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1. Ordering for Communication-Efficient Quickest Change Detection in a Decomposable Graphical Model

   https://doi.org/10.1109/TSP.2021.3077302  

   Chen, Yicheng ; Blum, Rick S. ; Sadler, Brian M ( May 2021 , IEEE Transactions on Signal Processing)

   null (Ed.)

   A quickest change detection problem is considered in a sensor network with observations whose statistical dependency structure across the sensors before and after the change is described by a decomposable graphical model (DGM). Distributed computation methods for this problem are proposed that are capable of producing the optimum centralized test statistic. The DGM leads to the proper way to collect nodes into local groups equivalent to cliques in the graph, such that a clique statistic which summarizes all the clique sensor data can be computed within each clique. The clique statistics are transmitted to a decision maker to produce the optimum centralized test statistic. In order to further improve communication efficiency, an ordered transmission approach is proposed where transmissions of the clique statistics to the fusion center are ordered and then adaptively halted when sufficient information is accumulated. This procedure is always guaranteed to provide the optimal change detection performance, despite not transmitting all the statistics from all the cliques. A lower bound on the average number of transmissions saved by ordered transmissions is provided and for the case where the change seldom occurs the lower bound approaches approximately half the number of cliques provided a well behaved distance measure between the distributions of the sensor observations before and after the change is sufficiently large. We also extend the approach to the case when the graph structure is different under each hypothesis. Numerical results show significant savings using the ordered transmission approach and validate the theoretical findings. 

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2. Decentralized random-field estimation for sensor networks using quantized spatially correlated data and fusion-center feedback

   Dogandzic, Aleksandar ; Qiu, Kun ( December 2008 , IEEE transactions on signal processing)

   In large-scale wireless sensor networks, sensor-processor elements (nodes) are densely deployed to monitor the environment; consequently, their observations form a random field that is highly correlated in space.We consider a fusion sensor-network architecture where, due to the bandwidth and energy constraints, the nodes transmit quantized data to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. In addition to saving energy, this feedback ensures reliability and robustness to node and fusion-center failures. We assume that the sensor observations follow a linear-regression model with known spatial covariances between any two locations within a region of interest. We propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the random field and its parameters. We also derive a simple suboptimal scheme for estimating the unknown parameters, apply our estimation approach to the no-feedback scenario, discuss field prediction at arbitrary locations within the region of interest, and present numerical examples demonstrating the performance of the proposed methods. 

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3. Asymptotic analysis of a new low complexity encryption approach for the Internet of Things, smart cities and smart grid

   https://doi.org/10.1109/ICSGSC.2017.8038576  

   Samudrala, Ananth Narayan ; Blum, Rick S. ( July 2017 , 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC))

   Abstract (WSN) using encrypted non-binary quantized data is studied. In a WSN, sensors transmit their observations to a fusion center through a wireless medium where the observations are susceptible to unauthorized eavesdropping. Encryption approaches for WSNs with fixed threshold binary quantization were previously explored. However, fixed threshold binary quantization limits parameter estimation to scalar parameters. In this paper, we propose a stochastic encryption approach for WSNs that can operate on non-binary quantized observations and has the capability for vector parameter estimation. We extend a binary stochastic encryption approach proposed previously, to a nonbinary generalized case. Sensor outputs are quantized using a quantizer with R + 1 levels, where R in {1.2. 3 ...}, encrypted by flipping them with certain flipping probabilities, and then transmitted. Optimal estimators using maximum-likelihood estimation are derived for both a legitimate fusion center (LFC) and a third party fusion center (TPFC) perspectives. We assume the TPFC is unaware of the encryption. Asymptotic analysis of the estimators is performed by deriving the Cramer-Rao lower bound for LFC estimation, and the asymptotic bias and variance for TPFC estimation. Numerical results validating the asymptotic analysis are presented. 

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4. Optimal Data Rate Allocation for Dynamic Sensor Fusion over Resource Constrained Communication Networks

   Jung, H.& T. ( May 2021 , American Control Conference)

   We consider a dynamic sensor fusion problem where a large number of remote sensors observe a common Gauss-Markov process and the observations are transmitted to a fusion center over a resource constrained communication network. The design objective is to allocate an appropriate data rate to each sensor in such a way that the total data traffic to the fusion center is minimized, subject to a constraint on the fusion center's state estimation error covariance. We show that the problem can be formulated as a difference-of-convex program, to which we apply the convex-concave procedure (CCP) and the alternating direction method of multiplier (ADMM). Through a numerical study on a truss bridge sensing system, we observe that our algorithm tends to allocate zero data rate to unneeded sensors, implying that the proposed method is an effective heuristic for sensor selection. 

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5. Estimating the Spread of Wildland Fires via Evidence-Based Information Fusion

   https://doi.org/10.1109/TCST.2022.3183645  

   Soderlund, Alexander ; Kumar, Mrinal ( March 2023 , IEEE Transactions on Control Systems Technology)

   This article presents a new evidential reasoning approach for estimating the state of an evolving wildfire in real time. Given assumed terrain information and localized wind velocity distribution parameters, a probabilistic representation (i.e., the belief state) of a wildfire is forecast across the spatiotemporal domain through a compilation of fire spread simulations. The forecast is updated through information fusion based on observations provided by: 1) embedded temperature sensors and 2) mobile vision agents that are advantageously directed toward locations of information extraction based on the current state estimate. This combination of uncertain sources is performed under the evidence-based Dempster’s rule of combination and is then used to enact sensor reconfiguration based on the updated estimate. This research finds that the evidential belief combination vastly outperforms the standard forecasting approach (where no sensor data are incorporated) in the presence of imprecise environmental parameters. 

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