Longitude Values Research Articles

Dependence of the main ionospheric trough position on local time, longitude and geomagnetic activity in the southern winter hemisphere

Based on the meticulous identification of ionization troughs, performed earlier from the CHAMP satellite data, twoadditional issueswereresolved: (1) the longitudinal effect characteristics in the position of the main ionospheric trough (MIT) were corrected, and (2) for the first time,the dependence of theMITposition on geomagnetic activity was determined foralllocaltime hours. A large dataset from the CHAMP satellite in the southern winter hemisphere under high solar activity was utilized. According to the refined data the amplitude of the longitudinal effect in the MITposition changes from ∼ 3° to ∼ 5° in the course of the day. The shape of the longitudinal effectvarieswith local time, however, the MIT in the eastern hemisphere isconsistentlylocated at higher latitudes than in the western hemisphere. The main reason for the longitudinal effect is the dependence of the equatorward boundary of auroral diffuse precipitation on the tilt angle of the Earth’s dipole. The dependence on geomagnetic activitywas determined as a linear regression ΛT = Λ0 − aKp, where Λ is the geomagnetic latitude, and the Kp indexisconsidered for the previous6 h. The latitude Λ0 and coefficient a exhibited pronounced dependence on local time, with Λ0 increasing and a decreasing when moving from night to day. Because the amplitude of the longitudinal effect decreases with increasing magnetic activity, the value of a alsodependson longitude. Consequently, coefficienta wasdetermined separatelyforthe eastern and western hemispheres. Theaveragevaluesof a vary from 1.3 − 1.4° during the day to 1.8 − 1.9° at night. Thedifference between theeastern and western hemispheres is ∼ 0.3°.

Decision Tree-Based Direction Detection Using IMU Data in Autonomous Robots

Location detection plays a crucial role in various applications. In this study, a machine learning (ML) method using inertial measurement unit (IMU) data was developed to determine direction with the Global Positioning System (GPS). In this study, an electronic board was designed using an Arduino Mega, Altimu-10 IMU sensor, GPS module, and SD card module. This electronic board was placed on a car to create a new dataset. This dataset consists of 1952x11 data. The dataset was obtained using accelerometer (x, y, z), gyroscope (x, y, z), compass (x, y, z), and GPS sensor data. The Decision Tree Algorithm was proposed for direction determination in this study. The angles between each position and the previous position were calculated using the latitude and longitude values obtained from the collected data. Then, the data were divided into 4 classes: North, East, South, and West, based on specific angle ranges. Finally, a direction detection model was developed using IMU data in the proposed method, achieving an accuracy of approximately 82.11%.

WOMAN SAFETY APP

In today’s world, people using smart phones have increased rapidly and hence, a smart phone can be used efficiently for personal security or various other protection purposes. The heinous incident that outraged the entire nation has woken us to go for the safety issues and so a host of new apps have been developed to provide security systems to women via their phones. This project presents, an Android Application for the Safety of Women, this app can be activated by just shaking phone three times, whenever need arises. By just three times shake identifies the location of place through GPS and sends a message comprising this location URL to the registered contacts to help the one in dangerous situations. The unique feature of this application is to send the message to the registered contacts after the phone is shook for three times. Instant location tracking information via SMS helps to find the location of the victim quickly and can be rescued safely. A wide range of tracking systems has been developed for tracking vehicles and displaying their position on a map, applications has been developed which tracks the mobility of a human being. Now a days tracking a person’s mobility has become a crucial issue, specifically in case of women. If tracking a criminal could be implemented as a system which is cost effective and can be used for tracking a human being using a GPS equipped mobile phone rather than using a handheld GPS receiver. The main aim of our project is to develop an application that reduces the overall cost of tracking based on GPS system, which is satellite-based service, and it is available 24X7 everywhere in the whole world. GPS system can be used to get location which includes details like latitude, longitude and altitude values along with the time details, etc. It is a free of cost service available to every individual. In order to track the movement of the person we have used Google maps for mapping the location sent by the mobile phone. The mobile phone fetches the GPS location which communicates with the server using General Packet Radio Service (GPRS). This system is a low-cost service which is wireless data communication system. Mobile phone equipped with GPS receiver are easily available in the market, it is a latest technology. The mobile phone technology has enabled us to communicate across the world. The GPRS is one of the best and cheapest mode of communication available today. Key Words: Android, GPS, URL, Registered Contacts

