- Rui Zhang;Ping Ji;Dinkar Mylaraswamy;Mani Srivastava;Sadaf Zahedi;
Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous (i.e., dirty) data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis (PCA) model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic (ROC) curves.
2013年03期 v.18 209-219页 [查看摘要][在线阅读][下载 1764K] [下载次数：36 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：34 ] - Fan Li;Xiao He;Siyuan Chen;Libo Jiang;Yu Wang;
Shortest path routing protocol intends to minimize the total delay between every pair of destination node and source node. However, it is also well-known that shortest path routing suffers from uneven distribution of traffic load, especially in dense wireless networks. Recently, several new routing protocols are proposed in order to balance traffic load among nodes in a network. One of them is Circular Sailing Routing (CSR) which maps nodes on the surface of a sphere and select routes based on surface distances. CSR has been demonstrated with better load balance than shortest path routing via simulations. However, it is still open that what load distribution CSR can achieve. Therefore, in this paper, we theoretically analyze the traffic load distribution of CSR in a dense circular wireless network. Using the techniques developed by Hytti and Virtamo, we are able to derive the traffic load of any point inside the network. We then conduct extensive simulations to verify our theoretical results with grid and random networks.
2013年03期 v.18 220-229页 [查看摘要][在线阅读][下载 2190K] [下载次数：22 ] |[网刊下载次数：0 ] |[引用频次：1 ] |[阅读次数：56 ] - Qiao Fu;Bhaskar Krishnamachari;Lin Zhang;
Vehicular Delay Tolerant Networks (DTN) use moving vehicles to sample and relay sensory data for urban areas, making it a promising low-cost solution for the urban sensing and infotainment applications. However, routing in the DTN in real vehicle fleet is a great challenge due to uneven and fluctuant node density caused by vehicle mobility patterns. Moreover, the high vehicle density in urban areas makes the wireless channel capacity an impactful factor to network performance. In this paper, we propose a local capacity constrained density adaptive routing algorithm for large scale vehicular DTN in urban areas which targets to increase the packet delivery ratio within deadline, namely Density Adaptive routing With Node deadline awareness (DAWN). DAWN enables the mobile nodes awareness of their neighbor density, to which the nodes' transmission manners are adapted so as to better utilize the limited capacity and increase the data delivery probability within delay constraint based only on local information. Through simulations on Manhattan Grid Mobility Model and the real GPS traces of 4960 taxi cabs for 30 days in the Beijing city, DAWN is demonstrated to outperform other classical DTN routing schemes in performance of delivery ratio and coverage within delay constraint. These simulations suggest that DAWN is practically useful for the vehicular DTN in urban areas.
2013年03期 v.18 230-241页 [查看摘要][在线阅读][下载 2363K] [下载次数：54 ] |[网刊下载次数：0 ] |[引用频次：3 ] |[阅读次数：84 ] - Kenneth Ezirim;Shamik Sengupta;
Among cognitive radio networks there is a persistent trend of competition to acquire under-utilized and idle channels for data transmission. The competition for spectrum resources often results in the misuse of the spectrum resources as networks experience contention in attempt to access unoccupied spectrum bands. The competitive scenario causes cognitive radio networks to incur a huge amount of loss, which constitutes a major problem of self-coexistence among networks. As a way to minimize these losses we present a self-coexistence mechanism that allows cognitive radio networks to coexist with each other by implementing a risk-motivated channel selection based on deference structure. Cognitive radio networks form deference structure community to have more efficient access to a channel of interest and can defer transmission to one another on that channel, thereby minimizing the chances of conflicts. As part of the decision making process to become a member of a deference structure community, cognitive radio networks rely on a risk-motivated channel selection scheme to evaluate the tentative deference structure channel. We provide numerical and simulation results that demonstrates the benefits of the proposed self-coexistence mechanism and show how it helps networks to coordinate their spectrum activities, minimize contention experienced and improve their utility. We also emphasize on the importance of the deference structure community size with regards to the average performance of member networks.
