2016 Fiscal Year Research-status Report
Endorsement Based Offline Mobile Payment System for Disaster Areas
| Project/Area Number |
15K15981
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
高 俊涛 奈良先端科学技術大学院大学, 情報科学研究科, 助教 (30732961)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Keywords | secure routing / byzantine attack / adaptive trasmission / power constraint / traffic scheduling |
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| Outline of Annual Research Achievements |
This year we proposed a monitoring scheme to secure packets route against Byzantine attacks in mobile payment systems; a adaptive packet transmission algorithm which only transmits packets when wireless channel condition is good and does not transmit packets when wireless channel condition is bad, thus saving battery power; and finally a back-pressure based traffic scheduling algorithm that enables bank vehicles/agents to deliver electric money/goods effectively to mobile payment system users.
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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
Reusing our previous methods/knowledge and simulation codes are the two major reasons that our research proceeded smoothly. Specifically, for secure communication research, we reused the security technique, monitoring mechanism, proposed in mobile payment system to prevent byzantine attack; for saving battery power of smart phones, we formulated the problem as an optimization problem with constraints and reused our knowledge of lyapunov optimization method to solve it; for delivering electric money/goods effectively from a bank to disaster areas, we surveyed the latest research advances in traffic scheduling that can reduce vehicle delay in road networks and found that back-pressure based algorithm is promising. We modified previous simulation codes to verify our new proposed schemes.
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| Strategy for Future Research Activity |
We plan to apply deep learning algorithms to further improve our mobile payment system. To improve system security, we are going to design deep learning algorithms that can learn various attack patterns from training data with labels and then detect attacks and warn users of these attacks. Since labeling training data costs a lot of human power and time, the second step is to design deep learning algorithms that can learn attack patterns from training data without labels, one promising method to achieve this is to combine deep learning and reinforcement learning. Another future work is to apply deep learning for priority traffic signal control and vehicle routing such that bank agents of mobile payment system can reach disaster areas quickly.
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| Causes of Carryover |
1) To develop deep learning based algorithms, we need to train deep neural networks with data of high dimensions, which requires high performance computers especially with GPU computing capabilities. 2) We plan to attend two domestic conferences and two international conferences, which involve conference registration accommodation, food, transportation fees. 3) We will also submit our work to an international journal, summarizing our proposed mobile payment system, which requires publication fee.
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| Expenditure Plan for Carryover Budget |
1) Buying GPGPU workstation GU-1000: 300,000 Yen 2) Two domestic conferences: 200,000 Yen 3) Two international conferences: 500,000 Yen 4) one international journal: 100,000 Yen
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