Design and Implementation of Smart Blind Stick for Obstacle Detection and Navigation System

The design, advancement, and evaluation of an IoT -enabled smart stick that can detect and alert users to obstacles is presented in this study to help visually impaired persons traverse the outside world. A water sensor deployed to identify puddles and other wet surfaces in the user's route, and ultrasonic sensors for detecting obstacles are all used in the suggested design. Additionally, after correctly detecting and recognising items, the user receives voice feedback through earphones regarding various obstacles and objects. The suggested smart stick has two modes: the first detects and recognises obstacles and provides speech feedback; the second mode employs ultrasonic sensors for detecting and response through vibration motors to inform about the direction of the obstacle. The suggested method enables users to alternate between the two modes based on their preferences and the surroundings. Additionally, the user's latitude and longitude values are recorded and uploaded to the IoT platform for efficient tracking through global system for wireless communication modules, allowing the user or device to be tracked in real time on the dashboard. An IoT-enabled environment generates an alert signal in the form of an SMS with Google Maps link generated using latitude and longitude coordinates, and this serves as a panic button for emergency assistance. The smart stick's design includes features such a long battery life, size adjustment, waterproofing, and light weight. Robust features, portability, stability, ease of access, and energy efficiency are all guaranteed by the entire design.

Upward and Poleward (but Not Phenological) Shifts in a Forest Tenebrionid Beetle in Response to Global Change in a Mediterranean Area.

There is an increasing volume of literature on the impact of climate change on insects. However, there is an urgent need for more empirical research on underrepresented groups in key areas, including species for which the effects of climatic change may seem less evident. The present paper illustrates the results of a study on a common forest tenebrionid beetle, Accanthopus velikensis (Piller and Mitterpacher, 1783), at a regional scale within the Mediterranean basin. Using a large set of records from Latium (central Italy), changes in the median values of elevation, latitude, longitude, and phenology between two periods (1900-1980 vs. 1981-2022) were tested. Records of A. velikensis in the period 1981-2022 showed median values of elevation and latitude higher than those recorded in the first period. Thus, in response to rising temperatures, the species became more frequent at higher elevation and in northern places. By contrast, A. velikensis does not seem to have changed its activity pattern in response to increased temperatures, but this might be an artifact due to the inclusion of likely overwintering individuals. The results obtained for A. velikensis indicate that even thermally euryoecious species can show changes in their elevational and latitudinal distribution, and that poleward shifts can be apparent even within a small latitudinal gradient.

Machine learning empowered prediction of geolocation using groundwater quality variables over YSR district of India

Machine Learning (ML) has been used in the prediction of geolocation with improved accuracies in this work. The pre-processed data was subjected to prediction analytics using 22 machine learning algorithms over regression mode. It was observed that Extra Trees Regressor performed well with better accuracies in predicting latitude, longitude, and Haversine distance, respectively. Regression models like CatBoost, Extreme Gradient boosting, Light Gradient boosting machine, and Gradient boosting regressor were also tested. The R2 values were computed for each case, and we obtained 0.96 (Longitude), 0.98 (Latitude), and 0.96 (Haversine), respectively. The evaluation of models was done using metrics like MAE, MASE, RMSE, R2, RMSLE, and MAPE and R2 is considered most important than others. The effect of data point was calculated using Cooks’ distance, and the variable fluoride has a significant impact on the prediction accuracy of Longitude followed by RSC, Cl, SO4, SAR, NO3, NA, Ca, EC and pH variables. In the prediction of latitude, the SAR variable played a significant role, followed by Na and TH. According to the t-SNE manifold, three longitude values were quite different from the others. This work is supported by some of the manifests like Cooks’ distance outlier detection, feature importance plot, t-SNE manifold, prediction error plot, residuals plot, RFECV plot, and validation curve. This work is done to report that the challenge of predicting both latitude and longitude on a common ground is solved partially, if not completely, and machine learning tools can be used for this purpose. Haversine distance can be obtained from latitude and longitude and can be used in the prediction of geolocation.

