Energy Modeling of Wireless Sensor Nodes Based on Petri Nets

Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. Accurately understanding the energy consumption characteristics of each sensor node is a critical step for the design of energy saving strategies. This paper develops a detailed probabilistic model based on Petri nets to evaluate the energy consumption of a wireless sensor node. The model factors critical components of a sensor node, including processors with emerging energy-saving features, wireless communication components, and an open or closed workload generator. Experimental results show that this model is more flexible and accurate than Markov models. The model provides a useful simulation platform to study energy saving strategies in wireless sensor networks

Effective Stochastic Modeling of Energy-Constrained Wireless Sensor Networks

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 a 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

Idle energy minimization by mode sequence optimization

[[abstract]]This article presents techniques for reducing idle energy by mode-sequence optimization (MSO) under timing constraints. Our component-level CoMSO algorithm computes energy-optimal mode-transition sequences for different lengths of idle intervals. Our system-level SyMSO algorithm shifts tasks within slack intervals while satisfying all timing and resource constraints in the given schedule. Experimental results on a commercial software-defined radio show that these new techniques can reduce idle energy by 50--70%, or 30--50% of total system energy over previous offline-optimal but unsequenced techniques based on localized break-even-time analysis, thanks to rich options offered by mode sequencing.[[fileno]]2030236030039[[department]]資訊工程學