## 2 Related Work  
A limited selection of works exists tackling the problem of power draw characteristics in DoS attacks against embedded WSN devices. Most studies focus on aspects like battery consumption of devices within normal operational environments [31; 32]. Within this, there is an even smaller number of studies that focus on physical power analysis of attacked devices [33]. The work presented by [PERSON] et al. [33] focuses on the detection of compromised devices and the study presents a successful algorithm which helps discover compromised devices based on projected and actual power consumption. The methodology revolves around the utilisation of an off-the-shelf device: a high end power analyser called Monsoon which can analyse power draw at an incredibly high rate of 5 GHz. The team focused on the use of this technology to analyse the power draw of each device to create large baseline data sets that provide a reference point to baseline operation. The baseline operation is calculated by analysing a predefined amount of time during which normal communication/computation happens. This method of analysis is closely replicated within our work, with an average power draw calculated over a set period being used in the study. This likely provides accurate results as it works with an average power draw, not a peak.  
A similar concept of calculating a baseline power draw is also used by [PERSON] et al. [32]. They estimated the average power use of IoT devices like the Phillips Hue, to accurately predict performance of many IoT technologies. Power consumption was based on power usage per data packet sent and was calculated from the power usage figure claimed by the manufacturer, divided by the number of packets sent during the active time. This was used to create an average power consumption per device that could be modelled and scaled easily. However, there is some doubt in the accuracy within this model compared to the model employed by [PERSON] et al. [33]. For example, devices are not likely to consume the same amount of energy per packet, as every packet can vary in size or content. A paper tackling similar power draw and IoT performance metrics like that of [PERSON] et al. [32] is [PERSON] et al. [31]. Here, the methodology is similar to that of [PERSON] et al. [33], sharing the set time frame allocation framework to calculate power draw. There will likely be concrete reasons to use a particular methodology, especially because these three papers are similar. Our paper follows within the footsteps of previous work [31; 33] as the area of focus appears to be similar.  
A large issue preceding the direct comparison from the power draw of an idling device to a device that is being attacked is the unrealistic usage scenario that that would create. Devices within industries are likely to always have some sort of processing duties (in between standby/low power mode cycling). A few realistic use case scenarios could be based on initially connecting to the gateway and initialising the routing for the network, and then a series of requests can be cycled to provide an update to the user. Multiple realistic usage scenarios have been outlined within research ranging from air quality monitoring systems [34] to critical health devices [35]. With multiple ranging baselines throughout the literature, average use is likely all dependent on the frequency of packet transfer of the devices. However, some sources claim 10 years at a data transmission rate of 128 bytes per 10 s [36] and others claim 306 h [37] on one charge with frequent data transfer (as it was a vital sign monitoring system). Likely, an in between of this would be the best choice for an appropriate baseline power draw for the study, increasing the comparability of the study's results to real-world examples where a default operation time has already been established. Basing the impact of any possible attacks on the decrease of operational time would likely result in an unbiased proposal for ranking the effectiveness of each propositioned attack.