Description:FIELD OF THE INVENTION
The present disclosure relates to a smart tractor-trailer tracking system and a method for tracking tractor-trailers. In more particular manner, the present invention relates to a smart system, and method for tracking tractors and trailers, wherein the system is configured to utilize GPS, RFID (Radio Frequency Identification), or BLE (Bluetooth Low Energy) technology. The proposed system allow user to determine, which trailer is attached to which tractor, where both are located in real-time, and how many kilometers each trailer has traveled, consignment records using a web and mobile application.
BACKGROUND OF THE INVENTION
Tractors, commonly used for forwarding, and trailers, primarily used for carrying goods, operate in tandem for efficient logistics and transportation. However, several operational challenges arise in managing this system. It is often difficult to determine which trailer is currently attached to which tractor, or to track the exact time of attachment and detachment. Furthermore, identifying the current location of both tractors and trailers remains problematic, especially when they are idle and not engaged in active transportation.
There is also a lack of accurate data on the number of kilometers each trailer has traveled, along with the complete history of consignments carried. In the absence of such tracking, it becomes difficult to ensure optimal utilization of tractors across multiple trailers. This inefficiency can lead to underutilization of assets, delays in delivery, potential misuse or theft, and challenges in planning regular maintenance.
Therefore, in order to address the aforementioned drawbacks, there is a need for a system that enables precise and real-time tracking of tractors and trailers, ensuring improved operational transparency, asset utilization, and overall management efficiency.

SUMMARY OF THE INVENTION
The present disclosure relates to a smart tractor-trailer tracking system and a method for tracking tractor-trailers. In more particular, the present invention provides a system and method to perform real-time tracking of tractors and trailers, ensuring improved operational transparency, asset utilization, and overall management efficiency. The system includes a GPS device that needs to be installed on each tractor, and an RFID (radio frequency identification) tag or BLE (Bluetooth low energy) device, fitted in each trailer. The system further includes a sensor installed on the tractor, configured to read the RFID/BLE tag when a trailed is attached with the tractor. The system is further configured to record the date, time, and location, at which the trailer is attached or detached from the tractor, wherein the system is configured to calculate the distance travelled by each trailer tracking the movement, when attached to a tractor. All the tracking data is sent to a central server through GPS device, wherein the central server in connection to the user interface, displays to the user information such as, which tractor is attached to which trailer, location of each trailer or tractor, their current status, history of attachment and detachments, and total kilometres travelled by each trailer. The system facilitates in better fleet management, higher security, efficient operations, increasing turn-around time of assets, and easy maintenance planning.
The present disclosure seeks to provide a smart tractor-trailer tracking system. The system comprises: a GPS device configured to be installed on a tractor and configured to track a real-time location of the tractor; a tag selected from one of a Radio Frequency Identification (RFID) tag or a Bluetooth Low Energy (BLE) tag configured to be attached to a trailer, wherein said RFID/BLE tag comprises a unique identification for the trailer, wherein the RFID/BLE tag is uniquely paired with its assigned trailer using a secure identification (ID) and a challenge-response handshake mechanism, wherein the RFID/BLE reader transmits a unique challenge to the tag, and the tag responds with with at least one of an encrypted key or token or serial number to verify its authenticity before a pairing event is recorded; an RFID/BLE reader configured to be installed on the tractor to detect an attachment and a detachment of the trailer to the tractor by reading the RFID/BLE tag; a communication module configured to transmit data from the GPS device and the RFID/BLE reader to a cloud server, wherein said cloud server configured to receive and store the tracking data selected from real-time location data from the GPS device, attachment and detachment events, selected from a unique identification of the attached or detached trailer, a unique identification of the tractor, a location, and a time, and calculated distance traveled by the trailer during an attached period; a web application accessible via a network, coupled to the cloud server to display a real-time status of tractors and trailers, including which tractor is attached to which trailer, current locations of individual or all tractors and individual or all trailers, identification of idle tractors and trailers and their parked locations, a history of attachment and detachment events, a history of consignments associated with specific tractors, and total kilometers traveled by each trailer; and a graphical user interface accessible via a user computing device, coupled to the cloud server to display the real-time status, current locations, idle asset identification, attachment/detachment history, consignment history, and total kilometers traveled by each trailer.
The present disclosure also seeks to provide a method for tracking tractor-trailers. The method comprises: tracking a real-time location of a tractor using a GPS device installed on the tractor; detecting an attachment of a trailer to the tractor by reading an RFID/BLE tag on the trailer using an RFID/BLE reader on the tractor; recording, by a cloud server, an attachment event including a unique identification of the attached trailer, a unique identification of the tractor, a location of attachment, and a time of attachment; detecting a detachment of the trailer from the tractor by the RFID/BLE reader; wherein detecting an attachment or detachment further comprises using a sensor to detect a user-defined proximity of the RFID/BLE tag to the RFID/BLE reader; recording, by the cloud server, a detachment event including the unique identification of the detached trailer, the unique identification of the tractor, a location of detachment, and a time of detachment; transmitting location data from the GPS device and attachment/detachment event data from the RFID/BLE reader to the cloud server via a communication module; calculating, by the cloud server, a distance traveled by the trailer during an attached period based on the real-time location data and the recorded attachment and detachment events; and displaying, via at least one of a web application and a graphical user interface, real-time status of tractors and trailers, current locations, idle asset identification, a history of attachments and detachments, a history of consignments, and total kilometers traveled by each trailer.
An objective of the present disclosure is to provide a smart tractor-trailer tracking system.
