DDS-P: Stochastic models based performance of IoT disaster detection systems across multiple geographic areas
Israel Araújo,
Luis Guilherme Silva,
Carlos Brito,
Dugki Min,
Jae-Woo Lee,
Tuan Anh Nguyen,
Erico Leão,
Francisco A. Silva
Affiliations
Israel Araújo
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piaui (UFPI), Picos, Piaui 64607-670, Brazil
Luis Guilherme Silva
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piaui (UFPI), Picos, Piaui 64607-670, Brazil
Carlos Brito
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piaui (UFPI), Picos, Piaui 64607-670, Brazil
Dugki Min
Department of Artificial Intelligence, Graduate School, Konkuk University, Seoul 05029, South Korea; Corresponding author at: Department of Artificial Intelligence, Graduate School, Konkuk University, Seoul 05029, South Korea.
Jae-Woo Lee
Department of Mechanical and Aerospace Engineering, Konkuk University, Seoul 05029, South Korea
Tuan Anh Nguyen
Department of Artificial Intelligence, Graduate School, Konkuk University, Seoul 05029, South Korea; Konkuk Aerospace Design-Airworthiness Research Institute (KADA), Konkuk University, Seoul 05029, South Korea; Corresponding author at: Department of Artificial Intelligence, Graduate School, Konkuk University, Seoul 05029, South Korea.
Erico Leão
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piaui (UFPI), Picos, Piaui 64607-670, Brazil
Francisco A. Silva
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piaui (UFPI), Picos, Piaui 64607-670, Brazil
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.
