Device to device (D2D) communication is a promising paradigm to support data sharing in the fifth generation (5G) small cell networks. However, D2D communication is more vulnerable to security and privacy threats due to direct wireless connection between the proximity devices. In this paper, we exploit blockchain technology to propose a new distributed and secure data sharing framework named D2D blockchain, where we deploy a set of Access Points (APs) to verify end users' transactions. Besides, in order to enable the end users to receive the required data on time in the D2D blockchain, we propose two new schemes for the transaction confirmation procedure including a relay-assisted transaction relaying scheme and a Delegated Proof-of-Stake (DPoS) based lightweight block verification scheme. Furthermore, to incentivize the relay devices to help in relaying transactions in congested areas, and to encourage the verifiers to contribute their resources in block verification, we design a two-stage contract theory based joint optimization scheme. To measure the quality of the relayed transaction and the verified block, we formulate two models: value of transaction relaying (VoTR) and value of block verification (VoBV), and then design the optimal contract for each relay device and each verifier. Security analysis and numerical results illustrate that the proposed D2D blockchain and the designed joint optimization scheme are secure and efficient for data sharing among end users.

Efficient and smart business processes are heavily dependent on the Internet of Things (IoT) networks, where end-to-end optimization is critical to the success of the whole ecosystem. These systems, including industrial, healthcare, and others, are large scale complex networks of heterogeneous devices. This introduces many security and access control challenges. Blockchain has emerged as an effective solution for addressing several such challenges. However, the basic algorithms used in the business blockchain are not feasible for large scale IoT systems. To make them scalable for IoT, the complex consensus-based security has to be downgraded. In this article, we propose a novel lightweight proof of block and trade (PoBT) consensus algorithm for IoT blockchain and its integration framework. This solution allows the validation of trades as well as blocks with reduced computation time. Also, we present a ledger distribution mechanism to decrease the memory requirements of IoT nodes. The analysis and evaluation of security aspects, computation time, memory, and bandwidth requirements show significant improvement in the performance of the overall system.

In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.

Crowdsensing systems collect various types of data from sensors embedded on mobile devices owned by individuals. These individuals are commonly referred to as workers that complete tasks published by crowdsensing systems. Because of the relative lack of control over worker identities, crowdsensing systems are susceptible to data poisoning attacks which interfering with data analysis results by injecting fake data conflicting with ground truth. Frameworks like TruthFinder can resolve data conflicts by evaluating the trustworthiness of the data providers. These frameworks somehow make crowdsensing systems more robust since they can limit the impact of dirty data by reducing the value of unreliable workers. However, previous work has shown that TruthFinder may also be affected by data poisoning attack when the malicious workers have access to global information. In this paper, we focus on partially observable data poisoning attacks in crowdsensing systems. We show that even if the malicious workers only have access to local information, they can find effective data poisoning attack strategies to interfere with crowd sensing systems with TruthFinder. First, we formally model the problem of partially observable data poisoning attack against crowdsensing systems. Then, we propose a data poisoning attack method based on deep reinforcement learning, which helps malicious workers jeopardize with TruthFinder while hiding themselves. Based on the method, the malicious workers can learn from their attack attempts and evolve the poisoning strategies continuously. Finally, we conduct experiments on real-life data sets to verify the effectiveness of the proposed method.