Another object of the present disclosure is to provide a method for tracking tractor-trailers.
Another object of the present disclosure is to provide a system configured to accurately tracks the attachment and detachment of trailers to tractors, along with the exact time and location of such events.
Another object of the present disclosure is to enable real-time location tracking of both tractors and trailers using GPS, RFID, or BLE technologies.
Yet, another object of the present disclosure is to record and display the total distance traveled by each trailer along with its consignment history, for better planning and efficient use of transport assets.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.

BRIEF DESCRIPTION OF FIGURES
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates a block diagram of a smart tractor-trailer tracking system in accordance with an embodiment of the present disclosure; and
Figure 2 illustrates a flow chart of a method for tracking tractor-trailers, in accordance with an embodiment of the present disclosure.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have been necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
DETAILED DESCRIPTION:
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to “an aspect”, “another aspect” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase “in an embodiment”, “in another embodiment” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
The functional units described in this specification have been labeled as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may include executable code and may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executable of an identified device need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the device and achieve the stated purpose of the device.
Indeed, an executable code of a device or module could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices. Similarly, operational data may be identified and illustrated herein within the device, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, as electronic signals on a system or network.
Reference throughout this specification to “a select embodiment,” “one embodiment,” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Thus, appearances of the phrases “a select embodiment,” “in one embodiment,” or “in an embodiment” in various places throughout this specification are not necessarily referring to the same embodiment.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided, to provide a thorough understanding of embodiments of the disclosed subject matter. One skilled in the relevant art will recognize, however, that the disclosed subject matter can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.
In accordance with the exemplary embodiments, the disclosed computer programs or modules can be executed in many exemplary ways, such as an application that is resident in the memory of a device or as a hosted application that is being executed on a server and communicating with the device application or browser via a number of standard protocols, such as TCP/IP, HTTP, XML, SOAP, REST, JSON and other sufficient protocols. The disclosed computer programs can be written in exemplary programming languages that execute from memory on the device or from a hosted server, such as BASIC, COBOL, C, C++, Java, Pascal, or scripting languages such as JavaScript, Python, Ruby, PHP, Perl or other sufficient programming languages.
Some of the disclosed embodiments include or otherwise involve data transfer over a network, such as communicating various inputs or files over the network. The network may include, for example, one or more of the Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and/or any other delivery or tunneling mechanism for carrying data. The network may include multiple networks or sub networks, each of which may include, for example, a wired or wireless data pathway. The network may include a circuit-switched voice network, a packet-switched data network, or any other network able to carry electronic communications. For example, the network may include networks based on the Internet protocol (IP) or asynchronous transfer mode (ATM), and may support voice using, for example, VoIP, Voice-over-ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular telephone network configured to enable exchange of text or SMS messages.
Examples of the network include, but are not limited to, a personal area network (PAN), a storage area network (SAN), a home area network (HAN), a campus area network (CAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a virtual private network (VPN), an enterprise private network (EPN), Internet, a global area network (GAN), and so forth.
Figure 1 illustrates a block diagram of a smart tractor-trailer tracking system (100) in accordance with an embodiment of the present disclosure.
Referring to Figure 1, the system (100) comprises: a GPS device (102) configured to be installed on a tractor and configured to track a real-time location of the tractor.
In an embodiment, a tag (104) is selected from one of a Radio Frequency Identification (RFID) tag or a Bluetooth Low Energy (BLE) tag configured to be attached to a trailer, wherein said RFID/BLE tag (104) comprises a unique identification for the trailer, wherein the RFID/BLE tag is uniquely paired with its assigned trailer using a secure identification (ID) and a challenge-response handshake mechanism, wherein the RFID/BLE reader transmits a unique challenge to the tag, and the tag responds with at least one of an encrypted key or token or serial number to verify its authenticity before a pairing event is recorded.
In an embodiment, an RFID/BLE reader (106) is configured to be installed on the tractor to detect an attachment and a detachment of the trailer to the tractor by reading the RFID/BLE tag.
In an embodiment, a communication module (108) is configured to transmit data from the GPS device (102) and the RFID/BLE reader (106) to a cloud server (110), wherein said cloud server (110) configured to receive and store the tracking data selected from real-time location data from the GPS device (102), attachment and detachment events, selected from a unique identification of the attached or detached trailer, a unique identification of the tractor, a location, and a time, and calculated distance traveled by the trailer during an attached period.
In an embodiment, a web application (112) accessible via a network, coupled to the cloud server (110) to display a real-time status of tractors and trailers, including which tractor is attached to which trailer, current locations of individual or all tractors and individual or all trailers, identification of idle tractors and trailers and their parked locations, a history of attachment and detachment events, a history of consignments associated with specific tractors, and total kilometers traveled by each trailer.
In an embodiment, a graphical user interface (114) is accessible via a user computing device (116), coupled to the cloud server (110) to display the real-time status, current locations, idle asset identification, attachment/detachment history, consignment history, and total kilometers traveled by each trailer.
In another embodiment, the system (100) further comprises a sensor (118) associated with the RFID/BLE reader (106), configured to detect the presence of the RFID/BLE tag (104) within a user-defined proximity, wherein the sensor is selected from a magnetic sensor or a proximity sensor, said sensor being configured to detect the presence of the RFID/BLE tag by measuring changes in a magnetic field or distance and confirming an attachment or detachment event only when the detected proximity remains stable for a user-defined duration, wherein the RFID/BLE reader and the sensor are further configured to jointly apply an intelligent filtering technique that analyses signal strength and timing patterns to filter out false positives and negatives, wherein the intelligent filtering technique flags an event as valid only when both the RFID/BLE signal and the sensor's proximity reading are consistent for a specified time period.
