The Internet of Things (IoT) is also known as the Internet of everything. As an important part of the new generation of intelligent information technology, the IoT has attracted the attention both of researchers and engineers all over the world. Considering the limited capacity of smart products, the IoT mainly uses cloud computing to expand computing and storage resources. The massive data collected by the sensor are stored in the cloud storage server, also the cloud vulnerability will directly threaten the security and reliability of the IoT. In order to ensure data integrity and availability in the cloud and IoT storage system, users need to verify the integrity of remote data. However, the existing remote data integrity verification schemes are mostly based on the RSA and BLS signature mechanisms. The RSA-based scheme has too much computational overhead. The BLS signature-based scheme needs to adopt a specific hash function, and the batch signature efficiency in the big data environment is low. Therefore, for the computational overhead and signature efficiency issues of these two signature mechanisms, we propose a scheme of data integrity verification based on a short signature algorithm (ZSS signature), which supports privacy protection and public auditing by introducing a trusted third party (TPA). The computational overhead is effectively reduced by reducing hash function overhead in the signature process. Under the assumption of CDH difficult problem, it can resist adaptive chosen-message attacks. The analysis shows that the scheme has a higher efficiency and safety.INDEX TERMS Internet of Things, cloud computing, provable data integrity, privacy preserving, public auditability, short signature, ZSS signature.

The energy consumption of China's steel industry accounted for 53% of the global steel industry energy consumption in 2014. This paper aims to analyze the energy saving potential of China's steel industry, according to its development plan of the next decade, and find the key of energy conservation. A multivariate energy intensity (MEI) model is developed for energy saving potential analysis based on the research on China's energy statistics indexes and methods, which is able to capture the impacts of production routes, technology progress, industrial concentration, energy structure, and electricity (proportion and generation efficiency). Different scenarios have been set to describe future policy measures in relation to the development of the iron and steel industry. Results show that an increasing scrap ratio (SR) has the greatest energy saving effect of 16.8% when compared with 2014, and the maximum energy saving potential reaches 23.7% after counting other factors. When considering coal consumption of power generation, the energy saving effect of increasing SR drops to 7.9%, due to the increase on the proportion of electricity in total energy consumption, and the maximum energy saving potential is 15.5%, and they can increase to 10.1% and 17.5%, respectively, with improving China's power generation technology level.Energies 2018, 11, 948 2 of 16The advantages of BU models are the disadvantages of TD models and vice versa. Due to disciplinary and structural differences, these two types of models cannot demonstrate wide ranges of future projection of energy trajectories at both the macroscopic and technical levels. Ref.[10] tried to complement each type model's advantages and disadvantages by exchanging information between a detailed BU model with simplified macroeconomic module; Proença and Aubyn [11] integrated TD and BU models by representing detailed energy technologies within the TD framework; Fujimori etc. Ref.[12] developed an Integrated Assessment Models (IAM)/computable general equilibrium (CGE) model that was integrating detailed energy end-use technologies.These studies focused on coupling existing models without exploring the calculation approach or EI impact factors of the steel industry besides existing ones. According to the development plan issued by the Chinese government in recent years, the production situation of China's steel industry will change significantly by in next decade, including both macro and technical impact factors, such as raw materials, production routes, technology progress, industrial concentration, energy structure, and electricity (proportion in energy consumption and generation efficiency). However, using existing models in early studies cannot comprehensively analyze the future energy consumption trends of China's steel industry, as no existing models are able to capture the influence of all these factors. Furthermore, the energy statistics system are the basis of energy related analysis and research, and data calculated according to their definition and statistical met...

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Last few years, many security schemes are designed for RFID system since the release of the EPC Class 1 Generation 2 standard. In 2010, Yeh et al. proposed a new RFID authentication protocol conforming to EPC Class 1 Generation 2 standard. Yoon pointed that their protocol still had two serious security problems such as DATA integrity problem and forward secrecy problem. Then he proposed an improved protocol which claimed to eliminate the weakness in 2011. This paper shows that Yoon' s protocol had no resistance to replay attack and did not resolve the problem of data forge and tag's location privacy. An improved protocol is also proposed to protect RFID system from all major attacks. By comparing to other authentication protocols with respect of security and performance, the results shows that the proposed protocol is feasible for RFID tags which are low cost and resourceconstrained devices.

Because of wireless sensor networks are usually deployed in hostile environment without continuous supervision, node compromise is the most critical threats of security issues of wireless sensor networks. In this paper, we proposed a method to identify malicious nodes in wireless sensor networks which have deployed with a reputation system through using time series analysis on node reputation. We focus a tricky type of attack nodes in reputation sensor network. These nodes can launch attacks or abnormal behaviors abstemious to against the detection of reputation system. We give a detail of definition of these nodes in the paper and call them sub-aggressiveness malicious nodes. Our scheme to identify these nodes combined time series analysis with k-means clustering. With a series simulation, the results reveal that the proposed scheme has good performance and effectiveness to identify sub-aggressiveness malicious nodes which are hardly to be detected by reputation threshold mechanism of reputation network.