# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 5 Conclusions  
The findings of this study highlight the substantial impact of the proposed jamming mitigation algorithm on network resilience, energy efficiency, and communication stability within heterogeneous WSNs. By implementing an adaptive cluster-based mitigation strategy, the system successfully counteracts jamming attacks in BLE, LoRa, and hybrid BLE-LoRa networks, ensuring reliable data transmission even in interference-prone environments. The results demonstrate that, in BLE-only configurations, the mitigation algorithm significantly reduces retransmission rates and energy consumption by dynamically re-organizing cluster heads and optimizing transmission power. Similarly, in LoRa-based networks, where energy usage is inherently higher due to longer transmission distances, the proposed strategy mitigates energy drain by intelligently rerouting traffic and selecting alternative communication paths. The hybrid BLE-LoRa approach, leveraging the strengths of both technologies, showcases the highest adaptability under jamming conditions, effectively balancing energy efficiency and transmission range through real-time protocol switching. The comparative analysis validates that intelligent network adaptation not only enhances the robustness of WSNs against external interference but also extends the operational lifetime of battery-powered devices, making it a crucial advancement for large-scale IoT deployments. The core contribution of this research lies in its novel integration of a cluster-based jamming mitigation algorithm with multi-protocol network switching, setting it apart from conventional static anti-jamming strategies. Unlike prior approaches that rely on frequency hopping or power adaptation alone, the proposed method dynamically adjusts network topology and transmission parameters based on real-time interference detection. This innovation ensures that affected nodes rapidly adapt to changing conditions, reducing packet loss and minimizing energy-intensive retransmissions. Furthermore, the study provides a comprehensive empirical evaluation of the trade-offs between BLE, LoRa, and hybrid BLE-LoRa configurations, offering practical insights into their energy-performance dynamics under jamming scenarios. The results not only reinforce the effectiveness of adaptive mitigation strategies in heterogeneous IoT networks but also establish a foundation for future research on AI-driven jamming resilience mechanisms. By integrating intelligent decision making with multi-protocol flexibility, this work paves the way for more secure and energy-efficient WSN architectures, addressing critical challenges in wireless communication for smart cities, industrial automation, and large-scale sensor deployments. While our proposed mitigation strategy significantly improves resilience against jamming attacks in HWSNs, several challenges remain. One major area for future work is the extension of our algorithm to address more sophisticated and adaptive jamming techniques, such as adversarial machine-learning-based jammers that dynamically alter their interference patterns. Additionally, although our approach effectively balances energy efficiency and interference mitigation, further research is needed to optimize protocol switching decisions under varying network loads and mobility conditions. Another potential direction is the exploration of collaborative mitigation strategies that integrate multiple nodes' intelligence to detect and counteract jamming at a network-wide level rather than relying solely on cluster-based adaptation. Finally, while our experimental setup provides valuable real-world validation, large-scale deployments in diverse environments, such as industrial IoT and smart city applications, could offer deeper insights into scalability and long-term network performance under prolonged jamming scenarios. By considering these aspects, future studies can build upon our work to develop more robust and adaptive solutions for securing HWSNs against evolving interference threats.