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Abstract The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users’ privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations.

Objective Geothermal resources are valuable clean resources, and the Enshi Basin contains abundant geothermal resources. It is very important to expore the origin of geothermal resources. Methods Starting from the chemical and isotopic characteristics of geothermal water, combined with the hydrological and geothermal geological conditions of the Enshi Basin, the following works have been performed. The geochemical characteristics and main ion recharge sources of geothermal water in the Enshi Basin are discussed and analyzed by the Piper diagram method and main ion correlation analysis method. In this study, the appropriate silica temperature scale method is used to solve the problem that it is difficult to accurately measure the heat storage temperature. Using hydrogen and oxygen isotope testing technology, the recharge source, circulation depth and recharge elevation of geothermal water are determined. Results Results show that the hydrochemical type of geothermal water in the Enshi Basin is mainly SO4·Cl-Na water, and the main ions in the groundwater are SO42-, Cl- and Na+. There is a good positive correlation between TDS in geothermal water and Na+, Ca2+, Mg2+, Cl- and SO42-, while TDS in geothermal water is much higher than that in cold springs.It is due to the large burial depth, long runoff path and strong dissolution leaching action, which makes it easier to extract relevant ions from the surrounding rock and results in ion concentrations much higher than those of surface water. The hot water in the study area is mainly supplied by atmospheric precipitation, and the 14C and 34S isotopic characteristics of geothermal water show that the storage environment of geothermal water from the edge of the basin to the center of the basin is gradually closed, the retention time of geothermal water is gradually longer, and the degree of water rock reaction is gradually stronger. The results of the water-rock balance show that the concentration of SiO2 in hot water is controlled by the dissolution balance of quartz.Using the SiO2 geothermal temperature scale, the estimated thermal storage temperature is 55.74-58.24 ℃, the burial depth of thermal storage is 1 793-1 906 m, and the circulating depth of hot water is 1 823-1 936 m. The recharge elevation of geothermal water is estimated to be 1 022.64-1 109.00 m, according to the elevation effect of δO of atmospheric precipitation. Conclusion According to the elevation range of the study area, the geothermal water recharge area is mainly the low and middle mountain area of Cambrian Ordovician carbonate rocks in the western part of the basin.

Abstract Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.

Abstract Many shortest link scheduling algorithms adopt non-fading SINR interference model, which assumes that the received signal power will always remain determinate as long as the transmission power of the corresponding sender is fixed. In fact, because environment always influences the propagation of radio signals, the received signal power is by no means a certain value. Rayleigh fading is a statistical model for radio signals propagation. It assumes that the strength of a signal on a receiver is a random variable, varying with the Rayleigh distribution. This paper proposes a shortest link scheduling algorithm under the Rayleigh fading model (SLSRF). The SLSRF partitions the wireless network area into hexagons and colors the hexagons with three different colors such that two neighboring hexagons have different colors. The senders of the links scheduled simultaneously are arranged in hexagons with the same color. The correctness of the SLSRF is proved through theoretical analysis, and the efficiency is illustrated by elaborate simulations. Our simulation results demonstrate that the schedule delay of SLSRF is less than that of some results under the non-fading SINR interference model. Furthermore, we extend the SLSRF to a distributed version, which is suitable for large wireless networks.

Abstract The latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.