This paper proposes an optimal design of network and a routing mechanism that is independent from fixed protocols. It provides an optimized route for diversified mesh network, which can support interorganizational communication in a large-scale operation. Decentralization of the system ensures that every protocol acts independently and selects the best optimal path during transmission of data without modifying their architecture and technology. Incorporation of definite source configuration improves the mobility of the systems. Each sensor is individually processed to balance the data load and prevent congestion. Simple transmit-receive test is performed by circulating messages of increasing size between end sensors and network destination. The proposed technique is considered to be effective in terms of interoperability speed, data accuracy and bit error rate (BER) with an increment of 27.13%, 99.98%, and 15.12%, respectively. Finally, the test demonstrates its expediency in terms of adaptability and scalability.

1. [1] V. Saiz-Rubio and F. Rovira-Más, “From smart farming towards agriculture 5.0: A review on crop data management,” Agronomy, Vol.10, No.2, doi: 10.3390/agronomy10020207, 2020.
2. [2] A. I. Montoya-Munoz and O. M. C. Rendon, “An approach based on fog computing for providing reliability in IoT data collection: A case study in a colombian coffee smart farm,” Applied Sciences, Vol.10, No.24, doi: 10.3390/app10248904, 2020.
3. [3] R. S. Concepcion et al., “Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation,” Int. J. Advances in Intelligent Informatics, Vol.6, No.3, pp. 261-277, doi: 10.26555/ijain.v6i3.435, 2020.
4. [4] J. D. Alejandrino et al., “Feasibility of Television White Space Spectrum Technologies for Wide Range Wireless Sensor Network: A survey,” 2019 IEEE 11th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM48295.2019.9072794, 2019.
5. [5] M. R. Ramli, P. T. Daely, D. S. Kim, and J. M. Lee, “IoT-based adaptive network mechanism for reliable smart farm system,” Computers and Electronics in Agriculture, Vol.170, Article No.105287, doi: 10.1016/j.compag.2020.105287, 2020.
6. [6] J. Alejandrino et al., “Visual classification of lettuce growth stage based on morphological attributes using unsupervised machine learning models,” 2020 IEEE Region 10 Conf. (TENCON), pp. 438-443, doi: 10.1109/TENCON50793.2020.9293854, 2020.
7. [7] J. D. Alejandrino et al., “A machine learning approach of lattice infill pattern for increasing material efficiency in additive manufacturing processes,” Int. J. of Mechanical Engineering and Robotics Research, Vol.9, No.9, pp. 1253-1263, doi: 10.18178/ijmerr.9.9.1253-1263, 2020.
8. [8] S. Misra et al., “An adaptive learning approach for fault-tolerant routing in Internet of Things,” 2012 IEEE Wireless Communications and Networking Conf. (WCNC), pp. 815-819, doi: 10.1109/WCNC.2012.6214484, 2012.
9. [9] S. A. Chelloug, “Energy-Efficient Content-Based Routing in Internet of Things,” J. of Computer and Communications, Vol.3, No.12, pp. 9-20, doi: 10.4236/jcc.2015.312002, 2015.
10. [10] Y. Wei, J. Wang, and J. Wang, “A delay/disruption tolerant routing algorithm for IOT in harsh environment,” Proc. of 2013 6th Int. Conf. on Intelligent Networks and Intelligent Systems (ICINIS), pp. 143-146, doi: 10.1109/ICINIS.2013.43, 2013.
11. [11] L. Ngqakaza and A. Bagula, “Least Path Interference Beaconing Protocol (LIBP): A Frugal Routing Protocol for The Internet-of-Things,” Wired/Wireless Internet Communications: WWIC 2014, Lecture Notes in Computer Science, Vol.8458, doi: 10.1007/978-3-319-13174-0_12, 2014.
12. [12] E. Jung, I. Cho, and S. M. Kang, “IotSilo: The agent service platform supporting dynamic behavior assembly for resolving the heterogeneity of IoT,” Int. J. of Distributed Sensor Networks, Vol.2014, doi: 10.1155/2014/608972, 2014.
13. [13] J. Guo et al., “Resource aware routing protocol in heterogeneous wireless machine-to-machine networks,” 2015 IEEE Global Communications Conf. (GLOBECOM), doi: 10.1109/GLOCOM.2014.7417203, 2015.
14. [14] M. Surligas, A. Makrogiannakis, and S. Papadakis, “Empowering the IoT heterogeneous wireless networking with software defined radio,” 2015 IEEE 81st Vehicular Technology Conf. (VTC Spring), doi: 10.1109/VTCSpring.2015.7145802, 2015.
15. [15] H. Fotouhi, D. Moreira, and M. Alves, “mRPL: Boosting mobility in the Internet of Things,” Ad Hoc Networks, Vol.26, pp. 17-35, doi: 10.1016/j.adhoc.2014.10.009, 2015.
16. [16] S. C. Lauguico et al., “A comparative analysis of machine learning algorithms modeled from machine vision-based lettuce growth stage classification in smart aquaponics,” Int. J. of Environmental Science and Development, Vol.11, No.9, pp. 442-449, doi: 10.18178/ijesd.2020.11.9.1288, 2020.
17. [17] Y. He and H. T. Wai, “Estimating Centrality Blindly from Low-pass Filtered Graph Signals,” arXiv preprint, arXiv:1910.13137, 2019.
18. [18] M. Kovatsch, M. Lanter, and Z. Shelby, “Californium: Scalable cloud services for the Internet of Things with CoAP,” 2014 Int. Conf. on the Internet of Things (IOT), pp. 1-6, doi: 10.1109/IOT.2014.7030106, 2014.
19. [19] J. M. Ladrido et al., “Comparative survey of signal processing and artificial intelligence based channel equalization techniques and technologies,” Int. J. of Emerging Trends in Engineering Research, Vol.7, No.9, pp. 311-322, doi: 10.30534/ijeter/2019/14792019, 2019.
20. [20] A. Coluccia, A. Fascista, and G. Ricci, “Robust CFAR Radar Detection Using a K-nearest Neighbors Rule,” 2020 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 4692-4696, doi: 10.1109/ICASSP40776.2020.9054283, 2020.
21. [21] J. Sterle et al., “Application-Driven OAM Framework for Heterogeneous IoT Environments,” Int. J. of Distributed Sensor Networks, doi: 10.1155/2016/5649291, 2016.
22. [22] S. C. Lauguico et al., “Implementation of Inverse Kinematics for Crop-Harvesting Robotic Arm in Vertical Farming,” Proc. of 2019 IEEE Int. Conf. on Cybernetics and Intelligent Systems (CIS) and IEEE Conf. on Robotics, Automation and Mechatronics (RAM), pp. 298-303, doi: 10.1109/CIS-RAM47153.2019.9095774, 2019.
23. [23] S. C. Lauguico et al., “Lettuce life stage classification from texture attributes using machine learning estimators and feature selection processes,” Int. J. of Advances in Intelligent Informatics, Vol.6, No.2, pp. 173-184, doi: 10.26555/ijain.v6i2.466, 2020.
24. [24] J. Alejandrino et al., “Congestion Detection in Wireless Sensor Networks Based on Artificial Neural Network and Support Vector Machine,” 2020 IEEE 12th Int. Conf. on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), doi: 10.1109/HNICEM51456.2020.9400062, 2020.