상수관망 관리를 위한 3D 공간정보 시스템 및 모델 프로토타입 개발

Recently, water supply problems and the advent of the 4th Industrial Revolution, smart water supply network management using digital twin technology is becoming needs. In this study, to develop a prototype of the model and system 3D visualization information system for water supply network management based on own technology without restrictions on licenses. Based on the data from the ME(2017), an inventory DB according to the Level classification was established, and for 3D visualization based on 2D information systems, 9-steps of implementation process and verification were conducted. Comparing and analyzing the values of latitude and longitude of polygons, it was confirmed that the reliability and accuracy of the two-dimensional information system implemented in this study were secured. Therefore, 3D visualization was performed using this, and the process of designing data linkage plans within the program and implementing 3D visualization was established. The results of this study are considered insignificant in the construction of a network management space server with high economic benefits and high reliability and accuracy of GIS.

Autonomous method for a strapdown inertial navigation system calibration in operation in operation

Strapdown inertial navigation systems (SINS) are widely used in modern air transport. The service life of such systems is decades. During this time, the characteristics of such systems may deteriorate due to the degradation of inertial sensors - gyroscopes and accelerometers. Under these conditions, in order to maintain the required accuracy of the SINS during the entire period of operation, it is necessary either to periodically carry out routine maintenance with the onboard system, or to continuously adapt the used sensor measurement model in terms of compensating for errors that arise. The second direction, which we will call calibration, has the advantage over the first one that it is carried out automatically by the system itself and does not require any additional equipment or additional maintenance work. The known method of SINS recalibration is based on the integration of information from the SINS and from the receiver of satellite radio navigation signals (SRNS). Such calibration, firstly, significantly depends on the availability and quality of satellite information, and secondly, it is possible only for integrated navigation systems in which the inertial subsystem is supplemented by a SRNS receiver. In contrast to the known method, this article solves the problem of autonomous SINS calibration, which is carried out after each flight. The solution is based on a linearized inertial navigation error model based on the boundary conditions of the state vector (at the beginning and end of the flight). The external information used consists only of the initial values of the coordinates and the current measurements of the baroaltimeter. As a result of the decision at the end of the flight, the errors of gyroscopes and accelerometers are determined, as well as the error in determining the final values of latitude and longitude in the SINS. Sensor errors can be used in subsequent system activations, which improves the accuracy of its operation as a whole. The analysis of the suitability and effectiveness of the developed methodology was carried out on a typical flight simulation program.

Pemanfaatan Teknologi Augmented Reality Sebagai Media Navigasi Berbasis Aplikasi Android

The navigation media on the Universitas Teknologi Yogyakarta campus environment currently still uses conventional guidance media in the form of printed pictures and mock-up plans, so it is considered ineffective and inefficient in providing directions for students, staff, or campus visitors, especially in the outdoor areas where signage is rarely installed. This research aims to assist 3D virtual navigation services by utilizing augmented reality technology based on longitude and latitude coordinate point data of an area, which is implemented in the form of an Android mobile application. The research method used is the case study method, with a qualitative approach. This application development model uses the waterfall model. The stages used in this research include planning, data collection, analysis, design, implementation, and application testing using Black box testing with the test case method, which produces application testing that descriptively explains the application work process. Application test results show that route search navigation, information features, and application menus work well. The combination of augmented reality technology and a global positioning system can be implemented to recognize various navigation routes based on longitude and latitude numerical values very well.

Taxi fare prediction based on multiple machine learning models

The use of taxis as a fundamental mode of transportation in everyday life has led to the increased popularity of various ride-hailing applications such as Uber and Lyft, enabling users to conveniently request and view the predicted fare for their desired destination. Accurately predicting the fare is thus of significant importance. In this study, machine learning models were employed to forecast taxi fares based on factors such as distance and passenger count. As the initial data only contained latitude and longitude values, the Haversine formula was utilized to calculate the distance between two locations. Moreover, the raw data was plagued with inconsistencies such as negative fares and grossly exaggerated distances, which were resolved by implementing four data cleaning criteria. Following the preprocessing stage, three distinct models (i.e., linear regression, decision tree, and random forest) were trained and evaluated using the root mean square error metric. The results indicated that the random forest model produced the smallest error (1.264), followed by the decision tree model with a similar error rate (1.277), and lastly, the linear regression model with the highest error (1.718). Thus, the random forest model demonstrated superior performance and is recommended for accurate fare predictions.

