Extrema-retentive data buffering and simplification

Systems and storage media containing instructions for asset tracking are provided. An example system includes a server and an asset tracking device onboard an asset. The asset tracking device to receive raw data pertaining to the asset, determine a dynamic group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data, perform the dataset simplification algorithm on a set of data that contains the raw data and the dynamic group of carry-over data points to determine one or more data points in the set of data to be recorded, and transmit the recorded data points to the server.

FIELD

The present disclosure relates to telematics, and in particular to the collection of data from assets tracked by telematics systems.

BACKGROUND

In the field of telematics, the location of an asset, such as a vehicle, and other data pertaining to the asset, may be monitored by an asset tracking device integrated within the asset itself or by another asset tracking device placed onboard the asset. The location of the asset may be tracked through the use of a satellite navigation system, such as a Global Positioning System (GPS), Global Navigation Satellite System (GNSS), cellular tower network, or other system. Other data may be collected through sensors (e.g., accelerometers, temperature sensors), or through Electronic Control Units (ECUs) of the asset, from which a variety of data may be obtained, such as, in the case of the asset being a vehicle: engine speed, battery temperature, fuel level, tire pressure, and other data. Such data may be made available over a Controlled Area Network (CAN) accessible directly by an ECU or by an asset tracking device through a communication port (e.g., an OBD2 port). In any case, such data may be transmitted to, and recorded at, a back-end data collection system to be used in the provision of a telematics service, such as a fleet management tool, or for analysis.

SUMMARY

According to an aspect of the disclosure, a method for asset tracking in accordance with an extrema-retentive data buffering and simplification process is provided. The method involves obtaining a stream of raw data, adding data points from the stream of raw data to a data buffer in a first cycle of data, performing a dataset simplification algorithm on the first cycle of data to determine whether one or more data points from the first cycle of data are to be recorded, preparing the data buffer for a second cycle of data, including determining a group of carry-over data points to be included in the second cycle of data, continuing to add data points from the stream of raw data to the data buffer in the second cycle of data.

According to another aspect of the disclosure, an asset tracking device that employs an extrema-retentive data buffering and simplification process is provided. The device includes an interface layer to receive a stream of raw data, a data buffer to store cycles of raw data received by the interface layer along with dynamic groups of carry-over data points, a memory to store simplified data derived from the raw data, and a controller. The controller, when triggered, performs a dataset simplification algorithm on a set of data that contains a cycle of raw data and a dynamic group of carry-over data points, to determine whether one or more data points in the set of data are to be recorded in the memory as part of the simplified data. Further, the controller, when triggered, prepares the data buffer for a new cycle of raw data by pre-filling the data buffer with a dynamic group of carry-over data points that includes: (i) a last data point from an immediately previous cycle of raw data, (ii) a last data point determined to be recorded in the memory from the immediately previous cycle of raw data or an earlier cycle of raw data.

According to yet another aspect of the disclosure, a system that employs an extrema-retentive data buffering and simplification process is provided. The system includes a server and an asset tracking device onboard an asset. The asset tracking device is to receive raw data pertaining to the asset, determine a dynamic group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data, perform the dataset simplification algorithm on a set of data that contains the raw data and the dynamic group of carry-over data points to determine one or more data points in the set of data to be recorded, and transmit the recorded data points to the server.

According to yet another aspect of the disclosure, a non-transitory machine-readable storage medium that is executable by one or more processors to perform an extrema-retentive data buffering and simplification process is provided. The instructions cause the one or more processors to: obtain raw data pertaining to an asset, determine a group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data, perform the dataset simplification algorithm on a set of data that contains the raw data and the group of carry-over data points to determine one or more data points in the set of data to be recorded, and transmit the recorded data points to the server.

The group of carry-over data points may include (i) a last data point added to the first cycle of data, and (ii) where it is determined that one or more data points from the first cycle of data are to be recorded: a last data point determined to be recorded from the first cycle of data, else a last recorded data point from an earlier cycle of data. The group of carry-over data points may further include (iii) a data point between data point (i) and data point (ii) that deviates the most from a reference line defined through data point (i) and data point (ii).

The data buffer may be limited in size to contain no more than four data points, including data point (i), data point (ii), data point (iii), and (iv) a new data point added to the data buffer to be considered for recordation.

Performing the dataset simplification algorithm may involve comparing, against a first threshold value, a distance of a data point to be considered for recordation to a reference line in accordance with the dataset simplification algorithm. The method may further involve comparing, against a second threshold value that is less than the first threshold value, a distance of data point (iii) to its relevant reference line as defined by the dataset simplification algorithm, and when it is determined that the distance of data point (iii) to the relevant reference line is greater than the second threshold value, forcing data point (i) to be recorded.

The method may further involve, following performance of the dataset simplification algorithm: generating (i) a placeholder data point, immediately following (ii) a last data point in the first cycle of data, that deviates as much as possible, within an allowable error threshold of the dataset simplification algorithm, from a first reference line defined through data point (ii) and (iii) a last data point in the first cycle of data determined to be recorded by the dataset simplification algorithm; determining whether any data points between data point (ii) and data point (iii) deviate, in excess of the allowable error threshold of the dataset simplification algorithm, from a second reference line defined through data point (i) and (iv) a data point between data point (ii) and data point (iii) that deviates the most from the first reference line; and, when it is determined that there is at least one data point between data point (ii) and data point (iii) that deviates, in excess of the allowable error threshold of the dataset simplification algorithm, from the second reference line, forcing data point (ii) to be recorded.

The dataset simplification algorithm may be performed each time a new data point is added to the data buffer from the stream of raw data. Preparing the data buffer for the second cycle of data may involve removing, each time a new data point is to be added to the data buffer, a least significant data point from the data buffer, and retaining the remaining data points in the data buffer.

The dataset simplification algorithm may be performed upon satisfaction of a data logging trigger, wherein satisfaction of the data logging trigger includes the data buffer being filled, a timer expiring, or a data point obtained from the stream of raw data deviating from an expected range in excess of a threshold amount.

The raw data may pertain to an asset, and describes a property, state, or operating condition of the asset. The stream of raw data may be obtained from a data source onboard an asset, and the data source comprises an electronic control unit (ECU) of the asset or a sensor onboard the asset.

DETAILED DESCRIPTION

Telematics systems tend to track very high numbers of assets, and each of these assets could potentially produce a very large quantity of data to be tracked. Therefore, to avoid collecting inordinate amounts of data, telematics systems typically employ one or more data sampling, reduction, filtering, or simplification techniques that reduce the amount of data collected from those assets. Advanced versions of such techniques have the aim of identifying only the most operationally-salient data generated at each asset to be recorded, while discarding the remainder.

Such techniques may be applied directly at the asset level so that decisions are made as to which data points should be recorded, and which data points should be discarded, before any data is transmitted to back-end systems. For periods of time during which data points were discarded, the telematics system can estimate the status of the asset based on the nearest data points that were sent to the telematics system (e.g., by interpolation or extrapolation). In this way, a telematics system can provide an accurate picture of the state of any of its assets in real time, or at any point in the past, without unduly burdening the telematics system with excessive data transmission or data storage requirements.

