Dongliang Xie

Sequential and adaptive sampling for matrix completion in network monitoring systems

End-to-end network monitoring is essential to ensure transmission quality for Internet applications. However, in large-scale networks, full-mesh measurement of network performance between all transmission pairs is infeasible. As a newly emerging sparsity representation technique, matrix completion allows the recovery of a low-rank matrix using only a small number of random samples. Existing schemes often fix the number of samples assuming the rank of the matrix is known, while the data features thus the matrix rank vary over time. In this paper, we propose to exploit the matrix completion techniques to derive the end-to-end network performance among all node pairs by only measuring a small subset of end-to-end paths. To address the challenge of rank change in the practical system, we propose a sequential and information-based adaptive sampling scheme, along with a novel sampling stopping condition. Our scheme is based only on the data observed without relying on the reconstruction method or the knowledge on the sparsity of unknown data. We have performed extensive simulations based on real-world trace data, and the results demonstrate that our scheme can significantly reduce the measurement cost while ensuring high accuracy in obtaining the whole network performance data.

Recover Corrupted Data in Sensor Networks: A Matrix Completion Solution

Affected by hardware and wireless conditions in WSNs, raw sensory data usually have notable data loss and corruption. Existing studies mainly consider the interpolation of random missing data in the absence of the data corruption. There is also no strategy to handle the successive missing data. To address these problems, this paper proposes a novel approach based on matrix completion (MC) to recover the successive missing and corrupted data. By analyzing a large set of weather data collected from 196 sensors in Zhu Zhou, China, we verify that weather data have the features of low-rank, temporal stability, and spatial correlation. Moreover, from simulations on the real weather data, we also discover that successive data corruption not only seriously affects the accuracy of missing and corrupted data recovery but even pollutes the normal data when applying the matrix completion in a traditional way. Motivated by these observations, we propose a novel Principal Component Analysis (PCA)-based scheme to efficiently identify the existence of data corruption. We further propose a two-phase MC-based data recovery scheme, named MC-Two-Phase, which applies the matrix completion technique to fully exploit the inherent features of environmental data to recover the data matrix due to either data missing or corruption. Finally, the extensive simulations with real-world sensory data demonstrate that the proposed MC-Two-Phase approach can achieve very high recovery accuracy in the presence of successively missing and corrupted data.

Tradeoff Between Throughput and Energy Consumption in Multirate Wireless Sensor Networks

In wireless networks, multirate capability will facilitate using channel resource and reducing the bit error rate effectively. Current contention-based MAC protocols, however, behave inefficiently in multirate situations, leading to the performance anomaly problem. This paper researches the throughput deterioration in multirate wireless sensor networks. Initially, this paper refers to the notion of fairness to solve the performance deterioration problem and demonstrates that the utility maximization of throughput will be achieved when each contending node receives an equal proportion of channel occupancy time. Besides, considering the intrinsic tradeoff between throughput optimization and energy consumption optimization, the paper converts the tradeoff to the regulation of single nodes channel access probability, which is determined by nodes data rate and energy level. Finally, based on the contention-based MAC algorithm, the paper proposes an improved MAC protocol. Simulation validates that different application demands between throughput and lifetime can be adjusted flexibly and efficiently by regulating the tradeoff factor in the algorithm.

Decentralized Context Sharing in Vehicular Delay Tolerant Networks with Compressive Sensing

Vehicles equipped with various types of sensors can act as mobile sensors to monitor the road conditions. To speed up the information collection process, the monitoring data can be shared among vehicles upon their encounters to facilitate drivers to find a good route. The vehicular network experiences intermittent connectivity as a result of the mobility, which makes the inter-vehicle contact duration a scarce resource for data transmissions and the support of monitoring applications over vehicular networks a challenge. We propose a novel compressive sensing (CS)-based scheme to enable efficient decentralized context sharing in vehicular delay tolerant networks, called CS-Sharing. To greatly reduce the data transmission overhead and speed up the monitoring processing, CS-sharing exploits two techniques: sending an aggregate message in each vehicle encounter, and quick collection of information taking advantage of data sharing and the sparsity of events in vehicle networks to significantly reduce the number of measurements needed for global information recovery. We propose a novel data structure, and an aggregation method that can take advantage of the random and opportunistic vehicle encounters to form the measurement matrix. We prove that the measurement matrix satisfies the Restricted Isometry Property (RIP) property required by the CS technique. Our results from extensive simulations demonstrate that CS-Sharing allows vehicles in a large network to quickly obtain the full context data with the successful recovery ratio larger than 90%.

Paths selection-based resequencing queue length in concurrent multipath transfer

Traditionally, concurrent multipath transfer CMT is used to achieve aggregate bandwidth in next generation networks, which are expected to be heterogeneous, integrating access networks employing different technologies. Simultaneous data transfer over multiple destination addresses could increase throughput, but causes data reordering at the destination. A lot of studies assumed that the receiver buffer RBUF in CMT is unlimited, actually, its not. However, limited RBUF indeed affects the whole performance and even causes RBUF blocking problem in the real situation. In this paper, we analyze the RBUF blocking problem brought by simultaneous data transfer with limited RBUF. Then, we propose a new analytical model of reliable delivery to predict the Stream Control Transmission Protocol throughput and the length expectation of the resequenceing queue in CMT. Based on the model mentioned earlier, the paths selection problem is formulated as a knapsack problem, which maximizes the overall throughput and limits the resequencing queue length expectation to the fixed RBUF size. Through the computer simulations, the proposed analytical model can predict the throughput of CMT accurately. It is proved that our proposed paths selection has achieved much better performance than the others in CMT.Copyright

