Wireless sensor network advances enable different high-specification sizes with increased ease of deployment to increase lifespan service in the Internet of Things. Sensor service is related to energy source, which is usually battery powered. Estimating sensor lifetime before deployment avoids service interruption. Petri net is an intelligent language for scheduling, managing, and modelling complex concurrent systems. It can be used as a problem-solver algorithm. Many different classes of Petri net model (which uses given data and structure to find final answers to problems) evaluate sensor energy behaviour. However, representing all interactions in a node and network increases model complexity in terms of structure comprehensiveness and reuse. Existing methods can only be applied at certain areas, with inefficient estimation speeds, and they do not account for temperature impacts on sensor lifetime. The complexity of model structure design is burdensome for non-expert users, and existing methods fail to provide common background for quick estimation of sensor battery lifetime, and advanced hardware and network re-building call for improved models. This study develops a high-coloured Petri net model with a robust structural solution for developing and modifying complex sensor workflows, applying the coloured tokens concept to allow network traffic to be distinguished according to the same structural elements. The dynamicity of a network is abstracted as tokens representing timing sets of sensors operations within a network. A case study of low power and lossy network is studied through this research work, demonstrating efficient and fast (one-second) estimation of sensor nodes’ lifetime. The model is easily operated by non-expert users, with graphical rather than mathematical interface structure. The same model can be easily modified by changing its related tokens values and be extended to include any other energy consumption sources in a node as a function of temperature for evaluation.