Modeling and estimation of solar radiation of Karachi through artificial neural network (ANN) using temperature and dew-point

The most influential source of energy in our lives is solar energy. Solar energy reaches the earth in three different forms, i.e., Global, diffused, and Direct Solar Radiation. The Solar flux at the earth's surface depends on the intensity of these radiations and is a function of the values of latitude and longitude. The earth's temperature and hence dewpoint are greatly affected by solar flux. This idea is used for predicting solar radiation with input parameters, temperature, and dewpoint along with day number and month. The method of prediction of solar radiation used in the study is Artificial Neural Network (ANN). ANN has four variables in the input, ten neurons in the hidden layer, and three output parameters GSR. DSR and BSR. Six different types of errors, namely, Root Mean Square error (RMSE), Mean Absolute Error (MABE), Mean percent error (MAPE), Chi-square, Coefficient of Determination, Kolmogorov Smirnov, have been calculated for training, testing, and validation mode to check the accuracy of estimation. The values of all the errors are low, which indicates the prediction of solar radiation is reliable.

Impact of Cancer-Related Virtual Visits on Travel Distance, Travel Time, and Carbon Dioxide (CO2) Emissions during the COVID-19 Pandemic in Manitoba, Canada.

CancerCare Manitoba (CCMB) introduced virtual visits at the beginning of the COVID-19 pandemic to replace many in-person visits. This study examines the impact of virtual visits for cancer care on travel distance, travel time, and carbon dioxide (CO2) emissions. We included all visits to CCMB for invasive and in situ cancers from 1 April 2020 to 31 December 2022. Data were extracted from CCMB's electronic health record. The percentage of visits done virtually by month was reported by age, gender, cancer diagnosis, and regional health authority of residence. Postal codes for patients' residences and clinic locations were converted into latitude and longitude values. Travel distance, travel time, and CO2 emissions associated with travel were estimated. The percentage of virtual visits was highest during the months when COVID-19 restrictions were present in Manitoba and represent more than 50% of such monthly visits. Virtual visits increased with age, were highest among men with urogenital cancer, and were lowest among northern Manitoba residents. The median travel time per visit ranged from 30 min in Winnipeg to 15 h in the Northern Region. The estimated travel distance saved varied from 420,000 to 750,000 km per month. Estimated travel time saved varied from 5500 to 9600 h per month. Estimated CO2 emissions prevented varied from 87 to 155 metric tons per month. Virtual care is an important tool for better supporting those living with cancer by substantially decreasing travel distance and time. Virtual care also contributes to reducing greenhouse gas emissions.

English

Soil health in terms of plant available nutrients is crucial to estimate the yield potential of agricultural land in an area. Based on the availability of soil health data, it is possible to set yield targets using various yield prediction models. This study was initiated to estimate the concentration of DTPA-extractable micronutrients in Tehsil Muzaffargarh and to develop a database that can be used to develop predictive models. Soil samples (n=11537) were collected from Muzaffargarh (0-15 cm depth). The latitude and longitude values of sampling points were recorded for the purpose of georeferencing the soil samples. Micronutrients such as zinc (Zn), copper (Cu), iron (Fe) and manganese (Mn) were extracted with diethylenetriamine penta-acetate (DTPA) extraction solution and their concentrations were determined with atomic absorption spectrophotometer. Water soluble boron (B) was determined spectrophotometrically using Azomethine-H as colour developing reagent. Critical limits established by the National Agricultural Research Center (NARC) Islamabad, Pakistan, were used as a benchmark for grading micronutrient status. The ordinary kriging technique was used to visualize the micronutrient status in the surveyed area and digital maps were prepared using Quantum Geographic Information System (QGIS) software. Results showed that the range of DTPA-extractable micronutrients Zn, Fe, Mn, and Cu ranged from 0.10-5.92, 0.01-12.51, 0.09-10.00 and 0.03-8.08 µg/g, respectively. Boron concentration ranged from 0.02-1.98µg/g. Most of the soils in the studied area had adequate levels of Zn (61.9%) and Cu (48.6%) while 45.8, 43.3 and 55.4 % of the soil samples fell into the marginal category in terms of Cu, Mn, and B, respectively. The results revealed the widespread deficiency of DTPA-extractable Fe (< 4.5mg kg-1 in 74% soil samples) in Tehsil Muzaffargarh. The digitized maps developed from this study would serve as primary source to locate micronutrient deficient areas and also set site-specific yield targets along with other biophysical factors. However, these maps need ground truthing after crop harvest each year.

