FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
(SEE SECTION 10, RULE 13)
DEVICE FOR COMPUTING OPTIMAL DISTANCE TRAVELLED BY A VEHICLE AND METHOD THEREOF
The following specification particularly describes the invention and the manner in
which it is to be performed.

DEVICE FOR COMPUTING OPTIMAL DISTANCE TRAVELLED BY A VEHICLE AND METHOD THEREOF
TECHNICAL FIELD
[0001] The present invention relates to a device and a method to compute optimal distance travelled by a vehicle. Particularly, the present invention relates to Course over Ground (COG) based technique for computing optimal distance travelled by a vehicle on a curvilinear path. BACKGROUND
[0002] Conventionally, distance travelled by a vehicle is calculated using odometer which is part of the vehicle. However, for a fleet operator that manages a plurality of vehicles, computing the distance travelled by each of these vehicles is both arduous and time consuming. To ease the process, the fleet operator may remotely observe and identify specific location points at pre-determined intervals and subsequently collect the data on the said points. These points can be referred as sampling points, which are utilized in computation of the total distance travelled by a vehicle. Further, this technique to collect data at the sampling points is called a sampling technique. The prior art defines a time-based and distance-based sampling techniques that identifies the sampling point according to pre¬determined intervals of time and distance. However, there is no disclosure of a sampling technique that considers the trajectory of the path being travelled, i.e., whether the path is straight line path or a curved path.
[0003] Further, in areas where internet access is limited or metered, it becomes imperative to reduce the sampling rate, i.e., identify a minimum number of sampling points. This is done in order to efficiently utilize network resources in such areas and further reduce the associated cost for the metered connection. However, by reducing the sampling rate, the accuracy of the distance computation is also affected.
[0004] Thus, there arises a need for a method that considers and optimizes all the above-mentioned limitations without affecting the accuracy of the distance computation.

SUMMARY
[0005] The present disclosure overcomes one or more shortcomings of the prior art and provides additional advantages discussed throughout the present disclosure. Specifically, the present invention discloses a device and a method for transmitting a relevant location data to a remote server for updating a location of a vehicle. The present invention also discloses a server and a method for computing optimal distance travelled by a vehicle.
[0006] The main object of the invention is to provide a method and device for computing optimal distance travelled by a vehicle, particularly when the vehicle is traversing a curvilinear path. The present invention is particularly beneficial for distance computation in hilly and mountainous regions, where there are numerous hair pin bends in the traversed path.
[0007] Another object of the invention is to provide for an optimal combination of time based, distance based, and Course of Ground (CoG) based sampling techniques for optimal distance computation.
[0008] According to an aspect of the present disclosure, methods, devices, and servers are provided for computing optimal distance travelled by a vehicle.
[0009] In a non-limiting embodiment of the present disclosure, the present application discloses a method for transmitting a relevant location data to a remote server for updating a location of a vehicle The method comprises generating the relevant location data of the vehicle and receiving Global Positioning System (GPS) data. Further the method comprises decoding the GPS data to obtain values for a plurality of parameters. Herein, the plurality of parameters comprises at least a Course-over-Ground COG parameter corresponding to the location of the vehicle. The method further comprises determining whether to transmit the relevant location data to the remote server for updating the location of the vehicle based on a value of the COG parameter.

[0010] In one non-limiting embodiment, the method further comprises transmitting the relevant location data to the remote server for updating the location of the vehicle, when the value of the COG parameter is greater than a COG threshold value.
[0011] In one non-limiting embodiment, the method further comprises obtaining vehicle data. Here, the vehicle data comprises a start time, a current time, and a current speed. Further, the method comprises computing an elapsed time based on the start time and the current time and thereby determining whether the elapsed time is greater than a time threshold value when the value of the COG parameter is not greater than a COG threshold value. Further, on determination of the elapsed time being greater than the time threshold value, it is further determined whether the current speed is greater than a speed threshold value. Further, the method comprises determining whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle, based on the current speed. The method further comprises transmitting the relevant location data to the remote server for updating the location of the vehicle, when the current speed is greater than the speed threshold value. [0012] In yet another non-limiting embodiment, the method further comprises obtaining the vehicle data when the elapsed time is less than or equal to the time threshold value.
[0013] In yet another non-limiting embodiment, the method further comprises obtaining the vehicle data when the current speed is less than or equal to the speed threshold value.
[0014] In yet another non-limiting embodiment, the plurality of parameters comprises a latitude parameter, a longitude parameter and a current speed parameter of the vehicle.
[0015] In yet another non-limiting embodiment, the relevant location data further comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.

