18153884. SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK simplified abstract (Honeywell International Inc.)
Contents
- 1 SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does this technology ensure data security and privacy for the vehicle operation data collected from cloud-based and edge-based sources?
- 1.9.3 What are the potential limitations or challenges in implementing this technology on a large scale across different types of vehicles and environments?
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK
Organization Name
Inventor(s)
Angelo Koutsogiannis of Gilbert AZ (US)
Kalimulla Khan of Bangalore (IN)
Ramkumar Rajendran of Madurai (IN)
SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 18153884 titled 'SYSTEMS AND METHODS FOR A VEHICLE PROCESSING SYSTEM WITH A FOG-BASED FRAMEWORK
Simplified Explanation
The patent application describes a method for generating vehicle operation insights using cloud-based and edge-based data.
- Cloud-based application framework receives vehicle operation data from a cloud-based database before transit.
- The framework also receives edge-based vehicle data in real-time during transit from edge computing devices.
- Vehicle operation insights are generated during transit based on the combination of cloud-based and edge-based data.
Potential Applications
This technology could be applied in the automotive industry for real-time monitoring and analysis of vehicle performance, predictive maintenance, and optimization of driving behavior.
Problems Solved
This technology solves the problem of efficiently collecting and analyzing vehicle operation data in real-time, allowing for proactive maintenance and performance optimization.
Benefits
The benefits of this technology include improved vehicle performance, reduced maintenance costs, enhanced safety, and optimized driving practices.
Potential Commercial Applications
A potential commercial application of this technology could be in fleet management systems, where real-time insights into vehicle operation can help optimize routes, reduce fuel consumption, and improve overall fleet efficiency.
Possible Prior Art
One possible prior art could be systems that collect and analyze vehicle data for maintenance purposes, but the combination of cloud-based and edge-based data in real-time for generating insights may be a novel aspect of this technology.
Unanswered Questions
How does this technology ensure data security and privacy for the vehicle operation data collected from cloud-based and edge-based sources?
This article does not address the specific measures or protocols in place to protect the confidentiality and integrity of the vehicle operation data.
What are the potential limitations or challenges in implementing this technology on a large scale across different types of vehicles and environments?
The article does not discuss the scalability or adaptability of this technology to various vehicle models, operating conditions, or geographical locations.
Original Abstract Submitted
Disclosed are methods, systems, and one or more computer-readable mediums for performing, by one or more processors located off-board a vehicle, operations including receiving, by the fog based application framework before transit of the vehicle, vehicle operation data from a cloud based database of a cloud based computing system; receiving, by the fog based application framework before and/or in real-time during transit of the vehicle, edge-based vehicle data from one or more edge computing devices of the vehicle; and generating, by the fog based application framework during transit of the vehicle, a plurality of vehicle operation insights based on the vehicle operation data and the edge-based vehicle data.
