Writing a Statistical Hypothesis

PROACTIVE DEPLOYMENT OF UAVs FOR OPTIMIZED QUALITY-OF-EXPERIENCE

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PROACTIVE DEPLOYMENT OF UAVs FOR OPTIMIZED QUALITY-OF-EXPERIENCE 3

In this assignment, you will write a null and alternate hypothesis for the research problem statement that you wrote in 

Module 5.

 In your document (400 words minimum), present the problem statement that you wrote, the null and alternate hypothesis for that research problem, and a discussion on how probability (p-values) is used in statistical analysis.

Over the last decade, the unmanned aerial vehicles (UAVs) have become a significant element in military operations. Additionally, today UAVs are being used for different purposes, including public, scientific, and commercial purposes. Depending on the needs of different users, different types of UAVs are under development. The research area in the UAV domain is, therefore, evolving because of the increase in different types and the number of UAVs. The evolution in UAVs is being shaped by the varying and increasing expectancy by the users of the UAVs (Chen et al., 2017). It is important to focus on the application of the UAVs since it is a valuable source for both research and education purposes that could help in solving different aspects of surveillance, inspection, and other data acquisition applications. The technology used in UAVs is light-weight and cost-effective because of the miniaturization sensors used for capturing images and reliability that can be used in different fields of operations. A novel algorithm that is based on a conceptor-based echo state network should be proposed to solve these problems.

The problem to be addressed in this study is the issue of proactive deployment of the cache-enabled UAVs while optimizing the quality-of-experience (QoE) of the devices that are wireless in the cloud radio access network (CRAN). The network can influence human-centric information, including the locations of the users, job, and the form of devices that can be used in protecting the distribution of the content requested (Ma et al., 2017).

Research hypothesis

The null hypothesis (Ho) is that an algorithm can yield 33.3% using real pedestrian mobility. The alternative hypothesis (Ha) is that the patterns are significantly more reliable than a benchmark solution without UAVs.

Research questions

R1: What is the best approach that can be proposed in seeking to find the user-UAV associations in the optimal locations of the UAVs to be used within six months?

R2: How will the proactive deployment of the cache-enabled UAVs impact the pedestrians in the optical locations of the UAVs within a period of six months?

Testable hypothesis

H1: There is a significant relationship between an approach used in seeking to find the user-UAV associations and the optimal locations of the UAVs.

H2: There is a significant relationship between the proactive deployment of the cache-enabled UAVs and the pedestrians in the optical locations of the UAVs.

References

Chen, M., Mozaffari, M., Saad, W., Yin, C., Debbah, M., & Hong, C. S. (2017). Caching in the sky: Proactive deployment of cache-enabled unmanned aerial vehicles for optimized quality-of-experience. IEEE Journal on Selected Areas in Communications, 35(5), 1046-1061

Ma, L., Fu, T., Blaschke, T., Li, M., Tiede, D., Zhou, Z., … & Chen, D. (2017). Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS International Journal of Geo-Information, 6(2), 51.