Behavior Pattern Detection for Data Assimilation in Agent-Based Simulation of Smart Environments

Rai, Sanish; Hu, Xiaolin

doi:10.1109/wi-iat.2013.106

Cited by 13 publications

(13 citation statements)

References 18 publications

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“…Other studies have used real-time data to augment crowd simulation models [57]. In particular, behaviour detection models have been used to facilitate data assimilation in ABMs of smart environments [29]. Attempts to improve management and visualization of results of models that rely on dynamic spatial data are also of note [28].…”

Section: Related Workmentioning

confidence: 99%

“…In an attempt to address the challenges related to data management and dynamic visualization of the results of dynamic data-driven ABMs, parallel simulation under the framework of Distributed Dynamic Data-driven Simulation and Analysis System (4D-SAS) has been proposed [28]. Furthermore, there is evidence that behaviour patterns of agents as captured by sensor observations can facilitate and improve data assimilation in dynamic data driven simulation models [29]. Whereas dynamic-data assimilation has largely been successful in mathematical models [30,31], there have only been limited attempts to implement data-driven ABMs [32].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Oloo

Wallentin

2017

IJGI

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Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used "at the end" to validate the conceptual understanding. In today's data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Oloo

Wallentin

2017

IJGI

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“…It can be used to learn previous input sequences and estimate future state. An algorithm to detect behaviour patterns based on HMM is proposed in [7]. They used binary inputs to learn the behaviour of conference events.…”

Section: Related Workmentioning

confidence: 99%

“…posture and movement transition [3], and activity recognition [4] to longer resolution such as longterm behaviour patterns [5]. Computational intelligence techniques such as neural networks [6], Hidden Markov Model [7], fuzzy logic [8], mixture model [5], etc. as well as semantic approaches [9], [10] have been used to model, learn, and discover behavioural patterns.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mobility entropy in an Ambient Intelligent environment

Chernbumroong

Lotfi

Langensiepen

2014

2014 IEEE Symposium on Intelligent Agents (IA)

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Abstract-Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term.In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues.

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“…Subsequently, an increasing number of ABMs have been designed to incorporate these dynamic data [18,19]. These dynamic data can be employed to improve modeling accuracy and augment analytical capabilities.…”

Section: Introductionmentioning

confidence: 99%

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

Guan

et al. 2016

IJGI

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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena modeling, dynamic data injection, and real-time visualization. To tackle these problems, we propose a distributed dynamic-data driven simulation and analysis system (4D-SAS) specifically for massive spatial agent-based modeling to support real-time representation and analysis of geospatial phenomena. To accomplish large-scale geospatial problem-solving, the 4D-SAS system was spatially enabled to support geospatial model development and employs high-performance computing to improve simulation performance. It can automatically decompose simulation tasks and distribute them among computing nodes following two common schemes: order division or spatial decomposition. Moreover, it provides streaming channels and a storage database to incorporate dynamic data into simulation models; updating agent context in real-time. A new online visualization module was developed based on a GIS mapping library, SharpMap, for an animated display of model execution to help clients understand the model outputs efficiently. To evaluate the system's efficiency and scalability, two different spatially explicitly agent-based models, an en-route choice model, and a forest fire propagation model, were created on 4D-SAS. Simulation results illustrate that 4D-SAS provides an efficient platform for dynamic data-driven geospatial modeling, e.g., both discrete multi-agent simulation and grid-based cellular automata, demonstrating efficient support for massive parallel simulation. The parallel efficiency of the two models is above 0.7 and remains nearly stable in our experiments.

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Behavior Pattern Detection for Data Assimilation in Agent-Based Simulation of Smart Environments

Cited by 13 publications

References 18 publications

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

Prediction of mobility entropy in an Ambient Intelligent environment

4D-SAS: A Distributed Dynamic-Data Driven Simulation and Analysis System for Massive Spatial Agent-Based Modeling

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