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Modelfree probabilistic localization of wireless sensor network nodes in indoor environments
"... Abstract. We present a technique that makes up a practical probabilistic approach for locating wireless sensor network devices using the commonly available signal strength measurements (RSSI). From the RSSI measurements between transmitters and receivers situated on a set of landmarks, we construc ..."
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Abstract. We present a technique that makes up a practical probabilistic approach for locating wireless sensor network devices using the commonly available signal strength measurements (RSSI). From the RSSI measurements between transmitters and receivers situated on a set of landmarks, we construct appropriate probabilistic descriptors associated with a device’s position in the contiguous space using a pdf interpolation technique. We then develop a localization system that relies on these descriptors and the measurements made by a set of clusterheads positioned at some of the landmarks. The localization problem is formulated as a composite hypothesis testing problem. We develop the requisite theory, characterize the probability of error, and address the problem of optimally placing clusterheads. Experimental results show that our system achieves an accuracy equivalent to 95 % < 5 meters and 87 % < 3 meters. 1
Optimized scheduled multiple access control for wireless sensor networks
 IEEE Trans. Automat. Contr
, 2009
"... Abstract—We consider wireless sensor networks with multiple sensor modalities that capture data to be transported over multiple frequency channels to potentially multiple gateways. We study a general problem of maximizing a utility function of achievable transmission rates between communicating nod ..."
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Abstract—We consider wireless sensor networks with multiple sensor modalities that capture data to be transported over multiple frequency channels to potentially multiple gateways. We study a general problem of maximizing a utility function of achievable transmission rates between communicating nodes. Decisions involve routing, transmission scheduling, power control, and channel selection, while constraints include physical communication constraints, interference constraints, and fairness constraints. Due to its structure the formulation grows exponentially with the size of the network. Drawing upon largescale decomposition ideas in mathematical programming, we develop a cuttingplane algorithm and show that it terminates in a finite number of iterations. Every iteration requires the solution of a subproblem which is NPhard. To solve the subproblem we (i) devise a particular relaxation that is solvable in polynomial time and (ii) leverage polynomialtime approximation schemes. A combination of both approaches enables an improved decomposition algorithm which is efficient for solving large problem instances. Index Terms—Mathematical programming/optimization, multiple frequency channels, routing, transmission scheduling, wireless sensor networks. I.
Statistical Anomaly Detection with Sensor Networks IOANNIS CH. PASCHALIDIS
"... We seek to detect statistically significant temporal or spatial changes in either the underlying process the sensor network is monitoring or in the network operation itself. These changes may point to faults, adversarial threats, misbehavior, or other anomalies that require intervention. To that end ..."
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We seek to detect statistically significant temporal or spatial changes in either the underlying process the sensor network is monitoring or in the network operation itself. These changes may point to faults, adversarial threats, misbehavior, or other anomalies that require intervention. To that end, we introduce a new statistical anomaly detection framework that uses Markov models to characterize the “normal ” behavior of the sensor network. We develop a series of Markov models, including treeindexed Markov chains which can model its spatial structure. For each model, an anomalyfree probability law is estimated from past traces. We leverage large deviations techniques to develop optimal anomaly detection rules for each corresponding Markov model, assessing whether its most recent empirical measure is consistent with the anomalyfree probability law. A series of simulation results, some with real sensor data, validate the effectiveness of the proposed anomaly detection algorithms.
ModelFree Stochastic Localization of CBRN Releases∗
"... Abstract—We present a novel twostage methodology for ..."
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Abstract—We present a novel twostage methodology for
1Statistical Traffic Anomaly Detection in TimeVarying Communication Networks ∗
"... Abstract—We propose two methods for traffic anomaly detection in communication networks where properties of normal traffic evolve dynamically. We formulate the anomaly detection problem as a binary composite hypothesis testing problem and develop a modelfree and a modelbased method, leveraging tec ..."
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Abstract—We propose two methods for traffic anomaly detection in communication networks where properties of normal traffic evolve dynamically. We formulate the anomaly detection problem as a binary composite hypothesis testing problem and develop a modelfree and a modelbased method, leveraging techniques from the theory of large deviations. Both methods first extract a family of Probability Laws (PLs) that represent normal traffic patterns during different timeperiods, and then detect anomalies by assessing deviations of traffic from these laws. We establish the asymptotic NewmanPearson optimality of both methods and develop an optimizationbased approach for selecting the family of PLs from past traffic data. We validate our methods on networks with two representative timevarying traffic patterns and one common anomaly related to data exfiltration. Simulation results show that our methods perform better than their vanilla counterparts, which assume that normal traffic is stationary. Index Terms—Statistical anomaly detection, large deviations theory, set covering, binary composite hypothesis testing, cybersecurity. I.
1Optimizing Warehouse Forklift Dispatching Using a Sensor Network and Stochastic Learning ∗
"... Abstract—The authors report on a successful deployment of an inexpensive mobile wireless sensor network in a commercial warehouse served by a fleet of forklifts. The aim is to improve forklift dispatching and reduce the costs associated with the delays of loading/unloading delivery trucks. To that e ..."
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Abstract—The authors report on a successful deployment of an inexpensive mobile wireless sensor network in a commercial warehouse served by a fleet of forklifts. The aim is to improve forklift dispatching and reduce the costs associated with the delays of loading/unloading delivery trucks. To that end, an integrated system including both hardware and software is constructed. First, the forklifts are instrumented with sensor nodes that collect an array of information, including the forklifts ’ physical location, usage time, bumping/collision history, and battery status. The hardware’s capability is enhanced with a theoretically sound hypothesis testing technique to capture the rather elusive location information, and the collection of the data is done in an efficient eventdriven manner. The information acquired combined with inventory information is fed into a sophisticated actorcritic type stochastic learning method to generate dispatching recommendations. Because noise is inevitable in such wireless sensor networks, the performance of the algorithm is investigated under different noise levels. In combining wireless sensing with stateoftheart decision theory, this work extends beyond the standard use of wireless sensor networks as monitoring devices. I.
1Formation Detection with Wireless Sensor Networks
"... We consider the problem of detecting the formation of a set of wireless sensor nodes based on the pairwise measurements of signal strength corresponding to all transmitter/receiver pairs. We assume that formations take values in a discrete set and develop a composite hypothesis testing approach whic ..."
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We consider the problem of detecting the formation of a set of wireless sensor nodes based on the pairwise measurements of signal strength corresponding to all transmitter/receiver pairs. We assume that formations take values in a discrete set and develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test (LT). We also adapt one prevalent supervised learning approach, Multiple Support Vector Machines (MSVMs), and compare it with our probabilistic methods. Due to the highly variant measurements from the wireless sensor nodes, and these methods ’ different adaptability to multiple observations, our analysis and experimental results suggest that GLT is more accurate and suitable for formation detection. The formation detection problem has interesting applications in posture detection with Wireless Body Area Networks (WBANs), which is extremely useful in health monitoring and rehabilitation. Another valuable application we explore concerns autonomous robot systems.