SMA-WSN: Failure-Aware Slime-Mold-Inspired Clustering and Per-Hop Versus End-to-End Delivery Analysis in Agricultural Wireless Sensor Networks.

This project introduces a failure‑aware clustering and routing framework for agricultural wireless sensor networks (WSNs) that uses the Slime Mold Algorithm (SMA). In farms, sensor nodes often fail because of harsh weather, drained batteries, or physical obstacles. To cope with these problems, the system applies a slime‑mold‑inspired optimization method to pick cluster heads and create the best routing paths on the fly. The clustering process is failure‑aware: if a cluster head or any intermediate node stops working, the network automatically finds a new route for the data. The framework also compares two delivery styles—per‑hop and end‑to‑end—by measuring packet delivery ratio, latency, and energy use. Simulations model how sensors are placed,

how they consume energy, and how long the network lasts under different agricultural conditions. Results show that SMA‑based clustering makes the network more resilient, spreads energy use more evenly among nodes, and improves data‑delivery reliability compared with traditional clustering methods. This research provides a solid base for building robust, self‑healing communication protocols for precision agriculture.

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