2019 Fiscal Year Final Research Report
Mutual Information Passing between Sensor Information Environment and Physical Wireless Environment for High Density Wireless Sensor Networks
| Project/Area Number |
17H03264
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Shinshu University |
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Principal Investigator |
Takyu Osamu 信州大学, 学術研究院工学系, 准教授 (40453815)
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| Co-Investigator(Kenkyū-buntansha) |
藤井 威生 電気通信大学, 先端ワイヤレス・コミュニケーション研究センター, 教授 (10327710)
太田 真衣 福岡大学, 工学部, 助教 (20708523)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Keywords | 無線センサネットワーク / 確率伝播 |
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| Outline of Final Research Achievements |
This research studies a mutual information exchanging between environment of sensing information and environment of physical wireless communications for specifying individual sensing information under access from a lot of sensors. There are four research achievements. First one is an optimal construction of wireless resource based on environments of sensing information and physical wireless communication. Second one is a distinguishing scheme among a desired signal and the others with using amplitude probability distribution. Third one is an estimation scheme of arrival timing of co-channel interference from the other system. Fourth one is the mutual information exchanging based on graph cut algorithm between environments of sensing information and physical wireless communications. From these achievements, we confirm the good communication link quality even under the access from a lot of sensors owing to the constructed mutual information exchanging.
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| Free Research Field |
無線通信方式
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| Academic Significance and Societal Importance of the Research Achievements |
センサ情報空間・無線物理空間相互情報パッシング法は多数のセンサによる無線通信の確立を可能にし、IoT社会に向けたセンサの普及促進に貢献できると期待される。また、本研究では、センサ情報の物理環境を無線通信に取り入れた高品質化を実現しており、今後の無線通信のさらなる高品質化に向けて、伝送すべき情報の特徴が有益であることを指摘した。現在、機械学習や人工知能（AI)などの潜在的な特徴傾向を導出する技術が発達しており、本研究で注目したセンサ情報空間の分析精度のさらなる向上が期待され、多数のセンサによる同時アクセスをより促進できる可能性が高まったといえる。
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