Performance analysis of chirp spread spectrum system under mobility scenario

Qaraqe, Marwa; Althunibat, Saud; Badarneh, Osamah S.; Mesleh, Raed

doi:10.1016/j.phycom.2020.101233

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…LoRa is a well-known communication technology that uses the real layer of the chirp spread spectrum (CSS), with the higher layer being OSI-based. Because the quality of service (QoS) is inadequate, the radio boundaries are changed [4]. To increase service quality in LoRa networks, a specific software-based computer algorithm adjusts the transmission factor and carrier frequency (CF) radio limits [5].…”

Section: Introductionmentioning

confidence: 99%

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

Arasu¹,

Rani²,

Geoffery³

2022

Intelligent Automation &Amp; Soft Computing

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LoRa is well-known for its extensive communication range, inexpensive efficiency, and reduced or less power consumption in end devices. Enddevice 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 LoR-aWAN, 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.

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Section: Introductionmentioning

confidence: 99%