With the decision as to which data points to discard being made at the asset level, there is a significant need for temporary data storage at the asset (or on asset tracking devices at the asset). This need may be met through the use of one or more data buffers that are periodically refreshed as new data is collected. A data simplification process may be performed directly on these data buffers so that the decision as to which data points are to be discarded, and which are to be recorded, can be made prior to the transmission, or more permanent storage, of the data, thereby limiting the need for, and cost of, more permanent data storage and/or data transmission.

However, as will be described in greater detail below, it has been determined that certain data points may be inadvertently overlooked by the data simplification process (i.e., not adequately considered for recordation) when a data simplification process is applied directly within a data buffer. In particular, it has been determined that certain extrema data points (i.e., global or local maxima or minima, peaks or troughs) can sometimes be inadvertently excluded from recordation when such data points occur near the transition point when a data buffer is about to be refreshed (e.g., when the data buffer is filled with a first cycle of data and is cleared in preparation to ingest a second cycle of data). These extrema data points may have otherwise been recorded had they occurred elsewhere in the buffer cycle. Extrema data points are often of particular interest in the field of telematics, as they often communicate the most salient information (e.g., the top speed of a speeding vehicle, or a lowest oil temperature on a cold winter day).

As described herein, a data buffering process and a data simplification process are combined in such a way that the efficiency of applying a data simplification process directly within a data buffer is retained without the inadvertent exclusion of those certain extrema data points from being adequately considered for recordation. This process may be referred to as an extrema-retentive data buffering and simplification process.

FIG.1is a schematic diagram of an example system100for asset tracking that involves an extrema-retentive data buffering and simplification process.

The system100includes an asset102. For exemplary purposes, the asset102is shown as a vehicle, namely a commercial transport truck. However, the asset102may include any type of vehicular asset, such as a passenger vehicle, construction equipment vehicle, sporting vehicle, utility vehicle, naval vessel, aircraft, or any other vehicular asset. The asset102may also include any non-vehicular asset, such as a transport trailer, shipping container, pallet, shipped item, power generator, other machine, or any other non-vehicular asset.

In some examples, an asset tracking device (not shown) may be coupled to the asset102to track data pertaining to the asset102. In other examples, the asset102may include an integrated asset tracking device that tracks data pertaining to the asset102directly. In still other examples, the term “asset tracking device” may describe a computer program, whether embodied in software, firmware, or a combination thereof, that resides onboard a computing device onboard the asset102, and which performs the functions described herein. In either case, a processor/controller on the asset102and/or asset tracking device obtains data, such as the location of the asset102, and other types of data, from sensors (e.g, for accelerometer data), electronic control units (ECUs) of the asset102(e.g., for vehicle speed data), or other data sources (e.g., GPS devices). In the case of location data, the location of the asset102may be obtained from a locating system such as a Global Positioning System (GPS), a Global Navigation Satellite System (GNSS), a cellular tower network, Wi-Fi networks, or another location system. In the case of sensor data, that data may be obtained from a sensor onboard the asset102or a sensor on an asset tracking device coupled to the asset102, if applicable. In the case of data from an ECU, that data may be obtained from the ECU (e.g., through a Controlled Area Network (CAN) bus) or, if an asset tracking device is coupled to the asset, through a communication port such as an onboard diagnostic port (e.g., OBD2 port) of the asset102. Any of the above sources of data may be referred to as a data source. Such data is typically collected in a stream in real-time, and thus the data collected from such a data source is indicated generally as raw data stream110.

The raw data stream110may contain data that has passed through one or more pre-filtering steps, such as, for example, a low-pass filter, and may also contain data that has not passed through any such pre-filtering steps. Thus, although the term “raw data” is used, it is to be understood that such data refers to any data that is fed into the extrema-retentive data buffering and simplification process112.

The system100further includes a telematics system120with back-end data collection systems to record data captured from the asset102and other assets, including location data, trip/travel histories, accelerometer data, vehicle speed data, engine data, and other data pertaining to the assets it tracks. The telematics system120may further store user accounts and other data associated with the assets and/or asset tracking devices for the provision of telematics services. The telematics system120includes one or more servers or computing devices to store such data and to provide a telematics service and/or data analysis based on the recorded data. In particular, the telematics system120includes at least one server with a communication interface to communicate with the asset102(directly or through an asset tracking device coupled to the asset102, if applicable) via one or more computing networks and/or telecommunication networks, a memory to store data and programming instructions, and a controller to execute the methods performed by the telematics system120as described herein. The telematics system120may provide a telematics service, including live tracking, record keeping, and reporting services to end user (client) devices, and may further store or forward the data collected from the asset102and other assets to other systems for further analytics purposes.

Only a small portion of the data in the raw data stream110that is collected at the asset102is ultimately transmitted to the telematics system120. The remainder of the raw data is discarded as being redundant or not sufficiently operationally-salient for the purposes of the telematics system120. Raw data from the raw data stream110is buffered at the asset (or at an asset tracking device thereon) and periodically (or aperiodically) simplified, in what is described herein as an extrema-retentive data buffering and simplification process112. By application of the process112, a simplified data stream114is produced, which is ultimately transmitted to the telematics system120.

The extrema-retentive data buffering and simplification process112is described in greater detail throughout this description. However, for illustrative purposes, attention is next directed towardFIGS.2-3C, where some of the shortcomings of non-extrema-retentive processes are explained.

FIG.2illustrates the application of a data buffering and simplification process to a data buffer200as the data buffer200is filled with a series of data points. The process shown may suffer from the issues described above with respect to certain extrema data points being improperly missed by the application of a dataset simplification algorithm.

In the example shown, the data buffer200provides space for five data points to be temporarily stored. At step202, the data buffer200is in its first “cycle” of data. The term “first” used herein does not necessarily refer to the initial cycle of data that fills the data buffer200before any other, but rather, refers to the first of any pair of adjacent cycles of data (potentially after several cycles of operation of the data buffer200). Here, four data points, P1, P2, P3, and P4, are stored in the data buffer200, leaving space for a fifth data point to be added. These data points may represent any sort of data pertaining to an asset that may be collected by a telematics system, including any property, state, or operating condition of a vehicle, such as, for example, GPS latitude, GPS longitude, altitude, vehicle speed, engine speed, accelerometer X magnitude, accelerometer Y magnitude, accelerometer Z magnitude, oil pressure, battery temperature, battery voltage, etc. The value of each data point is shown plotted on a data-time plot above the data buffer200(with units and axis labels omitted for clarity) to illustrate that the collected data is changing over time. The data points are added chronologically to the data buffer200from left to right, with each data point being plotted approximately above its respective slot in the data buffer200.

At step204, a fifth point, P5, is added to the data buffer200, which is now completely filled. With the data buffer200filled, the application of a dataset simplification algorithm to the data buffer200is triggered, at step206. The dataset simplification algorithm determines, in this case, that point P3(highlighted in bold) is to be recorded into memory220. Data points that are recorded in the memory220are designated to be transmitted to a telematics system.

