2016 Fiscal Year Research-status Report
Oligopoly Mobile Data Offloading Market Analysis and Intelligent Network Selection System
| Project/Area Number |
16K18109
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| Research Institution | Waseda University |
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Principal Investigator |
張 成 早稲田大学, 理工学術院, 助教 (40755089)
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Keywords | mobile data offloading / Markov decision process / wireless LAN |
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| Outline of Annual Research Achievements |
In this project, we study mobile data offloading problem from both from network side perspective and mobile users’ perspective.
For the first year, we formulated MU’s offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish optimal policy by a dynamic programming based algorithm. Since time complexity of the dynamic programming based offloading algorithm is still high, we then propose a low time complexity heuristic offloading algorithm with low time complexity.
We have published several international conference papers on Asia-Pacific Network Operations and Management Symposium, IEEE Consumer Communications & Networking Conference and domestic conferences papers. We have submitted a transaction paper to IEICE Transactions on Communications.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have processed our research based on our initial plan. Firstly, we have developed an intelligent network selection algorithm that optimizes users’ monetary costs, quality of service (QoS) and battery consumption. Secondly, we validated our algorithm by a python based simulator, which was developed by ourselves. Thirdly, research results have been published on several international conference papers and domestic conferences, such as IEICE general conference, IEICE Technical Report on Communication Quality. We also have submitted a transaction paper.
Furthermore, we are developing a prototype system on iOS will be implemented for validating our proposed algorithm through real world data.
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| Strategy for Future Research Activity |
Firstly, we will finish the development of iOS prototype app as soon as possible, and then recruit volunteer mobile users to get real world data on the mobile users’ mobile pattern as well as LAN access points distribution to validate our proposed algorithm.
Secondly, we will proposed a learning algorithm to into answer this question by proposing a reinforcement learning based algorithm to deal with the case when mobiles users’ mobile pattern is unknown in advance.
Thirdly, we will publish our developed iOS app in Apple’s app store and plan to publish at least two international conference papers and one journal papers.
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| Causes of Carryover |
For the first year, most of the traveling cost has been covered by other budgets of the university, so that more money will be reserved for supporting the publications and presentations of our research results at conferences venues for the next year.
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| Expenditure Plan for Carryover Budget |
The reserved budget will be used for supporting probably more publications and travelings in the next years. Meanwhile, we will use some budget for paying students who will do experiment for the project.
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