چكيده به لاتين
With the rapid expansion of wireless technologies and the widespread emergence of the Internet of Things, optimizing energy consumption in networks composed of battery-powered nodes has become a major challenge in wireless sensor network research for IoT applications. This issue is especially critical in scenarios where nodes are deployed under severe energy constraints, making battery replacement or recharging infeasible. In such cases, employing energy management methods such as clustering algorithms or equipping nodes with environmental energy harvesters plays a key role in extending the network’s operational lifetime. In clustering based routing algorithms, cluster heads assume a portion of the energy intensive communication tasks, thereby reducing transmission and reception overhead for ordinary nodes. Furthermore, hardware heterogeneity among nodes in a cluster facilitates the use of high-capacity nodes to handle more demanding communication tasks, and equipping them with environmental energy harvesting systems significantly enhances network stability and lifetime.
To thoroughly analyze network behavior under these complex conditions, analytical models like colored Petri nets (CPNs)
provide an efficient alternative to costly and time consuming real world implementations. These models can accurately represent both discrete events such as message exchange and cluster head selection and continuous processes such as gradual energy harvesting and consumption. In the present study, a comprehensive model for such networks is developed using CPN Tools, taking into account key factors including battery capacity, environmental energy harvesting rate, sunlight intensity, sensor response time, and the interval between operational rounds. This approach provides a quantitative assessment of each factor’s impact on the network’s energy consumption. The model’s hierarchical structure offers high scalability, simplifying any changes to the number or characteristics of nodes. Moreover, node heterogeneity, diverse climatic conditions, and energy harvesting mechanisms during sleep intervals between rounds are seamlessly integrated into this unified framework.
By distinguishing the behavior of heterogeneous nodes and concurrently modeling discrete and continuous components, the proposed model can monitor node energy levels in real time and evaluate the effectiveness of clustering schemes in various studies. Additionally, by enabling diverse assumptions, the model allows for highly accurate simulations of real-world conditions. Finally, to validate the proposed model, a corresponding implementation was carried out in the OMNeT++ simulation environment, and a comparison of the results shows that the outputs of the hierarchical timed colored Petri net model closely align with actual network behavior in terms of detailed implementation.
