The emergence of Wireless Body Area Networks (WBAN) provides users with ubiquitous wireless communication services, such as continuous exchange of medical information in real time. Therefore, WBAN is considered to be one of the effective wireless sensor technologies for improving medical services. However, the characteristics of WBAN make it subject to multiple attacks, such as the leakage of private information of users, and WBAN is also inefficient. In this paper, a certificateless ring signature scheme CLRS is proposed. In addition, we propose a public auditing scheme with identity privacy protection, which combines the certificateless ring signature technology for cloud-assisted body area network. Through the analysis of security, it shows that the scheme can resist the existing attack methods such as forging attacks. Finally, the comparison between theoretical analysis and experimental simulation shows that the scheme has obvious efficiency advantages compared with the existing scheme. INDEX TERMS Public auditing, privacy preserving, certificateless ring signature, wireless body area networks.

Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

Aiming at the problem that there are many paths in data forwarding in soft define network (SDN) network, and the optimal path is difficult to find, combined with the advantages of ant colony algorithm and Genetic algorithm (GA), a routing control strategy based on the ant colony genetic fusion algorithm is proposed. This algorithm absorbs the advantages of high speed in the early stage of GA search and high efficiency in the late stage of ant colony algorithm search; the improved ant colony genetic fusion algorithm is applied to the SDN routing process for simulation tests. The simulation experiment shows that the throughput and link utilization of the proposed algorithm are about 30% higher than that of the single optimal particle swarm optimization algorithm.

Since the emergence of Software Defined Network (SDN), it has been widely concerned by the academia and the industry. However, Distributed Denial of Attack (DDoS) can pose a threat to the SDN. Many papers use machine learning algorithms to detect DDoS attacks in SDN, trying to find a balance between detection accuracy and processing time. The purpose of this paper is to propose a DDoS attack detection and defense mechanism based on the Self‐Organizing Mapping (SOM) under SDN environment. The experimental results show that this mechanism can not only maintain the proper precision, but also reduce the processing time. In addition, it can also restore port communication.

Aiming at the problem that the optimal parameters of the least square support vector regression (LSSVR) localization model in Internet of Things (IoT) are difficult to determine, a positioning method based on the improved particle swarm optimization (PSO) algorithm is proposed. First, the positioning model is constructed by LSSVR, then the PSO algorithm is improved by adaptively adjusting the inertia weights and the learning factors, and finally the improved PSO algorithm is used to search the optimal parameters of the LSSVR positioning model, which avoids the blindness of the parameter search. The simulation results show that the positioning accuracy of the proposed algorithm is improved by 25.9% and 19.7%, respectively, compared with the LSSVR and PSO‐LSSVR algorithms, and has better positioning stability and real‐time performance.