ASIS Edge Computing Model to Determine the Communication Protocols for IoT Based Irrigation

Authors

  • S. Premkumar Department of Computer Science & Engineering, Faculty of Engineering & Technology, Annamalai University, Tamilnadu, India https://orcid.org/0000-0001-9350-3819
  • AN. Sigappi Department of Computer Science & Engineering, Faculty of Engineering & Technology, Annamalai University, Tamilnadu, India https://orcid.org/0000-0002-2166-8312

DOI:

https://doi.org/10.13052/jmm1550-4646.18321

Keywords:

Internet of things, Edge computing, Irrigation system, Smart farming, Networkprotocol, cloud infrastructure, Zigbee, LoRa, LoWPAN, iFOGSIM

Abstract

Internet of Things (IoT) provide a promising Smart irrigation facilitator for continual monitoring and control of environmental parameters, thereby leading to a huge volume of data to be efficiently collected, transferred, processed and stored. The deployment of cloud-based infrastructure with on-field connectedness, allowing information exchange among IoT nodes, and the usage of energy scavenging (e.g., solar power) in feeding them, become necessary, since agricultural fields are in lack of wired energy supply and, often, a reliable (Internet) network coverage. Therefore, these issues can be addressed through the integration of Edge computing in IoT scenarios. An efficient strategy is required to select the best communication technology with a motive of increasing the network performance between the IoT devices, Edge device and cloud. Application Specific Infrastructure Selection (ASIS) is an edge computing model developed to select the appropriate communication protocols according to the infrastructure requirements of three different real time scenarios namely: Assembly line automation, Smart parking system and Automatic irrigation system are deployed to get the most suitable application specific protocol from ZigBee, LoRa (Long Range) and LoWPAN (Low-Power Wireless Personal Area Network) to implement in real-time basis. ASIS model is proposed as a network resource manager that is capable of sensing, acting, signal processing, and/or communication abilities to perform a protocol selection according to their physical and technological limitations. Further Edge based ASIS model is developed to enhance the network performance even better when compared with cloud-based model. Automatic irrigation system is extended in the Edge based ASIS model. The overall ASIS system is evaluated by means of network parameters such as network usage, network delay and power consumption. The ASIS model and Edge based ASIS model is deployed in iFogSim simulator that compares each protocol used in the above IoT scenarios. Finally, the scenario of Automatic irrigation system is modeled using Edge based ASIS model where ZigBee with edge performs better compared with cloud-based model. Experimental results show that ASIS based Edge implementation lessen the overall network parameters in contrast to non-edge deployment in automatic irrigation scenario.

Downloads

Download data is not yet available.

Author Biographies

S. Premkumar, Department of Computer Science & Engineering, Faculty of Engineering & Technology, Annamalai University, Tamilnadu, India

S. Premkumar Research Scholar, Computer Science and Engineering, Annamalai University, India. He has finished Master of Engineering (CSE) in Annamalai University. Currently he is also serving as a Project fellow (CSE) under UGC India granted DST-PURSE scheme at Annamalai University. His interested areas are Artificial Intelligence, Internet of Things, Edge computing and Cloud computing.

AN. Sigappi, Department of Computer Science & Engineering, Faculty of Engineering & Technology, Annamalai University, Tamilnadu, India

AN. Sigappi, Received her Ph.D in Computer Science and Engineering from Annamalai University in 2013. She did her Master Degree in Computer science and engineering from Anna University. Currently she is serving as a Professor in the Department of Computer Science and Engineering, Annamalai University, India. Her areas of interest include Image Processing, Machine Learning, Data Analytics and Internet of things. She has published more than 25 research articles in international journals and conferences.

References

J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, Oct. 2017, doi: 10.1109/JIOT.2017.2683200.

L. Bajer and O. Krejcar, “Design and Realization of Low Cost Control for Greenhouse Environment with Remote Control,” IFAC-PapersOnLine, vol. 48, no. 4, pp. 368–373, 2015, doi: 10.1016/j.ifacol.2015.07.062.

J. A. Stankovic, “Research Directions for the Internet of Things,” IEEE Internet Things J., vol. 1, no. 1, pp. 3–9, Feb. 2014, doi: 10.1109/JIOT.2014.2312291.

L. Lou, Q. Li, Z. Zhang, R. Yang, and W. He, “An IoT-Driven Vehicle Detection Method Based on Multisource Data Fusion Technology for Smart Parking Management System,” IEEE Internet Things J., vol. 7, no. 11, pp. 11020–11029, Nov. 2020, doi: 10.1109/JIOT.2020.2992431.