[0016] In another non-limiting embodiment of the present disclosure, the present disclosure discloses a method for computing optimal distance travelled by a vehicle. The method includes obtaining relevant location data from a telematics device. The method further includes storing and updating the relevant location data in a database. Further, the method includes computing optimal distance travelled by the vehicle based on the updated location data. [0017] In yet another non-limiting embodiment, the relevant location data further comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.
[0018] In another non-limiting embodiment of the present disclosure, the present disclosure discloses a telematics device transmitting a relevant location data to a remote server (128) for updating a location of a vehicle. The telematics device includes a memory and a location module configured to receive GPS data. Further the telematics device further includes a control unit configured to generate the relevant location data of the vehicle and decode the GPS data to obtain values for a plurality of parameter. Here, the plurality of parameters comprises at least a Course-over-Ground (COG) parameter corresponding to the location of the vehicle. Further the control unit is configured to determine whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle based on a value of the COG parameter.
[0019] In another non-limiting embodiment, the control unit of the telematics device 110 is further configured to transmit the relevant location data to the remote server for updating the location of the vehicle, when the value of the COG parameter is greater than a COG threshold value.
[0020] In another non-limiting embodiment, the control unit of the telematics device is further configured to obtain vehicle data. Here, the vehicle data comprises a start time, a current time and a current speed. Further the control unit is configured to compute an elapsed time based on the start time and the current time and further determine whether the elapsed time is greater than a time threshold value when the value of the COG parameter is less than or equal to a COG threshold value. Furthermore, the control unit is configured to determine

whether the current speed is greater than a speed threshold value when the elapsed
time is greater than the time threshold value. The control unit is further configured
to determine whether to transmit the relevant location data to the remote server for
updating the location of the vehicle, based on the current speed. The control unit
is further configured to transmit the relevant location data to the remote server for
updating the location of the vehicle, when the current speed is greater than the
speed threshold value.
[0021] In another non-limiting embodiment, the memory of the telematics
device is configured to store vehicle data.
[0022] In another non-limiting embodiment, the control unit of the telematics
device is further configured to obtain the vehicle data when the elapsed time is
less than or equal to the time threshold value.
[0023] In another non-limiting embodiment, the control unit of the telematics
device is further configured to obtain the vehicle data when the current speed is
less than or equal to a speed threshold value.
[0024] In another non-limiting embodiment, the plurality of parameters
further comprises a latitude parameter, a longitude parameter and a current speed
parameter of the vehicle.
[0025] In another non-limiting embodiment, the relevant location data of the
telematics device comprises a current time, a current latitude, a current longitude
and a current speed of the vehicle.
[0026] In another non-limiting embodiment of the present disclosure, the
present disclosure discloses a remotes server configured to compute an optimal
distance travelled by a vehicle. The server includes a distance calculation module
configured to receive relevant location data from a telematics device. Further the
distance calculation module is further configured to store and update the relevant
location data in a database; and subsequently compute optimal distance travelled
by the vehicle based on the updated location data. Here, the server also includes a
memory configured to store the database.

[0027] In another non-limiting embodiment, the relevant location data of the remote server comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.
BRIEF DESCRIPTION OF THE DRAWINGS [0028] FIG. 1A illustrate exemplary curvilinear path 100A, travelled by a vehicle, associated with a plurality of points.
[0029] FIG. 1B illustrate exemplary scenario 100B, to compute a distance travelled by a vehicle, in a curvilinear path, using a plurality of points. [0030] FIG. 1C illustrates a block diagram 100 of a system for compute an optimal distance travelled by a vehicle, in accordance with some aspects of the present disclosure.
[0031] FIG. 2 illustrates an exemplary method 200 for transmitting a relevant location data to a remote server (128) for updating a location of a vehicle, in accordance with some aspects of the present disclosure.
[0032] FIG. 3 illustrates an exemplary method 300 for transmitting a relevant location data to a remote server for updating a location of a vehicle, in accordance with some aspects of the present disclosure.
[0033] FIG. 4 illustrates an exemplary method 400 for computing an optimal distance travelled by a vehicle, in accordance with some aspects of the present disclosure.
[0034] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown. DETAILED DESCRIPTION
[0035] In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration”. Any embodiment or

implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.
[0036] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure. [0037] The terms “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[0038] The terms like “server” and “remote server” have been used interchangeably throughout the disclosure. Further, the terms like “device” and “telematics device” have been used interchangeably throughout the disclosure.
[0039] Referring to Figure 1A that depicts an exemplary path 100A for a vehicle travelling on a curvilinear through points a, b, c, d, and e. Typically, it is easier and convenient for a GPS enabled telematics device to accurately calculate the distance between point a and point b, as the vehicle travels the shortest distance between these points in a straight line. However, it is difficult for the GPS enabled telematics device to compute the path from point a to point e, as the device computes the minimal straight-line distance (i.e., a straight-line path from point a to point e), rather than traversing through the curvilinear path a - b - c - d -e. The direct path between a and e is much smaller than the curvilinear distance between a and e. This is because the conventional distance computation technique

calculates the shortest distance between 2 given points. Typically to overcome this, time-based and distance-based sampling techniques may be used to identify sampling points according to pre-determined intervals of time and distance.
[0040] Conventionally, in a time-based sampling technique, a time threshold is pre-programmed and preset within a control unit of a vehicle. The time elapsed from a source to the current location is calculated and elapsed time is noted. If the difference between elapsed time is greater than a preset time threshold, then the current location is identified as the sampling point. In a conventional distance-based sampling technique, the sampling point is identified after a preset distance threshold (e.g., 50m). However, the conventional techniques fail to consider the trajectory of the path travelled and may erroneously compute the distance.
[0041] Referring now to FIG. 1B which illustrates an exemplary scenario 100B of computing distance based on the identified sampling points. The total distance from point A to D can be computed, from the summation of the distance travelled from one sampling point to another sampling point, i.e, A+B, B+C, C+D. Further, it is common knowledge that if there are more sampling points, then the accuracy in distance computation is considerably enhanced.
[0042] However, it remains a problem to be solved for metered connections, that when a distance is to be computed at a remote end, it becomes imperative to reduce the sampling rate to save costs. It is also known from prior art that sampling rate may be reduced if the speed is constant or if the source (vehicle) is slow moving.
[0043] Thus, the present disclosure provides a method to compute an optimal distance travelled by a vehicle, based on a COG parameter that denotes the heading of the vehicle. CoG may be defined as the actual direction of progress of a vehicle, between two points, with respect to the surface of the earth. It is a measure of the heading direction of a vehicle, measured in terms of angle. As long as the COG is near constant it can be assumed that the vehicle is in a straight-line

path. The present disclosure discloses controlling the sampling rate based on the value of the COG parameter. Thus, if the variance in COG is high the sampling rate also increases. Further, if the vehicle is travelling on a straight line path, the sampling rate is sparse or negligible.
[0044] FIG. 1C illustrates a block diagram 100 of a system for compute an optimal distance travelled by a vehicle, in accordance with some aspects of the present disclosure. The proposed system 100 comprises a telematics device 110 of a vehicle that communicates with a remote server 128 over a wireless network 118. The telematics device 110 may comprise a memory 112, a location module 114, and a control unit 116. The location module 114 configured to receive GPS data. Further the control unit 116 is configured to generate a relevant location data of the vehicle and decode the GPS data to obtain values for a plurality of parameters. Here, the plurality of parameters comprises at least a Course-over-Ground (COG) parameter corresponding to the vehicle’s location. Further the control unit 116 is configured to determine whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle based on a value of the COG parameter.
[0045] Here, the relevant location data implies the location information of a particular sampling point that is essential in computing the optimal distance travelled by the vehicle. To accurately estimate the distance travelled, the vehicle must sample the location data of only those locations that facilitate optimal computation of the distance travelled. Thus, the present disclosure ensures not only accurately estimates the distance but also utilizes the limited network resources frugally and most efficiently. Thus, the relevant location data comprises a current time, a current latitude, a current longitude and a current speed of the vehicle
[0046] In one non-limiting embodiment, the location module 114 may obtain GPS data from a satellite. Further, GPS data received from the satellite in usually in form of strings. Typically, two types of strings are considered for the conventional location determination: GPGGA – global positioning system fix data