At step208, with the relevant data points from the first cycle of data recorded (i.e., point P3), the data buffer200is prepared for the next cycle of data (i.e., the second cycle of data). Preparing the data buffer200for the next cycle of data may involve clearing, refreshing, or emptying the data buffer200of any existing data points. That is, any data points remaining in the data buffer200that are not stored in memory220are discarded (i.e., deleted or overwritten).

At step210, new data points are added to the data buffer200, beginning with data point P6, to start the next cycle of data. New data points may continually be added in this way until another dataset simplification algorithm is triggered. These new data points will similarly either be recorded in memory220(for transmission to a telematics system) or discarded.

In this way, large quantities of data may be collected at an asset and analyzed for relevance (by a dataset simplification algorithm) so that only the operationally-salient data points are transmitted to the telematics system. Several threads of the process described herein may run in parallel for different sets of data. For example, separate data buffers200may be designated for GPS data, accelerometer data, or other vehicle data, and each of these separate data buffers200may be subject to the real-time buffering and simplification process described above.

Although this process makes efficient use of memory and data transmission resources, this process may be at risk of certain shortcomings, as will be seen below. As shown inFIGS.3A-3C, certain extrema data points may be missed near the edges of a set of data when a dataset simplification algorithm is applied to a data buffer when the data buffer transitions from a first cycle of data to a second cycle of data.

FIG.3Ashows an example data-time plot of a series of data points being added to a data buffer300, and further shows the application of a data buffering and simplification process to the data buffer300as the data buffer300transitions from a first cycle of data to a second cycle of data.

The data buffer300provides space for a number of data points to be temporarily stored (including at least the six data points shown), which may represent any sort of data pertaining to an asset that may be collected by a telematics system. The value of each data point is shown plotted on a data-time plot above the data buffer300(with units omitted for clarity) to illustrate that the collected data is changing over time, chronologically, from left to right, with each data point being plotted approximately above its respective slot in the data buffer300.

Any number of data points may have been added to the data buffer300prior to the addition of data point P1. After data point P6is added, the data buffer300is filled, a dataset simplification algorithm is triggered, and the data buffer300is cleared in preparation for new data points to be added in a second cycle of data. The dataset simplification algorithm is applied on points P1-P6and any earlier points added to the data buffer300. In the example shown, the particular kind of dataset simplification algorithm that is applied is a direct (unmodified) application of the Ramer-Douglas-Peucker (RDP) algorithm, an iterative process in which a curve of data is simplified into a similar curve composed of fewer points, within a certain margin of error (i.e., an allowable error threshold). However, the process could be illustrated with other curve simplification algorithms (path simplification algorithms) that directly simplify a set of data. Direct application of the RDP algorithm results in the recordation of the first point in the data buffer300(not shown), the last point in the data buffer (P6), and any significant data points between the first and last points that fall outside of a predefined margin of error (in this case, P2). Thus, data points P2and P6are highlighted in bold to indicate that they have been flagged to be recorded and transmitted to the telematics system. A trend line is defined between the points recorded from the first cycle of data (i.e., points P2and P6), and remains to be defined in the second cycle of data after a dataset simplification algorithm is applied in that cycle of data.

However, it should be noted that the highest data point in the first cycle (P5) was not recorded. This is because, despite P5being the highest point in the dataset, it occurred near a point that was forcibly recorded by the RDP algorithm (namely, the last point in the dataset, P6), and therefore would not be recorded unless it fell outside the margin of error between the last point in the dataset and the previous recorded point. Rather, P6is erroneously recorded as the “peak” data point in the curve.

Although it is one of the goals of the RDP algorithm to filter out data points that lie within the margin of error between two neighbouring points, there is a bias in the RDP algorithm against saving peak points that occur near the first and last points in the dataset. The RDP algorithm typically saves the first and last points in a set of data, and then evaluates the contribution of each of the interior points toward the overall shape of the data. Since the first and last points are automatically saved, other points that appear near these first or last points are less to have an impact on the overall shape of the curve, and therefore are less likely to be saved. This is a problem when these points actually represent the peaks (or troughs) in a larger set of data. This is particularly a problem when data is stored temporarily in a buffer prior to simplification. For example, a data point that is situated near the end of a buffer may be a peak data point if viewed in the greater context of the current buffer plus the immediately following buffer, but this data point may be discarded (i.e., “missed”) because it did not have a significant impact on the overall shape of the first buffer. In other words, there is a bias in the RDP algorithm that is expected to cause a significant proportion of peak data points to be prematurely determined to be missed merely for being collected near the beginning or end of the data buffer300. Similar biases may be present in other curve/path simplification algorithms.

Extrema data points (peaks or troughs) are of particular interest in telematics data, and therefore there is a significant advantage to be attained by ensuring that these points are captured. For example, it may be of particular interest to record the highest speed that a speeding vehicle is travelling, regardless of whether those highest speed points fall within a margin of error of a dataset simplification algorithm.

Although the issue described here results from application of the RDP algorithm, it is expected that the same issue will be observed upon the application of any sort of dataset simplification algorithm that forces the first or last data point in a dataset to always be recorded. Similar issues may be applicable in other curve simplification or path simplification techniques. Techniques to avoid this bias against capturing extrema points that are found near the end of the data buffer300are proposed herein, including inFIG.3B, below.

FIG.3Bshows another example data-time plot of the same series of data points being added to the data buffer300. However, in contrast to the data buffering and simplification process described inFIG.3A, inFIG.3B, the last data point added to the data buffer300(P6) is always excluded from consideration for recordation (shown in grey). Further, the last data point in the data buffer300is always discarded. Thus, since P6is excluded from consideration, in this case, P5is now identified by the dataset simplification algorithm as point worth recording (shown in bold), and thus the peak point is successfully captured. Application of this strategy therefore avoids the specific edge case shown inFIG.3Ain which a peak point near the last point in the data buffer300is missed.

FIG.3Cshows another example data-time plot of yet a series of data points being added to the data buffer300. In this case, as inFIG.3B, the last data point added to the data buffer300is always excluded from consideration for recordation (P6). However, in this case, the last data point added to the data buffer300is itself the peak point. Under application of the RDP algorithm, the non-peak point, P5, is erroneously recorded as the “peak” data point. Thus, it can be seen that the strategy employed inFIG.3Bintroduces a new bias in which a peak point is missed if that peak point is itself the last data point added to the data buffer300.

Thus, there exists a need for a data buffering and simplification process that ensures that extrema data points (peaks and troughs) are captured without biases against extrema data points that occur near the transition of a data buffer from one cycle to the next. The present disclosure proposes such a process, referred to herein as an extrema-retentive data buffering and simplification process.

