We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

Federated learning is an emerging machine learning technique which enables distributed model training using local datasets from large-scale nodes, e.g., mobile devices, but shares only model updates without uploading raw training data. This technique provides promising privacy preservation for mobile devices while simultaneously ensuring high learning performance. The majority of existing work focused on designing advanced learning algorithms with an aim to achieve better learning performance. However, the challenges such as incentive mechanisms for participating in training and worker (i.e., mobile devices) selection schemes for reliable federated learning, have not been explored yet. These challenges have hindered the widespread adoption of federated learning. To address the above challenges, in this paper, we first introduce reputation as the metric to measure the reliability and trustworthiness of the mobile devices. We then design a reputation-based worker selection scheme for reliable federated learning by using a multi-weight subjective logic model. We also leverage the blockchain to achieve secure reputation management for workers with non-repudiation and tamper-resistance properties in a decentralized manner. Moreover, we propose an effective incentive mechanism combining reputation with contract theory to motivate high-reputation mobile devices with high-quality data to participate in model learning. Numerical results clearly indicate that the proposed schemes are efficient for reliable federated learning in terms of significantly improving the learning accuracy.

The drastically increasing volume and the growing trend on the types of data have brought in the possibility of realizing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resource of vehicles, mobile edge computing integrated with vehicular networks gives rise to Vehicular Edge COmputing and Networks (VECONs) for providing powerful computing and massive storage resources. However, vehicular edge computing servers consisted of roadside units cannot be fully trusted, which may result in serious security and privacy challenges. We exploit consortium blockchain and smart contract technologies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level security for data sharing in VECONs.

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device contain massive user’s private data, which is challenging current detection approaches as user privacy has attracted more and more public concerns. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-k selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that our framework accurately and timely detects anomalies and also reduces the communication overhead by 50% compared to the federated learning framework that does not use gradient compression scheme.

In the Internet of Vehicles (IoV), data sharing among vehicles is critical to improving driving safety and enhancing vehicular services. To ensure security and traceability of data sharing, existing studies utilize efficient Delegated Proof-of-Stake consensus scheme as hard security solutions to establish blockchainenabled IoV (BIoV). However, as the 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 past 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.

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-to-grid 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.