A model-based approach to improving the dependability of IoT-enabled weather monitoring systems

Abstract

Low-cost Internet of Things (IoT) weather stations enhance spatial and temporal coverage for hyperlocal forecasting, especially in remote or hard-to-reach areas where traditional monitoring infrastructure is limited. However, their dependable operation is affected by component reliability, message delivery performance, and energy-related constraints, particularly battery depletion and solar recharge variability. This paper presents a dependability analysis of a real IoT-enabled weather monitoring platform based on a Weather Monitoring Approach (WMA), modeled using Stochastic Petri Nets (SPNs) to evaluate availability and reliability, while explicitly modeling energy autonomy as a cross-cutting operational constraint that affects continuous operation. Results show that the proposed WMA significantly increases operational availability, reduces failure probability, and improves energy autonomy by reducing the likelihood of battery depletion and extending operational continuity. In addition, the optimized communication configuration substantially decreased the latency required for near-certain message delivery, highlighting the impact of transmission tuning on system dependability. The proposed WMA provides a means to analyze configuration and design changes that can further improve system dependability, demonstrating how the combination of reliability modeling, energy autonomy mechanisms, and efficient communication strategies can substantially enhance the dependability of IoT-enabled weather monitoring systems and support continuous operation in regions with limited maintenance accessibility.