In Industrial Internet of Things (IIoT), Peer-to-Peer (P2P) energy trading ubiquitously takes place in various scenarios, e.g., microgrids, energy harvesting networks, and vehicle-togrid networks. However, there are common security and privacy challenges caused by untrusted and nontransparent energy markets in these scenarios. To address the security challenges, we exploit the consortium blockchain technology to propose a secure energy trading system named energy blockchain. This energy blockchain can be widely used in general scenarios of P2P energy trading getting rid of a trusted intermediary. Besides, to reduce the transaction limitation resulted from transaction confirmation delays on the energy blockchain, we propose a credit-based payment scheme to support fast and frequent energy trading. An optimal pricing strategy using Stackelberg game for credit-based loans is also proposed. Security analysis and numerical results based on a real dataset illustrate that the proposed energy blockchain and credit-based payment scheme are secure and efficient in IIoT.

In the Internet of Vehicles (IoV), data sharing among vehicles is critical to improve driving safety and enhance vehicular services. To ensure security and traceability of data sharing, existing studies utilize consensus schemes as hard security solutions to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, defending against voting collusion between the candidates and compromised high-stake vehicles becomes challenging. To address the challenge, in this paper, we propose a two-stage soft security enhancement solution: (i) miner selection and (ii) block verification. In the first stage, we design a reputation-based voting scheme to ensure secure miner selection. This scheme evaluates candidates' reputation using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among active miners, a newly generated block is further verified and audited by standby miners. To incentivize the participation of the standby miners in block verification, we adopt the contract theory to model the interactions between active miners and standby miners, where block verification security and delay are taken into consideration. Numerical results based on a real-world dataset confirm the security and efficiency of our schemes for data sharing in BIoV.Index Terms-Internet of Vehicles, blockchain, reputation management, delegated proof-of-stake, contract theory, security Internet of Vehicles Active minerA malicious active miner RSU RSU Wireless communication Current block manager Standby miner Block verification Miner voting collusion Compromised vehicle Miner voting collusionBlock verification

Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where ''fine'' local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches. INDEX TERMS Federated learning, vehicular edge computing, model aggregation, contract theory.

While tremendous efforts have been dedicated to developing environmentally friendly films made from natural polymers and renewable resources, in particular, multifunctional films featuring extraordinary mechanical properties, optical performance, and ordered nanostructure, challenges still remain in achieving all these characteristics in a single material via a scalable process. Here, we designed a green route to fabricating strong, super tough, regenerated cellulose films featuring tightly stacked and long-range aligned cellulose nanofibers self-assembled from cellulose solution in alkali/urea aqueous systems. The well-aligned nanofibers were generated by directionally controlling the aggregation of cellulose chains in the hydrogel state using a preorientation-assisted dual cross-linking approach; i.e., a physical cross-linking was rapidly introduced to permanently reserve the temporarily aligned nanostructure generated by preorienting the covalent cross-linked gels. After a structural densification in air-drying of hydrogel, high strength was achieved, and more importantly, a record-high toughness (41.1 MJ m–3) in anisotropic nanofibers-structured cellulose films (ACFs) was reached. Moreover, the densely packed and well-aligned cellulose nanofibers significantly decreased the interstices in the films to avoid light scattering, granting ACFs with high optical clarity (91%), low haze (<3%), and birefringence behaviors. This facile and high-efficiency strategy might be very scalable in fabricating high-strength, super tough, and clear cellulose films for emerging biodegradable next-generation packaging, flexible electronic, and optoelectronic applications.

The development of a facile and fast method to construct anisotropic hydrogels with the ability to induce unidirectional growth of cells remains challenging. In this work, we demonstrated anisotropic cellulose hydrogels (ACHs) that are composed of nanoscale aligned nanofibers by dissolving cotton liner pulp in alkali/urea aqueous solution. On the basis of directionally controlling the architecture of cellulose chains with a facial prestretching strategy in chemical gel state and locking the highly ordered nanostructure through the formation of close physical networks via strong self-aggregation forces among neighboring cellulose nanofibers, ACHs, combing with a long-range aligned structure, entirely differential mechanical performances along the parallel and perpendicular directions of the hydrogel orientation and optical birefringence, were constructed. The aggregation of hydrogen bonds in anisotropic and isotropic hydrogels are of significant difference, confirmed by nuclear magnetic resonance technology. Importantly, ACHs with microgroove-like structure promote the adhesion and orientation of cardiomyocytes. Our work demonstrated the bottom-up fabrication of polysaccharide-based hydrogels with anisotropic structure and properties, paving the way to potentially apply them in cardiomyocytes in vitro culture system.