# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 3 Energy Drain Attacks in WSNs
### Literature Review for Energy Drain Attacks  
In 2019, [PERSON] et al. [23] presented an overview of security issues for energy depletion in low-power wireless networks, including WSNs and the IoT. They defined these attacks as energy depletion attacks (EDAs) and analyzed the effects of these attacks. Consequently, countermeasures for EDAs were described and compared. The authors categorized existing methods according to the attack target under layered architecture, the purpose of attacks, and the target network type. Figure 5 illustrates the categorization of EDA methods. Furthermore, the advantages and disadvantages of each method were compared. Open issues and analysis for further research trends were provided, and conclusions were drawn that existing solutions cannot satisfy all requirements. This result implies that specific requirements should be prioritized and defined depending on the application requirements. In this survey, the authors discuss the representative attacks in each layer. First, in the physical layer, jamming and communications in hidden channels were given. Garage data verification and duty-cycle manipulation were used for the medium access control layer. Loop and stretch forwarding attacks are described in the network layer. Finally, duty-cycle manipulation and malformed applications can affect security. As for these attacks, defense  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Mode** & **Sensing** & **Processing** & **Communications** & **Stand by** & **Init, Actuation, Log** \\ \hline Percent & 6.0\% & 12.0\% & 51.0\% & 24.4\% & 21.6\% \\ \hline \end{tabular}
\end{table}
Table 4: Energy consumption in sensor node [18].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mode** & **Sensing** & **Processing** & **Reception** & **Transmission** & **Stand by** & **Idle** \\ \hline Percent & 4.4\% & 6.7\% & 26.7\% & 33.3\% & 24.4\% & 4.4\% \\ \hline \end{tabular}
\end{table}
Table 3: Energy consumption in the sensor node [17].  
or countermeasures in each layer were described in a layered architecture. Among mentioned methods, statistical techniques, intrusion detection, and end-to-end security can be applied to prevent attacks in all layers. Most schemes were simulated using NS2, NS3, and C++. By contrast, multiple channel hopping and enhancing security schemes were implemented on the real sensor device.  
Similar to [23], [PERSON] et al. addressed energy attacks by including diverse mobile devices in [24]. In this study, the authors addressed that energy attacks on mobile devices can be of the DoS type. In approaching a systematic study on smartphones, they extended it to cover hardware components, software resources, network communications, and malicious applications on smartphones. A notable contribution is to present malicious motivation and use cases for energy attacks on the smartphone as well as classifying the existing methods based on the goals, targets, control, locations of launch, elements of the attack, and process of the attack. The authors concluded that attacking network elements is a more efficient attack than software elements.