V. Chauhan, M. Patel, S. Tanwar, S. Tyagi, and N. Kumar, “IoT Enabled Real-Time urban transport management system,” Computers & Electrical Engineering, vol. 86, p. 106746, Sep. 2020, doi: 10.1016/j.compeleceng.2020.106746.

H. M. Khan, A. Khan, F. Jabeen, and A. U. Rahman, “Privacy preserving data aggregation with fault tolerance in fog-enabled smart grids,” Sustainable Cities and Society, vol. 64, p. 102522, Jan. 2021, doi: 10.1016/j.scs.2020.102522.

W. Yang et al., “EdgeKeeper: a trusted edge computing framework for ubiquitous power Internet of Things,” Front Inform Technol Electron Eng, Jan. 2021, doi: 10.1631/FITEE.1900636.

E. Saavedra, G. del Campo, and A. Santamaria, “Smart Metering for Challenging Scenarios: A Low-Cost, Self-Powered and Non-Intrusive IoT Device,” Sensors, vol. 20, no. 24, p. 7133, Dec. 2020, doi: 10.3390/s20247133.

M. Kumar, K. S. Raju, D. Kumar, N. Goyal, S. Verma, and A. Singh, “An efficient framework using visual recognition for IoT based smart city surveillance,” Multimed Tools Appl, Jan. 2021, doi: 10.1007/s11042-020-10471-x.

F. B. Poyen, A. Ghosh, P. Kundu, S. Hazra, and N. Sengupta, “Prototype Model Design of Automatic Irrigation Controller,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–17, 2021, doi: 10.1109/TIM.2020.3031760.

N. Abdullah et al., “Towards Smart Agriculture Monitoring Using Fuzzy Systems,” IEEE Access, vol. 9, pp. 4097–4111, 2021, doi: 10.1109/ACCESS.2020.3041597.

W. Yu et al., “A Survey on the Edge Computing for the Internet of Things,” IEEE Access, vol. 6, pp. 6900–6919, 2018, doi: 10.1109/ACCESS.2017.2778504.

M. Capra, R. Peloso, G. Masera, M. R. Roch, and M. Martina, “Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World,” Future Internet, vol. 11, no. 4, p. 100, Apr. 2019, doi: 10.3390/fi11040100.

H. Bangui, S. Rakrak, S. Raghay, and B. Buhnova, “Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions,” Electronics, vol. 7, no. 11, p. 309, Nov. 2018, doi: 10.3390/electronics7110309.

J. Kang and D.-S. Eom, “Offloading and Transmission Strategies for IoT Edge Devices and Networks,” Sensors, vol. 19, no. 4, p. 835, Feb. 2019, doi: 10.3390/s19040835.

M. Syafrudin, N. Fitriyani, G. Alfian, and J. Rhee, “An Affordable Fast Early Warning System for Edge Computing in Assembly Line,” Applied Sciences, vol. 9, no. 1, p. 84, Dec. 2018, doi: 10.3390/app9010084.

K. S. Awaisi et al., “Towards a Fog Enabled Efficient Car Parking Architecture,” IEEE Access, vol. 7, pp. 159100–159111, 2019, doi: 10.1109/ACCESS.2019.2950950.

A. Goap, D. Sharma, A. K. Shukla, and C. Rama Krishna, “An IoT based smart irrigation management system using Machine learning and open source technologies,” Computers and Electronics in Agriculture, vol. 155, pp. 41–49, Dec. 2018, doi: 10.1016/j.compag.2018.09.040.

M. Chernyshev, Z. Baig, O. Bello, and S. Zeadally, “Internet of Things (IoT): Research, Simulators, and Testbeds,” IEEE Internet Things J., vol. 5, no. 3, pp. 1637–1647, Jun. 2018, doi: 10.1109/JIOT.2017.2786639.

E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund, “Industrial Internet of Things: Challenges, Opportunities, and Directions,” IEEE Trans. Ind. Inf., vol. 14, no. 11, pp. 4724–4734, Nov. 2018, doi: 10.1109/TII.2018.2852491.

C. Del-Valle-Soto, L. J. Valdivia, R. Velázquez, L. Rizo-Dominguez, and J.-C. López-Pimentel, “Smart Campus: An Experimental Performance Comparison of Collaborative and Cooperative Schemes for Wireless Sensor Network,” Energies, vol. 12, no. 16, p. 3135, Aug. 2019, doi: 10.3390/en12163135.

