This research explores the challenges and opportunities associated with the transmission of Big Data over Mobile Ad-Hoc Networks (MANETs). Big Data applications demand efficient and scalable communication solutions, and MANETs offer a decentralized and dynamic infrastructure that can potentially support such requirements. However, the inherent characteristics of MANETs, including limited bandwidth, variable connectivity, and resource constraints, present unique obstacles for transmitting large volumes of data.

The study investigates various strategies and protocols tailored for the transmission of Big Data in MANETs. This includes the exploration of data partitioning, compression techniques, and adaptive routing protocols to optimize data transfer efficiency. Additionally, the research delves into the impact of node mobility, network topology changes, and energy consumption on the performance of Big Data transmission in MANETs.

Simulation experiments are conducted using NS-3 (Network Simulator 3) to assess the proposed strategies under diverse MANET scenarios. Key performance metrics, such as throughput, latency, and energy consumption, are analyzed to evaluate the effectiveness of the different approaches in managing the unique challenges posed by Big Data transmission.

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