In another embodiment, the cloud server (110) is further configured to generate alerts for attachment and detachment events upon receiving the data from the GPS device and the RFID/BLE reader using a secure communication protocol, said secure communication protocol comprising an encrypted data transmission channel and a device-level authentication mechanism, wherein the device-level authentication mechanism verifies the authenticity of the on-board device against a pre-registered device list stored on the cloud server, wherein the encrypted data transmission channel utilizes Transport Layer Security (TLS) or Secure Sockets Layer (SSL) to establish a secure, end-to-end connection between the on-board devices and the cloud server.
In another embodiment, the web application (112) and the graphical user interface (114) are further configured to provide secure login for different user roles, including a driver and an administrator, wherein the web application (112) and the graphical user interface (114) are further configured to display a live mapping of tractors and trailers.
In another embodiment, wherein the cloud server (110) is further configured to detect idle assets by analyzing real-time location data against a set of predefined criteria, wherein the criteria includes a speed threshold below which the asset is considered stationary and a time duration for which the asset must remain stationary, and wherein both the speed threshold and the time duration are configurable by a user, thereby process concurrent data streams from multiple tractors and trailers using a load-balanced architecture, said load-balanced architecture comprising a data ingestion pipeline that queues incoming data packets and processes them in parallel using a distributed service framework, wherein the data ingestion pipeline is further configured to validate the integrity of each data packet upon receipt by performing a checksum verification and to discard any corrupted packets.
In another embodiment, the cloud server (110) is further configured to generate reports for management based on the stored tracking data and calculate the distance travelled by the trailer during an attached period by applying a Haversine technique or a map-matching technique to the real-time GPS coordinates, wherein the server utilizes a Kalman filter or a smoothing technique to filter out GPS inaccuracies and interpolates between data points to estimate missing segments during intermittent signal loss.
In an embodiment .the RFID/BLE reader is further configured to dynamically adjust its signal strength based on environmental conditions detected via an integrated ambient sensor, wherein the ambient sensor monitors electromagnetic noise, temperature, and physical vibrations from the tractor, and wherein the RFID/BLE reader applies an adaptive gain control to maintain optimal tag detection reliability under fluctuating environmental conditions, thereby reducing false attachment or detachment triggers caused by electromagnetic interference or mechanical shocks; and wherein the intelligent filtering technique comprises a multi-stage validation process, wherein: (a) a first stage compares instantaneous Received Signal Strength Indicator (RSSI) values of the RFID/BLE tag to a dynamically updated baseline to reject transient spikes or drops in signal; (b) a second stage computes a time-weighted moving average of both RSSI and proximity readings to identify stable connection patterns; and (c) a third stage applies a correlation check between the detected attachment event and concurrent changes in tractor acceleration data obtained from an onboard accelerometer, wherein an attachment event is validated only when both sensor correlations and RFID/BLE stability criteria are satisfied.
In an embodiment, the RFID/BLE reader is designed to dynamically regulate its signal strength based on environmental variations detected through an integrated ambient sensor, thereby achieving a significant technical improvement in maintaining consistent and reliable tag detection in real-world operating conditions. The ambient sensor continuously monitors factors such as electromagnetic noise, temperature fluctuations, and mechanical vibrations generated by the tractor during movement or operation. For instance, when the tractor traverses an area near high-voltage power lines producing electromagnetic interference, the sensor detects the elevated noise levels and instructs the reader to reduce its gain, mitigating false readings caused by spurious signals. Conversely, in open fields with minimal interference, the reader automatically amplifies its signal to ensure extended detection coverage. This adaptive gain control mechanism provides a distinct technical effect of ensuring stable and accurate tag recognition even under dynamically changing conditions, significantly reducing false attachment or detachment triggers that can otherwise disrupt operational workflows.
To further enhance robustness, the reader employs an intelligent filtering technique comprising a multi-stage validation process that goes beyond conventional single-threshold detection methods. In the first stage, instantaneous RSSI values from the RFID/BLE tag are continuously compared to a dynamically updated baseline, effectively eliminating transient spikes or sudden drops that typically arise from momentary environmental disturbances or signal reflections. In the second stage, the system computes a time-weighted moving average of both RSSI and proximity readings to establish stable connection patterns over time, filtering out short-lived fluctuations. In the third stage, the system cross-validates any detected attachment event by correlating it with tractor acceleration data obtained from an onboard accelerometer. An attachment event is deemed valid only when the accelerometer confirms a corresponding physical action, such as a hitching maneuver, and the RFID/BLE signal stability criteria are simultaneously satisfied. For example, if a sudden RSSI increase is detected but there is no corresponding acceleration profile indicative of trailer engagement, the system disregards the event as a false positive.
The combination of adaptive gain control and multi-layered validation ensures superior detection accuracy compared to traditional static-power, threshold-based systems. The technical advancement lies in the reader’s ability to self-optimize in real time, adapting to both environmental and operational contexts, and delivering reliable trailer attachment status under conditions where prior systems would fail due to noise, mechanical shocks, or sensor limitations. This results in fewer operational interruptions, increased safety, and enhanced trust in the automated tracking system’s output.
In an embodiment, the cloud server further comprises a distributed data ingestion module configured to handle real-time streams from multiple tractors, wherein said module: (a) assigns a unique cryptographic session token to each incoming data stream upon authentication; (b) uses a priority-based queueing mechanism to categorize incoming events into critical (attachment/detachment) and non-critical (periodic GPS updates) streams; and (c) applies event-driven processing such that critical events are immediately validated and pushed to the user interface with sub-second latency, while non-critical data is aggregated and processed in scheduled intervals to optimize bandwidth and computational load.
