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Comparative analysis of RPL protocol objective functions impact on energy consumption in low-power and lossy networks

This bachelor thesis explores the impact of different RPL protocol objective functions on energy consumption in low-power and lossy networks. Given the critical role of low-power and lossy networks in applications like environmental monitoring, industrial automation, and smart cities, optimising energy consumption is paramount. Using the Contiki-NG operating system and the Cooja network simulator, this study conducts a comparative analysis of two primary objective functions: Objective Function Zero and Minimum Rank with Hysteresis Objective Function. Simulations are performed under 18 network scenarios including different node densities (10, 30, 50), node positioning, and receive ratios. The findings indicate that Objective Function Zero generally consumes less energy, particularly in high-density networks and environments with high packet delivery ratios. Conversely, Minimum Rank with Hysteresis Objective Function may be more suitable for dynamic and unpredictable environments despite its higher energy consumption. This research offers practical recommendations for selecting objective functions to optimise the energy consumption of constrained networks, contributing to the development of more sustainable IoT solutions.

Author: Deniss Bogdans

Supervisor: Jeļena Baranova

Degree: Bachelor

Year: 2024

Work Language: English

Research on effective finding of network anomalies in Intrusion Detection Systems using ML/DL methods

In this paper, the process of testing datasets and ML/DL methods using the Weka platform in order to obtain results of their effectiveness and how high the accuracy of detecting network anomalies they provide is described. During the experiment, many ML/DL algorithms and the most common datasets (KDD99, NSL-KDD, UNSW-NB15) were analyzed, as a result of which it was revealed that the best result of detection accuracy and time required on the experiment was produced by the NSL-KDD dataset.

Author: Dmitrijs Bulaks

Supervisor: Jeļena Baranova

Degree: Bachelor

Year: 2024

Work Language: English

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