and GPRMC -Recommended minimum specific GPS/Transit data. The telematics device 110 fetches a GPS NMEA (National Marine Electronics Association.) string from the satellite. NMEA is a standard data format supported by a variety of GPS manufacturers. Some important parameters present in the GPS NMEA string are Latitude, Longitude, Speed in knots and CoG.
[0047] In one non-limiting embodiment, the CoG value can be received from the GPS NMEA string every 1 sec.
[0048] Furthermore, the present disclosure particularly useful for curved or circular roads. and in areas with limited network resources. Further, the present system is operable on low-bandwidth connection. The present disclosure may create a lower database size on the remote server 128 thus lowering the recurring costs for a remote monitoring service. Furthermore, the present system aids the fleet operator to calculate the exact distance travelled by a vehicle from one point to the other point. Further, the present disclosure, may additionally facilitate the fleet operator with travel suggestions, on the run, to choose the most optimum path based on the travel curve traversed by the driver.
[0049] In one non-limiting embodiment, the optimal COG threshold value can be computed over a learning algorithm which compares the distance over two methods – one with frequent sampling and one with COG sampling and feedbacks the error back to the system 100. An average LMS algorithm may then be employed to compute the threshold adjustments till the same settles down at a point. Another method to train the system would use sample test vectors and compute the weightage to create a minimal error over the perfect computation. [0050] In one non-limiting embodiment, the control unit 116 may be configured to convert the speed data obtained from GPS string, from knots to Km/hr. Further, the distance travelled in meters is computed by estimating the time spanned and dividing the obtained speed by the estimated time.
[0051] In another non-limiting embodiment, the control unit 116 of the telematics device 110 is further configured to obtain vehicle data. Here, the vehicle data comprises a start time, a current time and a current speed. Further the

control unit 116 is configured to compute an elapsed time based on the start time and current time and further determine whether the elapsed time is greater than a time threshold value when the value of the COG parameter is not greater than the COG threshold value. Furthermore, the control unit 116 is configured to determine whether the current speed is greater than a speed threshold value when the elapsed time is greater than the time threshold value. The control unit 116 is further configured to determine whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle, based on the current speed; and transmit the relevant location data to the remote server (128) for updating the location of the vehicle, when the current speed is greater than the speed threshold value.
[0052] In one non-limiting embodiment, the system may comprise one or more sensors comprising an acceleration sensor, a gyroscope, a geo-positioning sensor, a vehicle speed sensor etc. The sensors may be communicatively coupled to the control unit 116 and configured to obtain the vehicle data such as a start time, a current time and a current speed of the vehicle, this data may be communicated to the control unit 116 for further processing. The control unit 116 of the telematics device 110 is further configured to obtain the vehicle data. [0053] In another non-limiting embodiment, the memory 112 of the telematics device 110 is configured to store vehicle data.
[0054] In another non-limiting embodiment, the system interpolates various factors such as continuously varying speed of the vehicle, elapsed time and the constantly changing trajectory of the path travelled. The control unit 116 of the telematics device 110 is configured to obtain the vehicle data either when the elapsed time is not greater than the time threshold value or when the current speed is not greater than a speed threshold value.
[0055] In another non-limiting embodiment of the present disclosure, the present disclosure discloses a remote server (128) configured to compute an optimal distance travelled by a vehicle. The remote server (128) includes a distance calculation module configured to receive relevant location data from a telematics device 110. Further the distance calculation module is further

configured to store and update the relevant location data in a database (124) and subsequently compute optimal distance travelled by the vehicle based on the updated location data. Here, the remote server (128) also includes a memory 112 configured to store the database 124.
[0056] In one non-limiting embodiment, the COG threshold value can be computed using a learning algorithm which compares the distance over two methods – one with frequent data transmission and one with COG based data transmission and feedbacks the error back to the system. An average LMS algorithm can then be employed to compute the threshold adjustments till the same settles down at a point.
[0057] In another embodiment, the present disclosure uses sample test vectors and computes the weightage for various parameters such as speed, time and COG in order to optimally compute the distance traveled at the remote server (128). [0058] FIG. 2 illustrates an exemplary method 200 for determining relevant location data for updating a vehicle’s location, in accordance with some aspects of the present disclosure. The present application discloses a method 200 for transmitting a relevant location data to a remote server (128) for updating a location of a vehicle. The method 200 comprises at step 202 generating the relevant location data of the vehicle and at step 206 receiving Global Positioning System (GPS) data. Further the method 200 comprises at step 206, decoding the GPS data to obtain values for a plurality of parameters. Herein, the plurality of parameters comprises at least a Course-over-Ground (COG) parameter corresponding to the vehicle’s location. The method comprises at step 208, determining (208) whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle based on a value of the COG parameter.
[0059] In one non-limiting embodiment, the method further comprises transmitting the relevant location data to the remote server for updating the location of the vehicle, when the value of the COG parameter is greater than a COG threshold value.

