This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Vehicular ad hoc network (VANET) is an emerging technology that can significantly improve the efficiency of transportation systems and mitigate traffic accidents by exchanging traffic-related messages or announcements. Nevertheless, there has not been a consensus on how to generate, distribute, and validate trustworthy announcements in such an untrusted wireless environment. Security and privacy, inspiration mechanism, and resource integration are significant challenges for announcement generation and dissemination. In this paper, a secure and trustworthy announcement dissemination scheme is realized for location-based service (LBS) application in VANET. A blockchain-assisted vehicular cloud (VC) architecture is proposed to harvest underutilized heterogeneous resources of vehicles participating in VANET. Moreover, the technologies of blockchain and smart contract are adopted to classify vehicles into different levels automatically by bidding for bonuses. What’s more, vehicles can generate trustworthy announcements with the help of neighbor vehicles by adopting the technology of threshold signature. Meanwhile, the reputation of announcements is evaluated for trust management. Formal security analysis shows that the proposed scheme satisfies fundamental security and privacy requirements in VANET. Experimental results show that the proposed scheme is robust and efficient.

Vehicular ad hoc network (VANET) enables a more efficient and secure traffic environment by integrating several emerging technologies. The technology of certificateless aggregate signature (CLAS) is widely utilized to guarantee secure communications in VANET. However, the legitimation of vehicles’ public keys cannot be verified effectively in existing CLAS protocols for VANET. Thus, a malicious key generation center (KGC) can masquerade as any vehicle and generate legitimate signatures. In this paper, a reliable malicious KGC-resistant CLAS protocol, called RelCLAS, is proposed to eradicate this problem. RelCLAS solves the key escrow problem owning to the combination of registration-based encryption (RBE) and CLAS. Partial public keys selected by vehicles are stored in a key accumulator to prevent malicious KGC from forging valid signatures. Formal security proof demonstrates that RelCLAS satisfies the fundamental security requirements of VANET. Extensive simulations illustrate that RelCLAS is efficient, reliable, and feasible.

Due to the advent of healthcare wireless medical sensor networks (HWMSNs) technology, the traditional model of medical treatment has been transformed. In HWMSNs environments, sensor nodes (e.g., wearable devices) collect medical data from patients and transmit them to doctors for diagnosis. Several certificateless aggregate signature (CLAS) schemes have been put forward to guarantee secure transmission of medical data and privacy of patients in HWMSNs. However, the utilization of a centralized key generation center (KGC) implies elevated risk of insecurity and privacy disclosure. Practical and destructive coalition attacks resulting from the collusion of an insider signer with a malicious KGC are difficult to resist. Therefore, a security-enhanced certificateless designated verifier anonymous aggregate signature (CL-DVAAS) scheme is devised to solve these problems. As introduced in the YOSO (You Only Speak Once) model, an ever-changing, unpredictable committee replaces the role of the KGC for system initialization and key distribution. The serverless computing paradigm empowers the proposed scheme with excellent attack prevention and privacy protection capabilities. The proposed scheme is provably secure against various types of attacks in the random oracle model. Moreover, the performance evaluation and comparison illustrate that our scheme outperforms the state-of-the-art solutions.

With an increasing number of IoT devices being deployed in healthcare, massive amounts of electronic health records (EHRs) are generated and shared in the cloud. To preserve data privacy, one promising data-sharing tool named attribute-based encryption (ABE) has been widely employed. However, it is a challenge to achieve flexible data sharing without loss of confidentiality when authorized users are dynamic. Another challenge is how to guarantee fleet data access time when resource-limited devices are used. In this paper, a dynamic access policy ABE (DAP-ABE) system for EHRs in the cloud is proposed. The cloud server can update the access policy without sensitive information, while decryption keys of authorized users do not need to be updated. Authorized users enjoy approximately 0.07ms data access by outsourcing the majority of the decryption overhead to the cloud server. Furthermore, a verification procedure is embedded in DAP-ABE to check the identities of patients in the data sharing stage, which ensures that no malicious user can upload invalid EHRs. Extensive experiments demonstrate the feasibility and efficiency of the DAP-ABE system.