Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

A Novel Search Engine for Internet of Everything Based on Dynamic Prediction

In recent years, with the rapid development of sensing technology and deployment of various Internet of Everything devices, it becomes a crucial and practical challenge to enable real-time search queries for objects, data, and services in the Internet of Everything. Moreover, such efficient query processing techniques can provide strong facilitate the research on Internet of Everything security issues. By looking into the unique characteristics in the IoE application environment, such as high heterogeneity, high dynamics, and distributed, we develop a novel search engine model, and build a dynamic prediction model of the IoE sensor time series to meet the real-time requirements for the Internet of Everything search environment. We validated the accuracy and effectiveness of the dynamic prediction model using a public sensor dataset from Intel Lab.

Guest editorial:Special issue on data intelligence for IoT

The Internet of Things(IoT)is increasingly deployed to enable smart applications.Various types of data are accumulated continuously during the running of these applications.Managing and using these IoT data to derive intelligence for making the smart world reality is attracting both industrial and academic efforts.Though quite some progress has been made in this area,there is still a need for high data intelligence in IoT applications.

Mechanical properties of a novel, lightweight structure inspired by beetle's elytra

A new kind of bio-inspired, lightweight structure was designed and built from carbon fibre prepreg based on the cross-sectional microstructure of a beetle's elytra. The compression strength and failure process of the resulting structure was analysed using the finite element method; while at the same time, a quasi-static compression experiment was performed using an electronic universal testing machine to verify the effectiveness and accuracy of this finite element method. This bio-inspired structure was compared against a conventional honeycomb structure using FEM, revealing that for a given porosity and load parallel to the axis of the core tubes the respective compressive and specific compressive strengths of the bioinspired structure are much higher at 84.3 MPa and194.7 MPa/(g cm-3); thus demonstrating that this bioinspired structure has superior compressive capability.