2013年03期 v.18 242-249页 [查看摘要][在线阅读][下载 392K] [下载次数：22 ] |[网刊下载次数：0 ] |[引用频次：1 ] |[阅读次数：24 ] - Liang Hu;Zhengyu Zhang;Feng Wang;Kuo Zhao;
The Internet of Things emphasizes the concept of objects connected with each other, which includes all kinds of wireless sensor networks. An important issue is to reduce the energy consumption in the sensor networks since sensor nodes always have energy constraints. Deployment of thousands of wireless sensors in an appropriate pattern will simultaneously satisfy the application requirements and reduce the sensor network energy consumption. This article deployed a number of sensor nodes to record temperature data. The data was then used to predict the temperatures of some of the sensor node using linear programming. The predictions were able to reduce the node sampling rate and to optimize the node deployment to reduce the sensor energy consumption. This method can compensate for the temporarily disabled nodes. The main objective is to design the objective function and determine the constraint condition for the linear programming. The result based on real experiments shows that this method successfully predicts the values of unknown sensor nodes and optimizes the node deployment. The sensor network energy consumption is also reduced by the optimized node deployment.
2013年03期 v.18 250-258页 [查看摘要][在线阅读][下载 747K] [下载次数：134 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：66 ] - Jun Li;Baochun Li;
In the current era of cloud computing, data stored in the cloud is being generated at a tremendous speed, and thus the cloud storage system has become one of the key components in cloud computing. By storing a substantial amount of data in commodity disks inside the data center that hosts the cloud, the cloud storage system must consider one question very carefully: how do we store data reliably with a high efficiency in terms of both storage overhead and data integrity? Though it is easy to store replicated data to tolerate a certain amount of data losses, it suffers from a very low storage efficiency. Conventional erasure coding techniques, such as Reed-Solomon codes, are able to achieve a much lower storage cost with the same level of tolerance against disk failures. However, it incurs much higher repair costs, not to mention an even higher access latency. In this sense, designing new coding techniques for cloud storage systems has gained a significant amount of attention in both academia and the industry. In this paper, we examine the existing results of coding techniques for cloud storage systems. Specifically, we present these coding techniques into two categories: regenerating codes and locally repairable codes. These two kinds of codes meet the requirements of cloud storage along two different axes: optimizing bandwidth and I/O overhead. We present an overview of recent advances in these two categories of coding techniques. Moreover, we introduce the main ideas of some specific coding techniques at a high level, and discuss their motivations and performance.
2013年03期 v.18 259-272页 [查看摘要][在线阅读][下载 4087K] [下载次数：170 ] |[网刊下载次数：0 ] |[引用频次：35 ] |[阅读次数：42 ] - Li Chen;Baochun Li;Bo Li;
Datacenters have become increasingly important to host a diverse range of cloud applications with mixed workloads. Traditional applications hosted by datacenters are throughput-oriented without delay requirements, but newer generations of cloud applications, such as web search, recommendations, and social networking, typically employ a tree-based Partition-Aggregate structure, which may incur bursts of traffic. As a result, flows in these applications have stringent latency requirements, i.e., flow deadlines need to be met in order to achieve a satisfactory user experience. To meet these flow deadlines, research efforts in the recent literature have attempted to redesign flow and congestion control protocols that are specific to datacenter networks. In this paper, we focus on the new array of deadline-sensitive flow control protocols, thoroughly investigate their underlying design principles, analyze the evolution of their designs, and evaluate the tradeoffs involved in their design choices.
