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A hierarchical distributed fog computing architecture for big data analysis in smart cities

Authors:
A Hierarchical Distributed Fog Computing Architecture for
Big Data Analysis in Smart Cities
Bo Tang
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
btang@ele.uri.edu
Zhen Chen
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
chen@ele.uri.edu
Gerald Hefferman
Warren Alpert Medical School
Brown University
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
gerald_hefferman@brown.edu
Tao Wei
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
wei@ele.uri.edu
Haibo He
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
he@ele.uri.edu
Qing Yang
Department of Electrical,
Computer, and Biomedical
Engineering
University of Rhode Island
qyang@ele.uri.edu
ABSTRACT
The ubiquitous deployment of various kinds of sensors in
smart cities requires a new computing paradigm to support
Internet of Things (IoT) services and applications, and big
data analysis. Fog Computing, which extends Cloud Com-
puting to the edge of network, fits this need. In this pa-
per, we present a hierarchical distributed Fog Computing
architecture to support the integration of massive number
of infrastructure components and services in future smart
cities. To secure future communities, it is necessary to build
large-scale, geospatial sensing networks, perform big data
analysis, identify anomalous and hazardous events, and offer
optimal responses in real-time. We analyze case studies us-
ing a smart pipeline monitoring system based on fiber optic
sensors and sequential learning algorithms to detect events
threatening pipeline safety. A working prototype was con-
structed to experimentally evaluate event detection perfor-
mance of the recognition of 12 distinct events. These exper-
imental results demonstrate the feasibility of the system’s
city-wide implementation in the future.
CCS Concepts
Computer systems organization Distributed ar-
chitectures; Computing methodologies Parallel com-
puting methodologies; Machine learning; Security and
privacy Network security;
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ASE BD&SI 2015, October 07-09, 2015, Kaohsiung, Taiwan
c
2015 ACM. ISBN 978-1-4503-3735-9/15/10. . . $15.00
DOI: 10.1145/2818869.2818898
Keywords
Fog computing; smart city; big data analysis; distributed
computing architecture; pipeline safety monitoring
1. INTRODUCTION
In the past decade, the concept of Smart City has drawn
great interest in both the science and engineering fields as
a means to overcome the challenges associated with rapidly
growing urbanization. A smart city is an urbanized area
where multiple sectors cooperate to achieve sustainable out-
comes through the analysis of contextual, real-time infor-
mation. Smart cities reduce traffic congestion and energy
waste, while allocating stressed resources more efficiently
and improving quality of life. Smart city technologies are
projected to become massive economic engines in the com-
ing decades, and are expected to be worth a cumulative 1.565
trillion dollars by 2020, and 3.3 trillion dollars by 2025. To-
day, companies are actively vying for a central role in the
smart city ecosystem, creating an expanding number of tech-
nologies and employment opportunities. Already, IBM, In-
tel, GE, and many other companies have initiated projects
to integrate their products and services into a smart city
framework [1]. Hundreds of millions of jobs will be created to
facilitate this smart city conversion; in June 2014, Intel and
the city of San Jose, CA began collaborating on a project
implementing Intel’s Smart City Demonstration Platform,
installing a network of air quality and climate sensors which
alone fostered 25,000 new high tech jobs in San Jose [2].
While rapid urbanization provides numerous opportuni-
ties, building smart cities presents many challenges, such
as large-scale geospatially distributed sensing networks, big
data analysis, machine-to-machine communication, etc. Cur-
rently, the “pay-as-you-go” Cloud Computing paradigm is
widely used in enterprises to address the emerging chal-
lenges of big data analysis because of its scalable and dis-
tributed data management scheme. However, data centers
in the Cloud faces great challenges on the burden of ex-
ploding amount of big data and the additional requirements
of location awareness and low latency at the edge of net-
work necessary for smart cites. Fog Computing recently,
proposed by Cisco, extends the Cloud Computing paradigm
to run geo-distributed applications throughout the network
[6]. In contrast to the Cloud, the Fog not only performs
latency-sensitive applications at the edge of network, but
also performs latency-tolerant tasks efficiently at powerful
computing nodes at the intermediate of network. At the
top of the Fog, Cloud Computing with data centers can be
still used for deep analytics.
In this paper, we introduce a hierarchical distributed Fog
Computing architecture for big data analysis in smart cities.
Due to the natural characteristic of geo-distribution in big
data generated by massive sensors, we distribute intelligence
at the edge of a layered Fog computing network. The com-
puting nodes at each layer perform latency-sensitive appli-
cations and provide quick control loop to ensure the safety
of critical infrastructure components. Using smart pipeline
monitoring as a use case, we implemented a prototypical
4-layer Fog-based computing paradigm to demonstrate the
effectiveness and the feasibility of the system’s city-wide im-
plementation in the future.
