Power minimization is a serious issue in wireless sensor networks to extend the lifetime and minimize costs. However, in  Wireless sensor networks are becoming increasingly prevalent in a wide range of areas from surveillance [6] to monitoring temperature, humidity, and other environmental parameters [12, 10] . Wireless sensor network are usually comprised of nodes powered by batteries in locations where maintenance access may be difficult. Minimizing energy consumption in these networks would go a long ways toward extending the lifetime of the network and increasing the usability. However, in order to obtain a thorough understanding of the energy consumption characteristics in these networks, accurate modeling methods need to be developed. Although the primary energy consumption in wireless sensor networks is for communication [2] , the processor also serves a key role and there is a need to examine the energy characteristics of the embedded processors of these sensor nodes. A sound method of modeling provides a stable platform upon which the energy characteristics of innovative technology can be analyzed. One example of this is the capability of processors to power down to a low power mode after some time of no activity, which we will refer to as the Power Down Threshold. However, once powered down, the processor requires time Power Up Delay to reach operating mode again. In this paper, we compare two different processor models: a Markov chain and a Petri net against software simulation. We show that the Petri net model is more accurate than the Markov chain. Section 2 introduces the use of both Markov models and Petri nets. Section 3 presents related work. Section 4 develops a model of a processor using a Markov model and a Petri net. Section 5 compares the results obtained by the simulation, Markov model, and Petri net, and Section 6 concludes this paper. Markov models have long been used to model systems dealing with exponentially distributed arrival and service rates. Markov models are composed of event chains where the likelihood of a system being in a particular state is independent of being in any other state. We can see how this restriction limits the usefulness of this method. Petri nets, on the other hand, are far more useful than Markov models. Initially Petri nets were based on Markov models, but since then, features of Petri nets' have been extended over the years that has blurred these similarities. Unlike Markov models, Petri nets are a simulation based approach. Petri nets can be thought of as a directed graph of nodes and arcs which are called places and transitions respectively. With Markov models, closed form equations can be derived that can be used to provide analysis about the modeled systems. Petri nets require that the modeled system be simulated for extended periods of time so that the steady state probability of the system is reached. Computer program such as TimeNet 4.0 [13] are available that can assist in creating and simulating Petri nets. Figure 1 depicts a simple Petri net that contains two places P 0 and P 1 and a transition T 0. When a token exists in the input place of a transition as shown in P 0, this enables
The work of REF ] presents a Petri net modelling the energy consumption of a processor in WSNs.
Energy Modeling of Processors in Wireless Sensor Networks Based on Petri Nets
2008 International Conference on Parallel Processing - Workshops
2008 International Conference on Parallel Processing - Workshops
Computer Science