Algorithms and scenarios for efficient and secure big data delivery, management, and analysis over the Internet of Things

Abstract

All recent technology findings could be involved and combined to strengthen and support the “Internet of Things” (IoT) sector. The novel technology of “Multi-Access Edge Computing” or “Mobile Edge Computing” (MEC) rises rapidly in the industry as well as the “Digital Twins”. MEC is the middle-layer between mobile devices and cloud, which offers scalability, reliability, security, efficient control, and storage of resources. In addition, digital twins form a communication model that will enhance the whole system by improving the latency, the overhead, and the energy consumption. The overall paper is focused on the biggest challenges that researchers in the field of IoT have to overcome in order to gain a more efficient communication environment in terms of technology integration, efficient energy, data delivery, storage spaces, security, and real-time control and analysis. First, IoT, surveillance, haptics, and other devices have been configured, installed, and programmed with suitable algorithms. Then, the databases and the broker devices have been also installed and programmed to work efficiently with the IoT devices. Moreover, a framework has been proposed in order to reduce the traffic and the latency by merging the processing of the data generated by the IoT devices at the edge of the network. Machine learning algorithms have been also tested and compared in order to make the best choice for each case. The evaluation of critical parts of the proposed IoT systems have been performed both with emulation/simulation software and in real environments with real devices. The results have shown that data delivery and offloading have been done more efficiently, the energy consumption and the processing have been improved, and the security, the complexity, the control, and the reliability have been enhanced.