Mathematical Programming Techniques for Sensor Networks

Sorokin, Alexey V.; Boyko, Nikita; Boginski, Vladimir; Uryasev, Stan; Pardalos, Pãnos M.

doi:10.3390/a2010565

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…The other form of the MDAP we are interested in is multi-sensor association with S ≥ 3 sensors. This scenario is common in centralized tracking systems, where sensors that are distributed around a surveillance region report raw measurements to a central node [119] [13]. When each sensor sends its local tracks to a central node for track association and fusion, a MDAP must be solved.…”

Section: Multidimensional Assignmentmentioning

confidence: 99%

Machine Learning Methods for Data Association in Multi-Object Tracking

Emami,

Pardalos,

Elefteriadou

et al. 2018

Preprint

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Data association and track-to-track association, two fundamental problems in single-sensor and multi-sensor multi-target tracking, are instances of an NP-hard combinatorial optimization problem known as the multidimensional assignment problem (MDAP). Over the last few years, data-driven approaches to tackling MDAPs in tracking have become increasingly popular. We argue that viewing multi-target tracking as an assignment problem conceptually unifies the wide variety of machine learning methods that have been proposed for data association and track-to-track association. In this survey, we review recent literature, provide rigorous formulations of the assignment problems encountered in multi-target tracking, and review classic approaches used prior to the shift towards data-driven techniques. Recent attempts at using deep learning to solve NPhard combinatorial optimization problems, including data association, are discussed as well. We highlight representation learning methods for multi-sensor applications and conclude by providing an overview of current multi-target tracking benchmarks.

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Section: Multidimensional Assignmentmentioning

confidence: 99%

Machine Learning Methods for Data Association in Multi-Object Tracking

Emami,

Pardalos,

Elefteriadou

et al. 2018

Preprint

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“…The management and the organization of WSN have been investigated by many researchers. Depending on the application and the goal of researchers, several combinatorial optimization models and solution techniques have been proposed [1]. These models and problems include, among others, sensor localization and tracking [2][3][4][5], sensor scheduling [6][7][8][9][10][11] communication neutralization [12,13] and energy consumption [14,15].…”

Section: Introductionmentioning

confidence: 99%

Complexity Results for Wireless Sensor Network Scheduling

Jarray

2010

WSN

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We study the problem of scheduling multi sensors to visit and observe a group of sites at discrete time points over a planning horizon of given length. We show that scheduling under a given number of visits for each site and in each period is an NP-complete problem by providing equivalence with a problem in discrete tomography. We also give a polynomial time algorithm to schedule the sensors under a given number of visits in each period

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“…Although the wireless network is quite different from wired network, and the routing protocols in the wired network can not be applied directly in the wireless network, some technologies of wired networks are still simulated by wireless networks in their developments [35]. One of such technologies is backbone [15], which can be used for reducing the communication overhead and increasing network reliability.…”

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confidence: 99%