Network-Assisted Environmental Scanning with Dynamic Bandwidth Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies face challenges in efficiently utilizing network capacity for network-assisted scanning of surrounding environments, particularly in vehicular use cases, where bandwidth optimization and precise driving recommendations are needed while balancing cost and data quality.
Innovation Solution
The system prioritizes environmental data collection and transmission based on available cellular RAN resources, using edge compute locations and cloud-based datacenters, and applies data prioritization and scheduling to optimize bandwidth usage, enabling efficient data retrieval and scalable crowd-sourcing for sensory data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If environmental data is collected and transmitted using cellular RAN resources, then spatial resolution and data quality are improved, but network bandwidth burden and costs increase
Solution Approach 1:
The system dynamically changes transmission parameters including data sampling rate, resolution, and frequency based on vehicle speed, location, and network conditions. When vehicles move quickly through an area, sampling rate is reduced; when stationary or slow-moving, higher resolution data is collected. This parameter adaptation maintains spatial resolution quality while significantly reducing overall network bandwidth consumption.
Solution Approach 2:
The system implements selective data transmission where only portions of environmental data meeting specific criteria (novelty, urgency, relevance) are transmitted to the server. Collection agents perform local filtering and prioritization, transmitting only the most valuable data subsets rather than all collected sensory data, thereby reducing network burden while preserving essential spatial information.
2Measurement precision
If high-fidelity mapping updates are performed, then spatial resolution is improved, but costs and network bandwidth consumption increase
Solution Approach 1:
Collection agents perform preliminary data processing, filtering, and prioritization before transmission. The system pre-identifies and transmits only the most valuable data portions (novel, urgent, or highly relevant information) while discarding redundant data. This preliminary action at the edge reduces the volume of data requiring high-fidelity transmission while maintaining mapping quality.
Solution Approach 2:
The system applies different data quality levels to different spatial regions based on their importance and update needs. High-fidelity data is transmitted only for critical areas (construction zones, hazard locations, frequently accessed regions) while lower-resolution data suffices for less important areas. This local quality differentiation maintains essential mapping fidelity while reducing overall data transmission volume.
3Reliability
If environmental data is collected from multiple collection agents, then data coverage and reliability are improved, but network resource utilization and coordination complexity increase
Solution Approach 1:
Collection agents autonomously determine their own data transmission schedules and priorities based on local conditions (vehicle speed, sensor data novelty, network availability). Each agent independently prioritizes its own data queue and makes transmission decisions without requiring centralized coordination for every data packet. This self-service approach maintains comprehensive data coverage from multiple agents while significantly reducing network coordination complexity.
Solution Approach 2:
The system dynamically adjusts the number and density of active collection agents based on spatial and temporal conditions. In areas requiring high monitoring density, more agents are activated; in less critical areas, fewer agents operate or enter sleep mode. This dynamic agent deployment maintains reliable data coverage where needed while reducing overall network resource utilization and coordination overhead.
Data Source
AI summary
A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving one or more pieces of environmental data from a plurality of collection agents, each collection agent operating with a plurality of sensors configured to sense data relating to a respective location area of an environment, wherein the one or more environmental data is received responsive to determining that a portion of the environmental data is necessary for updating a consumption point report. Based on the received data, an updated consumption point report may be generated, which may be transmitted to a data consumption point.