An Exact Algorithm for Maximum Entropy Sampling

Ko, Chun-Wa; Lee, Jon; Queyranne, Maurice

doi:10.1287/opre.43.4.684

Cited by 273 publications

(211 citation statements)

References 6 publications

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“…As shown by Ko et al [13] the problem of sensors network optimization is NPhard. To solve such problems the Simulated Annealing (SA) algorithm is efficient.…”

Section: Choice Of the Algorithmmentioning

confidence: 99%

Optimization of sensor networks for the estimation of atmospheric pollutants sources

Kouichi

Turbelin

Ngae

et al. 2016

WIT Transactions on Ecology and the Environment

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This study describes a process to design a sensor network. This network could include: wireless mobile sensors deployed by first responders in hazardous material operations, stationary sensors used to protect an area against accidental, or intentional, contaminations or stationary air quality monitoring stations. The objective of the network is the estimation (localization -quantification) of releases sources. The design of such a network has an important issue in determining the optimal placement of sensors. This paper presents the first application of the renormalized data assimilation method to address this issue. It is associated with a classical optimization algorithm (simulate annealing) to solve the combinatory optimization problem consisting of finding the optimal configuration of sensors among a set of potential positions. Three scenarios, corresponding with three different cost functions, are proposed. The first one consists of optimizing the design of a network deployed in emergency situations. Experimental data from a wind tunnel experiment are used. The objective is to characterize the source to minimize error in measurement forecasts. The second one is to optimize the design of the same network but in a situation where the source can be anywhere in the domain. To that end, an entropic criterion is used. The last one consists of optimizing the design of a stationary network. The objective is to characterize the source with varying meteorological conditions (experimental meteorological data are used).

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“…As shown by Ko et al [13] the problem of sensors network optimization is NPhard. To solve such problems the Simulated Annealing (SA) algorithm is efficient.…”

Section: Choice Of the Algorithmmentioning

confidence: 99%

Optimization of sensor networks for the estimation of atmospheric pollutants sources

Kouichi

Turbelin

Ngae

et al. 2016

WIT Transactions on Ecology and the Environment

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“…These works concentrate on the advancement of data, regularly characterized as either joint entropy or data pick up (delta entropy). It is regular practice to utilize the covetous (nearsighted) arrangement towards this determination issue [40], [38], [39], with ensured execution limits, because of the handling complexities of discovering the ideal arrangement [28], [29], [30]. SmartContext expands upon the insatiable arrangement of Krause and Guestrin [40], however is adjusted to utilizing estimation precision rather than data pick up.…”

Section: Literature Surveymentioning

confidence: 99%

Functional Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

2016

IJSR

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Context data brings new open doors for productive and compelling applications and services on mobile devices. An extensive variety of examination has abused context dependency, i.e. the relations in the middle of context(s) and the result, to accomplish huge, evaluated, execution picks up for an assortment of applications and services. These works ordinarily need to manage the difficulties of numerous context sources prompting an inadequate preparing dataset, and the difficulties of energy hungry context sensors. Regularly, they address these difficulties in an application particular and specially appointed way. We free mobile application planners and analysts from these weights by giving a systematic way to deal with these difficulties. Specifically, we 1) characterize and measure the context-dependency of three central sorts of mobile use (went by sites, telephone calls, and application use) in an application freethinker yet viable way, giving understanding into the execution of potential application. 2) Address the test of information meager condition when managing various context sources in an efficient way. 3) Present SmartContext to address the energy challenge via consequently selecting among context sources while guaranteeing a base precision for every estimation. Our examination and discoveries depend on one year of utilization and context follows gathered, in actuality, settings from 24 iPhone clients. We show discoveries with respect to the context dependency of three sorts of mobile utilization from 24 clients, yet our philosophy and the lessons we learn can be promptly stretched out to different sorts of use and additionally framework assets. Our discoveries control the improvement of context mindful frameworks, and highlight the difficulties and assumptions in regards to the context dependency of mobile use.

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“…Algorithms for determining the best sensor placements for measuring a scalar field modeled by a GP have used entropy [15], mutual information [6], and variance reduction [16]. Mutual information is commonly used because it is submodular [6] [16], but we will instead use the average reduction in variance…”

Section: Sensor Placementmentioning

confidence: 99%

Branch and bound for informative path planning

Binney

Sukhatme

2012

2012 IEEE International Conference on Robotics and Automation

113

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Abstract-We present an optimal algorithm for informative path planning (IPP), using a branch and bound method inspired by feature selection algorithms. The algorithm uses the monotonicity of the objective function to give an objective functiondependent speedup versus brute force search. We present results which suggest that when maximizing variance reduction in a Gaussian process model, the speedup is significant.

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An Exact Algorithm for Maximum Entropy Sampling

Cited by 273 publications

References 6 publications

Optimization of sensor networks for the estimation of atmospheric pollutants sources

Optimization of sensor networks for the estimation of atmospheric pollutants sources

Functional Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

Branch and bound for informative path planning

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