METHOD AND SYSTEM FOR MANAGING OVER-THE-AIR UPGRADE IN EDGE DEVICES

1 . A method for managing an upgrade rollout to a plurality of edge devices, the method comprising:

identifying, by at least one processor of a cloud server, a first set of edge devices among the plurality of edge devices for an upgrade rollout, wherein the identifying is based on a communication history indicating successful communication frequency between each edge device and the cloud server;

pushing the upgrade to the first set of edge devices;

receiving feedback from the first set of edge devices, wherein the feedback comprises information related to operational metrics of at least one of software and hardware components of the first set of edge devices;

determining a fault percentage representing edge devices from the first set wherein the operational metrics deviate from baseline performance values beyond predetermined tolerance thresholds; and

determining, after a predefined time duration (Td) from completion of pushing the upgrade, whether to continue with the upgrade rollout to remaining edge devices of the plurality of edge devices.

2 . The method of claim 1 , wherein the communication history comprises at least one of:

a frequency of heartbeat signals transmitted from each edge device to the cloud server;

a success rate of data transmissions between each edge device and the cloud server;

a temporal recency of last successful communication with the cloud server; and

a signal strength metric associated with network connectivity.

3 . The method of claim 2 , wherein identifying the first set of edge devices further comprises: determining a working status of hardware and software components for each edge device based on the heartbeat signals, wherein edge devices are selected for the first set when the working status indicates an operating condition without detected irregularities.

4 . The method of claim 1 , wherein the operational metrics comprise at least one of:

processor utilization percentage; memory consumption values; battery discharge rate;

component uptime duration; data throughput measurements; and error frequency indicators.

5 . The method of claim 4 , wherein the baseline performance values are generated by:

monitoring the operational metrics during a pre-upgrade period; calculating average performance values for each metric; and storing said average performance values as reference baselines for post-upgrade comparison.

6 . The method of claim 5 , wherein the predetermined tolerance thresholds comprise:

expected performance degradation values inherent to the upgrade process; and acceptable deviation ranges determined through pre-deployment testing, wherein an edge device is classified as faulty when operational metric deviation exceeds the sum of expected degradation and acceptable deviation range.

7 . The method of claim 1 , wherein responsive to determining to continue with the upgrade rollout, the method further comprises: determining a deployment multiplier (M) based on the fault percentage; and identifying a second set of edge devices from the remaining edge devices, wherein a size of the second set equals the product of the deployment multiplier and a size of the first set.

8 . The method of claim 7 , wherein determining the deployment multiplier comprises:

assigning a first multiplier value when the fault percentage is below a first threshold, indicating accelerated deployment; assigning a second multiplier value when the fault percentage is between the first threshold and a second threshold, indicating maintained deployment pace; and wherein the first multiplier value exceeds the second multiplier value.

9 . The method of claim 8 , further comprising: iteratively repeating the identifying, pushing, receiving, and determining steps for subsequent sets of edge devices, wherein a cumulative number of edge devices receiving the upgrade in iteration i follows an arithmetic progression defined by: Ni=K+(i−1)(M×K), where K represents the size of the first set, and M represents the deployment multiplier determined for each iteration.

10 . The method of claim 9 , further comprising: dynamically modifying rollout parameters during deployment execution, wherein the modification comprises adjusting at least one of: the deployment multiplier between iterations based on emerging performance patterns; the predefined time duration based on feedback collection rate; and selection criteria for subsequent device sets based on accumulated deployment data.

11 . The method of claim 7 , further comprising: maintaining a deployment log tracking upgrade status for each edge device; generating a rollout trajectory prediction based on current fault percentage trends; and dynamically adjusting the deployment multiplier based on the rollout trajectory prediction.

12 . The method of claim 1 , wherein responsive to determining not to continue with the upgrade rollout, the method further comprises: terminating further upgrade deployment to the remaining edge devices; and initiating a rollback procedure for the first set of edge devices to restore a previous firmware version.

13 . The method of claim 11 , wherein the determination not to continue occurs when: the fault percentage exceeds a critical threshold value; or a predetermined number of consecutive edge devices report critical component failures.

14 . The method of claim 1 , wherein the predefined time duration (Td) is determined based on at least one of: geographic distribution of the plurality of edge devices; expected vehicle operational cycles associated with the edge devices; network traffic patterns for the communication infrastructure; and statistical confidence intervals for feedback data collection.

15 . The method of claim 1 , wherein receiving feedback comprises: collecting periodic status reports from each edge device in the first set; aggregating operational metrics across multiple reporting cycles; and identifying performance anomalies through statistical deviation analysis.

16 . The method of claim 1 , wherein the plurality of edge devices comprises multiple device models identified by distinct stock keeping units (SKUs), the method further comprising: segregating the upgrade rollout by SKU categories; and maintaining separate fault percentage thresholds for each SKU based on device-specific performance characteristics.

17 . A system for managing an upgrade rollout to a plurality of edge devices, the system comprising:

a memory configured to store operational data and deployment parameters; and

at least one processor in communication with the memory, wherein the at least one processor is configured to:

identify a first set of edge devices among the plurality of edge devices for an upgrade rollout, wherein the identifying is based on a communication history indicating successful communication frequency between each edge device and a cloud server;

push the upgrade to the first set of edge devices;

receive feedback from the first set of edge devices, wherein the feedback comprises information related to operational metrics of at least one of software and hardware components of the first set of edge devices;

determine a fault percentage representing edge devices from the first set wherein the operational metrics deviate from baseline performance values beyond predetermined tolerance thresholds; and

determine, after a predefined time duration (Td) from completion of pushing the upgrade, whether to continue with the upgrade rollout to remaining edge devices of the plurality of edge devices.

18 . A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations for managing an upgrade rollout to a plurality of edge devices, the operations comprising:

identifying a first set of edge devices among the plurality of edge devices for an upgrade rollout, wherein the identifying is based on a communication history indicating successful communication frequency between each edge device and a cloud server;

pushing the upgrade to the first set of edge devices;

receiving feedback from the first set of edge devices, wherein the feedback comprises information related to operational metrics of at least one of software and hardware components of the first set of edge devices;

determining a fault percentage representing edge devices from the first set wherein the operational metrics deviate from baseline performance values beyond predetermined tolerance thresholds; and

determining, after a predefined time duration (Td) from completion of pushing the upgrade, whether to continue with the upgrade rollout to remaining edge devices of the plurality of edge devices.