FIG.4is a flowchart of an example method400for asset tracking in accordance with an example extrema-retentive data buffering and simplification process. The method400may be understood to be one example of the extrema-retentive data buffering and simplification process112ofFIG.1, and it is to be understood that the blocks of the method400may be performed by an integrated tracking device onboard the asset102ofFIG.1or an asset tracking device coupled to the asset102. For greater clarity, the method400is described with reference toFIG.5, which illustrates the functional elements involved in an extrema-retentive data buffering and simplification process500, which may be similar to the process112ofFIG.1, in a manner that shows the flow of data points through the process. Thus, attention is directed toFIG.4andFIG.5simultaneously.

InFIG.5, the data buffer504and the memory506may refer to different portions of memory onboard an integrated tracking system or asset tracking device that tracks an asset, such as the asset102ofFIG.1. The dataset simplification algorithm505and data point carry-over algorithm520are algorithms that may be executed by a controller (e.g., processor) of such an integrated tracking system or asset tracking device. Similarly the raw data stream510may be similar to the raw data stream110ofFIG.1, and the simplified data stream514may be similar to the simplified data stream114ofFIG.1.

Referring now toFIG.4andFIG.5together, at block402, the raw data stream510pertaining to an asset is obtained. The stream of raw data may be obtained from a data source onboard the asset, which may include an electronic control unit (ECU) of the asset or a sensor onboard the asset. Such data may be obtained directly, through a Controlled Area Network (CAN), or through a communication port (e.g., OBD2 port) if the data is obtained by an asset tracking device coupled to the asset. In cases where the asset is a vehicle, the raw data may describe a property, state, or operating condition of the vehicle (e.g., outside temperature, accelerometer data, GPS data, engine speed, etc., as described above).

At block404, data points from the raw data stream510are added to the data buffer504in the first cycle of data511(i.e., the current cycle of data). In other words, data points from the raw data stream510are batched or grouped together into small chunks for the determination of whether any of such data points are to be recorded. The data points are added to the data buffer504chronologically, and in real-time, as the data is obtained.

At block406, a dataset simplification algorithm505is performed on the first cycle of data511to determine whether one or more data points from the first cycle of data511are to be recorded (i.e., as simplified data, or in a simplified set of data) shown as to-be-recorded data points512. The unrecorded data points are discarded, shown as discarded data points513. The recorded data points512(the points in the simplified data) are ultimately transmitted as a simplified data stream514to a telematics system.

The dataset simplification algorithm that is applied may be the Ramer-Douglas-Peucker (RDP) algorithm, or a variation thereof (or another curve simplification or path simplification algorithm). The RDP algorithm is an iterative process in which a curve of data is simplified into a similar curve composed of fewer points, within a certain margin of error (i.e., an allowable error threshold). Briefly described, the RDP algorithm, and variations thereof, involve comparing, against a threshold value (i.e., an allowable error threshold), the distance of each data point under evaluation to a particular reference line to make a determination as to how significant that data point is to the overall trend of the data, and therefore whether it should be recorded. In the RDP algorithm, and variations thereof, each data point is measured against a reference line joining the two nearest data points on either side of that data point that are already determined to be recorded, beginning with the first and last points in the set of data. If a data point deviates from the relevant reference line in excess of the allowable error threshold, and in a greater proportion than any of its neighboring data points that are also being compared against the same reference line, then that data point is recorded. Beginning with a reference line defined between the first and last points in the set of data, the point with the greatest error from that reference line is recorded, and two new reference lines are defined: a first reference line between the first point and the to-be-recorded point, and a second reference line between the to-be-recorded point and the last point. The process carries forward iteratively, on each newly defined reference line, until no points deviate from their relevant reference lines in excess of the threshold error value. The distance between a data point and its reference line may be measured directly (i.e., the shortest distance), as per the RDP algorithm, or in some cases, as a “vertical” distance (in the data dimension), or even as a “horizontal” distance (in the time dimension, if applicable), in variations of the RDP algorithm. As described herein, the last data point in the set of data is not necessarily saved, as it is often carried-over to the next cycle of the data buffer for further evaluation, and further, the first data point in the set of data is often a previously recorded point, the recordation of which is not duplicated.

In some examples, the dataset simplification algorithm505may be triggered periodically. In other examples, rather than being performed strictly periodically, the dataset simplification algorithm may be performed upon satisfaction of a data logging trigger. A data logging trigger being satisfied may include, for example, the data buffer being filled, a timer expiring, or when a more advanced aperiodic trigger is satisfied. An example of an aperiodic trigger is one which is satisfied when any given condition is met in the raw data. For example, an aperiodic trigger may be satisfied when a particular type of data being collected exceeds a threshold value (e.g., a dataset simplification algorithm is triggered when a vehicle is determined to be travelling in excess of 100 km/h). Another example of an aperiodic trigger is one which is satisfied when there appears to be a significant change in the raw data being collected. For example, an aperiodic trigger may be satisfied when a data point obtained from the raw data stream510deviates from an expected range in excess of a threshold amount. For example, when GPS data shows that the asset has travelled outside of its expected trajectory (based on previously recorded GPS data points), a dataset simplification algorithm may be triggered. Thus, a data logging trigger may be satisfied before the data buffer504is completely filled. Further, any number of data logging triggers, whether periodic or aperiodic, may be checked in parallel.

In some examples, the dataset simplification algorithm505may be performed each time a new data point is added to the data buffer504from the stream of raw data510. Thus, each time a new data point is obtained, that new data point may be immediately evaluated, with the benefit of the context provided by carry-over data points521, to determine whether that new data point is worth saving. In such examples, depending on the size of the data buffer504, it can be expected that the dataset simplification algorithm505will have been performed several times by the time the data buffer504becomes filled. Such a process may be particularly well suited toward identifying extrema data points quickly, once they are added to the data buffer504, without the dataset simplification algorithm505being biased by a long series of data points following an extrema data point.

At block408, the data buffer504is prepared for a second cycle of data. Preparing the data buffer504for the second cycle of data involves the data point carry-over algorithm520determining a group of carry-over data points521(i.e., a dynamic group of carry-over data points) to be included in the second cycle of data, and pre-filling the data buffer504with that group of carry-over data points521(i.e., adding the carry-over data points521to the data buffer504after the data buffer504being cleared, or retaining the carry-over data points521in the data buffer504without the entire data buffer504being cleared). The group of carry-over data points521may be selected from one or more of: the first cycle of data511(i.e., the immediately previous cycle of raw data), and an earlier cycle of data. These data points are retained from these earlier cycles of data to be carried over to the second cycle of data. That is, the carry-over data points521include data points from either the first cycle of data511, an earlier cycle of data, or both. In some cases, the carry-over data points521may include data points selected for recordation from the results of the application of the dataset simplification algorithm505. Thus, most often, when the dataset simplification algorithm505is performed on a set of data, that set of data contains new raw data points from the raw data stream510along with a group of carry-over data points521. The data points that are selected to be recorded may be from the new batch of raw data points from the raw data stream510or from the carry-over data points521, if not already recorded.