In an embodiment, the cloud server incorporates a distributed data ingestion module specifically engineered to efficiently process and manage high-volume, real-time telemetry streams originating from multiple tractors operating simultaneously across geographically dispersed locations. Upon authentication of each incoming data stream, the module assigns a unique cryptographic session token, which serves as a secure and traceable identifier for the data throughout its lifecycle within the system. This ensures that data integrity and authenticity are maintained at all times, preventing unauthorized access or spoofing of telemetry inputs—a critical requirement in fleet management operations where unverified data could lead to erroneous decision-making.
The module further introduces a technical advancement by implementing a priority-based queueing mechanism that categorizes incoming events into critical and non-critical streams. Critical events, such as trailer attachment or detachment, are instantly flagged due to their direct impact on operational safety and logistics planning. For example, if a trailer unintentionally detaches during transit, the system elevates this event’s processing priority, ensuring it bypasses routine queue delays. Non-critical data, such as periodic GPS position updates, is classified into a lower priority tier, thereby preventing routine telemetry from overwhelming the system and compromising responsiveness to urgent conditions.
The event-driven processing architecture of this module enables critical events to be validated and transmitted to the user interface with sub-second latency, ensuring that fleet managers receive actionable alerts in real time. For instance, in the event of an unexpected detachment on a highway, the alert is immediately pushed to the operator dashboard, enabling prompt intervention and reducing the risk of accidents or cargo loss. Meanwhile, non-critical data is aggregated and processed at scheduled intervals, optimizing both bandwidth consumption and computational resource allocation. This approach delivers the technical effect of ensuring system scalability and efficiency even under peak load conditions, avoiding bottlenecks that commonly occur in conventional, non-prioritized telemetry processing systems. This embodiment demonstrates a clear technical advancement by providing a secure, scalable, and latency-optimized ingestion framework that balances real-time responsiveness with efficient resource utilization, thereby significantly enhancing the reliability and operational effectiveness of the tractor-trailer tracking ecosystem.
In an embodiment, the secure communication protocol further implements a rolling key exchange mechanism based on ephemeral key pairs, wherein: (a) each on-board device periodically generates a temporary public/private key pair; (b) the cloud server verifies each data packet using the ephemeral key before accepting the transmission; and (c) upon detection of any key mismatch or packet tampering, the cloud server triggers an immediate revocation of the session and initiates a secure re-authentication handshake with the device, ensuring continuous protection against replay attacks and unauthorized data injection.
In an embodiment, the secure communication protocol is enhanced through a rolling key exchange mechanism utilizing ephemeral key pairs, providing a substantial advancement in maintaining data confidentiality, integrity, and resilience against evolving cybersecurity threats in distributed telematics systems. Each on-board device periodically generates a temporary public/private key pair, ensuring that encryption credentials are frequently refreshed, thereby minimizing the window of opportunity for potential attackers to compromise a valid key. This continuous renewal of keys prevents the long-term exploitation of static credentials, which are a known vulnerability in traditional systems.
Upon transmission, every data packet sent by the on-board device is verified by the cloud server using the corresponding ephemeral public key before acceptance. This verification process ensures that the data originates from an authenticated source and has not been altered in transit. For example, if a malicious actor attempts to intercept and inject a falsified detachment event into the network, the cloud server will reject the packet due to the absence of a valid ephemeral signature. This guarantees that only authenticated, untampered data is processed by the system, maintaining the reliability of critical operational information.
If any key mismatch, signature failure, or packet tampering is detected, the cloud server immediately revokes the existing session and initiates a secure re-authentication handshake with the affected device. This automated response ensures uninterrupted protection by promptly isolating potential security breaches and re-establishing a trusted communication channel without manual intervention. Such proactive mitigation prevents replay attacks, where previously intercepted valid messages might otherwise be resent by an attacker to manipulate system behavior, as well as unauthorized data injection attempts that could compromise fleet safety and decision-making.
The technical effect of this embodiment is a robust, self-healing security framework that adapts dynamically to potential threats, significantly improving the resilience of the tractor-trailer tracking system against cyberattacks. The technical advancement lies in its ability to combine cryptographic agility, real-time verification, and autonomous breach recovery, ensuring that sensitive telemetry data remains continuously protected throughout its transmission lifecycle.
In an embodiment, idle asset detection further comprises: (a) computing a geofence boundary dynamically around the last known position of the asset using real-time GPS coordinates; (b) monitoring deviations of the asset’s location within said geofence using a Kalman-filtered trajectory prediction model; and (c) classifying the asset as idle only when its actual movement variance remains below a computed statistical threshold of the predicted trajectory for a configurable time window, thereby preventing false idle status due to GPS drift or momentary signal fluctuations.
In an embodiment, idle asset detection is enhanced through a dynamic geofencing and trajectory prediction approach that significantly improves accuracy over conventional static-location or simple threshold-based methods. The system begins by computing a geofence boundary around the last known position of the asset using real-time GPS coordinates, wherein the geofence is not a fixed perimeter but is dynamically recalculated as new positional data is received. This allows the boundary to adapt to changes in the asset’s operational context, such as when it is relocated to a new site or moved within a large storage yard, ensuring the detection mechanism remains relevant to the asset’s actual environment.
