Abduladheem Fadhil Khudhur’s scientific contributions

The fast growth in Internet-of-Vehicles (IoV) applications is rendering energy efficiency management of vehicular networks a highly important challenge. Most of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous environments. Based on Large Energy-Aware Fog (LEAF) computing, this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network scenarios. The main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected devices. The proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous infrastructures. Among the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave, while different kinds of networking protocols-wired and wireless-can also be employed. The novelty of this work is threefold: for the first time, the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in mind. Unlike existing fog computing simulators, data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and applications. The results of evaluation show that LEAF can cover up to 98.75% of the distance, with devices ranging between 1 and 1000, showing significant energy-saving potential through A wide-area network (WAN) usage reduction. These findings indicate great promise for fog computing in the future-in particular, models like LEAF for planning energy-efficient IoV infrastructures.

In this research, an advanced Large Energy-Aware Fog (LEAF) computing based technique was introduced that allows for the modeling of large-scale vehicular network scenarios for executing thousands of streaming applications on a distributed, heterogeneous infrastructure. Compute nodes can be interconnected with different types of wired or wireless networking protocols, and edge devices can be mobile and join or leave the topology during the simulation. This level of realism permits research on energy-conserving fog computing architectures leading to more informed decisions in the planning of future infrastructure. Furthermore, the proposed model enables online decision making based on power usage, which can be used to implement energy-aware task placement strategies or routing policies. These algorithms can make direct use of LEAF’s ability to trace the power usage of infrastructure back to the responsible applications in order to identify and mitigate potential inefficiencies. Moreover, different kinds of energy-saving mechanisms can be integrated into simulations. What further distinguishes LEAF from existing fog computing simulators is the combination of analytical and numerical modeling approaches. Instead of modeling network traffic in detail, all data flows, and power models are represented by parameterizable, mathematical equations. This method leads to results that are easy to analyze and ensures scalability to hundreds or thousands of devices and applications with percentage of distance between 1 to 1000 was covered up to 98.75% using the LEAF technique. The research collects findings of various papers on the energy usage of different compute and networking equipment, including a detailed derivation of WAN connection parameters, to provide the reader with examples on how to model and parameterize LEAF experiments. Furthermore, the evaluation results indicate that fog computing may indeed be able to conserve energy in the future, mainly by reducing WAN usage.