Lexicographical order Max-Min fair source quota allocation in mobile Delay-Tolerant Networks

There is a big potential to enable more efficient data dissemination in mobile Delay-Tolerant Networks (DTNs) with the concurrent use of multi-copy forwarding and social metrics. However, this also leads to the possibility of severely overloading the relay nodes with high social metrics, and consequent performance degradation. We propose a fair source quota allocation algorithm to effectively alleviate the load while ensuring their dissemination fairness, i.e, Lexicographical order Max-Min Fairness(LMMF). In this paper, A fair source quota allocation algorithm along with an implementation scheme was presented to take advantage of the features of social networks and social forwarding for higher delivery performance. Extensive simulations based on trace data demonstrate that our mechanism greatly reduces the delivery-ratio degradation caused by uneven load while ensuring fairness among the network users.

Distributed Multi-dimensional Pricing for Efficient Application Offloading in Mobile Cloud Computing

Offloading computation intensive applications to mobile cloud is promising for overcoming the problems of limited computational resources and energy of mobile devices. However, without considering the competition relationship of mobile users and cloudlets in the mobile cloud computing system, existing studies lack an incentive mechanism for the system to achieve efficient application offloading and cloud resource provisioning. In this paper, we design MPTMG, a Multi-dimensional Pricing mechanism based on Two-sided Market Game. We propose three types of prices: a multi-dimensional price corresponding to multi-dimensional resource allocation, a penalty price to encourage fair and high quality cloud services, and a benefit discount factor to motivate more even provisioning of resources on different dimensions in the cloud. Based on these prices, we propose a distributed price-adjustment algorithm for efficient resource allocation and QoS-aware offloading scheduling. We prove that the algorithm can converge in a finite number of iterations to the equilibrium core allocation at which the mobile cloud system achieves the Pareto efficiency by maximizing the total system benefit. To the best of our knowledge, this is the first paper that applies economic theories and pricing mechanisms to manage application offloading in mobile cloud systems. The simulation results demonstrate that our proposed pricing mechanism can significantly improve the system performance.

SCTP performance improvement based on virtual receiver window

Abstract This article puts forward one algorithm for stream control transfer protocol (SCTP) improvement with limited receiver buffer (RBUF). As is well known, SCTP is one of the most important transfer control protocol, but most researches focus on the situations without the RBUF limit. In this study, we analyze the impact of the RBUF size on the performance. Computer simulations show that the network utility is low in reliable transfer, when the RBUF size is smaller than bandwidth delay product. By studying the transmission sequence number (TSN) transfer progress, we find that the peer receiver window (PEER_RWND), which lags behind the true receiver window (RWND), leads to the poor network utility. To improve SCTP performance with limited RBUF, the virtual receiver window (VIRTUAL_RWND) is introduced. Based on the VIRTUAL_RWND, one algorithm is proposed to increase the sending rate. Computer simulations have evaluated an excellent performance of the proposed algorithm at both ideal link without lost packet and nonideal link with lost packet.

Exploiting time-varying graphs for data forwarding in mobile social Delay-Tolerant Networks

With the rapid shift from end-to-end communications to content-based data sharing, there are increasing interests in exploiting mobile social Delay-Tolerant Networks (social DTNs) to deliver data, where the forwarding decision is usually made by comparing the social metrics of encountered nodes. Existing studies mostly derive long-term statistical social metrics without considering the temporal impact from node mobility. We exploit the time-varying contact graphs to analyze the dynamics of social DTNs based on two groups of datasets. Based on the analysis, we derive the time-varying characteristics of node contacts, durative and periodicity, and apply them to more accurately predict the corresponding time-varying social metrics (TSMs). We further propose a two-stage opportunistic forwarding strategy to select relays based on TSMs. Our simulation results verify the importance of the two properties we observe and the effectiveness of our algorithm in tracking time-varying social metrics. We also show the potential of our algorithm in finding general time varying metrics to improve the data dissemination performance of other opportunistic forwarding schemes.

Network Codes-based Multi-Source Transmission Control Protocol for Content-centric Networks

With the rapid shift from end-to-end communications to content-based data retrieval, there are increasing interests in exploiting Content-centric Networks (CCN) to deliver data. As the special characteristics of CCN, in-network caching and naming-based routing make traditional TCP-like transmission control protocol unsuitable. Although there are some existing efforts on improving the congestion control in CCN, the big issue of redundant transmissions caused by multiple sources has received little attention. To eliminate the redundancy and speed up the transmission, we propose a complete Network Codes-based Multi-Source Transmission Control Protocol (MSTCP), which provides an efficient and controllable multi-source content retrieval service over CCN. MSTCP takes advantage of random network coding to make full use of the coded data responded by different sources to speed up decoding and data receiving at the request side. Moreover, we design a scheduling algorithm based on a simple Expected Reception Deadline (ERD) to efficiently control the number of coded packets to send at each source. This not only effectively eliminates the redundant transmissions in CCN, but also helps to significantly speed up the information retrieval. Extensive simulations show that our mechanism greatly reduces the redundancy while speeding up the content retrievals by the network users.