In this paper, the blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless sensor networks (WSNs). In our system, the nodes storing the data are rewarded with digital money. The more the data stored by the node, the more the reward it achieves. Moreover, two blockchains are constructed. One is utilized to store data of each node and another is to control the access of data. In addition, our proposal adopts the provable data possession to replace the proof of work (PoW) in original bitcoins to carry out the mining and storage of new data blocks, which greatly reduces the computing power comparing to the PoW mechanism. Furthermore, the preserving hash functions are used to compare the stored data and the new data block. The new data can be stored in the node which is closest to the existing data, and only the different subblocks are stored. Thus, it can greatly save the storage space of network nodes.

With the rapid development of Artificial Intelligence of Things (AIoT) technology and applications, the cloud–edge computing system provides convenient and low-latency storage and decentralized computing services to reduce computing requirements in the cloud environment. However, data integrity verification is still an urgent issue researchers have widely discussed in AIoT cloud–edge computing environment. As a distributed and immutable ledger technology, blockchain provides an effective way of data verification in digital financial transactions. The research proposes a blockchain-based data integrity verification scheme to improve the data verification rate in cloud–edge computing environment. Based on the method, a detailed blockchain-based data integrity verification scheme without a trusted third party is proposed with a data upload phase and a data verification phase. The novel proposal system is built on Hyperledger Sawtooth and experimentally evaluated against two state-of-the-art systems in a simulated cloud–edge environment. The results demonstrate that the proposed scheme can efficiently verify data integrity with a high verification rate.

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based trustworthiness evaluation scheme for privilege management in VANETs is proposed. In the proposed scheme, to ensure the result of trustworthiness is valid, mutual authentication with conditional anonymity between the evaluator and the vehicle to be evaluated is first employed. Then, based on the vehicle's behavioral big data, the trustworthiness of each vehicle is evaluated by utilizing the fuzzy theory. Note that the privilege of a vehicle and the reliability of the vehicle's messages are determined by its trustworthiness. Moreover, the mobility of vehicles is also considered in this paper, since the location of a vehicle is not constant and the monitoring area of an RSU is limited. The results of theoretical and experimental analyses demonstrate that the proposed scheme performs well in terms of security and efficiency.

In practical structural health monitoring (SHM) process based on wireless sensor network (WSN), data loss often occurs during the data transmission between sensor nodes and the base station, which will affect the structural data analysis and subsequent decision making. In this paper, a method of recovering lost data in WSN based on compressive sensing (CS) is proposed. Compared with the existing methods, it is a simple and stable data recovery method and can obtain lower recovery data error for one‐dimensional SHM’s data loss. First, response signal x is measured onto the measurement data vector y through inner products with random vectors. Note that y is the linear projection of x and y is permitted to be lost in part during the transmission. Next, when the base station receives the incomplete data, the response signal x can be reconstructed from the data vector y using the CS method. Finally, the test of active structural damage identification on LF‐21M aviation antirust aluminum plate is proposed. The response signal gathered from the aluminum plate is used to verify the data recovery ability of the proposed method.