Energy consumption of energy-constrained nodes in wireless sensor networks (WSNs) is a fatal weakness of these networks. Since these nodes usually operate on batteries, the maximum utility of the network is dependent upon the optimal energy usage of these nodes. However, new emerging optimal energy consumption algorithms, protocols, and system designs require an evaluation platform. This necessitates modeling techniques that can quickly and accurately evaluate their behavior and identify strengths and weakness. We propose Petri nets as this ideal platform. We demonstrate Petri net models of wireless sensor nodes that incorporate the complex interactions between the processing and communication components of an WSN. These models include the use of both an open and closed workload generators. Experimental results and analysis show that the use of Petri nets is more accurate than the use of Markov models and programmed simulations. Furthermore, Petri net models are extremely easier to construct and test than either. This paper demonstrates that Petri net models provide an effective platform for studying emerging energy-saving strategies in WSNs. 1. Introduction and Motivations Application for wireless sensor networks (WSNs) has abounded since their introduction in early 2000. WSNs are being used from surveillance, environmental monitoring, inventory tracking, and localization. A sensor network typically comprises of individual nodes operating with some limited computation and communication capabilities, and powered by batteries with limited energy supply. Furthermore, these networks are situated at a location where they may not be easily accessible. Their distributed nature, small footprint, cheap, and wireless characteristics make them very attractive for these outdoor, unattended, and hostile environment applications. One of the motivating visions of WSNs was large-scale remote sensing such as large areas of a rainforest for environmental parameters such as humidity and temperature. However, given the remoteness of such a site, this can be a challenging problem. Modern WSNs were proposed for solving such problems, and it was envisioned that these WSN nodes could be sprinkled over an area from the back of an airplane as it flew over such an area. The nodes wherever they fell would automatically set up an ad hoc network, collect the necessary sensory information, and route the information to a base-station. Although great strides have been made in WSN designs and implementation, we are nowhere near meeting this original motivating problem. One reason why
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