2013年03期 v.18 273-285页 [查看摘要][在线阅读][下载 4132K] [下载次数：50 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：65 ] - Zhenhua Li;Zhi-Li Zhang;Yafei Dai;
In recent years, cloud sync(hronization) services such as GoogleDrive and Dropbox have provided Internet users with convenient and reliable data storing/sharing functionality. The cloud synchronization mechanism (in particular, how to deliver the user-side data updates to the cloud) plays a critical role in cloud sync services because it greatly affects the cloud operation cost (in terms of sync traffic) and user experience (in terms of sync delay). By comprehensively measuring tens of popular cloud sync services, we find that their cloud sync mechanisms differ greatly in sync performance and design granularity. Quite surprisingly, some very popular services (like GoogleDrive and 115 SyncDisk) utilize a quite coarse-grained cloud sync mechanism that may lead to severe traffic overuse. For example, updating 1-MB data may sometimes result in 260-MB sync traffic. In this paper, we conduct a comparative study of various cloud sync mechanisms by analyzing their respective pros and cons under different situations, unravel the pathological processes for their traffic overuse problems, and finally provide insights/solutions for better choosing/designing a cloud sync service.
2013年03期 v.18 286-297页 [查看摘要][在线阅读][下载 1362K] [下载次数：38 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：77 ] - Yuan Tian;Chuang Lin;Zhen Chen;Jianxiong Wan;Xuehai Peng;
The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.
2013年03期 v.18 298-307页 [查看摘要][在线阅读][下载 1574K] [下载次数：40 ] |[网刊下载次数：0 ] |[引用频次：9 ] |[阅读次数：65 ] - Wei Chen;Junwei Cao;Yuxin Wan;
Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of Service (QoS) and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.
2013年03期 v.18 308-317页 [查看摘要][在线阅读][下载 1820K] [下载次数：37 ] |[网刊下载次数：0 ] |[引用频次：8 ] |[阅读次数：75 ] - Feng Xie;Zhen Chen;Hongfeng Xu;Xiwei Feng;Qi Hou;
Collaborative filtering solves information overload problem by presenting personalized content to individual users based on their interests, which has been extensively applied in real-world recommender systems. As a class of simple but efficient collaborative filtering method, similarity based approaches make predictions by finding users with similar taste or items that have been similarly chosen. However, as the number of users or items grows rapidly, the traditional approach is suffering from the data sparsity problem. Inaccurate similarities derived from the sparse user-item associations would generate the inaccurate neighborhood for each user or item. Consequently, its poor recommendation drives us to propose a Threshold based Similarity Transitivity (TST) method in this paper. TST firstly filters out those inaccurate similarities by setting an intersection threshold and then replaces them with the transitivity similarity. Besides, the TST method is designed to be scalable with MapReduce framework based on cloud computing platform. We evaluate our algorithm on the public data set MovieLens and a real-world data set from AppChina (an Android application market) with several well-known metrics including precision, recall, coverage, and popularity. The experimental results demonstrate that TST copes well with the tradeoff between quality and quantity of similarity by setting an appropriate threshold. Moreover, we can experimentally find the optimal threshold which will be smaller as the data set becomes sparser. The experimental results also show that TST significantly outperforms the traditional approach even when the data becomes sparser.
2013年03期 v.18 318-327页 [查看摘要][在线阅读][下载 1267K] [下载次数：68 ] |[网刊下载次数：0 ] |[引用频次：9 ] |[阅读次数：98 ] <正>Tsinghua Science and Technology was started publication in 1996. It is an international academic journal sponsored by Tsinghua University and is published bimonthly. This journal aims at presenting the up-to-date scientific achievements in computer science, electronic engineering, and other IT fields. It is indexed by Ei and other
2013年03期 v.18 328页 [查看摘要][在线阅读][下载 52K] [下载次数：17 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：25 ] -
<正>Tsinghua Science and Technology (Tsinghua Sci Technol), an academic journal sponsored by Tsinghua University, is published bimonthly. This journal aims at presenting the up-to-date scientific achievements with high creativity and great significance in computer and electronic engineering. Contributions all over the world are welcome. Tsinghua Sci Technol is indexed by IEEE
2013年03期 v.18 329页 [查看摘要][在线阅读][下载 550K] [下载次数：10 ] |[网刊下载次数：0 ] |[引用频次：0 ] |[阅读次数：24 ] 下载本期数据