A radio-aware secure routing protocol for wireless sensor networks using predictive Markov filtering and bounded swarm optimization (RSMFSO)

Abstract Wireless sensor networks (WSNs) are one of the key enabling technologies for applications that are energy-constrained and vulnerable to insider attacks in adversarial environments. Conventional protocols optimized for swarms will soon fire up so many multi-hop paths as to exhaust node energy prematurely and yield increasingly higher flat power losses via ongoing over-promotion of available resources ( $$\:{d}^{4}$$ ) that exceed free-space limits. At the same time, traditional clustering mechanisms are susceptible to selective forwarding and blackhole attacks of malicious nodes. In order to address these interdependent problems, RSMFSO, a trust-aware and radio-aware routing protocol, is proposed. RSMFSO localizes a W = 5-round rolling state profile (PMF) to track the 5-round history of rolling state profiles and preemptively isolates compromised nodes prior to topology formation and cluster head selection. Simultaneously, free-space ( $$\:{d}^{2}$$ ) transmission with scene-dependent physical radio constraints is enforced within an optimization engine on the same biological principles. An automated shunting circuit breaker also dynamically circumvents high-effort clustering in late-stage network decay to prevent control packet cascades and dead cascades of control packets. Extensive empirical simulations executed over standard (Scenario A: N = 100 nodes, $$\:{E}_{init}$$ = 0.5 J) and high-density deployments (Scenario B: N = 300 nodes, $$\:{E}_{init}$$ = 0.5 J) verify that RSMFSO substantially extends operational network lifetime compared to evaluated baselines, achieving up to Round 400 with a non-failure node count of approx. 100% across both scales. Scenario B: In this scenario, RSMFSO delays the first node death until Round 500 (298 active nodes) and maintains 98 active operational nodes at Round 1000, compared with LEACH, MMABC, and WIFNCP protocols. Non-parametric statistical evaluations via two-tailed Wilcoxon Signed-Rank tests ( p < 0.05) and Friedman Mean Ranks (Rank 1.00) confirm that RSMFSO achieves statistically significant superiority over LEACH, MMABC, and WIFNCP across 30 independent Monte Carlo runs. With a less than 10% malicious node footprint, RSMFSO effectively mitigates the blackhole effect while maintaining high data throughput from the sensor field to the base station, making it ideal for mission-critical WSN deployments.