A Framework for Optimizing the Deployment of Wireless Sensor Networks

In order to tackle the problem of poor network connectivity and sensing coverage in random wireless sensor networks (WSNs) deployment, this paper proposes hierarchical-logic mapping and deployment algorithms, which use disk communication model and radio frequency propagation model to optimize sensing and connectivity coverage. To further accomplish the optimized approach, the study utilizes efficient image processing algorithm for classification of deployment terrain in order to improve deployment and connectivity coverage with required minimum number of nodes and transmission parameters. The Poisson random distribution process is used to simulate deployment while kernel-based support vector machine is used for terrain classification. The proposed approach is developed into an optimization and visualization framework for characterization and prediction of optimal deployment of WSNs. The performance of the framework is compared with actual small-scale stochastic deployment and existing simulators such as GloMoSim to demonstrate its optimal performance. Results show a significant difference and commonality. With the approach used in this paper, results show that deployment performance can be improved significantly. The availability of the deployment framework will ameliorate the current and future applications of WSN technology such as in military operations, post disaster recovery response, environmental quality, surveillance, and border security.