Parametrized pre-trained network (PPNet): A novel shape classification method using SPHARMs for MI detection

PurposeShape information contains descriptive knowledge for classification problems. A novel methodology for combining the valuable 3D and 3D + t shape information with the advantages of deep transfer learning is introduced. This method produces informative images from shape information, which are interpretable for deep CNNs previously trained using a large-scale image dataset. MethodsOur proposed structure can (1) generate 3D surfaces with optimal spatial resolution, (2) parametrize them using spherical harmonics shape descriptors (SPHARMs) with optimal frequency resolution, (3) employ registration to establish surface correspondences, and (4) convert the 3D corresponding point clouds to 2D images by spreading out the latitude and the longitude values of spherical components in the form of a flat coordinate system. The generated 2D images have the potential to feed into the pre-trained deep CNNs. The efficiency of the proposed method was assessed in myocardial infarction (MI) classification using two different datasets. ResultsFive well-known CNN architectures as fixed feature extractors were evaluated. Deep features derived from the output of convolutional and pooling layers were fed into the SVM classifier. We also explored the feasibility of adding additional information by assigning a color to each RGB channel. Subsequently, EfficinetNet-b0 reached the best performance, with an accuracy of 97.92% ± 1.47 using colored images containing additional information, respectively. ConclusionOur suggested approach by incorporating the transformed shape information and CNNs can be adopted as a robust computer-aided diagnosis system to assist clinicians in detecting various diseases such as MI.

System Control Holding Position on Quadcopter Using GPS with Waypoint

This study designs an autonomous quadcopter system that can work in outdoor areas using a waypoint and quadcopter system so that it can fly without being controlled to complete a given mission with accuracy that has a low offside value. The tests carried out were divided into 3 tests using 3 different missions using Post Hold flight mode and a height of 5 m, on each mission there were 3 waypoints with each waypoint distance of 10 m which later on each waypoint would perform a Holding Position. at each waypoint point and see the error value generated based on the waypoint point by calculating the Latitude and Longitude values between the vehicle and the waypoint point so as to get the error value between the vehicle and the waypoint point and then make a comparison with measurements using a measuring instrument at each waypoint point in carrying out missions.

Time Optimal Drag-Based Targeted De-Orbiting for Low Earth Orbit

Controlled de-orbiting plays a crucial role in any space mission to ensure landing of a satellite or capsule in the desired location and preventing damage to people and property on the ground caused by debris. The necessary orbital energy reduction from the initial conditions to the re-entry interface can be achieved using a de-orbit burn or exploiting drag modulation as a control mechanism. The current work perfectly fits in this scenario, proposing a novel algorithm to generate minimum-time optimal trajectories for a satellite ballistic de-orbiting from a Low Earth Orbit (LEO) to the atmospheric re-entry interface. The formulation is written in terms of modified equinoctial orbital parameters, particularly suitable for trajectory analysis and optimization, even in cases of oscillatory problems with large time scales as in the de-orbiting problem. The optimization problem is solved with the MATLAB software GPOPS-II using a hp adaptive Gaussian quadrature orthogonal collocation method. It is formulated as a single-stage optimization problem considering the exposed surface as a control variable. The cost function to be minimized is the final time, while the imposition of an event constraint on the altitude at the de-orbit point ensures its value is in an acceptable range. A novel class of solutions is defined for the algorithm implementation to guarantee the desired values of latitude and longitude. It has been used to generate high-precision optimal trajectories and corresponding control variable laws in different conditions. The identification of a common trend of solutions along an infinite-shaped pattern allowed the possibility to model a wide range of missions, involving different initial conditions and satellites. A subsequent Monte Carlo analysis showed the algorithm validity and robustness with a successful outcome on 500 cases and an error less of 0.5% for most of them.

