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1. Distributed In-Network Processing of k-MaxRS in Wireless Sensor Networks

   https://doi.org/10.5220/0006210701080117  

   Wonge-ammat, Panitan; Mas-ud Hussain, Muhammed; Trajcevski, Goce; Avci, Besim; Khokhar, Ashfaq (February 2017, Proceedings of the 6th International Conference on Sensor Networks (SENSORNETS 2017), Porto, Portugal, February 19-21, 2017)

   We address the problem of in-network processing of k-Maximizing Range Sum (k-MaxRS) queries inWireless Sensor Networks (WSN). The traditional, Computational Geometry version of the MaxRS problem considers the setting in which, given a set of (possibly weighted) 2D points, the goal is to determine the optimal location for a given (axes-parallel) rectangle R to be placed so that the sum of the weights (or, a simple count) of the input points in R’s interior is maximized. In WSN, this corresponds to finding the location of region R such that the sum of the sensors’ readings inside R is maximized. The k-MaxRS problem deals with maximizing the overall sum over k such rectangular regions. Since centralized processing – i.e., transmitting the raw readings and subsequently determining the k-MaxRS in a dedicated sink – incur communication overheads, we devised an efficient distributed algorithm for in-network computation of k-MaxRS. Our experimental observations show that the novel algorithm provides significant energy/communication savings when compared to the centralized approach. 

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2. Sequential change-point detection in high-dimensional Gaussian graphical models

   Hossein Keshavarz, George Michailidis (June 2020, Journal of machine learning research)

   null (Ed.)

   High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences. There has been recent work in offline detection and estimation of regime changes in the topology of sparse graphical models. However, the online setting remains largely unexplored, despite its high relevance to applications in sensor networks and other engineering monitoring systems, as well as financial markets. To that end, this work introduces a novel scalable online algorithm for detecting an unknown number of abrupt changes in the inverse covariance matrix of sparse Gaussian graphical models with small delay. The proposed algorithm is based upon monitoring the conditional log-likelihood of all nodes in the network and can be extended to a large class of continuous and discrete graphical models. We also investigate asymptotic properties of our procedure under certain mild regularity conditions on the graph size, sparsity level, number of samples, and preand post-changes in the topology of the network. Numerical works on both synthetic and real data illustrate the good performance of the proposed methodology both in terms of computational and statistical efficiency across numerous experimental settings. 

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3. Sequential change-point detection in high-dimensional Gaussian graphical models

   Keshavarz, Hossein; Michailidis, George; Atchade, Yves (June 2020, Journal of machine learning research)

   High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences. There has been recent work in offline detection and estimation of regime changes in the topology of sparse graphical models. However, the online setting remains largely unexplored, despite its high relevance to applications in sensor networks and other engineering monitoring systems, as well as financial markets. To that end, this work introduces a novel scalable online algorithm for detecting an unknown number of abrupt changes in the inverse covariance matrix of sparse Gaussian graphical models with small delay. The proposed algorithm is based upon monitoring the conditional log-likelihood of all nodes in the network and can be extended to a large class of continuous and discrete graphical models. We also investigate asymptotic properties of our procedure under certain mild regularity conditions on the graph size, sparsity level, number of samples, and pre- and post-changes in the topology of the network. Numerical works on both synthetic and real data illustrate the good performance of the proposed methodology both in terms of computational and statistical efficiency across numerous experimental settings. 

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4. Sequential change-point detection in high-dimensional Gaussian graphical models

   Keshavarz, H.; Michailidis, G.; Y Atchade, Y. (June 2020, Journal of machine learning research)

   High dimensional piecewise stationary graphical models represent a versatile class for modelling time varying networks arising in diverse application areas, including biology, economics, and social sciences. There has been recent work in offline detection and estimation of regime changes in the topology of sparse graphical models. However, the online setting remains largely unexplored, despite its high relevance to applications in sensor networks and other engineering monitoring systems, as well as financial markets. To that end, this work introduces a novel scalable online algorithm for detecting an unknown number of abrupt changes in the inverse covariance matrix of sparse Gaussian graphical models with small delay. The proposed algorithm is based upon monitoring the conditional log-likelihood of all nodes in the network and can be extended to a large class of continuous and discrete graphical models. We also investigate asymptotic properties of our procedure under certain mild regularity conditions on the graph size, sparsity level, number of samples, and pre- and post-changes in the topology of the network. Numerical works on both synthetic and real data illustrate the good performance of the proposed methodology both in terms of computational and statistical efficiency across numerous experimental settings. 

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5. Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

   https://doi.org/doi.org/10.48550/arXiv.2110.01119  

   Michal Yemini, Stephanie Gil (October 2021, ArXivorg)

   We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors may collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors’ and clusters’ detection quality, heterogeneity in their communication opportunities, and nonconvexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture. 

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