# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 3 Materials and Methods
### Energy Model
Nodes close to the coordinator or acting as relays for other nodes typically consume more transmission energy due to frequent packet forwarding, while distant nodes may have reduced transmission but higher reception energy as they receive data from multiple sources. Notably, packet retransmissions due to acknowledgment failures or interference further amplify energy demands, leading to increased network congestion and higher node power consumption. By quantifying these different types of energy, the model provides a comprehensive framework for analyzing network performance, optimizing routing protocols, and developing energy-efficient transmission strategies. It also helps identify potential inefficiencies, such as excessive retransmissions or redundant protocol overhead, that could be mitigated to enhance network longevity and resilience. Figure 1 shows the integration of Texas Instruments CC1352P LaunchPad and LPSTK-CC1352R sensor platforms within the heterogeneous IoT network, leveraging both LoRa and BLE communication protocols. The experimental setup includes sensor programming via Code Composer Studio, real-time data acquisition, and sensor deployment for network evaluation. The CC1352P board acts as a multi-protocol wireless microcontroller designed for low-power applications, capable of dynamically switching between LoRa and BLE communication based on network conditions. The mobile interface displays various environmental parameters, such as ambient temperature, humidity, and light sensor data, collected in real time. Additionally, the figure depicts the device being programmed and tested, emphasizing its role in adaptive communication switching strategies to mitigate jamming interference in wireless sensor networks. This algorithm, shown in Algorithm 1, proposes an advanced mitigation approach for random jamming in sensor networks by dynamically adapting network configurations and resource utilization to maintain stability and efficiency. The methodology integrates a multi-step process: initializing network clusters and routing tables, detecting jamming zones using resilience and retransmission metrics, and applying localized mitigation strategies (Algorithm 2). When a jamming zone is detected, affected nodes are reclassified based on energy thresholds, with low-energy nodes transitioning to a low-power mode, while nodes within the jamming zone are reassigned alternative routes derived from a prioritized routing table. If no routes are viable, nodes are isolated to preserve overall network functionality (Algorithm 3). Additionally, cluster reorganization redistributes nodes from impacted regions to neighboring clusters to balance load and connectivity. Transmission power adjustments are made based on node proximity to the coordinator node to optimize communication during mitigation (Algorithm 4). The iterative algorithm continuously monitors key metrics, recalibrating the network as necessary to adapt to evolving jamming conditions (Algorithm 5). The jamming detection mechanism in the proposed algorithm relies on a multi-metric evaluation framework that continuously monitors key network performance indicators, including packet retransmission rates, received signal strength indicator (RSSI) fluctuations, and abnormal energy consumption patterns. The decision criteria for identifying a jamming event are based on predefined adaptive thresholds, which were empirically derived from real-world experiments in heterogeneous LoRa-BLE networks. The algorithm operates as follows. (1) Each sensor node periodically collects RSSI and packet delivery success rate (PDSR) values, comparing them against their historical baselines. (2) If the RSSI drops below a dynamically adjusted threshold, while the packet loss rate exceeds a predefined retransmission threshold, the system interprets this as a potential interference anomaly.