In the present example, the carry-over data points521include, at minimum, the last (or latest) data point added to the first cycle of data511(that is, the last data point in the data buffer504if the data buffer504is filled, or, if the data buffer504is not filled, the most recently added point), and one previously recorded point. That previously recorded point is, where it is determined that one or more data points from the first cycle of data are to be recorded, the last (or latest) data point determined to be recorded from the first cycle of data511. Otherwise, where no data points are recorded from the first cycle of data511, that previously recorded point is the last (or latest) recorded data point from an earlier cycle of data. In either case, in the present example, the data point that was most recently identified as to-be-recorded is included as a carry-over data point521.

Carrying over the last data point added to the first cycle of data511maintains an amount of continuity between adjacent data buffer cycles and ensures that the point is not missed if that point happens to be an extrema data point (e.g., the circumstance that led to the missed “peak” point inFIG.3C). Carrying over the most recently recorded or to-be-recorded data point also maintains an amount of continuity between adjacent data buffer cycles to help ensure that any new data points that are to be considered for recordation are considered in the context of the most recently recorded data. That is, carrying over the most recently recorded or to-be-recorded data point helps ensure that new data points are not unnecessarily recorded unless they deviate significantly from that point, and thereby adds an amount of smoothing to the data recordation process.

In some examples, the carry-over data points521further include a point between the last point added and the last point determined to be recorded that deviates the most from a reference line (i.e., a trend line) defined through (a) the last point added and (b) the last point recorded or determined to be recorded, as the case may be. In other words, the carry-over data points521may include the point with the “greatest error” or the greatest deviance from the two other points that are being carried over. This “greatest error” data point may be measured against the reference line according to its shortest distance to the reference line (e.g., as per the RDP algorithm), its vertical distance to the reference line, its horizontal distance to the reference line, or by another calculation. When there is a “tie” for determining which of any set of points is the “greatest error” data point with respect to any given reference line, it may be determined that the data point which occurs later in the set of data is to be selected as the “greatest error” data point, as it has been determined that a data point situated later in a set of data may be more likely to have a greater impact on how that set of data is ultimately simplified, at least in view of the considerations regarding the transition of a data buffer from one cycle to the next, as described herein.

Carrying over this “greatest error” point helps to ensure that an extrema data point that occurs near the transition between data buffer cycles is not prematurely discarded merely for being within the margin of error of the dataset simplification algorithm that is applied (e.g., the circumstance that led to the missed “peak” point inFIG.3A). Carrying over this “greatest error” point into the next cycle allows the point to be considered in the greater context in comparison to the other data points being carried over (including the most recently recorded or to-be-recorded data point), and the newly added data points.

Thus, the first and last data points in any cycle of data (other than, in some cases, the initial cycle of data when the data buffer504begins operation) act as “reference” points that are considered in the analysis of the dataset simplification algorithm505, and may or may not themselves eventually be selected by the dataset simplification algorithm505to be recorded. Thus, most often, the last point in any cycle of data is carried over to the next cycle of data (without necessarily being recorded in the cycle of data it was carried over from), and the first point in any cycle of data is a previously-recorded point that was carried over from a previous cycle (which need not be recorded again). Thus, the feature of the RDP algorithm that forcibly causes the first and last points of a dataset to be recorded is eliminated, along with the accompanying biases against recording extrema data points that occur near these forcibly-saved points, but the information contained in these points is still considered by the dataset simplification algorithm.

Preparing the data buffer504for the second cycle of data may involve, in some examples, clearing, refreshing, or emptying the data buffer504of any existing data points. That is, any data points remaining in the data buffer504that are not stored in memory506are discarded (i.e., deleted or overwritten). In other examples, the data buffer504may only be partially cleared of the data points that are not recorded, while retaining the group of carry-over data points521. The method400then returns to block404, where new data points are added to the next (i.e., second) cycle of data. These new data points are temporarily stored in the data buffer504along with the group of carry-over data points521that is determined for that cycle of data.

Thus, the data buffer504is not completely cleared at the start of a new cycle, but rather, is primed, or pre-populated, with certain carry-over data points521. The group of carry-over data points521is dynamic, in that its composition will typically change from one cycle of data to the next, there is a new “last data point”, a new “last recorded data point”, and a new “greatest error” data point, to be carried over to the next cycle of data. The addition of these carry-over data points521to the next cycle of data provides the dataset simplification algorithm505with greater context to aid in the consideration of whether any of these carry-over data points521(and any future data points in that next cycle of data) should be recorded. That is, the dataset simplification algorithm may make the determination of whether any of these points is outside the acceptable margin of error based on a comparison to more contextually relevant data points. These benefits are illustrated by way of example inFIGS.6-7B, below.

In some examples, a group of carry-over data points need not necessarily be stored in a data buffer along with the raw data, but may be stored in separate memory, accessible by a dataset simplification algorithm. In such examples, when the dataset simplification algorithm is applied, it is applied to the raw data in the data buffer in addition to the carry-over data points stored in separate memory.

The method400may be embodied in instructions stored on a non-transitory machine-readable storage medium that is executable by a processor of a computing device. Such a non-transitory machine-readable storage medium may include ROM, RAM, flash memory, magnetic storage, optical storage, and similar, or any combination thereof, for storing instructions and data as discussed herein. Thus, an example non-transitory machine-readable storage medium may comprise instructions that when executed cause one or more processors to: obtain raw data pertaining to an asset; determine a group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data; perform the dataset simplification algorithm on a set of data that contains the raw data and the group of carry-over data points to determine one or more data points in the set of data to be recorded; and transmit the recorded data points to the server. An example non-transitory machine-readable storage medium may further comprise instructions to perform any of the functionality of the extrema-retentive data buffering and simplification processes described herein.

FIG.6illustrates the application of an example extrema-retentive data buffering and simplification process to a data buffer600as the data buffer600is filled with a series of data points. The example inFIG.6is similar to the example shown inFIG.2, but with the application of an extrema-retentive data buffering and simplification process as described herein. For continuity with the previous Figures, the data buffer600may be understood to be similar to the data buffer504ofFIG.5.

As inFIG.2, inFIG.6, the data buffer600provides space for five data points to be temporarily stored. At step602, the data buffer600is in its first “cycle” of data. Here, four data points, P1, P2, P3, and P4, are stored in the data buffer600, leaving space for a fifth data point to be added. The value of each data point is shown plotted on a data-time plot above the data buffer600(with units and axis labels omitted for clarity) to illustrate that the collected data is changing over time. The data points are added chronologically to the data buffer600from left to right, with each data point being plotted approximately above its respective slot in the data buffer600.

At step604, a fifth point, P5, is added to the data buffer600, which is now filled. With the data buffer600filled, the application of a dataset simplification algorithm to the data buffer600is triggered, at step606. The dataset simplification algorithm determines, for example, that point P3, highlighted in bold, is to be recorded into memory620.

At step608, with the relevant data points from the first cycle of data recorded (i.e., point P3), a set of carry-over data points is selected, retained (e.g., in the data buffer600), and the remainder of the data buffer600is cleared (i.e., deleted, refreshed, or emptied). In the present example, the data points that are carried over from into the second cycle of data include: (i) the last data point added to the first cycle of data (i.e., P5), the last data point determined to be recorded from the first cycle of data (i.e., P3), and (iii) the point between the last point added (P5) and the last point determined to be recorded (P3) that deviates the most from a trend line defined through the last point added and the last point determined to be recorded (i.e., P4). These three carry-over data points are ordered chronologically, and occupy the first three slots for data points in the data buffer600, leaving space for additional data points to be added to the data buffer600.

