High performance with low power consumption is an essential factor in wireless sensor networks (WSN). In order to address the issue on the lifetime and the consumption of nodes in WSNs, an improved ad hoc on-demand distance vector routing (IAODV) algorithm is proposed based on AODV and LAR protocols. This algorithm is a modified on-demand routing algorithm that limits data forwarding in the searching domain, and then chooses the route on basis of hop count and power consumption. The simulation results show that the algorithm can effectively reduce power consumption as well as prolong the network lifetime.

Relational database management systems are usually deployed on single-node machines and have strict limitations in terms of data structure. This means they do not work well with big data, and NoSQL has been proposed as a solution. To make data querying more efficient, indexes and memory cache techniques are used in NoSQL databases. In this paper, we propose a hierarchical indexing mechanism and a prototype distributed data-storage system, called HMIBase, which has hierarchical indexes for non-primary keys in tables and makes data querying more efficient. HMIBase uses HBase as the lower data storage and creates a memory cache for more efficient data transmission. HMIBase supports coprocessor-to-process update requests. It also provides a client with query and update APIs and a server to support RPCs from the client and finish jobs. To improve the cache hit ratio, we propose a memory cache replacement strategy, called Hot Score algorithm, in HMIBase. The experimental results show that Hot Score algorithm is better than other cache-replacement strategies.

Integration of the cloud desktop and cloud storage platform is urgent for enterprises. However, current proposals for cloud disk are not satisfactory in terms of the decoupling of virtual computing and business data storage in the cloud desktop environment. In this paper, we present a new virtual disk mapping method for cloud desktop storage. In Windows, compared with virtual hard disk method of popular cloud disks, the proposed implementation of client based on the virtual disk driver and the file system filter driver is available for widespread desktop environments, especially for the cloud desktop with limited storage resources. Furthermore, our method supports customizable local cache storage, resulting in user-friendly experience for thin-clients of the cloud desktop. The evaluation results show that our virtual disk mapping method performs well in the read-write throughput of different scale files.

User - analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user recommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidimensional algorithm is more accurate than a traditional recommendation algorithm.

With user-generated content, anyone can be a content creator. This phenomenon has infinitely increased the amount of information circulated online, and it is becoming harder to efficiently obtain required information. In this paper, we describe how natural language processing and text mining can be parallelized using Hadoop and Message Passing Interface. We propose a parallel web text mining platform that processes massive amounts of data quickly and efficiently. Our web knowledge service platform is designed to collect information about the IT and telecommunications industries from the web and process this information using natural language processing and data-mining techniques.

With the rapid development of the internet, internet of things, mobile internet, and cloud computing, the amount of data in circulation has grown rapidly. More social information has contributed to the growth of big data, and data has become a core asset. Big data is challenging in terms of effective storage, efficient computation and analysis, and deep data mining. In this paper, we discuss the significance of big data and discuss key technologies and problems in big-data analytics. We also discuss the future prospects of big-data analytics.

Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients. Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load (ETL) and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.

As cloud computing gains in popularity, data migrated off premises is exposed to more threats than ever before. This is because data is out of control of the owner while floating in the cloud. Traditional device-centric security systems are not efficient enough and need to be evolved to data-centric protection systems. Cloud telecommunications services require security measures in three domains: data storage, processing, and transmission. Data stored in the cloud requires a mechanism to protect it; data in transit needs to be protected either at the service or transmission level; and data being processed needs to be protected during the processing stage. In this paper, we propose a security model based on a new method of security domain division to provide on-demand, dynamic, and differentiated protection for cloud-based telecommunications services.

Mobile search is beset with problems because of mobile terminal constraints and also because its characteristics are different from the traditional Internet search model. This paper analyzes cloud computing technologies—especially mass data storage, parallel computing, and virtualization—in an attempt to solve technical problems in mobile search. The broad prospects of cloud computing are also discussed.