MMDP: A Mobile-IoT Based Multi-Modal Reinforcement Learning Service Framework

With the development of GPS technology, a new Mobile Internet of Things (M-IoT) is emerging, which perceives the city's rhythm and pulse day and night to collect a large scale of city data. It is urgent to innovate M-IoT service system for these large-scale and heterogeneous data. To cope with the problem, this article proposes a Mobile-IoT based multi-modal reinforcement learning service framework from data perspective, which has three highlights, i) Developing Action-aware High-order Transition Tensor (<inline-formula><tex-math notation="LaTeX">$AHTT$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>A</mml:mi><mml:mi>H</mml:mi><mml:mi>T</mml:mi><mml:mi>T</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-2964663.gif"/></alternatives></inline-formula>) to fuse the heterogeneous data from M-IoTs in a unified form. ii) Developing Multi-modal Markov Decision Process (<inline-formula><tex-math notation="LaTeX">$MMDP$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>M</mml:mi><mml:mi>M</mml:mi><mml:mi>D</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq2-2964663.gif"/></alternatives></inline-formula>) to model the multi-modal reinforcement learning for M-IoT service framework. iii) Developing Tensor Policy Iteration algorithm (<inline-formula><tex-math notation="LaTeX">$TPIA$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mi>I</mml:mi><mml:mi>A</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq3-2964663.gif"/></alternatives></inline-formula>) to solve the optimal tensor policy. Due to using tensor keeps the multi-modal relations of the context information in the process of solving the optimal policy. The proposed M-IoT service system provides more personalized service for taxi drivers. The experiment results shows that most taxi drivers earn more revenue according to the tensor policy.

• Publication year

  2020

• Venue

  IEEE Transactions on Services Computing

• Publication date

  2020-07-01

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1109/tsc.2020.2964663
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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