Smart Data Management in IoT: Leveraging Wireless Sensor Networks for Efficient Information Processing

The exponential growth of the Internet of Things (IoT) has resulted in an unprecedented surge in data, requiring the development of innovative methods to effectively handle and analyze information. This study investigates the (1)management of data in a smart manner within the IoT framework. It specifically examines the utilization of Wireless Sensor Networks (WSN) to accomplish effective information processing. The study examines two main elements: the utilization of Run-Length Encoding (RLE) for data compression and the incorporation of energy-aware extensions into the Ad Hoc On-Demand Distance Vector (AODV-EA) routing protocol. The purpose of employing Run-Length Encoding (RLE) is to enhance the efficiency of transmitting and storing data by effectively representing recurring sequences in sensor data. This compression technique is especially applicable for resource-constrained WSN where the conservation of bandwidth and energy is of utmost importance. The study investigates how communication protocols can be improved by integrating energy-conscious extensions into AODV. The objective of this approach is to enhance the energy efficiency of communication in WSN by taking into account the energy levels of each node in real-time when establishing routes. The NS-3 simulation framework is used to assess the proposed methodologies. NS-3 offers a flexible and expandable framework for simulating communication protocols and network scenarios. The study evaluates the performance of the integrated system by using simulation and analyzing important metrics such as accuracy, precision, Latency, data compression and energy efficiency. The research findings provide valuable insights into the field of Smart Data Management in IoT, demonstrating how the combination of data compression and energy-aware routing protocols can improve the efficiency of Wireless Sensor Networks for information processing.