# Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks
## 1 Introduction
Reactive jamming detects and interferes with active transmissions, causing packet loss and increasing retransmission rates, which significantly depletes battery life in low-power networks like BLE and LoRa. Random jamming alternates between active interference and idle periods, making it harder to detect while still degrading network performance [9]. Deceptive jamming injects malicious packets to create confusion in network protocols, potentially leading to resource exhaustion and misrouting in multi-protocol architectures. Heterogeneous networks, despite their advantages, remain susceptible to these attacks, particularly at the intersection of different communication protocols, where seamless switching and adaptive mitigation become challenging [10]. Effective countermeasures, such as frequency hopping, spread spectrum techniques, and AI-driven anomaly detection, are essential to ensure resilient and secure HWSN deployments, particularly in mission-critical applications such as industrial control systems, autonomous transportation, and emergency response networks. Jamming attacks represent a critical and quantifiable threat to the reliability of WSNs. Recent studies have shown that even low-power jamming can cause a packet loss rate exceeding 80% in LoRa-based networks when attackers operate at power levels as low as 14 dBm [11]. Similarly, in BLE networks, experimental results indicate that reactive jamming reduces successful packet delivery rates to below 10%, significantly disrupting device synchronization and data transmission in IoT applications [4]. In large-scale deployments, such as smart city infrastructures, the economic impact of jamming-related disruptions can be substantial, with estimations suggesting that network outages caused by interference could lead to financial losses of over USD 1 billion annually in critical industrial and urban monitoring systems [12]. These findings underscore the necessity for robust mitigation techniques that can adapt to diverse jamming conditions across heterogeneous network environments. The vulnerability of WSNs to jamming attacks is further exacerbated by the energy constraints of IoT nodes, making them susceptible to rapid power depletion when subjected to prolonged interference. Experimental analyses indicate that under jamming conditions, retransmission rates in multi-hop networks increase by up to 300%, causing a threefold surge in energy consumption and significantly reducing network lifespan [13]. Moreover, adversarial machine learning techniques have been employed to optimize jamming strategies, allowing attackers to maximize disruption while minimizing their own energy expenditure [14]. Given that many mission-critical applications, such as environmental monitoring and healthcare telemetry, rely on continuous and energy-efficient communication, the development of intelligent countermeasures that balance resilience and energy efficiency is paramount. This study addresses these challenges by proposing an adaptive, energy-aware mitigation framework tailored for heterogeneous networks, ensuring sustained connectivity in dynamic and interference-prone environments. Energy analysis plays a fundamental role in the design and optimization of HWSNs, particularly when exposed to jamming attacks, which can severely impact network sustainability and communication reliability [15]. In multi-protocol environments where devices dynamically switch between technologies such as BLE, LoRa, Wi-Fi, and NB-IoT, energy efficiency becomes a critical parameter influencing both network longevity and real-time adaptability. Jamming attacks exacerbate energy consumption by forcing nodes to engage in frequent retransmissions, power-intensive error correction, and defensive countermeasures such as frequency hopping or transmission power adjustments. Additionally, energy-draining attacks like reactive and deceptive jamming can disproportionately affect low-power communication protocols, leading to premature battery depletion and network fragmentation [15].