IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 8

objects that are not easily detectable or distinguishable
using traditional sensor technologies but not to monitor
the condition of objects [16]. Comparatively, WSNs can not
only provide information about the condition of the objects
and environment but also support multihop wireless communication. Some WSNs may be equipped with actuators
to perform appropriate physical actions. Ultimately, RFID
and WSNs can be combined [16].
Cloud Computing and Big Data
Based on virtualization technology and service-oriented
architecture (SOA), cloud computing enables the efficient
management of an extremely large shared pool of configurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned
and released with minimal management effort or service
provider interaction [17]. It has essential characteristics,
such as on-demand access, resource pooling (multitenant),
rapid elasticity, and measured service (a pay-as-you-go business model). Cloud computing can provide an important
thrust toward transforming the manufacturing sector [18].
Cloud manufacturing-a new service-oriented manufacturing paradigm [19]-is one significant effort that has attracted wide global attention [18].
With huge amounts of computing resources, the cloud
computing paradigm provides unprecedented capability
for the convenient handling of big data generated from
IoT-enabled manufacturing. The success or failure of the
IoT hinges on big data, which is a broad term for data sets
so large or complex that traditional data-processing technologies are inadequate [53]. It has three distinct V characteristics compared to traditional data sets: volume (i.e.,
large amounts of data, easily accounting for terabytes of
data), variety (i.e., the heterogeneity of data types, structured and unstructured data of text, video, images, and so
on), and velocity (i.e., the speed of data creation and time
frame of data processing to maximize the value) [20], though
others later proposed a fourth V (value) and a fifth V
(veracity). The lifecycle of big data comprises phases of
data acquisition, extraction, integration, analysis, and
interpretation [21]. With powerful storage and computing
capability, cloud computing plays a fundamental role in
the phases of big data's lifecycle. The demands from big
data also accelerate the development of cloud computing.
In manufacturing, big data can be applied in the full lifecycle of products, significantly impacting design innovation, manufacturing intelligence, cost reduction, quality,
efficiency, and customer satisfaction [22], e.g., designing
more precisely targeted products and making effective
promotion strategies based on acquired knowledge from
big data analysis.
Therefore, we can see that the IoT's core technologies
have great potential in reshaping the manufacturing sector
with pervasive real-time sensing, actuation, and powerful
data-processing capabilities. To unlock the IoT's potential
in manufacturing, several issues need to be addressed.
8

IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE Janu ar y 20 18

Research Issues of IoT-Enabled
Manufacturing
Reference Architectures and Standards
According to different perspectives, the conceptual architecture can be Internet-centric or thing-centric [23]. Gubbi et al.
[23] proposed a cloud-centric framework of the IoT, which
includes three layers: a network of things, cloud computing,
and applications. The cloud integrates ubiquitous devices by
providing scalable storage, computation time, and other
tools to build new IoT businesses. The European Union project for IoT architecture [24] is attempting to build a general
thing-centric framework that can be tailored according to
domain demands.
To organize huge amounts of heterogeneous devices that
provide and consume information available on the network
and cooperate level, the SOA approach is usually adopted
[3], [4], [25] in both Internet- and cloud-centric frameworks.
Each real-world device or system can offer its functionality
as services. Then various sophisticated services can be created via orchestrating those services. The cloud computing
paradigm has allowed the possibility for everything to be
provided as services in the long run, which is a concept
called XaaS [26].
To facilitate the interoperability, virtualization technology is widely used and researched, such as the virtualization
of computing, storage, and network resources in the area of
cloud computing. Cloud manufacturing tries to apply virtualization technology in the organization of various manufacturing resources and capabilities. He and Xu [18] concluded
that the generic architecture of cloud manufacturing consists of five layers: physical resource, virtual resource, core
service, application interface, and application. Even though
the IoT is claimed to be included in cloud manufacturing,
such architecture is actually cloud-centric.
From a data-handling perspective, Lee et al. [5] proposed
a five-"C" architecture for cyberphysical manufacturing
systems. The architecture comprises a smart connection
level to enable data acquisition through the networking of
sensors and machines, a data-to-information conversion
level to infer meaningful information from data, a cyberlevel
to act as central information hub, a cognitive level to generate a thorough knowledge of the monitored system, and a
configurable level to make machines in physical space selfconfigurable and self-adaptive when there is some feedback
delivered from cyberspace.
More recent work by Ning et al. [27] brought forward a
broader vision of the IoT, where physical perceptions,
cyberinteractions, social correlations, and even cognitive
thinking can be intertwined in the ubiquitous things' interconnections. Thus, the proposed hyperspace architecture
includes cyber, physical, social, and thinking space. Social
space refers to the logic architecture of social attributes
and interactions owned by human beings and other physical objects, or cyberentities. Thinking space addresses
thought- and idea-related issues.



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