At step610, new data points are added to the data buffer600, beginning with data point P6. The data points that were not recorded to the memory620(i.e., data points P1and P2) are discarded (i.e., deleted or overwritten). If the carry-over data points that were not already recorded (i.e., P4and P5) are not determined to be recorded in the second cycle of data, those points will also ultimately be discarded.

Thus, the data buffer600is not completely cleared between cycles, but rather, is primed or pre-populated with certain carry-over data points that aid in the determination of whether any of these carry-over data points, and any further data points added to the data buffer600, should be recorded.FIGS.7A and7B, shown below, show two illustrative scenarios in which the application of this data buffering and simplification process ensures that certain extrema data points (that may otherwise be missed as the data buffer600transitions from one cycle of data to the next) are captured.

FIG.7Ashows an example data-time plot of a series of data points being added to a data buffer700. For continuity with the previous Figures, the data buffer700may be similar to the data buffer600ofFIG.6. Further, the series of data points being added to the data buffer700is identical to that shown inFIG.3A. However,FIG.7Ashows the application of an extrema-retentive data buffering and simplification process to the data buffer700as the data buffer700transitions from a first cycle of data to a second cycle of data, thereby resulting in the capture of the peak data point that was missed inFIG.3A.

As shown, upon application of a dataset simplification algorithm to the first cycle of data, data point P2is determined to be recorded. However, in contrast to the algorithm applied inFIG.3A, inFIG.7A, the last data point (e.g., P6) is not necessarily recorded, but rather, is carried-over to the second cycle of data for further consideration. Further, the last recorded data point (P2), and the point between this last recorded point and the last point in the cycle (that is, P5), are also carried over to pre-populate the data buffer700in the second cycle of data. Thus, the first three slots of the data buffer600, in the second cycle, are shown to be occupied by P2, P5, and P6, chronologically. These carry-over data points are shown in dotted lines. New data points (P7, P8, P9) are added to the data buffer700in the second cycle of data.

With these carry-over data points included for consideration in the second cycle of data, the biases described above (inFIGS.3A-3C) which tend to result in certain extrema data points being missed by the dataset simplification algorithm, are avoided. As can be seen on the right-hand side ofFIG.7A, the peak data point (P5), which would have been missed by the application of an unmodified RDP algorithm if it fell within the margin of error, is very unlikely to fall within the margin of error when considered in the second cycle of data, and therefore is very likely to be recorded.

Similarly, as can be seen inFIG.7B, where the series of data points being added to the data buffer700is identical to that shown inFIG.3B, the highest data point in the first cycle (P6, which is also the last data point in the cycle), is carried over to the second cycle of data for consideration to be recorded, rather than being missed, as was the case inFIGS.3B-3C. Similarly, as can be seen on the right-hand side ofFIG.7B, the peak data point (P6), which would have been missed by the application of an unmodified RDP algorithm if it fell within the margin of error, is very unlikely to fall within the margin of error when considered in the second cycle of data, and therefore is very likely to be recorded.

Thus, between cycles of data, the data buffer700is pre-populated with carry-over data points that aid in the determination of whether any of these carry-over data points, and any further data points, should be recorded.

FIG.8is a schematic diagram of an example device800for asset tracking that employs an extrema-retentive data buffering and simplification process. The device800may be understood to be one example of an integrated tracking device onboard the asset102ofFIG.1or an asset tracking device coupled to the asset102.

The device800includes an interface layer802to receive raw data820pertaining to an asset (e.g., the asset102ofFIG.1). The raw data820is received from one or more data sources onboard the asset, such as, for example, an asset Electronic Control Unit (ECU)822or a sensor824onboard the asset. Another data source may include, for example, a GPS transceiver (whether integrated into the asset or an asset tracking device, if applicable). Another data source may include, in the case where the asset is tracked by an asset tracking device coupled to the asset, a communication port (e.g., onboard diagnostic port, OBD2 port) of the asset, from which the asset tracking device obtains data from an ECU or other data source onboard the asset, through a Controlled Area Network (CAN) or otherwise. The interface layer802includes the interfaces for receiving raw data820from such data sources, such as an interface for a GPS transceiver, an interface for an accelerometer, and an interface for a communication port of the asset.

The raw data820generally describes a property, state, or operating condition of the asset. For example, where the asset is a vehicle, the raw data820may describe the location of the vehicle, speed at which the vehicle is travelling, or an engine operating condition (e.g., engine oil temperature, engine RPM, engine cranking voltage).

The device800further includes a data buffer804to store the raw data820(i.e., cycles of raw data along with dynamic groups of carry-over data points) and a memory806to store simplified data826derived from the raw data820. The data buffer804and memory806may be located on the same, or separate, hardware, and may include read-only memory (ROM), random-access memory (RAM), flash memory, magnetic storage, optical storage, or similar, or any combination thereof.

Although only a single element is shown for raw data820, it is to be understood that the raw data820may include several data streams of several different data types, and this raw data820may include one or more target sets of data that are to be simplified separately, some of which may include one data dimension and others which may include multiple data dimensions.

The device800further includes a controller810to execute dataset simplification instructions812and data buffer cycling instructions814. The controller810may include one or more of a processor, microprocessor, microcontroller (MCU), central processing unit (CPU), processing core, state machine, logic gate array, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or similar, capable of executing, whether by software, hardware, firmware, or a combination of such, the actions performed by the controller810as described herein. The controller810includes a memory, which may include ROM, RAM, flash memory, magnetic storage, optical storage, and similar, or any combination thereof, for storing instructions and data as discussed herein, including the dataset simplification instructions812and data buffer cycling instructions814.

The dataset simplification instructions812are to cause the controller810, when triggered, to perform a dataset simplification algorithm on a cycle of data (i.e., a first cycle of data) to determine one or more data points from that cycle of data to be recorded in the simplified data826. That is, a dataset simplification algorithm is performed on a set of data that contains a cycle of raw data and a dynamic group of carry-over data points. The dataset simplification algorithm that is applied may be any algorithm that reduces a set of data into a smaller set of data of its most significant data points, such as, for example, a modified version of the RDP algorithm (e.g., modified in the manner described above). As discussed above, performance of a dataset simplification algorithm, and the further cycling of the data buffer804, may be triggered upon satisfaction of a data logging trigger, such as whenever a data point is added to the data buffer804, when the data buffer804becomes filled, when a timer expires, or another trigger.

