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Localising runtime Anomalies in Service-Oriented Systems

- He, Qiang, Xie, Xiaoyuan, Wang, Yanchun, Ye, Dayong, Chen, Feifei, Jin, Hai, Yang, Yun

  • Authors: He, Qiang , Xie, Xiaoyuan , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Jin, Hai , Yang, Yun
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: IEEE Transactions on Services Computing ( Volume: 10, Issue: 1, Jan.-Feb. 1 2017 ) Vol. 10, p. 94-106
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  • Description: In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.

An analysis of human engagement behaviour using descriptors from human feedback, eye tracking, and saliency modelling

- Podder, Pallab, Paul, Manoranjan, Debnath, Tanmoy, Murshed, Manzur

Background subtraction for real-time video analytics based on multi-hypothesis mixture-of-Gaussians

- Haque, Mahfuzul, Murshed, Manzur

  • Authors: Haque, Mahfuzul , Murshed, Manzur
  • Date: 2012
  • Type: Text , Conference proceedings
  • Relation: 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 18th-21st Sept, 2012. p1-6
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  • Description: Robust background subtraction (BS) is essential for high quality foreground detection in most video analytics systems. Recent BS techniques achieve superior detection quality mostly by exploiting the complementary strengths of multiple background models or processing stages. Consequently, these techniques fail to meet the operational requirements of real-time video analytics due to high computational overhead where BS is just the primary processing task. In this paper, we propose a new BS technique, named multi-hypothesis mixture-of-Gaussians (MH-MOG), suitable for real-time video analytics. The essential idea is to maintain a single background model based on perception-aware mixture-of-Gaussians and then, generating multiple detection hypotheses with different processing bases. Finally, only during the detection stage, the complementary strengths of the hypotheses are exploited to achieve superior detection quality without significant computational overhead. Comprehensive experimental evaluation validates the efficacy of MH-MOG.

Data sharing in secure multimedia wireless sensor networks

- Usman, Muhammad, Jan, Mian, Xiangjian, He, Nanda, Priyadarsi

  • Authors: Usman, Muhammad , Jan, Mian , Xiangjian, He , Nanda, Priyadarsi
  • Date: 2016
  • Type: Text , Conference proceedings
  • Relation: 2016 IEEE Trustcom/BigDataSE/ISPA;Tianjin, China; 23-26 August 2016 p. 590-597
  • Full Text: false
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  • Description: The use of Multimedia Wireless Sensor Networks (MWSNs) is becoming common nowadays with a rapid growth in communication facilities. Similar to any other WSNs, these networks face various challenges while providing security, trust and privacy for user data. Provisioning of the aforementioned services become an uphill task especially while dealing with real-time streaming data. These networks operates with resource-constrained sensor nodes for days, months and even years depending on the nature of an application. The resource-constrained nature of these networks makes it difficult for the nodes to tackle real-time data in mission-critical applications such as military surveillance, forest fire monitoring, health-care and industrial automation. For a secured MWSN, the transmission and processing of streaming data needs to be explored deeply. The conventional data authentication schemes are not suitable for MWSNs due to the limitations imposed on sensor nodes in terms of battery power, computation, available bandwidth and storage. In this paper, we propose a novel quality-driven clustering-based technique for authenticating streaming data in MWSNs. Nodes with maximum energy are selected as Cluster Heads (CHs). The CHs collect data from member nodes and forward it to the Base Station (BS), thus preventing member nodes with low energy from dying soon and increasing life span of the underlying network. The proposed approach not only authenticates the streaming data but also maintains the quality of transmitted data. The proposed data authentication scheme coupled with an Error Concealment technique provides an energy-efficient and distortion-free real-time data streaming. The proposed scheme is compared with an unsupervised resources scenario. The simulation results demonstrate better network lifetime along with 21.34 dB gain in Peak Signal-to-Noise Ratio (PSNR) of received video data streams.
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