The booming Internet of Things makes smart healthcare a reality, while cloud-based medical storage systems solve the problems of large-scale storage and real-time access of medical data. The integrity of medical data outsourced in cloud-based medical storage systems has become crucial since only complete data can make a correct diagnosis, and public auditing protocol is a key technique to solve this problem. To guarantee the integrity of medical data and reduce the burden of the data owner, we propose an efficient privacy-preserving public auditing protocol for the cloud-based medical storage systems, which supports the functions of batch auditing and dynamic update of data. Detailed security analysis shows that our protocol is secure under the defined security model. In addition, we have conducted extensive performance evaluations, and the results indicate that our protocol not only remarkably reduces the computational costs of both the data owner and the third-party auditor (TPA), but also significantly improves the communication efficiency between the TPA and the cloud server. Specifically, compared with other related work, the computational cost of the TPA in our protocol is negligible and the data owner saves more than $2/3$ of computational cost. In addition, as the number of challenged blocks increases, our protocol saves nearly 90% of communication overhead between the TPA and the cloud server.

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.

In vehicle ad hoc networks (VANETs), if a legal vehicle node becomes malicious, then it is more likely to tamper with transferred data or provide false data easily. Because the malicious node is a valid internal user in VANETs, its behaviour is difficult to be detected only through some cryptographic methods. Then the behaviour may cause many serious traffic accidents. Based on the available (unencrypted) data only, how to detect out the internal malicious vehicle nodes by some lightweight methods needs to be researched in VANETs. Additionally, fog computing seamlessly integrates heterogeneous computing resources widely distributed in edge networks and then provides stronger computing services for users. Therefore, in this paper, we propose a malicious node detection scheme in fog computing-based VANETs, where the fog server uses the reputation calculation to score each suspicious node based on the correlation of acquired data and network topology. In our proposed scheme, we build a reputation mechanism to score each suspicious node according to the correlation between outlier detection of acquired data and influence of nodes. Based on our proposed experiments, our proposed scheme can efficiently and effectively detect out malicious vehicle nodes so that fog server can acquire more true data.