Designing Self-Organized Wireless Sensor Networks Using Deep Neural Networks
Sankara Rao Allada, Vasavi Ravuri, Reena Sahu, Parbhat Gupta, Deepa Bhoi, Rovin Tiwari · 2024
It is well understood that WSNs require powerful and robust network architectures and since there are many data-centric applications today WSNs are a significant factor. This abstract explains a new approach to address the problem of hierarchical design in autonomous WSNs using Deep Neural Networks (DNNs). In contrast, traditional scientific WSNs that crucially depend on optimal configuration or efficient centralized control might suffer from reacting to new conditions flexibly. In contrast to self-organized networks which offer each sensor node the option to decide based on the local data which makes it both more flexible and efficient. To do this, in the abstract of our work, we present a revolutionary concept of integrating DNNs with the network architecture without interfering with the current structures. This has intelligent data aggregation, intelligent resources, detect anomalies on the architecture, and build complex topology dynamically. The mathematical modelling and everything conducted pursuant thereto prove that the strategy based on the DNN would be more effective as compared with the traditional methodologies. For example, to prove that the proposed architecture is scalable and flexible, the DNN-driven network has achieved 93.5% higher in the topology construction effectiveness than the standard network. Further, the lifespan of the network is enhanced by a thirty two and a half percent, or twenty two and a half days, has been made getting closer to sustainable. The introduction of DNNs acts as a revolution by endowing the self-organized WSNs with flexibility, efficiency and accuracy. The gigantic leapt that has been deemed in the aspect of network designing and performing is signified in this abstract based on efforts being made for attempting to convert the wireless sensor networks into smart evolving entity for living in real operational world.