient’s vitals will be stored in the edge server and also analyzed. The chosen predictor model checks the vital data for any abnormality and based on abnormality data detection in the heart rate, the warning message is conveyed to the nearby health care center via Thingspeak cloud. By tracking the patient’s GPS location, the server cloud sends alert notification along with the GPS location to the nearby health center and to the registered mobile numbers.

SOLDIERS HEALTH MONTORING AND TRACKING USING GSM AND GPS

In enemy territory soldiers not only have to deal with the physical threat, but also with stress andfatigue caused by protracted operations or lack of sleep. So for the security purpose we need a tool for remote soldier performance and health monitoring. So in this project a tool is implemented using biosensors for health monitoring purpose. Also a GPS system is used in order to track the location of soldier. Additionally a GSM modem is also used to make the system wirelessly compatible. Any abnormalities in the readings of wireless body area sensor network (WBASNs) is considered as a trigger for GSM to establish the connection between the soldier and base unit and send current location and health status to the receiver. By using all this equipments we had tried to implement the basic guarding system for the soldier in low cost, light weighted, portable and precise device.

A Compact Handheld Sensor Package with Sensor Fusion for Comprehensive and Robust 3D Mapping.

This paper introduces an innovative approach to 3D environmental mapping through the integration of a compact, handheld sensor package with a two-stage sensor fusion pipeline. The sensor package, incorporating LiDAR, IMU, RGB, and thermal cameras, enables comprehensive and robust 3D mapping of various environments. By leveraging Simultaneous Localization and Mapping (SLAM) and thermal imaging, our solution offers good performance in conditions where global positioning is unavailable and in visually degraded environments. The sensor package runs a real-time LiDAR-Inertial SLAM algorithm, generating a dense point cloud map that accurately reconstructs the geometric features of the environment. Following the acquisition of that point cloud, we post-process these data by fusing them with images from the RGB and thermal cameras and produce a detailed, color-enriched 3D map that is useful and adaptable to different mission requirements. We demonstrated our system in a variety of scenarios, from indoor to outdoor conditions, and the results showcased the effectiveness and applicability of our sensor package and fusion pipeline. This system can be applied in a wide range of applications, ranging from autonomous navigation to smart agriculture, and has the potential to make a substantial benefit across diverse fields.

Analysis of Post-Processed Pseudorange-Based Point Positioning with Different Data Sources for the Current Galileo Constellations.

The Galileo satellite navigation system now provides initial services. With further satellite launches, the performance of Galileo will gradually improve, and new services will be introduced. This study aims to provide a comprehensive analysis of Galileo Single Point Positioning (SPP) using different broadcast ephemeris data sources. This study investigates the completeness of Galileo navigation message records from different institutions. The results show that IGS provides the best completeness across different data sources (ECR > 70%), while IGN exhibits the lowest completeness. Analyze the proportions of different data sources within the Galileo navigation message in the broadcast ephemeris files provided by IGS during the study period. The proportions of FNAV_258, INAV_513, INAV_516, and INAV_517 during the study period are 25.83%, 24.76%, 23.61%, and 25.80%, respectively, suggesting better data completeness for FNAV_258 and INAV_517 and poorer completeness for INAV_513 and INAV_516. Finally, this study explores SPP solutions for GPS and Galileo systems using different data sources. The results indicate that a higher ECR corresponds to better positioning performance. Although GPS exhibits smaller error fluctuations and smoother positioning results, Galileo's SPP positioning accuracy surpasses that of GPS. The introduction of dual-frequency observations effectively reduces data dispersion and enhances vertical positioning accuracy.

EVALUATION OF THE PERFORMANCE OF MULTI-GNSS ADVANCED ORBIT AND CLOCK AUGMENTATION – PRECISE POINT POSITIONING (MADOCA-PPP) IN JAPAN REGION

For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don’t have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0–4 millimetres accuracy can be guaranteed on all occasions.

Current Situation of Traditional Architecture Located inside Cultural Mayan Heritage Spaces in Remote Villages of Guatemala: Case of the Black Salt Kitchens

In the town of Sacapulas, located in the mountainous country of Guatemala, there is a constant risk of natural disasters. Floods and landslides occur frequently, resulting in the loss of human lives and cultural aspects. Important to the region, the creation of the black salt is most affected. This resource has been created since the time of the Mayans on the salt beach surrounding the town. However, from the 1940s onwards, this industry has shrunk. As a result, architectural expressions known as “salt kitchens” have almost disappeared, and there is no information on the subject available. By employing interviews, area survey, and GPS mapping, it was discovered that the location of the salt kitchens is determined by the shape of the beach. However, only one third of the beach area is accessible up to this day. It was discovered that the destruction of the salt kitchens is due to natural elements as well as owners reusing the land for other economically viable functions. To preserve their existence, the first plans of the salt kitchens were created, and will help future researchers if necessary.

Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach

SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages of location privacy leakage. Most present location privacy protection solutions are only relevant to 2D locations. However, with the widespread use of positioning‐enabled aircraft, sensing devices and acquisition equipment, there is an urgent need to achieve privacy protection for 3D spatial location data. Therefore, this article proposes a 3D geo‐indistinguishability perturbation method for 3D GPS Locations. The suggested approach calculates GPS coordinate perturbation accurately using the 3D Laplace mechanism, allowing for point‐to‐point perturbation while protecting the privacy of 3D spatial GPS coordinates. Experimental examination of real spatial location datasets demonstrates that the proposed 3D spatial location perturbation method outperforms other existing methods in terms of location service quality loss, data availability, and operational efficiency with the same privacy budget.

Pollinator-assisted plant phenotyping, selection, and breeding for crop resilience to abiotic stresses.

Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant-pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source-to-sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system. Pollinator-assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions.

Autonomous Navigated IOT Enabled Smart Wheel Chair - A Healthcare and Mobility solution

WHO disability statistics indicate that about 8 million people worldwide, or 1% of the total population, suffer from a mobility handicap. It is also known that the elderly residents in the area cannot move independently. Many hospital patients can force their hand with assistance but cannot move independently. We also understand that real-time health monitoring is necessary for patients, the elderly, and individuals with impairments. Therefore, this work aims to develop and provide a smart wheelchair that can be controlled with a single hand gesture and tracks many health indicators, such as blood pressure, body temperature, oxygen saturation, and pulse rate, using an Internet of Things-based real-time health monitoring system. Users can effortlessly maneuver the wheelchair using simple gesture control enabled by the Internet of Things (IoT), promoting independence and enhancing general well-being. This innovative approach to healthcare and mobility meets users' urgent requirements and establishes a new benchmark for assistive technology, a significant advancement in technology and healthcare. The benefit of this strategy is that the caregiver can view all of these health indicators and the wheelchair user's GPS location using a smartphone application. Taking care of oneself while employed by a firm might be easy.

Car Pooling System

Traffic jams, pollution, and wasteful use of resources are just a few of the transportation-related issues that have arisen as a result of urbanization. Adapting sustainable transportation networks is crucial to address these issues, and carpooling is one of the most important tactics in this effort. To support the development of a sustainable transportation ecology in metropolitan areas, this abstract provides a thorough foundation for a carpooling system. Utilizing contemporary technology such as GPS monitoring, data analytics, and mobile applications, the suggested Carpooling System maximizes vehicle occupancy and reduces travel times by pairing drivers and passengers in the most efficient way possible. Ride-sharing experiences are made easy and convenient by the system's integration of user preferences, including routes, departure hours, and passenger compatibility. To further ease traffic congestion, minimize carbon emissions, and encourage the economical use of transportation resources, the Carpooling System was created with sustainability as its primary goal. The approach promotes widespread adoption and involvement by offering incentives like cost-sharing and awards for eco- friendly behavior. The Carpooling System not only improves the environment but also the social and economic spheres by increasing social connections, encouraging community involvement, and saving consumers money on commuting. The concept helps create more inclusive and resilient metropolitan communities by encouraging a shared mobility culture. The importance of including carpooling in the larger context of sustainable urban mobility is generally highlighted by this abstract. A realistic way forward for livable, equitable, and ecologically conscious cities in the face of rising urbanization and transportation issues is provided by the suggested Carpooling System, which embraces innovation, technology, and cooperative efforts.

Design and analysis of 7-stage MOS current mode logic power gated MOSFETs in current starved voltage-controlled oscillator for the phase locked loop application

This paper presents a new process, voltage and temperature (PVT) tolerant 7-stage ring type current starved voltage-controlled oscillator (CS-VCO). In this, a 7-stage ring VCO is proposed using power gated technique for phase locked loop (PLL) application. PLL plays a major role in clock and data recovery, Global Positioning System (GPS) system and satellite communications. For the high-speed application of PLL it is designed using 7-stage inverter delay cell with MOS current mode logic (MCML) technique. The circuit undergoes process, voltage and temperature variations with different parameters such as average power, oscillation frequency, phase noise, tuning range and output noise. The Monte-Carlo analysis justifies the proposed design provides better results. The circuit is simulated under 45 nm CMOS technology using cadence virtuoso. The average power consumption of the proposed circuit is 29.368 µW with the oscillation frequency of 3.06 GHz. The output noise and the phase noise of the proposed VCO are -161.55 dB and -125.92 dBc/Hz respectively. It achieves the frequency tuning range (FTR) of 95.09%. The obtained simulation results are highly robust with PVT making the circuit suitable for PLL application.