A. H. Alavi and W. G. Buttlar, “An overview of smartphone technology for citizen-centered, real-time and scalable civil infrastructure monitoring,” Future Generation Computer Systems, vol. 93, pp. 651–672, Apr. 2019, doi: 10.1016/j.future.2018.10.059.

A. de M. Del Esposte et al., “Design and evaluation of a scalable smart city software platform with large-scale simulations,” Future Generation Computer Systems, vol. 93, pp. 427–441, Apr. 2019, doi: 10.1016/j.future.2018.10.026.

A. Medela, B. Cendón, L. González, and R. Crespo, “IoT Multiplatform Networking to Monitor and Control Wineries and Vineyards,” p. 10, 2013.

Y. Song, J. Ma, X. Zhang, and Y. Feng, “Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System,” JNW, vol. 7, no. 5, pp. 838–844, May 2012, doi: 10.4304/jnw.7.5.838-844.

G. V. Satyanarayana and S. Mazaruddin, “Wireless Sensor Based Remote Monitoring System for Agriculture Using ZigBee and GPS,” p. 5.

N. Sakthipriya, “An Effective Method for Crop Monitoring Using Wireless Sensor Network,” p. 6, 2014.

D. Rajesh, “Application of Spatial Data mining for Agriculture,” IJCA, vol. 15, no. 2, pp. 7–9, Feb. 2011, doi: 10.5120/1922-2566.

Yue Shaobo et al., “The appliacation of bluetooth module on the agriculture expert system,” in 2010 2nd International Conference on Industrial and Information Systems, Dalian, China, Jul. 2010, pp. 109–112. doi: 10.1109/INDUSIS.2010.5565902.

M. Haefke, S. C. Mukhopadhyay, and H. Ewald, “A Zigbee based smart sensing platform for monitoring environmental parameters,” in 2011 IEEE International Instrumentation and Measurement Technology Conference, Hangzhou, China, May 2011, pp. 1–8. doi: 10.1109/IMTC.2011.5944154.

PG Student, Department of ME, MCE, Hassan, India., P. D. S, and M. S. Srinath, “GSM based Automatic Irrigation Control System for Efficient Use of Resources and Crop Planning by Using an Android Mobile,” IOSRJMCE, vol. 11, no. 4, pp. 49–55, 2014, doi: 10.9790/1684-11414955.

P. Sarwade, N. Shinde, and S. Tingre, “FPGA Based Real Time Monitoring System for Agricultural Field,” vol. 3, no. 3, p. 9, 2017.

R. Castañeda-Miranda, E. Ventura-Ramos, R. del Rocío Peniche-Vera, and G. Herrera-Ruiz, “Fuzzy Greenhouse Climate Control System based on a Field Programmable Gate Array,” Biosystems Engineering, vol. 94, no. 2, pp. 165–177, Jun. 2006, doi: 10.1016/j.biosystemseng.2006.02.012.

K. P. Ferentinos, N. Katsoulas, A. Tzounis, C. Kittas, and T. Bartzanas, “A climate control methodology based on wireless sensor networks in greenhouses,” Acta Hortic., no. 1107, pp. 75–82, Dec. 2015, doi: 10.17660/ActaHortic.2015.1107.9.

J.-S. Lee, Y.-W. Su, and C.-C. Shen, “A Comparative Study of Wireless Protocols: Bluetooth, UWB, ZigBee, and Wi-Fi,” in IECON 2007 – 33rd Annual Conference of the IEEE Industrial Electronics Society, Taipei, Taiwan, 2007, pp. 46–51. doi: 10.1109/IECON.2007.4460126.

C. Saad, B. Mostafa, E. Ahmadi, and H. Abderrahmane, “Comparative Performance Analysis of Wireless Communication Protocols for Intelligent Sensors and Their Applications,” IJACSA, vol. 5, no. 4, 2014, doi: 10.14569/IJACSA.2014.050413.

A. Paventhan, S. K. Allu, S. Barve, V. Gayathri, and N. M. Ram, “Soil Property Monitoring Using 6LoWPAN-enabled Wireless Sensor Networks,” p. 7, 2012.

Z. Suryady, M. H. M. Shaharil, K. A. Bakar, R. Khoshdelniat, G. R. Sinniah, and U. Sarwar, “Performance evaluation of 6LoWPAN-based precision agriculture,” in The International Conference on Information Networking 2011 (ICOIN2011), Kuala Lumpur, Malaysia, Jan. 2011, pp. 171–176. doi: 10.1109/ICOIN.2011.5723173.

