Abstract
Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process’s pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.
Highlights
Wireless sensor networks (WSNs) have been widely employed in area monitoring, military applications, and fire detection applications [1,2,3,4,5,6,7]
Experimental results indicate that our framework improves the structs a hierarchical and structural simulation model by combining the tree structure, the execution time by up to 8% and the central processing unit (CPU) utilization cost by up to parameters, and the atomic DEVS models
The entity structures show the components of the WSNs hierarchically in various trees; Proposed Pruning: Our pruning algorithm selects entities from an SES tree for generating a PES and configures a sensor network’s environment parameters; PES base: This base includes pruned entity structures of the WSNs
Summary
Wireless sensor networks (WSNs) have been widely employed in area monitoring, military applications, and fire detection applications [1,2,3,4,5,6,7]. It is necessary to achieve a new SES/MB framework to effectively synthesize synthesizing the entity structures and behavioral models, including the environment pathem forItWSNs. construction process. WSN modeling construction (i.e., transformation process) to achieve time and cost savbehavioral models of a sensor network with the parameters of the network environment. The transformation process expresses the structure of the network as an SES tree, selects the DEVS behavioral models of a sensor network with the parameters of the network envispecific entities and the environment parameters using our pruning algorithm, and coverts ronment. Experimental results indicate that our framework improves the structs a hierarchical and structural simulation model by combining the tree structure, the execution time by up to 8% and the central processing unit (CPU) utilization cost by up to parameters, and the atomic DEVS models.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
