Due to the fast development in communication technology and the emerging usage of Internet of Things (IoT) devices that produce a huge amount of data, the fifth generation (5G) mobile network is introduced to support this development. This mobile network can provide many advanced communication features in cellular phones. But unfortunately, this technology faces many challenges. One of its defective challenges is the management of a massive number of devices running different services, so Software Defined Network (SDN) is proposed as a key technology to overcome this drawback. SDN architecture provides higher flexibility, scalability, cost-effectiveness, and energy efficiency in 5G mobile networks. There are usually different architectures for the SDN control plane. We study some of these architectures, and we conciliate the usage of Logically Centralized-Physically Distributed (LC-PD) controller management architecture in 5G networks. This architecture enables providing higher throughput, and lower latency compared to other control plane architectures. In this paper, we focus on the demonstration that the LC-PD control plane architecture improves communication efficiency and the Quality of Services (QoS) of running internet services in the 5G mobile network. We use the Mininet-WIFI emulator in our simulation tests. Our conducted simulations show that employing the LC-PD control plane architecture in 5G networks enhances the QoS of Internet services compared to other SDN implementations.

Wireless Sensor Networks (WSNs) have been adopted in various environmental pollution monitoring applications. As an important environmental field, water quality monitoring is a vital process to ensure the sustainable, important feeding of and as a life-maintaining source for many living creatures. To conduct this process efficiently, the integration of lightweight machine learning technologies can extend its efficacy and accuracy. WSNs often suffer from energy-limited devices and resource-affected operations, thus constraining WSNs’ lifetime and capability. Energy-efficient clustering protocols have been introduced to tackle this challenge. The low-energy adaptive clustering hierarchy (LEACH) protocol is widely used due to its simplicity and ability to manage large datasets and prolong network lifetime. In this paper, we investigate and present a modified LEACH-based clustering algorithm in conjunction with a K-means data clustering approach to enable efficient decision making based on water-quality-monitoring-related operations. This study is operated based on the experimental measurements of lanthanide oxide nanoparticles, selected as cerium oxide nanoparticles (ceria NPs), as an active sensing host for the optical detection of hydrogen peroxide pollutants via a fluorescence quenching mechanism. A mathematical model is proposed for the K-means LEACH-based clustering algorithm for WSNs to analyze the quality monitoring process in water, where various levels of pollutants exist. The simulation results show the efficacy of our modified K-means-based hierarchical data clustering and routing in prolonging network lifetime when operated in static and dynamic contexts.