Once the geofence is established, the system monitors the asset’s location deviations within this boundary using a Kalman-filtered trajectory prediction model. The Kalman filter integrates successive GPS readings with motion parameters, such as velocity and heading, to predict the most probable path of the asset while smoothing out random noise and positional errors inherent in GPS signals. For example, in environments with intermittent GPS coverage, such as urban canyons or areas with dense tree cover, the Kalman filter can maintain an accurate estimate of the asset’s likely position even when individual GPS fixes momentarily deviate due to multipath errors.
The asset is classified as idle only when its actual movement variance remains below a computed statistical threshold relative to the predicted trajectory for a configurable time window. This ensures that short-term deviations, such as those caused by GPS drift or brief vibrations from nearby equipment, do not erroneously trigger idle alerts. For instance, if a trailer is parked but GPS signals intermittently shift its reported location by a few meters, the system recognizes this as noise rather than true movement and correctly maintains the idle classification.
The technical effect of this embodiment is a highly reliable idle detection mechanism that minimizes false positives and provides fleet managers with accurate operational insights. The technical advancement lies in the combination of adaptive geofencing, predictive filtering, and statistical validation, enabling precise asset state determination even under challenging environmental conditions where traditional approaches would fail, thereby optimizing resource allocation and reducing unnecessary operational interventions.
In an embodiment,the calculation of distance traveled by the trailer during an attached period further comprises: (a) segmenting GPS data points into contiguous travel intervals using attachment and detachment timestamps as boundary markers; (b) applying a hybrid computation method wherein the Haversine formula is used for long-haul segments and map-matching to digital road networks is used for urban segments; and (c) correcting any discontinuities in data caused by signal outages by interpolating intermediate positions using a constrained optimization model that incorporates last-known velocity and heading vectors from the tractor.
In an embodiment, the calculation of distance traveled by the trailer during an attached period is achieved through a hybrid geospatial computation framework that ensures high accuracy and reliability under diverse operating conditions. The process begins by segmenting GPS data points into contiguous travel intervals, wherein attachment and detachment timestamps serve as boundary markers. This segmentation ensures that only those distances traveled while the trailer is actively connected to the tractor are considered, thereby eliminating erroneous data contributions from periods when the trailer is stationary or detached. For example, when a trailer is parked overnight and reattached in the morning, the system accurately separates and computes distances for each distinct movement phase without manual intervention.
To address the inherent challenges of calculating distances across varied terrains, the system employs a dual-method approach. For long-haul segments, where travel occurs primarily on highways with minimal directional changes, the Haversine formula is applied to compute great-circle distances between successive GPS coordinates, offering computational efficiency while maintaining precision over large distances. In contrast, for urban segments characterized by frequent turns, complex road layouts, and potential multipath GPS errors, the system utilizes map-matching to digital road networks. This technique aligns raw GPS points with actual road geometries, effectively correcting deviations caused by GPS inaccuracies and ensuring that calculated distances reflect true on-road travel.
Further enhancing the accuracy of the computed distances, the system incorporates a constrained optimization model to correct for data discontinuities resulting from GPS signal outages, such as when traveling through tunnels, underpasses, or heavily forested regions. This model leverages last-known velocity and heading vectors from the tractor to interpolate intermediate positions, ensuring continuity in the travel path. For instance, if GPS connectivity is lost while the vehicle is in a tunnel, the system predicts its trajectory through the tunnel using prior motion parameters and seamlessly integrates the reconstructed path with subsequent GPS fixes upon signal restoration.
The technical effect of this embodiment is a robust, context-aware distance computation method that delivers precise and dependable travel metrics regardless of environmental or connectivity challenges. The technical advancement resides in its combination of interval-based segmentation, adaptive hybrid computation, and predictive data recovery, which collectively enhance billing accuracy for distance-based logistics operations, support regulatory compliance, and improve the reliability of operational analytics for fleet managers.
Figure 2 illustrates a flow chart of a method (200) for tracking tractor-trailers, in accordance with an embodiment of the present disclosure.
Referring to Figure 2, the method (200) comprises plurality of steps as described under:
At step (202), the method (200) includes tracking a real-time location of a tractor using a GPS device installed on the tractor;
At step (204), the method (200) includes detecting an attachment of a trailer to the tractor by reading an RFID/BLE tag on the trailer using an RFID/BLE reader on the tractor;
At step (206), the method (200) includes recording, by a cloud server, an attachment event including a unique identification of the attached trailer, a unique identification of the tractor, a location of attachment, and a time of attachment;
At step (208), the method (200) includes detecting a detachment of the trailer from the tractor by the RFID/BLE reader, wherein detecting an attachment or detachment further comprises using a sensor to detect a user-defined proximity of the RFID/BLE tag to the RFID/BLE reader;
At step (210), the method (200) includes recording, by the cloud server, a detachment event including the unique identification of the detached trailer, the unique identification of the tractor, a location of detachment, and a time of detachment;
At step (212), the method (200) includes transmitting location data from the GPS device and attachment/detachment event data from the RFID/BLE reader to the cloud server via a communication module;
At step (214), the method (200) includes calculating, by the cloud server, a distance traveled by the trailer during an attached period based on the real-time location data and the recorded attachment and detachment events; and
At step (216), the method (200) includes displaying, via at least one of a web application and a graphical user interface, real-time status of tractors and trailers, current locations, idle asset identification, a history of attachments and detachments, a history of consignments, and total kilometers traveled by each trailer.
In an embodiment, the method (200) further comprises generating alerts for attachment and detachment events.
In an embodiment, the method (200) further comprises displaying a live mapping of the tractors and trailers on the web application and the mobile application.
