# Build-Launch-Consolidate Framework and Toolkit for Impact Analysis on Wireless Sensor Networks
## 2 Related Work
### Performance Analysis Considering Attack Scenarios, Placements, and Network Layouts  
The literature includes a wealth of studies on the effect of different treatments and attacks on WSNs. Unfortunately, few of these considered a structured approach and limited potential confounding factors to provide more comprehensive insights. For example, the study presented in [11] drew conclusions on the differences between blackhole and grayhole attacks based on a network of random distribution, without considering the effect of other factors. [PERSON] and [PERSON] [18] conducted an experiment to study a blackhole attack in various network sizes and attacker placements. They found that the impact of the attack on packet delivery was inversely proportional to the number of nodes and directly proportional to the number of attackers. Several studies have investigated the impact of various attacks on different topologies. For instance, Ref. [9] is an evaluation study of rank attacks on a grid topology network. The study considered dispersed adversary nodes at random over the grid in each multiple-attacker scenario. The analysis considers how the assault affected various network nodes. The findings show that an attack may have a significant negative effect on the network's performance, particularly if it is carried out in an area with a high forwarding load or involves several attackers [9]. Version attacks have been considered by multiple studies to investigate the effects of the topology on the attack's impact. Other studies [19; 20] investigated version attack detection in the context of cluster-based, random, and grid topologies. Their findings show that the scalability of their detection solution was reduced in randomly generated topologies compared to the grid topology. However, more realistic cluster-based topologies exhibit performance comparable to grid topologies. Moreover, the authors of [21] employed grid and random node placement techniques in their work to study how the performance of the mitigation changes with the topological properties. Under version attacks, the study noted that the amount of control messages approximately doubled for a grid corner network, and it tripled for a random network [21]. The rationale was that a grid topology had more consistent node densities than random placement, which resulted in topologies with varying node densities. Because of it having longer links than others, grid topology showed the highest average power consumption values for the attack-free condition [21]. [PERSON] et al. [22] conducted a study to investigate the impact of sinkhole attacks on a network consisting of 10 nodes in a tree layout with one attacker. The study was conducted in three scenarios, and it noted that the DODAG Information Object (DIO) messages within the network increased significantly as the attacker was placed further in the network and closer to the sink node. Although the Quality of Service metrics of the network were not measured in multiple studies, the overall findings suggest that the network became less stable, which is evident in the frequent DODAG formations. This study provides valuable insights into the potential vulnerabilities of networks to sinkhole attacks and highlights the need for further research to develop effective countermeasures to mitigate such attacks. [PERSON] and [PERSON] [23] studied the implications of a decreased rank attack on a simulated network in a grid pattern. Although the paper assessed varying attacker placements with regard to the sink, it did not explore the differences in the aftermath. Nonetheless, the findings of the study show that the attack had more impact when the attacker was placed in an active network area [23]. Alternatively, in [24], the study tackled a node reset attack, which includes both local repair and version attacks under a binary tree and mesh topologies. In the binary tree topology, each node has only one way to the sink. As a result, removing each node causes the graph to split.