Designing an Adaptive Intelligent Architecture for Efficient Sensor Integration and Monitoring in Telecommunications Systems

Abstract

The paper puts forward a new broad intelligent architecture, representing desired solutions to the common problems of sensor integration and monitoring in telecommunication networks. Because the sensor layouts are different for each location and there is a diverse range of running requirements, traditional approaches struggle. We propose scalable deep learning and AI techniques that are efficiently implemented in an open-source framework to overcome the plaque identification problem in our recent work. In this paper, we describe a data-centric middleware architecture that supports the necessary functionalities for adaptive and dynamic management of sensor networks, even to such an extent as to react intelligently upon changes in types, configurations, failures, etc. This kind of flexibility thus enhances data collection and monitoring in order to provide reliable real-time insights on network performance. See the experimental results revealing that the framework improves system responsiveness and operational efficiency. In this context, a section on a section on designing adaptive intelligent architecture for telecommunication systems is presented. This advanced framework surprisingly caters to one such need, i.e., sensor fusion and closing-the-loop with a bitwise-best checkout driven by AI and deep learning techniques. Our attitude is very adaptable, allowing for a variety of sensor configurations and performance requirements. This will improve real-time sensing and make sensor networks much easier to administer, which is expected to increase the overall performance of these networks.