In an embodiment, the method (200) further comprises detecting idle tractors and trailers based on the real-time location data and absence of movement thereby generating management reports based on the recorded tracking data.
The present invention provides a smart tracking system for monitoring the operation of tractors and trailers. The system comprises GPS, RFID, or BLE modules installed on both tractors and trailers to detect and record attachment and detachment events, real-time locations, and movement data. The system further includes a central server and associated web and mobile applications to collect, store, and display data such as trailer-to-tractor mapping, location status, idle time, distance traveled, and consignment history. This enables efficient asset utilization, operational transparency, and improved maintenance planning.
In an embodiment, the proposed system utilizes GPS, RFID (Radio Frequency Identification), or BLE (Bluetooth Low Energy) technology, through which it determine, which trailer is attached to which tractor, where both are located in real-time, and how many kilometres each trailer has travelled, consignment records using a web and mobile application.
In an embodiment, the system for tracking tractor trailer comprises: a GPS device installed on each tractor; RFID or BLE tag installed on each trailer; a sensor installed on the tractor to read the RFID or BLE tag of the trailer; a central server configured to receive and store the details recorded by the system, and sent through the GPS device, wherein the system is configured to record when a trailer is attached or detached, along with the date, time, and location, and wherein the system is further configured to calculate the distance travelled by each trailer by tracking the movement when attached to a tractor; a web application or a mobile application with a user interface configured to show live information related to, which tractor is attached to which trailer, location of the single or all tractor and single or all trailer, status and location of each tractor or trailer, history of attachment and detachment, history of all consignments with which tractors, and distance travelled by each trailer. The proposed system facilitates better fleet management, higher security, and efficient operations, thereby increasing the turn-around time of assets and easy maintenance planning.
The present invention relates to a smart tracking system comprising multiple integrated components for monitoring tractors and trailers. The system includes a GPS device mounted on the tractor to track its real-time location. Each trailer is equipped with an RFID or BLE tag containing a unique identification code. The tractor is fitted with an RFID or BLE reader or sensor to detect the presence of a trailer and identify its unique ID during attachment and detachment events. A communication module on the tractor sends all collected data to a cloud server in real time. The cloud server acts as a central storage unit, maintaining comprehensive tracking records, including trailer-to-tractor pairing, time and location of attachment and detachment, and ongoing location data. A web application is provided as a dashboard interface for administrative users or staff to monitor and manage all tractor and trailer activity. A mobile application mirrors the web dashboard, allowing users to access system data on handheld user devices for convenience and real-time decision-making.
In operation, when a tractor approaches a trailer, the reader detects the trailer’s tag. The system records the event by noting the trailer ID, tractor ID, location, and time. Similarly, when the trailer is detached, the system records the detachment. The GPS device continues to update the tractor’s location, and the system calculates the distance travelled by each trailer during the time it is attached. All relevant information, including live tractor-trailer pairing, current and historical locations, and distance travelled, is available on cloud server which user can be access through both the web and mobile applications.
The system features include live mapping of tractor and trailer connections, detection of idle assets, historical location tracking, alerts for attachment and detachment events, detailed reports for management use, secure login access for different user roles such as drivers and administrators, and total kilometer tracking for each trailer. The advantages of the invention include prevention of theft and misuse, time savings through idle asset detection, improved logistics and maintenance planning, enhanced fleet productivity, and better management of tires and other trailer components through usage data. Future developments of the invention may include AI-based predictive maintenance alerts, integration with enterprise resource planning (ERP) systems, automated billing using movement data, and drone-based visual monitoring of large yards.
The cloud server uses a scalable, load-balanced architecture to manage concurrent data from multiple tractors and trailers. Data is queued securely, processed in parallel by distributed services, and stored efficiently. This modular pipeline ensures high availability, real-time processing, and reliability without data loss, even under heavy traffic conditions. The secure communication protocols and authentication mechanisms are implemented between on-board devices and the cloud server. These include encrypted data transmission, device-level authentication, and session validation to ensure data integrity, prevent unauthorized access, and maintain a secure and trusted connection across all connected tractor-trailer systems.
The system can track at least one tractor or trailer and is built to handle any number, without limit. It can grow by adding more servers and resources as needed, so it keeps working smoothly and processes data in real-time, even as more vehicles are added. The system is designed to provide near real-time tracking updates with minimal latency, typically within a few seconds. This is achieved through efficient data transmission protocols, optimized network communication, and a responsive backend architecture. Continuous monitoring and adaptive resource management ensure that low-latency performance is maintained even under high traffic conditions.
The data volume generated per tractor or trailer can vary based on usage, but it typically ranges from a few megabytes to several hundred megabytes per day. This data is efficiently managed through structured storage strategies, compression techniques, and periodic archiving in the cloud to ensure optimized performance, scalability, and cost-effective long-term retention. The GPS device and RFID/BLE reader are primarily integrated into the vehicle’s power system (12V/24V) for continuous operation, but may also include backup batteries for redundancy. This hybrid power setup ensures reliable performance during engine off conditions or temporary power loss, maintaining uninterrupted tracking and pairing functionality. GPS connected with vehicle battery and having communication feature with Trailer device BLE, RFID, GPS through wire, wifi, RFID, Bluethooth etc.
