IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 11

sources. One feasible way is context awareness computing,
essential part of the corresponding system. Real-time input
which stores context information linked to sensor data to
data enabled by the IoT can be used to verify and adapt
decide what data to use and facilitate autonomous machinemodels or drive model executions (i.e., simulation). Simulato-machine communications [33]. When current big data are
tion results obtained from model executions can guide the
not enough, the challenges become how to identify what
IoT-enabled control and actuation of physical objects/sysdata are further needed and how to adjust manufacturing
tems. Such a smooth bidirectional connection forms a
the IoT in a low-cost and fast way, which are not easy to
closed loop that can make the state of physical objects conresolve. Thus, during data handling, proper metrics or rules
verge quickly toward the target state. This can also greatly
should be established to evaluate whether current data sets
reduce the cycle time for a model update, analysis, and veriare enough and what additional data are needed if the curfication and carry out prompt what-if analyses to respond
rent results are not satisfactory. This may also involve the
to abrupt changes [34]. Moreover, the models can act as a
incremental deployment of IoT facilities. Timeliness is anothfilter to ensure the reliability and robustness of high-level
er challenge when some applications require instant and
decision-making models rather than feeding (incomplete
responsive big data processing to maximize the benefit
and/or inconsistent) sensory signals directly from the sensgained from big data [21]. The streaming IoT infrastructure [35].
based big data processing, which
Basic models that either do or do
aims to deliver data analysis results
not interface with physical objects
as soon as possible through procan be combined or composed hierTo hide heterogeneity
cessing the freshest data sets, is
archically to support higher-level
and facilitate
noteworthy in this aspect.
decision-making models for manumanagement, physical
Finally, how to efficiently and
facturing and logistic applications
flexibly share big data among difin workshops, factories, or organiobjects are virtualized
ferent data owners and, at the same
zations. One big chal lenge is to
and represented
time, protect the privacy of the
generate simulation results no
owners is challenging. When big
later than the required time for the
as twin models
data are manufactured, they are
physical objects. Multiresolution
(avatars), and they
usually stored and processed in the
modeling and high- performance
are seamlessly and
cloud. More efforts should be made
computing with specially designed/
from both legislative and technical
general purpose acceleration hardclosely integrated
points of view to prevent unauware (e.g., graphics processing
in both the physical
thorized access to private data.
units and many/multiple-core proFine-grained and reconfigurable
cessors) can be used to hasten simand cyber spaces.
data-sharing mechanisms should
ulations [36]. Other challenges
be provided to facilitate efficient
include the online evolution of modand secure data sharing. The sharels without bringing interruptions
ing mechanism of big data may also intertwine with the
to the physical systems according to dynamic environments
business models that data owners use to make profits.
and the pervasive involvement of users in decision-making
activities [36].
Cyberphysical Models and Simulations
There are essentially two kinds of models: mechanism
Modeling and simulation are a particularly useful means
and nonmechanism (such as models established by using
when the intended system costs too much to be built,
machine-learning approaches). Increasingly, nonmechaphysical experiments are dangerous or expensive, or it
nism models (e.g., deep neural networks) have gained wide
takes a long time to know the results of the system due
attention and have considerable characteristics such as
to the changing parameters. There are many such scegood flexibility, adaptability, and self-learning ability. Big
narios in IoT-enabled manufacturing that require the
data can be used to build prediction, classification, and cogresearch and application of modeling and simulation thenitive models for optimal decision making on various levels
ory and technology to facilitate training, decision-makof manufacturing systems and across the full lifecycle of
ing, and so on.
products, such as demand forecasting.
To hide heterogeneity and facilitate management, physical objects are virtualized and represented as twin models
Open and Intelligent Product
(avatars), and they are seamlessly and closely integrated in
Open first refers to the potential involvement of vast
both the physical and cyber spaces [2]. Twin models
human resources in the world. A recent trend is that more
abstract the functions of physical objects [3]. Physical
people beyond the boundaries of organizations will collecobjects and twin models interact in a mutually beneficial
tively participate in an activity of the product design and
manner [34]. The simulation systems that comprise twin
manufacturing. Crowdsourcing [37] and socialized manumodels and other digital models will operate as an
facturing [27] are such efforts to tap into the competences
Ja nua r y 2 01 8

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE

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Table of Contents for the Digital Edition of IEEE Systems, Man, and Cybernetics Magazine - January 2018

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