DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas

Abstract

Effective management of catastrophic events in high-risk zones necessitates a holistic technological approach to protect ecosystems, biodiversity, and native populations. Limitations in sensor range and connectivity hamper real-time data gathering in secluded areas, while financial and technical hurdles hinder the creation of cost-effective, automated systems. This study presents stochastic models, the LoRaW protocol, and cloud technology to enhance sensor deployment simulations. Wireless Sensor Networks and LoRa technology are crucial for extensive monitoring and communication infrastructures. Stochastic Petri Net models optimize system components by assessing crucial performance indicators, such as average response time and system utilization, thus improving disaster response and supporting research hypotheses. • Utilizing Stochastic Petri Nets, the study enhances disaster detection via LoRaWAN. • By integrating SPN modeling with sensitivity analysis, system performance is simulated. • Advancing cost-efficient designs, the research optimizes server capacity planning. • Through the addition of processing cores, MRT is reduced and system throughput improves. • Highlighting disaster management infrastructure, the study aids in building robust systems.

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