A Hybrid Data Acquisition Model using Artificial Intelligence and IoT Messaging Protocol for Precision Farming

被引：2
作者：
Alejandrino, Jonnel [1 ]
Concepcion, Ronnie [1 ]
Almero, Vincent Jan [1 ]
Palconit, Maria Gemel [1 ]
Bandala, Argel [1 ]
Dadios, Elmer [2 ]
机构：
[1] De La Salle Univ, Elect & Commun Engn Dept, Manila, Philippines
[2] De La Salle Univ, Mfg & Engn Management Dept, Manila, Philippines
来源：
2020 IEEE 12TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM) | 2020年
关键词：
wireless sensor network; Internet of Things (IOT); smart farming; precision farming; Artificial Intelligence; Deep Neural Network based routing algorithm; Colony Optimization; NETWORK;
D O I：
10.1109/HNICEM51456.2020.9400152
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The emergence of the Internet-of-Things (IoT) technology had widened the application of the existing technologies we have been using. This paper then uses the IoT technology in the form of wireless sensor network (WSN) specifically designed and developed for smart farming applications. The advancement of communications is determined to be highly by artificial intelligence (AI). Nevertheless, messaging protocols must be considered to avert false node location and minimize redundant data. This paper proposes a hybrid of two novel algorithms namely, Multi-objective Message Queue Telemetry Transport (MMQTT) and Deep Neural Network based routing algorithm (DNNRA) and compress their performance with the Baseline. The experimental outcomes show that the proposed method is found capable of improving the energy efficiency of wireless sensor network, sensor cluster node selection and deployment, detection capability, jitter/delay at actual smart aquaponic setup validation.
引用
收藏
页数：6
相关论文
共 22 条
  • [1] Abidin A, 2017, PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE AND ENGINEERING (CONFLUENCE 2017), P708, DOI 10.1109/CONFLUENCE.2017.7943243
  • [2] Alejandrino, 2019, 2019 IEEE 11 INT C H, P2
  • [3] Alejandrino Jonnel, 2020, 2020 IEEE Region 10 Conference (TENCON), P438, DOI 10.1109/TENCON50793.2020.9293854
  • [4] Alejandrino J., VISUAL CLASSIFICATIO
  • [5] Alejandrino J., AUDIT PATTERN OPTIMI
  • [6] Alejandrino J.D., 2020, INT J MECH ENG ROBOT, V9, P1253
  • [7] Cloud-WBAN: An experimental framework for Cloud-enabled Wireless Body Area Network with efficient virtual resource utilization
    Bhardwaj, Tushar
    Sharma, Subhash Chander
    [J]. SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2018, 20 : 14 - 33
  • [8] A theoretical analysis of buffer occupancy for Intermittently-Connected Networks
    Boero, L.
    Cello, M.
    Gnecco, G.
    Marchese, M.
    Patrone, F.
    Sanguineti, M.
    [J]. PERFORMANCE EVALUATION, 2017, 115 : 108 - 131
  • [9] Concepcion II R., 2019, 2019 IEEE 15 INT C S
  • [10] Concepcion R.S., 2020, Int. J. of Advances in Intelligent Informatics, V6, P261, DOI [10.26555/ijain, DOI 10.26555/IJAIN.V6I3.435]
123