First Node Scheduling Data Transfers via Predicted Routes
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Solution Overview
Problem
Current telecommunications networks face challenges in maximizing the use of renewable energy sources for data transmissions, leading to increased carbon footprints due to high energy demands, and existing technologies lack efficient methods to manage energy usage and Quality of Experience (QoE) in mobile communications systems.
Innovation Solution
A method involving a first node that predicts data transfer routes and schedules data transfers based on the type of energy used by cells along the route, providing indications to devices to optimize data transmission and maximize the use of renewable energy sources, thereby improving energy efficiency and QoS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If data transmissions are scheduled without considering energy source type, then network productivity is maintained, but renewable energy usage is not maximized and carbon footprint increases
Solution Approach 1:
The system performs preliminary actions by predicting the device's future route and identifying green cells in advance. The network node determines a predicted route that passes through multiple cells and identifies which cells are green cells before the actual data transmission occurs, enabling proactive scheduling rather than reactive responses.
Solution Approach 2:
The system dynamically adjusts data transmission scheduling based on real-time conditions. It determines optimal scheduling by considering the predicted route, identified green cells, and current network conditions, allowing flexible adaptation to maximize renewable energy usage while maintaining productivity.
2Use of energy by moving object
If the network schedules data transfers based on predicted routes and energy types, then renewable energy usage is maximized, but network complexity increases
Solution Approach 1:
The system segments the network path into individual cells along the predicted route and evaluates each cell's energy characteristics independently. By dividing the overall routing decision into smaller, manageable units (individual cell assessments), the complexity of managing energy-aware scheduling is reduced while still achieving comprehensive renewable energy optimization.
3Use of energy by moving object
If data transmission is delayed to align with renewable energy availability, then energy efficiency improves, but transmission time increases
Solution Approach 1:
The system performs preliminary route prediction and green cell identification before actual data transmission. By pre-determining the optimal path through green cells and preparing scheduling decisions in advance, the system minimizes unnecessary delays while ensuring data transfers align with renewable energy availability.
Data Source
AI summary
A computer-implemented method performed by a first node (101). The method is for handling managing one or more indications. The first node (101) operates in a communications system (100). The first node (101) determines (304) a scheduling of a transfer of data to or from a device (130) along a predicted route (140). The predicted route (140) is to be followed by the device (130) during a time period. The determining (304) of the scheduling is further based on a type of energy used by a plurality of cells (121, 122) providing radio coverage along the predicted route (140). The first node (101) initiates (305) providing one or more indications to the device (130) based on a first result of the determining (304) of the scheduling. The device (130) receives (402) the one or more indications, determines (403) whether or not to perform the data transfer based on them, and initiates (404) performing the data transfer accordingly.