The RFID/BLE reader is typically paired with a proximity or magnetic sensor, which detects trailer presence based on distance or magnetic field changes. These sensors confirm attachment/detachment by measuring whether the BLE/RFID signal and physical proximity fall within a "predetermined threshold," ensuring accurate, reliable connection status even in dynamic vehicle environments. The system uses intelligent filtering methods that analyze signal strength, timing patterns, and sensor inputs to minimize false attachment or detachment detections. Brief or unstable signals are evaluated against predefined conditions, ensuring that only consistent and reliable events are recorded. This enhances accuracy and reduces noise in real-time tracking. Each BLE/RFID tag is uniquely paired with its assigned trailer using a secure ID and optional challenge-response handshake. The system verifies tag identity through encrypted UUID matching or token-based or serial number based validation, ensuring only authorized, pre-registered tags are accepted, preventing spoofing or false associations from nearby unassigned tags.
The cloud calculates trailer distance by logging GPS coordinates from the tractor during attachment periods. It uses Haversine or map-matching techniques to compute accurate paths, filtering noise with Kalman filters or smoothing techniques. In case of GPS gaps or signal loss, interpolation and timestamp continuity are used to estimate missing segments reliably. The cloud server identifies idle assets by continuously analyzing real-time location and movement patterns. If a vehicle remains within a limited geographic area and its speed stays below a defined threshold for a specified duration, it is flagged as idle. These thresholds—such as minimal movement over several minutes—are configurable and optimized based on operational requirements to ensure accurate detection without false alerts.
Alerts for attachment and detachment events are generated based on a combination of factors such as signal strength, proximity validation, duration of stable connection, and confirmation from supporting sensors. These rules ensure that only confirmed events trigger alerts, avoiding false notifications. Yes, these alert conditions can be customized by the user based on specific operational needs—such as sensitivity levels, time thresholds, or notification preferences—to align with their workflow and monitoring priorities. Continuous GPS capture, secure transmission, rapid cloud processing, dynamic asset pairing, optimized map rendering, adaptive refresh, efficient data paths, load balancing, and proactive latency monitoring together enable tractors and trailers to appear on live maps almost instantly, maintaining reliable, near real time visibility even under heavy traffic volumes and sudden network fluctuations. This invention solves the problem of accurately identifying which trailer is connected to which tractor in real time using BLE/RFID and proximity sensors. It reduces manual errors, prevents asset mismatch, allows automatic logging, and provides fast, scalable tracking—features often missing or unreliable in GPS-only or manual systems.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefit s, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
, Claims:1. A smart tractor-trailer tracking system, comprising:
a GPS device configured to be installed on a tractor and configured to track a real-time location of the tractor;
a tag selected from one of a Radio Frequency Identification (RFID) tag or a Bluetooth Low Energy (BLE) tag configured to be attached to a trailer, wherein said RFID/BLE tag comprises a unique identification for the trailer, wherein the RFID/BLE tag is uniquely paired with its assigned trailer using a secure identification (ID) and a challenge-response handshake mechanism, wherein the RFID/BLE reader transmits a unique challenge to the tag, and the tag responds with at least one of an encrypted key or token or serial number to verify its authenticity before a pairing event is recorded;
an RFID/BLE reader configured to be installed on the tractor to detect an attachment and a detachment of the trailer to the tractor by reading the RFID/BLE tag;
a communication module configured to transmit data from the GPS device and the RFID/BLE reader to a cloud server, wherein said cloud server configured to receive and store the tracking data selected from real-time location data from the GPS device, attachment and detachment events, selected from a unique identification of the attached or detached trailer, a unique identification of the tractor, a location, and a time, and calculated distance traveled by the trailer during an attached period;
a web application accessible via a network, coupled to the cloud server to display a real-time status of tractors and trailers, including which tractor is attached to which trailer, current locations of individual or all tractors and individual or all trailers, identification of idle tractors and trailers and their parked locations, a history of attachment and detachment events, a history of consignments associated with specific tractors, and total kilometers traveled by each trailer; and
a graphical user interface accessible via a user computing device, coupled to the cloud server to display the real-time status, current locations, idle asset identification, attachment/detachment history, consignment history, and total kilometers traveled by each trailer.

2. The system as claimed in claim 1, further comprising a sensor associated with the RFID/BLE reader, configured to detect the presence of the RFID/BLE tag within a user-defined proximity, wherein the sensor is selected from a magnetic sensor or a proximity sensor, said sensor being configured to detect the presence of the RFID/BLE tag by measuring changes in a magnetic field or distance and confirming an attachment or detachment event only when the detected proximity remains stable for a user-defined duration, wherein the RFID/BLE reader and the sensor are further configured to jointly apply an intelligent filtering technique that analyses signal strength and timing patterns to filter out false positives and negatives, wherein the intelligent filtering technique flags an event as valid only when both the RFID/BLE signal and the sensor's proximity reading are consistent for a specified time period.
3. The system as claimed in claim 1, wherein the cloud server is further configured to generate alerts for attachment and detachment events upon receiving the data from the GPS device and the RFID/BLE reader using a secure communication protocol, said secure communication protocol comprising an encrypted data transmission channel and a device-level authentication mechanism, wherein the device-level authentication mechanism verifies the authenticity of the on-board device against a pre-registered device list stored on the cloud server, wherein the encrypted data transmission channel utilizes Transport Layer Security (TLS) or Secure Sockets Layer (SSL) to establish a secure, end-to-end connection between the on-board devices and the cloud server; and wherein the web application and the graphical user interface are further configured to provide secure login for different user roles, including a driver and an administrator, wherein the web application and the graphical user interface are further configured to display a live mapping of tractors and trailers; and wherein the cloud server is further configured to detect idle assets by analyzing real-time location data against a set of predefined criteria, wherein the criteria includes a speed threshold below which the asset is considered stationary and a time duration for which the asset must remain stationary, and wherein both the speed threshold and the time duration are configurable by a user, thereby process concurrent data streams from multiple tractors and trailers using a load-balanced architecture, said load-balanced architecture comprising a data ingestion pipeline that queues incoming data packets and processes them in parallel using a distributed service framework, wherein the data ingestion pipeline is further configured to validate the integrity of each data packet upon receipt by performing a checksum verification and to discard any corrupted packets; and wherein the cloud server is further configured to generate reports for management based on the stored tracking data and calculate the distance travelled by the trailer during an attached period to the real-time GPS coordinates, wherein the server utilizes a Kalman filter or a smoothing technique to filter out GPS inaccuracies and interpolates between data points to estimate missing segments during intermittent signal loss.