2. RELATED WORK
2.1 Computing and Communication Architec-
ture for Smart Cities
The new challenges of big data analysis posed by smart
cities demand that researchers investigate and develop novel
and high-performance computing architectures. The rising
of Cloud Computing and Cloud Storage in industry provides
a solution to support dynamic scalability in many smart city
applications, such as large scale data management for smart
house [16], smart lighting [7] and video surveillance [10], and
intensive business and academic computing tasks in educa-
tion institutions [20]. However, the deployment of massive
numbers of sensors in future smart cities requires location
awareness and low latency, which are lacking in current com-
mercial Cloud Computing models. In [6], a Fog Comput-
ing platform is developed to extend the Cloud Computing
paradigm to the edge of the machine-to-machine network
to support the Internet of Things. The work described in
this paper develops this Fog Computing concept further, and
the new paradigm will be described in detail in the following
section.
2.2 Smart Computing Technologies in Smart
Cities
In addition to the large-scale data storage, the“smartness”
of infrastructure in future smart cities requires intelligent
data analysis for smart monitoring and actuation to achieve
automated decision making, thereby ensuring the reliabil-
ity of infrastructure components and the safety of public
health. Such “smartness”in smart cities derives from the em-
ployment of many advanced artificial intelligence algorithms
or the combination of several of them, including density
distribution modeling [18], supervised and non-supervised
machine learning algorithms [11] [17], and sequential data
learning [19], to name a few. The wide use of heteroge-
neous senors leads to another challenge to extract useful
information from a complex sensing environment at differ-
ent spatial and temporal resolutions [13]. Current state-of-
the-art methods usually shallow this problem: they firstly
apply supervised learning algorithms to identify pre-defined
patterns and use unsupervised learning algorithms to detect
data anomalies. Then, sequential learning methods with
spatial-temporal association are employed to infer local ac-
tivities or predefined events. Complex city-wide spatial and
longer temporal activities or behaviors could be further de-
tected at a higher layer [13]. It is worth noting that the
proposed hierarchical architecture in this paper is suitable
for such distributed employment of artificial intelligence al-
gorithms across multiple layers.
3. HIERARCHIC DISTRIBUTED FOG COM-
PUTING PLATFORM FOR SMART CITIES
The big data in smart cities exhibits a new characteris-
tic: geo-distribution [5]. This new dimension of big data
requires that the data needs to be processed near the sen-
sors at the edge, instead of the data centers in traditional
Cloud computing paradigm. It is necessary to offer low la-
tency responses to protect the safety of critical infrastruc-
ture components. Fog Computing is a suitable paradigm by
extending the Cloud Computing to the edge of network. Be-
cause the data is processed at the edge, quick control loops
are feasible using the Fog Computing model.
The proposed 4-layer Fog computing architecture is shown
in Fig. 1. At the edge of network, layer 4, is the sens-
ing network which contains numerous sensory nodes. Those
sensors are non-invasive, highly reliable, and low cost; thus,
they can be widely distributed at various public infrastruc-
tures to monitor their condition changes over time. Note
that massive sensing data streams are generated from these
sensors that are geospatially distributed, which have to be
processed as a coherent whole.
The nodes at the edge forward the raw data into the next
layer, layer 3, which is comprised of many low-power and
high-performance computing nodes or edge devices. Each
edge device is connected to and responsible for a local group
of sensors that usually cover a neighborhood or a small com-
munity, performing data analysis in a timely manner. The
output of the edge device has two parts: the first are re-
ports of the results of data processing to an intermediate
computing node at its next upper layer, while the second is
simple and quick feedback control to a local infrastructure
to respond to isolated and small threats to the monitored
infrastructure components.
Layer 2 consists of a number of intermediate computing
nodes, each of which is connected to a group of edge de-
vices at layer 3 and associates spatial and temporal data to
identify potential hazardous events. Meanwhile, it makes
quick response to control the infrastructure when hazardous
events are detected. The quick feedback control provided at
layers 2 and 3 acts as localized “reflex” decisions to avoid
potential damage [15]. For example, if one segment of gas
pipeline is experiencing a leakage or a fire, these computing
nodes will detect the threat and shutdown the gas supply
to this area. Meanwhile, the data analysis results at these
two layers are reported to the top layer, for large-scaled and
long-term behavior analysis and condition monitoring.
The top layer is a Cloud Computing data center, provid-
ing city-wide monitoring and centralized controlling. Com-
plex, long-term, and city-wide behavior analyses can be also
performed at this layer, such as large-scale event detection,
long-term pattern recognition, and relationship modeling, to
Figure 1: The 4-layer Fog computing architecture in smart cities, in which scale and latency sensitive appli-
cations run near the edge.
support dynamic decision making. This allows municipali-
ties to perform city-wide response and resource management
in the case of a natural disaster or a large-scale service in-
terruption.
In summary, the 4-layer Fog Computing architecture sup-
ports the quick response at neighborhood-wide, community-
wide, and city-wide levels, providing high computing perfor-
mance and intelligence in future smart cities.
