# Build-Launch-Consolidate Framework and Toolkit for Impact Analysis on Wireless Sensor Networks
###### Abstract  
The Internet of Things (IoT) and wireless sensor networks (WSNs) utilize their connectivity to enable solutions supporting a spectrum of industries in different and volatile environments. To effectively enhance the security and quality of the service of networks, empirical research should consider a variety of factors and be reproducible. This will not only ensure scalability but also enable the verification of conclusions, leading to more reliable solutions. Cooja offers limited performance analysis capabilities of simulations, which are often extracted and calculated manually. In this paper, we introduce the Build-Launch-Consolidate (BLC) framework and a toolkit that enable researchers to conduct structured and conclusive experiments considering different factors and metrics, experiment design, and results analysis. Furthermore, the toolkit analyzes diverse network metrics across various scenarios. As a proof of concept, this paper studies the flooding attacks on the IoT and illustrates their impact on the network, utilizing the BLC framework and toolkit. RPL; framework; tool; flooding; cybersecurity; Denial of Service; wireless sensor networks; Internet of Things +
Footnote †: journal: Sensor and Actuator Networks  
studies [8; 9; 10; 11; 12] have considered the impact of certain attacks (including flooding) on the IoT as well as the overheads associated with deploying mitigation for these attacks. To maximize the potential of WSNs and the IoT and ensure their reliability and performance, it is vital to consider carefully and tailor network topologies and physical specifications to the unique requirements of each scenario. Empirical studies require the ability to be replicated to verify findings and confirm conclusions. In the WSN context, replicating experiments is necessary to reflect the impact of any change in the network configuration, report different behaviors, and, consequentially, draw different conclusions [13]. Therefore, manual placement and configuration for a relatively large number of sensor nodes might be tedious, time-consuming, and lacking in accuracy [14]. For instance, Cooja [15], the simulation tool provided by Contiki [16], offers limited choices of automatic placement (either linear or ellipse) in addition to manual and random placement. In addition, Cooja does not provide network performance metrics for the entire network, which need to be extracted and calculated. Therefore, structured frameworks are essential so that research studies in a specific field can converge in terms of findings and by building on shared foundations. To fully understand the impact of an attack, it is crucial to analyze the network under various attacker positions, network topologies, and software/hardware implementations. Random configurations and placements may be utilized in experiments, but they have limited generalization, are difficult to replicate, and may be biased. If not controlled in a research experiment, these elements pose a potential confounding factor that hinders the conclusiveness and generalization of the findings. The lack of a consistent and structured approach, and automated tools that provide replication and reporting of WSN experiments motivated us to develop the Build-Launch-Consolidate (BLC) framework and toolkits. BLC enables researchers to conduct replicable experiments under predefined conditions, leveraging systems and methods that ensure replicable research and robust findings by addressing the following:  
* The framework provides a systematic approach to conducting experiments by enabling the user to run simulations on Cooja using a replicable setup of node types and their precise position. * The framework can be used to establish a more accurate understanding of different attacks on WSNs and their impact by considering different attack scenarios, network topologies, and attacker placements.