IoT Fleet Monitoring Analytics Engine for Job Status Automation
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Solution Overview
Problem
Fleet operators face challenges in tracking the location and activity of moving IoT devices, such as vehicles, which are often disconnected from cellular networks, making it difficult to monitor job status, driver behavior, and vehicle performance in real-time, requiring manual and cumbersome processes.
Innovation Solution
A system and method that utilize IoT devices connected to a cellular network to receive and analyze location information, job assignments, and vehicle data, employing analytics and machine learning to determine job status, automatically assign drivers and vehicles, and provide real-time tracking and alerts, with a user interface for fleet managers to monitor vehicle locations and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tracking processes are used for fleet devices, then device complexity is reduced, but productivity and monitoring accuracy deteriorate
Solution Approach 1:
The system enables self-service through automated job status determination. The analytics engine automatically evaluates location information and job assignments to determine job status without manual intervention. The system also performs automatic driver and vehicle assignment, allowing the fleet management system to serve itself rather than requiring manual tracking operations.
Solution Approach 2:
The patent replaces manual mechanical tracking processes with automated electronic systems. Location data from GPS devices is automatically processed by analytics engines that use algorithms to determine job status, replacing the need for manual record-keeping and status updates. This substitution of mechanical/manual operations with electronic automation resolves the contradiction between productivity improvement and system complexity.
2Loss of information
If real-time monitoring of disconnected devices is implemented, then information availability improves, but system complexity and energy consumption increase
Solution Approach 1:
The system uses an intermediary approach by leveraging cellular network infrastructure and cloud-based analytics engines as mediators between disconnected IoT devices and fleet operators. Devices transmit location data when connected, and the analytics engine processes this information to determine job status even when devices are temporarily disconnected. This intermediary system maintains information availability without requiring continuous direct connection or complex onboard processing in each device.
3Productivity
If automated job status determination is implemented, then productivity improves, but measurement precision requirements increase
Solution Approach 1:
The system applies parameter changes by evaluating multiple parameters simultaneously (location coordinates, job assignment data, geofence boundaries, movement patterns) rather than relying on a single measurement. The analytics engine processes these varying parameters to determine job status, allowing automated productivity improvement while maintaining robustness against individual parameter inaccuracies through multi-parameter evaluation.
Data Source
AI summary
In one example embodiment, a computer-implemented method and system for providing job status information for an IoT device are disclosed. The method providing job status information for an IoT device includes receiving location information of the IoT device; receiving job assignment information for the IoT device; and evaluating the location information to determine the job status for the IoT device based on a specified condition. The system for providing job status information for an IoT devices includes at least one IoT device, a data processing system and a user interface, wherein the data processing system further comprises a storage database, wherein the storage database receives location information of the IoT device and job assignment information for the IoT device; an analytics engine, wherein the analytics engine evaluates the location information to determine the job status for the IoT device based on a specified condition.