A. Augustin, J. Yi, T. Clausen, and W. Townsley, “A Study of LoRa: Long Range & Low Power Networks for the Internet of Things,” Sensors, vol. 16, no. 9, p. 1466, Sep. 2016, doi: 10.3390/s16091466.

M. A. Ertürk, M. A. Aydın, M. T. Büyükakkaşlar, and H. Evirgen, “A Survey on LoRaWAN Architecture, Protocol and Technologies,” Future Internet, vol. 11, no. 10, p. 216, Oct. 2019, doi: 10.3390/fi11100216.

F. Adelantado, X. Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melia-Segui, and T. Watteyne, “Understanding the Limits of LoRaWAN,” IEEE Commun. Mag., vol. 55, no. 9, pp. 34–40, 2017, doi: 10.1109/MCOM.2017.1600613.

S.-C. Hung, H. Hsu, S.-Y. Lien, and K.-C. Chen, “Architecture Harmonization Between Cloud Radio Access Networks and Fog Networks,” IEEE Access, vol. 3, pp. 3019–3034, 2015, doi: 10.1109/ACCESS.2015.2509638.

S. Kitanov, E. Monteiro, and T. Janevski, “5G and the Fog—Survey of related technologies and research directions,” in 2016 18th Mediterranean Electrotechnical Conference (MELECON), Lemesos, Apr. 2016, pp. 1–6. doi: 10.1109/MELCON.2016.7495388.

T. X. Tran, A. Hajisami, P. Pandey, and D. Pompili, “Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges,” IEEE Commun. Mag., vol. 55, no. 4, pp. 54–61, Apr. 2017, doi: 10.1109/MCOM.2017.1600863.

A. Greasley and C. Owen, “Modelling people’s behaviour using discrete-event simulation: a review,” IJOPM, vol. 38, no. 5, pp. 1228–1244, May 2018, doi: 10.1108/IJOPM-10-2016-0604.

S. Svorobej et al., “Towards Automated Data-Driven Model Creation for Cloud Computing Simulation,” presented at the Eighth EAI International Conference on Simulation Tools and Techniques, Athens, Greece, 2015. doi: 10.4108/eai.24-8-2015.2261129.

T. Ojha, S. Misra, and N. S. Raghuwanshi, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Computers and Electronics in Agriculture, vol. 118, pp. 66–84, Oct. 2015, doi: 10.1016/j.compag.2015.08.011.

K. L. Krishna, J. Madhuri, and K. Anuradha, “A ZigBee based energy efficient environmental monitoring alerting and controlling system,” in 2016 International Conference on Information Communication and Embedded Systems (ICICES), Chenai, Tamilnadu, India, Feb. 2016, pp. 1–7. doi: 10.1109/ICICES.2016.7518849.

J. Gutierrez, J. F. Villa-Medina, A. Nieto-Garibay, and M. A. Porta-Gandara, “Automated Irrigation System Using a Wireless Sensor Network and GPRS Module,” IEEE Trans. Instrum. Meas., vol. 63, no. 1, pp. 166–176, Jan. 2014, doi: 10.1109/TIM.2013.2276487.

S. Premkumar and A. Sigappi, “A Survey of Architecture, Framework and Algorithms for Resource Management in Edge Computing,” EAI Endorsed Transactions on Energy Web, p. 167788, Jul. 2018, doi: 10.4108/eai.23-12-2020.167788.

Y. Mao, J. Zhang, and K. B. Letaief, “Joint Task Offloading Scheduling and Transmit Power Allocation for Mobile-Edge Computing Systems,” in 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, USA, Mar. 2017, pp. 1–6. doi: 10.1109/WCNC.2017.7925615.

H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, and R. Buyya, “iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments: iFogSim: A toolkit for modeling and simulation of internet of things,” Softw. Pract. Exper., vol. 47, no. 9, pp. 1275–1296, Sep. 2017, doi: 10.1002/spe.2509.

Published

2022-02-04

How to Cite

Premkumar, S. ., & Sigappi, A. . (2022). ASIS Edge Computing Model to Determine the Communication Protocols for IoT Based Irrigation. Journal of Mobile Multimedia, 18(03), 885–916. https://doi.org/10.13052/jmm1550-4646.18321

Issue

Section

Computer Vision and its Application in Agriculture