Internet of Things (IoT) is the inter-networking paradigm based on many processes such as identifying, sensing, networking and computation. An IoT technology stack provides seamless connectivity between various physical and virtual objects. The increasing number of IoT applications leads to the issue of transmitting, storing, and processing a large amount of data. Therefore, it is necessary to enable a system capable to handle the growing traffic requirements with the required level of QoS (Quality of Service). IoT devices become more complex due to the various components such as sensors and network interfaces. The IoT environment is often demanding for mobile power source, QoS, mobility, reliability, security, and other requirements. Therefore, new IoT technologies are required to overcome some of these issues. In recent years new wireless communication technologies are being developed to support the development of new IoT applications. This paper provides an overview of some of the most widely used wireless communication technologies used for IoT applications.

Wireless sensor networks (WSNs) are characterized by heterogeneous traffic types (audio, video, data) and diverse application traffic requirements. This paper introduces three traffic classes following the defined model of heterogeneous traffic differentiation in WSNs. The requirements for each class regarding sensitivity to QoS (Quality of Service) parameters, such as loss, delay, and jitter, are described. These classes encompass real-time and delay-tolerant traffic. Given that QoS evaluation is a multi-criteria decision-making problem, we employed the AHP (Analytical Hierarchy Process) method for multi-criteria optimization. As a result of this approach, we derived weight values for different traffic classes based on key QoS factors and requirements. These weights are assigned to individual traffic classes to determine transmission priority. This study provides a thorough comparative analysis of the proposed model against existing methods, demonstrating its superior performance across various traffic scenarios and its implications for future WSN applications. The results highlight the model’s adaptability and robustness in optimizing network resources under varying conditions, offering insights into practical deployments in real-world scenarios. Additionally, the paper includes an analysis of energy consumption, underscoring the trade-offs between QoS performance and energy efficiency. This study presents the development of a differentiated services model for heterogeneous traffic in wireless sensor networks, considering the appropriate QoS framework supported by experimental analyses.