Soft computing tool for restoring failures within wireless sensor networks

The proposed soft computing tool is developed as a suite of software programs capable of envisaging the evolution within wireless sensor networks (WSN) by accomplishing a series of processes. These processes are: modelling a real problem scenario in computing form, formulating the problem as an optimization problem, evaluating the outcomes and finally, presenting the outcomes in a statistical form. We have examined many possible failing scenarios of WSN operations through the proposed tool, which was capable to restore WSN in the presence of failures to continue normal operations. Simulated Annealing (SA) is utilized as a search method, which is embedded within the soft computing tool to explore the restoration space comprising of possible alternative topologies generated for each failing scenario, taking lifespan into consideration. The tested scenarios and their restorations can be hard-coded into a real WSN, thus preparing WSN to evolve through failures. The simulation results of a typical WSN illustrates the capability of the proposed computing tool to restore 79% of the lost days when no restoration scheme was applied.

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