The data buffer cycling instructions814are to cause the controller810, when triggered, to prepare the data buffer804for a new cycle of raw data, as described above with respect toFIG.4andFIG.5. Preparing the data buffer804for a new cycle of data may immediately follow the performance of a dataset simplification algorithm and the determination of whether any points in the data buffer804are to be recorded. Preparing the data buffer804for a new cycle of raw data may involve determining a dynamic group of carry-over data points, derived from the raw data, to be used in the performance of a subsequent application of the dataset simplification algorithm. Preparing the data buffer804(i.e., transitioning to a new cycle of data) may later involve clearing at least a portion of the data buffer804and pre-filling the data buffer804with carry-over data points (i.e., adding carry-over data points to the data buffer804). The data buffer cycling instructions814are also to cause data points from the raw data820to be added to the data buffer804in a new cycle of data.

As described above, the dynamic group of carry-over data points may include (i) a last data point from an immediately previous cycle of raw data, (ii) a last data point determined to be recorded in the memory from the immediately previous cycle of raw data or an earlier cycle of raw data. The dynamic group of carry-over data points may further include, where there exists at least one data point between data point (i) and data point (ii): (iii) a data point between data point (i) and data point (ii) that deviates the most from a reference line defined through data point (i) and data point (ii).

In some examples, if the data buffer804is cleared between cycles, the carry-over data points may be stored temporarily in a separate memory. In other examples, the carry-over data points may be selectively excluded from the clearing of the data buffer804, and left in place, or rearranged within the data buffer804, as appropriate.

The device800further includes a communication interface808to transmit the simplified data826to a server830, remote from the device800. The server830may be part of a telematics system, such as the telematics system120ofFIG.1. The communication interface808may include a cellular modem, such as an LTE-M modem, CAT-M modem, or other cellular modem configured for communication via the network with which to communicate with the server830. The communication interface808may be configured for bidirectional communication with the server830to receive instructions from the server830, such as, for example, to make modifications to the instructions812,814, for updating.

The server830is to receive the simplified data826and provide the simplified data826for a telematics service or other purposes. Such information may be provided on an ongoing basis or in response to requests. In some examples, the server830may be termed an “asset tracking server” which receives simplified data826and which may provide an indication of a status of the asset on an ongoing basis or in response to a status request. In some examples, the server830may be one computing device that is part of a cloud computing network.

Thus, in a telematics system, an asset tracking device onboard an asset may receive raw data pertaining to the asset, determine a dynamic group of carry-over data points, derived from the raw data, to be used in the performance of a dataset simplification algorithm on the raw data, perform the dataset simplification algorithm on a set of data that contains the raw data and the dynamic group of carry-over data points to determine one or more data points in the set of data to be recorded, and transmit the recorded data points to the server. Such an asset tracking device may batch the received raw data into cycles along with dynamic groups of carry-over data points, and perform the dataset simplification algorithm on said cycles. A dynamic group of carry-over data points may include (i) a last data point from an immediately previous cycle of raw data, (ii) a last data point determined to be recorded from the immediately previous cycle of raw data or an earlier cycle of raw data, and (iii) a data point between data point (i) and data point (ii) that deviates the most from a reference line defined through data point (i) and data point (ii).

With reference toFIG.9andFIG.10, further techniques for increasing the reliability of an extrema-retentive data buffering and simplification process are discussed.

FIG.9illustrates the application of an example extrema-retentive data buffering and simplification process to a data buffer900that is limited in size to contain no more than one new data point, in addition to a dynamic group of carry-over data points, to be considered for recordation. The process described inFIG.9may be employed by any computing device, such as the asset tracking device800ofFIG.8, and combined with any of the methods described herein.

A data buffer can in principle contain practically any number of data points for which sufficient memory is available. However, larger data buffers require more memory, which can be scarce on an asset tracking device. Thus, it may be desirable to use a data buffer of minimal size, even while employing an extrema-retentive data buffering and simplification process that involves retaining a dynamic group of carry-over data points.

Thus, the size of a data buffer (i.e., batch size) may be limited to contain no more than one new data point added, in addition to a dynamic group of carry-over data points, to be considered for recordation. In the example shown inFIG.9, the data buffer900is limited in size to contain no more than four data points, including (i) the last data point added to the immediately previous cycle of data, (ii) the last data point determined to be recorded, (iii) the data point between data point (i) and data point (ii) that deviates the most from a reference line defined through data point (i) and data point (ii) (where such a point is present), and (iv) a new data point to be considered for recordation. In this example, the size of the data buffer900may be understood to be of size four. Viewed another way, such a process involves no data buffer, as there is only one new data point being considered for recordation at any time. The carry-over data points are retained primarily for reference in the performance of the dataset simplification algorithm. In other examples, the size of such a data buffer may have size greater or less than four, so long as it contains no more than one new data point in addition to one or more carry-over data points.

Otherwise, the data buffer900ofFIG.9is similar to the data buffer600ofFIG.6. At step902, the data buffer900is pre-filled with a group of carry-over data points comprising points P1(the last recorded data point), P3(the last data point added to the previous cycle of data), and P2(the “greatest error” data point from the previous cycle of data between points P1and P3). There is room for one new data point to be added to the data buffer900to be considered for recordation.

At step904, a new data point, P4, is added to the data buffer900. At step906, the data buffer900is simplified, resulting in the recordation of point P2in memory920. Thus, a new group of carry-over data points is determined, comprising points P2(now the last recorded data point), P4(now the last data point added to the data buffer), and P3(now the point of “greatest error” between points P2and P4.

At step908, the data buffer900is (partially) cleared (of point P1), and pre-filled with points P2, P3, and P4. Again, there is room for one new data point to be added to the data buffer900to be considered for recordation (e.g., point P5at step910).

Thus, an extrema-retentive data buffering and simplification process may be employed using data buffers of small sizes.

FIG.10illustrates the application of an example extrema-retentive data buffering and simplification process to a data buffer1000in which the least significant data points in the data buffer1000are replaced with new incoming data points. The process described inFIG.10may be employed by any computing device, such as the asset tracking device800ofFIG.8.

Thus, rather than the data buffer1000being cleared of all data points save for a small number of designated carry-over data points, each data point in the data buffer1000is carried over to the next cycle of data, except the least significant data point, which is ejected from the data buffer1000. The least significant data point may be determined as per the dataset simplification algorithm applied to the data buffer1000, and in some examples, may be determined to be the least significant data point following the last data point in the data buffer1000determined to be recorded (i.e., the data point that deviates the least from a line defined through the last recorded data point and the last data point in the buffer1000). In other examples, where there are multiple recorded or to-be-recorded data points in the data buffer1000, the least significant data point may be determined to be the data point that deviates the least from any reference line. In still other examples, the least significant data point may be determined to be the least significant data point between the two most recently recorded data points. Other techniques may be used to identify one or more “least significant” data points to remove from the data buffer1000, which may not necessarily be the first or last data points added to the data buffer1000. In any case, the size of the group of carry-over data points may be understood to be of any size less than n, where n is the size of the data buffer1000. Such a process provides for exposure of each new data point to the most contextually important (the most significant) data points recently added to the data buffer1000, which may provide for a more robust application of a dataset simplification algorithm.

