An Underwater Wireless Sensor Network (UWSN) is a collection of sensors which are deployed at different depths. The 3D deployment model enables sensing data from surrounding area and relay information to sink nodes at the surface level. This paper, proposes an energy efficient, high reachability 3D broadcast protocol for UWSN (EE-BUWSN). EE-BUWSN protocol takes into accounts the acoustic channel characteristics and the 3D nature of undersea area to reduce packet collisions and hop count in order to achieve high delivery ratio, with less energy consumption. Simulation results show that EE-BUWSN protocol outperforms Vector-Based Forwarding broadcast protocol (VBFB) and probability-based protocols in terms of reachability, latency, and energy consumption.
Software-defined networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which manages the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-nearest neighbor approach (kNN); for the second level, we use the extreme learning machine (ELM); and for the remaining levels, we utilize the hierarchical extreme learning machine (HELM) approach. In comparison with conventional supervised machine learning algorithms and other state-of-the-art methodologies based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves a good accuracy (84.29%), with an ability to detect new attacks that reaches 77.18%. Therefore, our approach presents an efficient approach for intrusion detection in SDNs.
With the convergence of the era of global news and the era of big data, the daily amount of news sent to the world is exploding. Users also face the problem of information overloads when they get massive information, which leads to how cloud servers push personalized data to users among massive data have become the focus of news companies. In order to obtain the push accuracy, the traditional recommendation system often makes deep mining of users’ privacy data, which makes users’ privacy cannot be guaranteed. In order to solve the above problems, this paper proposes a collaborative filtering algorithm recommendation system based on federated learning on end-edge-cloud. The exposure of data privacy was further prevented by adding Laplace noise to the training model through differential privacy technology. Finally, the training model and recommendation information is stored to the blockchain network to provide permanent storage, evidence chain and real-time traceability services.On the premise of protecting data privacy, this system provides cloud server with solutions to alleviate computing pressure, bandwidth pressure and improve news push accuracy through end-edge-cloud distributed learning.
