Earticle

In telematics and LBS(location-based service) applications, because moving objects usually move on spatial networks, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updates of moving objects' locations in an efficient way, we propose a new distributed grid scheme which utilizes node-based pre-computation technique to minimize the update cost of the moving objects' locations. Because our grid scheme manages spatial network data separately from the POIs(Point of Interests) and moving objects, it can minimize the update cost of the POIs and moving objects. Using our grid scheme, we propose a new knearest neighbor (k-NN) query processing algorithm which minimizes the number of accesses to adjacent cells during POIs retrieval in a parallel way. Finally, we show from our performance analysis that our k-NN query processing algorithm is better on retrieval performance than that of the existing SGRID.

Key management in multicast dynamic groups, where users can leave or join at their ease is one of the most crucial and essential part of secure communication. Because communication bandwidth, storage memory, and computational power are limited resources, group key management schemes for scalable secure multicast communications have to consider these factors however, most of proposed key management schemes focus on guarantying secure distribution of keys. Even though security assurance is critical, reducing the number of update messages, number of stored keys and computational load also needs equivalent attention. Scalability, security, computation, and communication complexities are some of the tradeoffs. Here, we propose an efficient secure scheme in such a way that solves these problems and that can be implemented easily. In our scheme, we applied very simple mathematical concept with modulo and XOR operations in order to make the system simple for implementation and management. As a novel feature, the scheme is scalable with key update message of order O(1) - logM N, storage overhead of order O(1) and computational load of order O(log(N/M))- O(1).

Recently, the concept of HPC as a Service (HPCaaS) was proposed to bring the traditional high performance computing field into the era of cloud computing. One of its goals aims to allow users to get easier access to HPC facilities and applications. This paper deals with related job submission and scheduling issues to achieve such goal. Traditionally, HPC users in supercomputing centers are required to specify the amount of processors to use upon job submission. However, we think this requirement might no longer be necessary for HPCaaS users since most modern parallel jobs are moldable and users usually could not know how to choose an appropriate amount of processors to allow their jobs to finish earlier. With moldable property, parallel programs can choose to exploit different parallelisms for execution just before their start of running. Therefore, we propose two moldable job scheduling approaches which take advantage of information of application speedup model to not only relieve HPC users’ burden of selecting an appropriate number of processors but also achieve even better system performance than existing methods. The experimental results indicate that the proposed approaches can achieve up to 78% and 89% performance improvement in terms of average turnaround time.

Anonymous user authentication schemes are essential to guarantee personal privacy in global mobility networks. Recently, Chung, Lee and Won proposed an improved authentication scheme with anonymity which remedies security weaknesses showed by Youn, Park and Lim. However, their improved scheme does not provide unlinkability. In this paper, we propose an anonymous authentication scheme which ensures user unlinkability. It is impossible for attackers to know particular sessions, that have already occurred several times, are originated from one same user. Moreover, we decrease the rounds of transmission among involved entities while consuming same computational power compared with previous schemes. Therefore, the proposed scheme is lightweight and suitable for the mobility networks.

Recent years, flash memory based Solid State Drives (SSDs) become a mainstream choice in mobile computers. To protect confidential data from being leaked, users have to run secure software to securely delete the needless files. However the traditional secure deletion software will not work for SSDs. To solve the problem, we proposed a smart secure deletion method called SmSD to clean up the sensitive data without erase the whole SSD. In addition, RAID technique is also employed to enhance the reliability and eliminate the potential risk caused by secure deletion.

This paper proposes a new weighted mining frequent pattern based on customer’s RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing. An existing recommendation system using traditional mining has the problem, such as delay of processing speed from a cause of frequent scanning a large data, considering equal weight value of every item, and accuracy as well. In this paper, to solve these problems, it is necessary for us to extract the most frequently purchased data from whole data, to consider the weight/importance of attribute of item in order to forecast frequently changing trends by emphasizing the important items with high purchasability and to improve the accuracy of personalized u-commerce recommendation. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.