Energy-saving Pushing Based on Personal Interest and Context Information

TL;DR

This paper proposes an energy-efficient unicast pushing strategy that leverages user interest prediction and context information to reduce energy consumption and increase throughput during off-peak times.

Contribution

It introduces a novel power allocation and scheduling algorithm that exploits network and user context to minimize energy use in personalized file pushing.

Findings

Unicast pushing consumes less energy than broadcasting under non-uniform popularity.

The proposed strategy achieves higher throughput with less energy when interest prediction uncertainty is low.

Considering both content placement and delivery energy leads to more efficient pushing strategies.

Abstract

Pushing files to users based on predicting the personal interest of each user may provide higher throughput gain than broadcasting popular files to users based on their common interests. However, the energy consumed at base station for pushing files individually to each user is also higher than broadcast. In this paper, we propose an energy-saving transmission strategy for pre-downloading the files to each user by exploiting the excess resources in the network during off-peak time. Specifically, a power allocation and scheduling algorithm is designed aimed to minimize the extra energy consumed for pushing, where network and user level context information are exploited. Simulation results show that when the energy of both content placement and content delivery is taken into account, the proposed unicast strategy consumes less energy and achieves higher throughput than broadcasting when the files popularity is not uniform and the personal interest prediction is with less uncertainty.