[0060] In yet another non-limiting embodiment, the plurality of parameters comprises a latitude parameter, a longitude parameter and a current speed parameter of the vehicle.
[0061] In yet another non-limiting embodiment, the relevant location data further comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.
[0062] FIG. 3 illustrates an exemplary method 300 for determining relevant location data for updating a vehicle’s location, in accordance with some aspects of the present disclosure. The method 300 comprises at step 302 obtaining GPS data and vehicle data.
In one of the embodiments, the GPS data is decoded to obtain values for a plurality of parameters. Herein, the plurality of parameters comprises at least a Course-over-Ground COG parameter corresponding to the location of the vehicle. Further, the vehicle data comprises a start time, a current time, and a current speed. At step 304, the method determines whether a value of the COG parameter is greater than a COG threshold value. If the COG parameter is greater than the COG threshold value, then the method 300 at step 312, transmits relevant location data on remote server 128. At step 314, updating the relevant location data on remote server 128. However, if the COG parameter is less than the COG threshold value, then the method 300 comprises at step 306, computing an elapsed time based on the start time and current time and at step 308, determining whether the elapsed time is greater than a time threshold value. Further, on determination of the elapsed time being greater than the time threshold value, it is further determined at step 310, whether the current speed is greater than a speed threshold value. On determination of the current speed greater than the speed threshold value, the relevant location data is transmitted on remote server 128 (step 312). [0063] For example, if the COG threshold value is 35 degrees, the relevant location data is transmitted when current COG value exceeds 35 degrees. If the COG value is equal to or less than the 35 degrees, then an elapsed time is computed based on the start time and current time, wherein the start time is time

stamp of start of ignition of the vehicle. If the elapsed time is less than the time
threshold value (say 5 sec), the vehicle speed is compared against a speed
threshold value (say 5kmph) in order to enable the transmission of relevant
location data of the current location to the remote server.
[0064] In another non-limiting embodiment, the memory of the telematics
device is configured to store vehicle data.
[0065] In another non-limiting embodiment, the control unit of the telematics
device is further configured to obtain the vehicle data when the elapsed time is
less than or equal to the time threshold value.
[0066] In another non-limiting embodiment, the control unit of the telematics
device is further configured to obtain the vehicle data when the current speed is
less than or equal to a speed threshold value.
[0067] In another non-limiting embodiment, the plurality of parameters
further comprises a latitude parameter, a longitude parameter and a current speed
parameter of the vehicle.
[0068] In another non-limiting embodiment, the relevant location data of the
telematics device comprises a current time, a current latitude, a current longitude
and a current speed of the vehicle.
[0069] FIG. 4 illustrates an exemplary method 400 for computing an optimal
distance travelled by a vehicle by the remote sever (128), in accordance with some
aspects of the present disclosure. The method 400 includes at step 402 obtaining
relevant location data from a telematics device 110. The method 400 further
includes at step 404, storing and updating the relevant location data in a database
124. Further, the method 400 further includes at step 406 computing optimal
distance travelled by the vehicle based on the updated location data.
[0070] In another non-limiting embodiment, the relevant location data of the
remote server (128) comprises a current time, a current latitude, a current
longitude and a current speed of the vehicle.
[0071] Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant

art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. [0072] Reference Numerals:

100 System
110 Telematics Device
112 Memory
114 Location Module
116 Control Unit
118 Wireless Network
122 Distance Calculation Module
124 Database
126 Memory
128 Remote Server

