Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Utilizing network traffic scheduling to improve the energy efficiency of smartphones has been studied extensively in the past few years. These studies usually take certain approaches and make some assumptions concerning traffic predictability, regardless of whether these assumptions hold or whether the approaches have been studied before. ,  conduct an analysis of existing work to find common approaches and assumptions among the proposed solutions.  find out the following: 1. A large part of the solutions target a specific (single) application or category of applications, and do not schedule the whole traffic transmitted on the smartphone. 2. A common assumption is that network traffic for smart phones is predictable. The focus of our work is to test these assumptions against real-world data and analyze whether the approaches presented in the literature are feasible. By leveraging two data sets from NetSense,  make several major contributions: 1.  demonstrate clearly, based on a large dataset, that background apps are the largest energy consumers for smart phones. 2. although some traffic traces exhibit long-term trends, in general traffic from a single app or a user is not predictable in the short-term. 3. achieving energy savings is difficult by scheduling traffic only from a specific app, since multi-app scenarios are so prevalent on today's smartphones.  also pinpoint future directions for traffic scheduling schemes.
['Vilen Looga', 'Zhonghong Ou', 'Yu Xiao', 'Antti Ylä-Jääski']
I am writing a research paper on a idea The great expectations of smartphone traffic scheduling
[{"from": "human", "value": "I am writing a research paper on a idea The great expectations of smartphone traffic scheduling"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Utilizing network traffic scheduling to improve the energy efficiency of smartphones has been studied extensively in the past few years. These studies usually take certain approaches and make some assumptions concerning traffic predictability, regardless of whether these assumptions hold or whether the approaches have been studied before. ,  conduct an analysis of existing work to find common approaches and assumptions among the proposed solutions.  find out the following: 1. A large part of the solutions target a specific (single) application or category of applications, and do not schedule the whole traffic transmitted on the smartphone. 2. A common assumption is that network traffic for smart phones is predictable. The focus of our work is to test these assumptions against real-world data and analyze whether the approaches presented in the literature are feasible. By leveraging two data sets from NetSense,  make several major contributions: 1.  demonstrate clearly, based on a large dataset, that background apps are the largest energy consumers for smart phones. 2. although some traffic traces exhibit long-term trends, in general traffic from a single app or a user is not predictable in the short-term. 3. achieving energy savings is difficult by scheduling traffic only from a specific app, since multi-app scenarios are so prevalent on today's smartphones.  also pinpoint future directions for traffic scheduling schemes."}]