The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

The proliferation of Internet of Things (IoT) devices in terrestrial environments necessitates efficient data transmission and management protocols to optimize network performance. This research paper presents a comprehensive study on the implementation of IoT networks in a terrestrial setting, focusing on the integration of LZW (Lempel-Ziv-Welch) compression with three prominent IoT protocols: CoAP (Constrained Application Protocol), M2M (machine-to-machine), and MQTT (Message Queuing Telemetry Transport). We did a thorough test of these protocols using the NetSim simulator. We looked at how well they worked in terms of throughput, en-to-end delay, and routing overhead, all of which are important for loT network efficiency. The point of our study is to find out what the pros and cons of these protocols are when they are combined with LZW compression in a terrestrial loT setting. The results of our analysis reveal significant variations in the performance of CoAP, M2M, and MQTT when subjected to data compression. Throughput efficiency, which directly impacts data transmission rates, is scrutinized alongside end-to-end delay, a critical factor in ensuring timely data delivery. Additionally, we explore the implications of protocol choice on routing overhead, a crucial metric for network resource utilization. This research contributes to the ongoing efforts to optimize IoT networks in terrestrial settings, offering valuable guidance to network architects and developers seeking to strike the right balance between protocol selection and data compression for improved IoT performance.

The Internet of Things (IoT) has experienced phenomenal growth, opening up a wide range of applications in many settings. Due to the properties of sound propagation, IoT networks operating in acoustic environments in particular present special difficulties. Data compression techniques can be used to minimize overhead and maximize resource utilization in these networks to increase performance. The performance of IoT networks in acoustic environments is examined in this study, with a focus on routing overhead, throughput, and typical end-to-end delay. Lempel-Ziv-Welch (LZW) data compression is used to reduce data size and boost communication effectiveness. Three well-known protocols—MQTT, CoAP, and Machine-to-Machine (M2M)—are assessed in relation to acoustic Internet of Things networks. To mimic different acoustic conditions and collect performance metrics, a thorough experimental setup is used. Different network topologies, data speeds, and compression settings are used in the studies to determine how they affect the performance metrics. According to the analysis's findings, all three protocols' routing overhead is greatly decreased by the LZW data compression approach, which enhances network scalability and lowers energy usage. Additionally, the compressed data size has a positive impact on network throughput, allowing for effective data transmission in acoustic contexts with limited resources. Additionally, using LZW compression is seen to minimize the average end-to-end delay, improving real-time communication applications. This study advances knowledge of IoT networks operating in acoustic environments and the effects of data reduction methods on their functionality. The results offer useful information for network engineers and system designers to optimize the performance of IoT networks in similar situations. Additionally, a comparison of the MQTT, CoAP, and M2M protocols' suitability for acoustic IoT deployments is provided, assisting in the choice of protocol for particular application needs.