We Claim:
1. A method (200) for transmitting a relevant location data to a remote server
(128) for updating a location of a vehicle, comprising:
generating (202) the relevant location data of the vehicle;
receiving (204) Global Positioning System (GPS) data;
decoding (206) the GPS data to obtain values for a plurality of parameters, wherein the plurality of parameters comprises at least a Course-over-Ground (COG) parameter corresponding to the location of the vehicle; and
determining (208) whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle based on a value of the COG parameter.
2. The method as claimed in claim 1, further comprises transmitting (312) the relevant location data to the remote server (128) for updating the location of the vehicle, when the value of the COG parameter is greater than a COG threshold value.
3. The method as claimed in claim 1, further comprises:
obtaining (302) vehicle data, wherein the vehicle data comprises a start time, a current time, and a current speed;
computing (306) an elapsed time based on the start time and the
current time;
determining (308) whether the elapsed time is greater than a time threshold value when the value of the COG parameter is less than or equal to a COG threshold value;
determining (310) whether the current speed is greater than a speed threshold value when the elapsed time is greater than the time threshold value;

determining whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle, based on the current speed; and
transmitting (312) the relevant location data to the remote server (128) for updating the location of the vehicle, when the current speed is greater than the speed threshold value.
4. The method as claimed in claim 3, further comprises obtaining the vehicle data when the elapsed time is less than or equal to the time threshold value.
5. The method as claimed in claim 3, further comprises obtaining the vehicle data when the current speed is less than or equal to the speed threshold value.
6. The method as claimed in claim 1, wherein the plurality of parameters further comprises a latitude parameter, a longitude parameter and a current speed parameter of the vehicle.
7. The method as claimed in claim 1, wherein the relevant location data comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.
8. A method (400) for computing optimal distance travelled by a vehicle comprising:
obtaining (402) a relevant location data from a telematics device; storing and updating (404) the relevant location data in a database; and
computing (406) optimal distance travelled by the vehicle based on the updated location data.

9. The method as claimed in claim 8, wherein the relevant location data
comprises a current time, a current latitude, a current longitude and a current
speed of the vehicle.
10. A telematics device (110) for transmitting a relevant location data
to a remote server (128) for updating a location of a vehicle, comprising:
a memory (112);
a location module (114) configured to receive GPS data; and
a control unit (116) configured to:
generate the relevant location data of the vehicle;
decode the GPS data to obtain values for a plurality of parameters, wherein the plurality of parameters comprises at least a Course-over-Ground (COG) parameter corresponding to the location of the vehicle; and
determine whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle based on a value of the COG parameter.
11. The telematics device as claimed in claim 10, wherein the control unit (116) is further configured to transmit the relevant location data to the remote server (128) for updating the location of the vehicle, when the value of the COG parameter is greater than a COG threshold value.
12. The telematics device as claimed in claim 10, wherein the control unit (116) is further configured to:
obtain vehicle data, wherein the vehicle data comprises a start time, a current time and a current speed;
compute an elapsed time based on the start time and the current time;

determine whether the elapsed time is greater than a time threshold value when the value of the COG parameter is less than or equal to a COG threshold value;
determine whether the current speed is greater than a speed threshold value when the elapsed time is greater than the time threshold value;
determine whether to transmit the relevant location data to the remote server (128) for updating the location of the vehicle, based on the current speed; and
transmit the relevant location data to the remote server (128) for updating the location of the vehicle, when the current speed is greater than the speed threshold value.
13. The telematics device as claimed in claim 10, the memory (112) is configured to store vehicle data.
14. The telematics device as claimed in claim 12, wherein the control unit (116) is further configured to obtain the vehicle data when the elapsed time is less than or equal to the time threshold value.
15. The telematics device as claimed in claim 12, wherein the control unit (116) is further configured to obtain the vehicle data when the current speed is less than or equal to the speed threshold value.
16. The telematics device as claimed in claim 10, wherein the plurality of parameters further comprises a latitude parameter, a longitude parameter and a current speed parameter of the vehicle.
17. The telematics device as claimed in claim 10, wherein the relevant location data comprises a current time, a current latitude, a current longitude and a current speed of the vehicle.

18. A remote server (128) configured to compute an optimal distance
travelled by a vehicle, comprising:
a distance calculation module (122) configured to:
receive a relevant location data from a telematics device; store and update the relevant location data in a database
(124); and
compute optimal distance travelled by the vehicle based on
the updated location data, and
a memory (126) configured to store the database (124).
19. The remote server as claimed in claim 18, wherein the relevant
location data comprises a current time, a current latitude, a current longitude
and a current speed of the vehicle.