In the present example, the data buffer1000is of size six. At stage1002, the data buffer1000is filled with points P1, P2, P3, P4, P5, and P6, where point P1is the most recently recorded data point, and points P2, P3, P4, and P5are additional carry-over data points from previous cycles of data. With the data buffer1000being filled, a dataset simplification algorithm is run on the data buffer1000. For simplicity, it is assumed that no additional data points are determined to be recorded. However, point P4is determined to be the least significant data point—that is, the data point that deviates the least from a reference line drawn through points P1and P6. This “least error” data point is therefore removed from the data buffer1000, freeing up room for a new data point to be added, as shown at stage1004. At stage1006, a new data point, P7, is added to the data buffer1000, which triggers the performance of another iteration of the dataset simplification algorithm.

It is to be understood that the process described herein may operate with any number of “least significant” data points being removed from the data buffer1000between each cycle of data (e.g., the removal of two “least significant” data points, three “least significant” data points, etc.). Further, when there is a “tie” for determining which of any set of points is the “least significant” data point with respect to any given reference line, it may be determined that the data point which occurs earlier in the set of data is to be selected as the “least significant” data point, as it has been determined that a data point situated later in a set of data may be more likely to have a greater impact on how that set of data is ultimately simplified, at least in view of the considerations regarding the transition of a data buffer from one cycle to the next, as described herein.

Thus, an extrema-retentive data buffering and simplification process may be employed using data buffers that contain large groups of carry-over data points to provide for a robust, well contextually-informed, dataset simplification process.

With reference toFIG.11,FIG.12A, andFIG.12B, further techniques for increasing the reliability of a data buffering and simplification process are discussed. As described herein, a data buffering and simplification process may further involve the application of an algorithm that forces the last point in a data buffer to be recorded under certain circumstances (rather than merely passing it over to the next cycle of data), in order to reduce the likelihood that a data point that falls outside the maximum allowable error threshold of the dataset simplification algorithm will be inadvertently missed. The processes described inFIG.11, andFIG.12AandFIG.12Bmay be employed by any computing device, such as the asset tracking device800ofFIG.8, and combined with any of the methods described herein.

FIG.11is another example data-time plot of a set of data to be simplified by a dataset simplification algorithm.FIG.11illustrates how, under some circumstances, a data point that falls outside the maximum allowable error threshold of a dataset simplification algorithm may be missed, and further illustrates a solution to mitigate against this risk.

A data buffer1100, in a first cycle of data, contains data points P1, P2, P3, P4, P5, P6, P7, P8, and P9. Upon simplification of the first cycle of data, suppose that only data point P1is recorded, and that data points P2-P8each fall within the allowable error threshold of the dataset simplification algorithm (K1), with reference to reference line (Line A), defined through P1and P9. Further, suppose that data point P2is the “greatest error” data point that deviates the most from reference line A, but within the allowable error threshold. Thus, under an extrema-retentive data buffering and simplification process, as described herein, data points P1, P2, and P9are carried into the next cycle of the data buffer1100, and data points P3-P8are discarded.

However, suppose that in the next cycle of data, a new data point P10is added to the data buffer1100that deviates significantly from, but within the error bounds of, the trend that would be predicted by reference line A. That is, suppose that data point P10is situated at or near an error bound (in either direction) defined around line A by the allowable error threshold of the dataset simplification algorithm. Data point P10may be referred to as a “worst case scenario” data point, because if it were to become recorded by the dataset simplification algorithm, then its inclusion in the simplified set of data could “bend” the simplified set of data away from certain data points from the first cycle of data (i.e., points P3-P8) that were previously discarded, causing one or more of such points to fall outside the allowable error threshold of the resulting simplified set of data (e.g., point P8).

The proposed solution is to generate a placeholder data point (P10) at the location of the “worst case scenario” data point. This “worst case scenario” data point is located immediately following the last data point in the first cycle of data (P9), and deviates as much as possible, within the allowable error threshold of the dataset simplification algorithm, from a reference line (line A) defined through the last point in the first cycle of data (P9) and the last data point in the first cycle of data determined to be recorded by the dataset simplification algorithm (P1), on the opposite side of the reference line (line A) as the “greatest error” data point). Then it is determined whether any data points between these two data points (P1and P9) deviate, in excess of the allowable error threshold of the dataset simplification algorithm, from a second reference line (line B) defined through the placeholder data point (P10) and the “greatest error” data point from the first cycle of data (P2) (where the “greatest error” data point is carried forward into the next cycle of data). If it is determined that there is at least one such data point (between P1and P9) that deviates, in excess of the allowable error threshold of the dataset simplification algorithm, from reference line B, then the last point in the buffer (P9) is forced to be recorded.

Recordation of the last point in the data buffer under these circumstances ensures that none of the data points between P1and P9fall outside the allowable error threshold, since those data points will fall within the error bounds of a line defined between P1and P9. Thus, the risk that a “worst case scenario” data point will result in certain data points falling outside the allowable error threshold is reduced. Such data points may be considered operationally salient, and it may be desired that such data points are captured, or at least remain within the allowable error threshold of the dataset simplification algorithm.FIGS.12A-12B, below, discuss further techniques to avoid such risks.

FIG.12Ais an example data-time plot of a set of data to be simplified by a dataset simplification algorithm.FIG.12Aindicates a distance representing a first threshold value (K1) that is involved in the dataset simplification algorithm (e.g., the allowable error threshold used in the application of a RDP algorithm or a variant thereof). In other words, performing the dataset simplification algorithm involves comparing, against the threshold value K1, a distance of each data point to be considered for recordation to its relevant reference line, in accordance with the dataset simplification algorithm. This threshold value K1is to be distinguished from the second threshold value K2, shown inFIG.12B, which is involved in an additional step of another example data buffering and simplification process.

WhereasFIG.12Ashows a set of data prior to simplification (using threshold value K1),FIG.12Bshows the same set of data after simplification. Following simplification, an extrema-retentive data buffering and simplification process may involve comparing, against the second threshold value K2(that is less than the first threshold value K1), the distance of the “greatest error” data point (the point between the last recorded data point and the last point in the buffer that deviates the most from a reference line defined through those two points), to its relevant reference line (defined through the aforementioned two points). The second threshold value K2may be a fixed amount less than the first threshold value K1, or may be proportionate to the first threshold value K1(e.g., K2may be 95% of K1). When it is determined that the distance of this “greatest error” data point to the relevant reference line is greater than the second threshold value K2, the last data point in the data buffer may be forced to be recorded. As with the technique described inFIG.11, this technique may reduce the risk that a “worst case scenario” data point will result in certain data points falling outside the allowable error threshold.

Thus, it should be seen that the data collected from an asset may be simplified into operationally-salient data for a telematics system through an extrema-retentive data buffering and simplification process. Such a process not only saves on data storage and data transmission costs that would otherwise be incurred if data points were saved periodically, but also ensures that extrema data points, that may be of particular interest in telematics, are likely to be recorded, even on asset tracking devices that temporarily store data in small data buffers.

It should be recognized that features and aspects of the various examples provided above can be combined into further examples that also fall within the scope of the present disclosure. The scope of the claims should not be limited by the above examples but should be given the broadest interpretation consistent with the description as a whole.