As one of the communication methods in future mobile communications, Machine Type Communication (MTC) is an important mobile communication method in Internet of Things (IoT). When many MTC devices want to access the network at the same time, each MTC device needs to perform independent identity authentication, which will cause network congestion. In order to solve this problem and improve the security of key agreement of MTC device, a dynamic group based effective identity authentication and key agreement scheme was proposed in Long Term Evolution-Advanced (LTE-A) networks. Based on symmetric bivariate polynomials, the proposed scheme was able to authenticate a large number of MTC devices at the same time and establish independent session keys between the devices and the network. In the proposed scheme, multiple group authentications were supported, and the updating of access policies was provided. Compared with the scheme based on linear polynomials, bandwidth analysis shows that the bandwidth consumptions of the proposed scheme during transmission are optimized:the transmission bandwidth between the MTC devices in the Home Network (HN) and the Service Network (SN) is reduced by 132 bit for each group authentication, the transmission bandwidth between the MTC devices within the HN is reduced by 18.2%. Security analysis and experimental results show that the proposed scheme is safe in actual identity authentication and session key establishment, and can effectively avoid signaling congestion in the network.

To solve the difficulty of attribute correlation in the process of simulating table data, an H model was proposed for describing not-temporal attribute correlation in table data. Firstly, the key attributes of the evaluation subject and the evaluated subject were extracted from the data set, by the twofold frequency statistics, four relationships of the key attributes were obtained. Then, the Maximum Information Coefficient (MIC) of each relationship was calculated to evaluate the correlation of each relationship, and each relationship was fitted by the Stretched Exponential (SE) distribution. Finally, the data scales of the evaluation subject and the evaluated subject were set. According to the result of fitting, the activity of the evaluation subject was calculated, and the popularity of the evaluated subject was calculated. H model was obtained through the association that was established by equal sum of activity and popularity. The experimental results show that H model can effectively describe the correlation characteristics of the non-temporal attributes in real data sets.

Due to the problem that the nodes having the same distance but different position in the complex environment, brings shortage to accuracy and stability of indoor positioning, a new indoor positioning algorithm with Dynamic Environment Attenuation Factor (DEAF) was proposed. This algorithm built a DEAF model and redefined the way to assume the value. In this algorithm, particle filtering method was firstly used to smooth the Received Signal Strength Indication (RSSI); then, the DEAF model was used to calculate the estimation distance of the node; finally, the trilateration was used to get the position of the target node. Comparative experiments had been done using several filtering models, and the results show that this dynamic environment attenuation factor model combined with particle filtering can resolve the problem of the environment difference very well. This algorithm reduces the mean error to about 0.68 m, and the result has higher positioning accuracy and good stability.

In recommendation system, it is difficult to predict the behavior of users on items and give the accurate recommendation. In order to improve the accuracy of recommendation system, the recommendation potential was introduced and a novel personalized hybrid recommendation model fused with recommendation potential was proposed. Firstly, the trend momentum was calculated according to the visits of items in recent short time and long time; then, the current recommendation potential was calculated utilizing trend momentum; finally, the hybrid recommendation model was achieved according to the fusion of recommendation potential and personalized recommendation model. The experimental results show that the personalized trend prediction fused with recommendation potential can improve the accuracy of recommendation system in a large scale.

There are two inefficient steps in GEP-Cluster algorithm: one is screening and aggregation of clustering centers and the other is the calculation of distance between data objects and clustering centers. To solve the inefficiency, an auto-clustering algorithm based on Compute Unified Device Architecture (CUDA) and Gene Expression Programming (GEP), named as CGEP-Cluster, was proposed. Specifically, the screening, and aggregation of clustering center step was improved by Gene Read & Compute Machine (GRCM) method, and CUDA was used to parallel the calculation of distance between data objects and clustering centers. The experimental results show that compared with GEP-Cluster algorithm, CGEP-Cluster algorithm can speed up by almost eight times when the scale of data objects is large. CGEP-Cluster can be used to implement automatic clustering with the clustering number unknown and large data object scale.

In a Virtual Machine (VM) computing environment, it is difficult to monitor and allocate the VM's memory in real-time. To overcome these shortcomings, a real-time method of monitoring and adjusting memory for Xen virtual machine called Xen Memory Monitor and Control (XMMC) was proposed and implemented. This method used hypercall of Xen, which could not only real-time monitor the VM's memory usage, but also dynamically real-time allocated the VM's memory by demand. The experimental results show that XMMC only causes a very small performance loss, less than 5%, to VM's applications. It can real-time monitor and adjust on demand VM's memory resource occupations, which provides convenience for the management of multiple virtual machines.

Some E-commerce sites cannot deploy independent recommender systems themselves due to limited resources. In order to help these sites deploy recommender systems quickly and conveniently, an E-commerce recommendation platform based on Software-as-a-Service (SaaS) pattern was proposed and implemented. This platform used unified scripts to collect user action information and provided recommendation services through standard interface. It realized a low coupling between platform and E-commerce sites so that the cost of implementation was reduced. The results of online operations show that this platform can help E-commerce sites increase the conversion rate and the volume of orders.