A Novel Theoretical Probabilistic Model for Opportunistic Routing with Applications in Energy Consumption for WSNs

Area of Science:

• Computer Science
• Network Engineering
• Stochastic Modeling

Background:

• Opportunistic networks (OppNets) present unique challenges for performance analysis due to their intermittent connectivity.
• Existing theoretical models often rely on Markov chains, which can be computationally intensive and indirect in parameter estimation.
• The need for efficient and direct methods to compute network parameters in OppNets is critical for performance optimization.

Purpose of the Study:

• To propose a new theoretical stochastic model for opportunistic networks based on percolation theory.
• To enable direct computation of key network parameters, including route possibilities and transmission success probabilities.
• To offer a flexible model capable of handling probabilistic values defined by intervals or density functions.

Main Methods:

• Development of a novel percolation stochastic model tailored for opportunistic networks.
• Direct analytical computation of network parameters: number of routes, successful transmission probability, expected broadcasts, and receptions.
• Validation through Monte Carlo simulations and provision of a reproducible computational toolbox (R-packet).

Main Results:

• The percolation model successfully computes network parameters directly, outperforming traditional methods in efficiency.
• The model demonstrates flexibility in handling diverse probability specifications (bounded intervals, density functions).
• Numerical examples confirm the model's applicability, including energy consumption estimation in OppNets.

Conclusions:

• The proposed percolation stochastic model offers a powerful and direct analytical tool for opportunistic network analysis.
• This novel approach enhances the efficiency and flexibility of modeling OppNet performance metrics.
• The R-packet toolbox facilitates the adoption and verification of the presented model.