4. The system as claimed in claim 1, wherein the RFID/BLE reader is further configured to dynamically adjust its signal strength based on environmental conditions detected via an integrated ambient sensor, wherein the ambient sensor monitors electromagnetic noise, temperature, and physical vibrations from the tractor, and wherein the RFID/BLE reader applies an adaptive gain control to maintain optimal tag detection reliability under fluctuating environmental conditions, thereby reducing false attachment or detachment triggers caused by electromagnetic interference or mechanical shocks; and wherein the intelligent filtering technique comprises a multi-stage validation process, wherein:
(a) a first stage compares instantaneous Received Signal Strength Indicator (RSSI) values of the RFID/BLE tag to a dynamically updated baseline to reject transient spikes or drops in signal;
(b) a second stage computes a time-weighted moving average of both RSSI and proximity readings to identify stable connection patterns; and
(c) a third stage applies a correlation check between the detected attachment event and concurrent changes in tractor acceleration data obtained from an onboard accelerometer, wherein an attachment event is validated only when both sensor correlations and RFID/BLE stability criteria are satisfied.
5. The system as claimed in claim 1, wherein the cloud server further comprises a distributed data ingestion module configured to handle real-time streams from multiple tractors, wherein said module:
(a) assigns a unique cryptographic session token to each incoming data stream upon authentication;
(b) uses a priority-based queueing mechanism to categorize incoming events into critical (attachment/detachment) and non-critical (periodic GPS updates) streams; and
(c) applies event-driven processing such that critical events are immediately validated and pushed to the user interface with sub-second latency, while non-critical data is aggregated and processed in scheduled intervals to optimize bandwidth and computational load.
6. The system as claimed in claim 3, wherein the secure communication protocol further implements a rolling key exchange mechanism based on ephemeral key pairs, wherein:
(a) each on-board device periodically generates a temporary public/private key pair;
(b) the cloud server verifies each data packet using the ephemeral key before accepting the transmission; and
(c) upon detection of any key mismatch or packet tampering, the cloud server triggers an immediate revocation of the session and initiates a secure re-authentication handshake with the device, ensuring continuous protection against replay attacks and unauthorized data injection.
7. The system as claimed in claim 5, wherein idle asset detection further comprises:
(a) computing a geofence boundary dynamically around the last known position of the asset using real-time GPS coordinates;
(b) monitoring deviations of the asset’s location within said geofence using a Kalman-filtered trajectory prediction model; and
(c) classifying the asset as idle only when its actual movement variance remains below a computed statistical threshold of the predicted trajectory for a configurable time window, thereby preventing false idle status due to GPS drift or momentary signal fluctuations.
8. The system as claimed in claim 1, wherein the calculation of distance traveled by the trailer during an attached period further comprises:
(a) segmenting GPS data points into contiguous travel intervals using attachment and detachment timestamps as boundary markers;
(b) applying a hybrid computation method wherein the Haversine formula is used for long-haul segments and map-matching to digital road networks is used for urban segments; and
(c) correcting any discontinuities in data caused by signal outages by interpolating intermediate positions using a constrained optimization model that incorporates last-known velocity and heading vectors from the tractor.
9. The system as claimed in claim 1, wherein the RFID/BLE tag is configured with a low-power wake-up protocol, wherein:
(a) the tag remains in a dormant state until a wake-up beacon from the tractor’s RFID/BLE reader is received;
(b) upon wake-up, the tag transmits its unique ID and encrypted session token within a defined response window; and
(c) the reader aborts pairing attempts if no authenticated response is received within the window, thereby conserving tag power and preventing unauthorized passive scanning.
10. A method for tracking tractor-trailers, the method comprising:
tracking a real-time location of a tractor using a GPS device installed on the tractor;
detecting an attachment of a trailer to the tractor by reading an RFID/BLE tag on the trailer using an RFID/BLE reader on the tractor;
recording, by a cloud server, an attachment event including a unique identification of the attached trailer, a unique identification of the tractor, a location of attachment, and a time of attachment;
detecting a detachment of the trailer from the tractor by the RFID/BLE reader;
wherein detecting an attachment or detachment further comprises using a sensor to detect a user-defined proximity of the RFID/BLE tag to the RFID/BLE reader;
recording, by the cloud server, a detachment event including the unique identification of the detached trailer, the unique identification of the tractor, a location of detachment, and a time of detachment;
transmitting location data from the GPS device and attachment/detachment event data from the RFID/BLE reader to the cloud server via a communication module;
calculating, by the cloud server, a distance traveled by the trailer during an attached period based on the real-time location data and the recorded attachment and detachment events; and
displaying, via at least one of a web application and a graphical user interface, real-time status of tractors and trailers, current locations, idle asset identification, a history of attachments and detachments, a history of consignments, and total kilometers traveled by each trailer.