Data gathering in delay tolerant wireless sensor networks using a ferry.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Delay-tolerant wireless sensor networks (WSNs) often rely on mobile ferries for data collection, offering reduced node energy consumption compared to multi-hop forwarding.
• However, ferry-based approaches can increase data delivery latency, posing challenges for time-sensitive applications.

Purpose of the Study:

• To propose an efficient data collection algorithm for ferry-based WSNs that optimizes both ferry travel time and network energy consumption.
• To enhance network lifetime and reduce overall energy usage in large-scale WSN deployments.

Main Methods:

• Dividing the sensing field into virtual grids with checkpoints to minimize ferry roundtrip travel time.
• Employing a Genetic Algorithm (GA) to solve the Traveling Salesman Problem (TSP) for optimal ferry path determination.
• Utilizing a previously developed Node Ranking Clustering Algorithm (NRCA) for selecting cluster heads based on residual energy and proximity to checkpoints.

Main Results:

• Simulations in MATLAB demonstrated improved network lifetime, reduced total energy consumption, and shorter ferry roundtrip travel times.
• Nonlinear ferry trajectories showed superior optimization compared to linear trajectories for network lifetime, energy consumption, and travel time.
• The proposed algorithm outperformed recent comparable algorithms in terms of network lifetime across various initial energy values.

Conclusions:

• The developed algorithm offers an efficient solution for data collection in delay-tolerant WSNs using mobile ferries.
• Optimizing ferry trajectories and employing intelligent clustering significantly enhances WSN performance metrics.
• The findings suggest that nonlinear paths and energy-aware clustering are crucial for maximizing the efficiency of ferry-based WSNs.