Network Simplification and K-Terminal Reliability Evaluation of Sensor-Cloud Systems

The sensor-cloud system (SCS) integrates sensors, sensor networks, and cloud for managing sensors, collecting and processing data, and decision-making based on data processed. Though the SCS has received tremendous attention from both academia and industry because of its numerous exciting applications, it still faces the challenge in reliability. The reliability of an SCS is generally referred to as the ability to perform required functions for a given period of time. This work is focused on the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-terminal reliability of an SCS, which is concerned with the successful communication between all pairs of network nodes belonging to a pre-specified subset <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>. The increased complexity and scale of real-life SCSs require new efficient techniques to evaluate their <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-terminal reliability. In this work we make novel contributions by proposing a network simplification method that can effectively remove all redundant network edges and vertices, leading to a significantly reduced network model for accurate and efficient <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-terminal reliability analysis. The method is based on graph decomposition and reconstruction through articulation vertices. Empirical studies show that the proposed simplification method integrated with the binary-decision-diagrams based evaluation algorithm can significantly speed up <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-terminal reliability analysis of large real-life SCSs.

[1]  Dongqing Xie,et al.  Cost-sensitive and sequential feature selection for chiller fault detection and diagnosis. , 2018 .

[2]  Gerardo Rubino,et al.  Static Network Reliability Estimation via Generalized Splitting , 2013, INFORMS J. Comput..

[3]  Gregory Levitin,et al.  Dynamic System Reliability , 2019 .

[4]  Liudong Xing,et al.  Binary Decision Diagrams and Extensions for System Reliability Analysis: Xing/Binary , 2015 .

[5]  Victor C. M. Leung,et al.  End-edge-cloud collaborative computation offloading for multiple mobile users in heterogeneous edge-server environment , 2020, Wireless Networks.

[6]  Liudong Xing,et al.  Reliability in Internet of Things: Current Status and Future Perspectives , 2020, IEEE Internet of Things Journal.

[7]  Gregory Levitin,et al.  Reliability evaluation for acyclic consecutively connected networks with multistate elements , 2001, Reliab. Eng. Syst. Saf..

[8]  Yuchang Mo,et al.  A Multiple-Valued Decision-Diagram-Based Approach to Solve Dynamic Fault Trees , 2014, IEEE Transactions on Reliability.

[9]  Saburo Muroga,et al.  Binary Decision Diagrams , 2000, The VLSI Handbook.

[10]  Yang Du,et al.  A Hybrid LSTM Neural Network for Energy Consumption Forecasting of Individual Households , 2019, IEEE Access.

[11]  Liudong Xing,et al.  Reliability Analysis of IoT Networks with Community Structures , 2020, IEEE Transactions on Network Science and Engineering.

[12]  Sy-Yen Kuo,et al.  Efficient and Exact Reliability Evaluation for Networks With Imperfect Vertices , 2007, IEEE Transactions on Reliability.

[13]  Kim-Kwang Raymond Choo,et al.  Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things , 2019, Future Gener. Comput. Syst..

[14]  Youngsuk Kim,et al.  Network reliability analysis of complex systems using a non-simulation-based method , 2013, Reliab. Eng. Syst. Saf..

[15]  Wei-Chang Yeh,et al.  A Novel Label Universal Generating Function Method for Evaluating the One-to-all-Subsets General Multistate Information Network Reliability , 2011, IEEE Transactions on Reliability.

[16]  Gregory Levitin,et al.  BDD-based reliability evaluation of phased-mission systems with internal/external common-cause failures , 2013, Reliab. Eng. Syst. Saf..

[17]  Hao Wang,et al.  Critical Internet of Things: An Interworking Solution to Improve Service Reliability , 2020, IEEE Communications Magazine.

[18]  Michael O. Ball,et al.  Computational Complexity of Network Reliability Analysis: An Overview , 1986, IEEE Transactions on Reliability.

[19]  Wei-Chang Yeh An improved sum-of-disjoint-products technique for the symbolic network reliability analysis with known minimal paths , 2007, Reliab. Eng. Syst. Saf..

[20]  Ning Jin,et al.  Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy , 2018, Energies.

[21]  Kishor S. Trivedi,et al.  A BDD-Based Algorithm for Reliability , 2000 .

[22]  Dmitri Loguinov,et al.  On Lifetime-Based Node Failure and Stochastic Resilience of Decentralized Peer-to-Peer Networks , 2005, IEEE/ACM Transactions on Networking.

[23]  Shudong Sun,et al.  A novel decision diagrams extension method , 2014, Reliab. Eng. Syst. Saf..

[24]  Vinod Vokkarane,et al.  A Study of Online Social Network Privacy Via the TAPE Framework , 2015, IEEE Journal of Selected Topics in Signal Processing.

[25]  Randal E. Bryant,et al.  Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.

[26]  Junho Song,et al.  Matrix-based System Reliability Analysis of Urban Infrastructure Networks: A Case Study of MLGW Natural Gas Network , 2007 .

[27]  Gerardo Rubino,et al.  Approximate Zero-Variance Importance Sampling for Static Network Reliability Estimation , 2011, IEEE Transactions on Reliability.

[28]  Liudong Xing,et al.  Infrastructure Communication Sensitivity Analysis of Wireless Sensor Networks , 2016, Qual. Reliab. Eng. Int..

[29]  Gregory Levitin,et al.  Optimal structure of multi-state systems with multi-fault coverage , 2013, Reliab. Eng. Syst. Saf..

[30]  Shyue-Kung Lu,et al.  OBDD-based evaluation of k-terminal network reliability , 2002, IEEE Trans. Reliab..

[31]  J. Hopcroft,et al.  Algorithm 447: efficient algorithms for graph manipulation , 1973, CACM.

[32]  Gerardo Rubino,et al.  Combination of conditional Monte Carlo and approximate zero-variance importance sampling for network reliability estimation , 2010, Proceedings of the 2010 Winter Simulation Conference.

[33]  Gregory Levitin,et al.  Reliability of non-repairable phased-mission systems with propagated failures , 2013, Reliab. Eng. Syst. Saf..

[34]  Kenneth J. Supowit,et al.  Finding the Optimal Variable Ordering for Binary Decision Diagrams , 1987, 24th ACM/IEEE Design Automation Conference.

[35]  Wei-Chang Yeh,et al.  A Modified Universal Generating Function Algorithm for the Acyclic Binary-State Network Reliability , 2012, IEEE Transactions on Reliability.

[36]  Paolo Gardoni,et al.  Matrix-based system reliability method and applications to bridge networks , 2008, Reliab. Eng. Syst. Saf..

[37]  Liudong Xing,et al.  A Multiple-Valued Decision Diagram Based Method for Efficient Reliability Analysis of Non-Repairable Phased-Mission Systems , 2014, IEEE Transactions on Reliability.

[38]  Zhiwei Ji,et al.  Semi-supervised learning for early detection and diagnosis of various air handling unit faults , 2018, Energy and Buildings.

[39]  Nikolaos Limnios,et al.  K-Terminal Network Reliability Measures With Binary Decision Diagrams , 2007, IEEE Transactions on Reliability.

[40]  S. Kuo,et al.  Determining terminal-pair reliability based on edge expansion diagrams using OBDD , 1999 .

[41]  Liudong Xing,et al.  Choosing a heuristic and root node for edge ordering in BDD-based network reliability analysis , 2014, Reliab. Eng. Syst. Saf..