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Efficient Heuristic for Optimal MILP-LoRa Adaptive Resource Allocation for Aquaculture

M. Iniyan Arasu1,*, S. Subha Rani1, G. Raswin Geoffery2

1 Department of ECE, PSG College of Technology, Coimbatore, 641004, Tamilnadu, India
2 Dr. Jayalalithaa Fisheries University, Nagapattinam, 611002, Tamilnadu, India

* Corresponding Author: M. Iniyan Arasu. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 729-742.


LoRa is well-known for its extensive communication range, inexpensive efficiency, and reduced or less power consumption in end devices. End-device energy consumption in LoRa networks is ludicrous because some end-devices use massive dissemination variables to reach the remote doorway. Furthermore, the batteries in these end devices deplete very quickly, reducing network life significantly. To address this issue, an optimal mixed-integer linear programming long-range technique (OMILP-LoRa) was used in this study. The primary goal of this research is to enable adaptive resource allocation using the unique OMILP-LoRa protocol. The ACCURATE heuristic and the OMILP model for LoRaWAN resource allocation are presented in this work. The ACCURATE method was used to dynamically modify the spreading factor (SF) and carrier frequency (CF) configurations for every LoRaWAN IoT devices. The results shows the ACCURATE heuristic produces results that are related to the optimal obtained through the OMILP-LoRa device for channel use, increasing the placement of LoRaWAN, steps to prevent collisions, and enhancing the complete system. The suggested method’s performance includes a comparison of the proposed approach to different existing methods, including the ILP, LoRa, and MILP methods.


Cite This Article

M. Iniyan Arasu, S. Subha Rani and G. Raswin Geoffery, "Efficient heuristic for optimal milp-lora adaptive resource allocation for aquaculture," Intelligent Automation & Soft Computing, vol. 33, no.2, pp. 729–742, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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