End User Premises Device for Off-Peak Content Caching
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Solution Overview
Problem
The high cost of mobile data plans and the lack of affordable fixed-broadband services in emerging markets hinder users from accessing rich content like video, and existing networks face challenges in efficiently managing data access during peak hours.
Innovation Solution
An end user premises device with processors and transceivers that schedule data retrieval during off-peak hours and store it locally for later access, using predictive algorithms to anticipate user needs and network topology for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is retrieved during peak hours, then user access speed is improved, but network congestion increases and data access cost increases
Solution Approach 1:
The system performs preliminary actions by retrieving data during off-peak hours before users need to access it. The network node schedules data retrieval in advance during periods of low network traffic, storing the data locally so that when users access it during peak hours, the data is already available without contributing to network congestion.
2Productivity
If data is retrieved during off-peak hours, then network congestion is reduced, but user access time is delayed
Solution Approach 1:
The system performs preliminary actions by retrieving data during off-peak hours before users need to access it. The network node schedules data retrieval in advance during periods of low network traffic, storing the data locally so that when users access it during peak hours, the data is already available without contributing to network congestion.
Solution Approach 2:
The system uses feedback mechanisms to monitor user access patterns, network traffic conditions, and data usage trends. This feedback information is used to dynamically adjust data retrieval scheduling decisions, ensuring that data is fetched at optimal times that balance network efficiency with user access requirements.
3Loss of energy
If data is cached locally, then access cost is reduced, but storage capacity requirements increase
Solution Approach 1:
The system applies local quality by selectively caching data based on its importance, popularity, and access patterns. Not all data is cached uniformly, but rather the system identifies which data items are most likely to be accessed and prioritizes caching those. This selective approach reduces storage requirements while still providing significant cost savings for the most critical data access needs.
Solution Approach 2:
The system dynamically adjusts caching parameters such as cache size, retention policies, and selection criteria based on network conditions, user behavior patterns, and data characteristics. By changing these parameters adaptively, the system optimizes the balance between storage capacity usage and data access cost savings.
4Adaptability or versatility
If predictive algorithms are used to anticipate user needs, then content delivery is personalized, but system complexity increases
Solution Approach 1:
The system implements self-service by using predictive algorithms that automatically learn from user behavior patterns and network conditions without requiring manual configuration or complex user input. The algorithms continuously adapt to changing user needs and network dynamics, providing personalized content delivery recommendations that are generated autonomously by the system itself.
Data Source
AI summary
An end user premises device is provided that includes a memory, one or more transceivers, and one or more processors. The one or more transceivers are configured to communicate with one or more stations in a network and a client device. The one or more processors are configured to receive a first user request for data from the client device using the one or more transceivers, determine a first point in time for retrieving the data based on an amount of charge in batteries of the one or more stations in the network, retrieve, at the first point in time, the data from a remote server via the network using the one or more transceivers, store the data in the memory, and in response to a second user request, transmit the data to the client device using the one or more transceivers.