Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a detailed measurement-driven model of mobile data traffic usage of smartphone subscribers, using a large-scale dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives.  first characterize individual subscribers’ routinary behavior, follod by a detailed investigation of subscribers’ temporal usage patterns (i.e., ”when” and ”how much” traffic is generated).  then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours.  show that the synthetic trace generated by our data traffic model consistently replicates a subscriber’s profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into temporal network resource usage.  also discuss relevant issues in traffic demands and describe implications in network solution evaluation and privacy.
['Eduardo Mucelli Rezende Oliveira', 'Aline Carneiro Viana', 'K. P. Naveen', 'Carlos Sarraute']
I am writing a research paper on a idea Mobile data traffic modeling: Revealing temporal facets
[{"from": "human", "value": "I am writing a research paper on a idea Mobile data traffic modeling: Revealing temporal facets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a detailed measurement-driven model of mobile data traffic usage of smartphone subscribers, using a large-scale dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives.  first characterize individual subscribers\u2019 routinary behavior, follod by a detailed investigation of subscribers\u2019 temporal usage patterns (i.e., \u201dwhen\u201d and \u201dhow much\u201d traffic is generated).  then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours.  show that the synthetic trace generated by our data traffic model consistently replicates a subscriber\u2019s profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into temporal network resource usage.  also discuss relevant issues in traffic demands and describe implications in network solution evaluation and privacy."}]