RESULTS OF SEMI-NATURAL TESTS OF THE AUTONOMOUS DETERMINATION OF THE VEHICLE LONGITUDE AND LATITUDE

Navigation parameters such as vehicle heading, linear speed and latitude and longitude are determined today, as a rule,using satellite navigation systems. However, there is a need for autonomous determination of navigation parameterswhen satellite navigation system signals are unavailable for one reason or another. For example, satellite navigationsystem signals can be jammed by electromagnetic countermeasures.Autonomous systems for determining the vehicle navigation parameters are inertial navigation systems, the basis ofwhich is the double integration of signals from accelerometers, as well as the integration of signals from gyroscopes toform one or another coordinate system, if we are talking about strapdown inertial navigation systems (SINS).Integration of the output signals of accelerometers and gyroscopes, which contain errors, leads to the accumulation ofSINS errors. To increase accuracy, SINS are integrated, for example, with satellite navigation systems. However, suchsystems cease to be completely autonomous.The article uses the method of autonomous determination of vehicle latitude and longitude, which is a new method ininertial navigation. The method is based on the strapdown inertial technology, which, unlike the standard methods usedin SINS, does not use the integration of the output signals of accelerometers and gyroscopes. To determine the angles oflatitude and longitude, the signals of the gyroscopes included in the inertial measurement unite (IMU) are used.As a result of a semi-natural experiment, the efficiency of the method of autonomous determination of latitude andlongitude was verified. Flight data of a small-sized aircraft were used for experimental verification. The data containedinformation about the angles of the heading, pitch, roll of the aircraft, the output signals of three micromechanicalgyroscopes, three micromechanical accelerometers, information about latitude, longitude and altitude measured by thereceiver of the satellite navigation system, as well as information about the horizontal and vertical components of speedand current time. Plots of calculated values of latitude and longitude and values measured using the receiver of thesatellite navigation system are given. From the given plots and tables, it can be seen that from a certain time, thecalculated values coincide with the "reference" ones, which confirms the efficiency of the method of autonomousdetermination of latitude and longitude.

Problematika Hukum Shalat Gerhana Saat Tidak Tampak

Abstract Eclipses are one of the rare natural phenomena. Apart from being a natural phenomenon, the appearance of an eclipse is the cause of the sunnah of the eclipse prayer. This phenomenon can be known by using contemporary methods of reckoning. To get to know more the author will discuss "Problematics of the Law of Eclipse Prayer When Invisible". The method used in this study is a qualitative type of library research. By understanding eclipses from an astronomical perspective, eclipses in the perspective of Islamic law, the types of eclipses, and the problems that are the reasons why it is sunnah or not to pray eclipses. Based on the results of the readings that the author did, there are several points, namely: astronomically, the eclipse phenomenon is defined as the closing of the observer's view of the celestial body by other celestial bodies that are closer to the observer caused by the value of astronomical longitude and the angle of inclination of the Moon's orbit around the Earth. A penumbral lunar eclipse is not a sunnah to khusuf prayer. When during an eclipse the weather is cloudy with thick clouds or heavy rain, then there are two opinions, namely: it is not sunnah to pray the eclipse "madzhab rukyah" and it is still sunnah to pray the eclipse "madzhab hisab".

MULTI-BRANCH DEEP LEARNING BASED TRANSPORT MODE DETECTION USING WEAKLY SUPERVISED LABELS

Abstract. Mobility data, based on global positioning system (GPS) tracking, have been widely used in many areas. These include, but not limited to: user direction guidance, analyzing travel patterns, and evaluating travel impacts. Transport Mode Detection (TMD) is an essential factor in understanding mobility within the transport system. A TMD model assigns a GPS point or a GPS trajectory to a particular transport mode based on the user’s current activity. However, the complexity of the prediction procedure increases with the number of modes that need to be predicted given the increasing overlaps in feature values between multiple transportation modes. Hence, this study proposes a two-branch deep learning-based TMD model that predicts multi-class transport modes to improve prediction accuracy. In addition, it proposed a weakly supervised labelling model using snorkel to improve the volume of labelled data and resulting TMD model prediction accuracy. We considered publicly available road networks, railway networks, bus routes, etc., for creating road, bus, train labels by overlaying GPS points on these transportation networks. We introduced a boolean (true/false) based soft-labelling function, where the same GPS point overlaid on road or railway network. The raw GPS data were used to generate point-level features such as speed, speed difference, acceleration, acceleration difference, initial bearing and bearing difference, all used as derived features for the TMD model. To construct the model we opted to use two branches where raw GPS latitude and longitude values were used in one and the derived mobility features are used in the other.

ANTI THEFT VEHICLE TRACHING SYSTEM USING INTERNET OF THINGS (IOS)

Nowadays security of a vehicle is up most important to everyone. Everyday hundreds of vehicles are beings toleninacity. To provide that an efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. An Anti-theft vehicle system is developed using IOT which is both userfriendly and cost effective with the help of the Microcontroller. He receives the location of the vehicle with the help of GPS module in the form of string i.e., the latitude and longitude values of the exact location of the vehicle. The Google Maps API is used to display the vehicle on the map in the Smart-phone application. Thus, users will be able to continuously monitor a moving vehicle using the Smart-phone application and determine the estimated distance and time for the vehicle to arrive at a given destination.