# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Physical Layer
The authors presented a jamming-resilient channel-hopping mechanism to meet the requirements of WirelessHART. In the proposed approach, multi-level randomness was investigated to reduce repetitive channel hopping patterns and improve resistance to jamming attacks. Recently, [PERSON] et al. [32] presented a simple jamming detection algorithm that is based on an investigation of performance metrics. The authors aimed at detecting areas of affected nodes with minimal energy expenditure through a predictive model of a jammer node attacking a network. ML has been introduced to detect jamming. [PERSON] et al. [33] first proposed the stochastic game framework for the anti-jamming problem with multiple users. Based on this model, a joint multi-agent anti-jamming algorithm (JMAA) was presented to obtain the optimal anti-jamming strategy by multi-agent reinforcement learning to select a channel. The proposed scheme can prevent external jamming attacks and avoid internal mutual interference among sensor nodes. The authors mentioned that JMAA does not cause additional overhead without estimating the jamming patterns or any specific parameter. Simulations demonstrated that the JMAA outperformed the frequency-hopping method. In addition to an anti-jamming algorithm with reinforcement learning, a novel approach using a neural network was proposed in [34]. The authors incorporated ZigBee technology based on multiple inputs and multiple outputs in which the decoding signal in the presence of a constant jamming attack is achieved. The optimized neural network can mitigate the unknown inferences for learning. Using this ML algorithm, a receiver can decode its data packet without the jamming attack. The enhanced ZigBee module consists of synchronization and learning-based jamming mitigation modules that are installed on a device by two antennas. Under this architecture, a novel baseband signal processing pipeline module was designed to mitigate the jamming signal and recover the ZigBee signal. For data training, a simple neural network by each individual packet was assumed. The preamble in the ZigBee frame was trained. [PERSON] et al. [35] addressed countermeasures against the frequency-hopping spread spectrum in WSNs by combining deep reinforcement learning (DRL) and a meta-trust region policy optimization (TRPO)-based method. The DRL is for the optimization of the jamming resource allocation problem, whereas the TRPO is used for the improvement of policy networks. Using these two schemes, jamming frequencies and bandwidths for multiple frequency-hopping sets were solved through the Markov decision process and the policy and value network were maximized with accumulated jamming reward within the trust region. In addition to these models, a novel meta-DRL-based method was proposed to address the jamming resource allocation problem for emerging new tasks. The DRL model was composed of two optimization steps, namely meta-training and fine-tuning. We also reviewed recent anti-jamming methods. Research can be classified into two categories based on target purposes, that is, detection and countermeasures for jamming and identifying jammers. Moreover, various ML algorithms have been proposed and used for the two purposes. Table 5 depicts a comparison of recent work in the aspects of advantages and disadvantages consequently. In addition to Table 5, [PERSON] et al. [36] provided performance metrics for the impact on energy consumption in industrial WSNs environments to detect jamming in real-time. Two simulations were accomplished to demonstrate that the types of jamming made a distinct impact according to application requirements. In summary, the anti-jamming method is likely to employ a data-driven approach, such as ML, if jamming nodes exist. However, since these approaches incur high complexity for computation and take a long time to train, these issues should be a concern.