Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks

Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring.

1. Introduction -- 2. Related Work -- 3. Materials and Methods -- 3.1. Energy Model -- 3.2. Scenario -- 3.3. Network Architecture and Connectivity -- 3.4. Jamming Detection and Mitigation Strategy -- 3.5. Statistical Analysis -- 4. Results and Discussion -- 4.1. Scenario 1: BLE-Based Communication with Cluster-Based Mitigation and a Jammer Node -- 4.2. Scenario2: LoRa-Based Communication with Cluster-Based Mitigation and a Jammer Node -- 4.3. Scenario 3: Adaptive Communication Switching Between LoRa and BLE with Cluster-Based Mitigation -- 4.4. Comparative Energy Consumption Analysis -- 4.5. Histogram with Probability Density Function -- 4.6. Potential Limitations -- 5. Conclusions -- References.