4. A PROTOTYPE OF SMART PIPELINE
MONITORING
In this section, we present the implementation of 4-layer
Fog Computing architecture for smart pipeline monitoring.
Pipelines play important role in resource and energy sup-
plying and are essential infrastructure components in cities.
However, several threats endanger the integrity of pipeline,
such as aging and sudden environmental changes. Those
threats lead to corrosion, leakage, and failure of pipelines,
with serious economic and ecologic consequences [3][4]. We
show that the hierarchical Fog Computing architecture is
suitable for accurate and real-time monitoring of city-wide
pipelines and provides quick responses when predefined threats
and hazardous events are detected.
4.1 Layer 4: Fiber Optic Sensing Networks
At layer 4, optical fibers are used as sensors to measure the
temperature along the pipeline. Optical frequency domain
reflectometry (OFDR) system is applied to measure the dis-
continuity of the regular optical fibers [12]. With the contin-
uous sweep method, the Rayleigh scatter (about -80dB) as a
function of length along the fiber under test can be obtained
via the Fourier transform. With the time-domain filter and
cross-correlation method, the extracted frequency patterns
at certain locations can be used to detect the ambient phys-
ical change, such as strain, stress and temperature [8]. For
the detailed description of OFDR interrogation system, we
refer interested readers to our previous work [9] [14].
4.2 Layer 3: Edge Device for Feature Extrac-
tion
Layer 3 is composed of parallelized small computing nodes,
or edge devices. Each edge device usually performs two com-
puting tasks. The first task is to identify potential threat
patterns on the incoming data streams from sensors using
machine learning algorithms, and the second one is to per-
form feature extraction for the computing at the upper layer
for further analysis. Considering a region governed by one
edge device with a total length of hundreds of meters, mil-
lions of temperature sensors in our high resolution sensing
network produce massive data streams and lead to a high
data rate. Instead of transmitting the raw sensor data to
layer 2, it is necessary to reduce the communication load
between the edge devices and the intermediate computing
nodes. Thereafter, raw sensor data is discarded.
4.3 Layer 2: Intermediate Computing Node
for Event Recognition
The intermediate computing nodes at layer 2 are con-
nected to tens and hundreds of edge devices, governing the
community-level sensors. The data streams from these edge
devices represent measurements at various locations. The
key is to associate the spatial and temporal data and to
identify potential hazardous events.
Assume an intermediate computing node connects nedge
devices, and denote a m×1 vector si(t) by the features
outputted from the i-th edge device at time t. Since the
sensors are static, the features output from each edge de-
vice contains the geospatial information. After receiving
all the features from nedge devices, we combine these n
groups of feature vectors into a mn ×1 feature vector x(t).
Hence, from time 1 to time t, this intermediate computing
node receives the data sequences X={x(1),· · · ,x(t)}, and
the task of event recognition at this layer is to recognize
the event pattern given its previous data sequences. After
that, we apply hidden Markov model (HMM) for modeling
the spatial-temporal pattern of each event in a probabilis-
tic manner. Specifically, at the learning state, we apply
the Baum Welch learning algorithm to estimate model pa-
rameters, and at the evaluation stage, we use maximum a
posteriori (MAP) rule for making classifications.
4.4 Layer 1: Cloud for Data Management
The top layer is at data centers of the Cloud, which col-
lects data and information from each intermediate comput-
ing node on layer 2. We build the Cloud using the open
source Hadoop, taking advantage of the power of clusters
and high performance computing and storage. At this layer,
very large-scale (city-wide) and very high-latency (years)
computing tasks will be performed, such as long-term natu-
ral disaster detection and prediction.
5. EXPERIMENTAL RESULTS AND ANAL-
YSIS
5.1 Sensor Data Collection
In our experiments, we built a prototype of pipeline mon-
itoring system. The layout of pipeline structure is shown
in Fig. 2. The optical fiber sensors were distributed along
this the pipeline such that the temperature of pipeline is
measured. The real-time data was collected from the fiber
sensor network along the prototypical pipeline system with
a temporal resolution of 1 second and a spatial resolution of
0.01 meters.
Figure 2: The layout of prototype pipeline system.
We simulated 12 events around the pipeline and collected
the sensor data of pipeline temperature. Each event includes
a heating and a cooling process. A heat source was placed
nearby, blowing the hot air towards the pipeline system. In
each experiment, 100 frames of sensing data were gathered,
where in the first 10 frames the system remained stable, from
11 to 40 frames the heat source was on, and from 41 to 100
the heat source was off.
5.2 Spatial-Temporal Event Recognition
We trained a HMM for each event. Each HMM has Q
hidden states, and the observation probability distribution
is modeled by a Gaussian mixture model (GMM) with K
Gaussian components. We performed 10-fold cross valida-
tion to evaluate the recognition performance. All the follow-
ing reported results were averaged over 10 runs. For each
sequential test data, we run online prediction, i.e., at time
frame t, a decision was made based on its currently and
previously observed sequence x0:t.
The online recognition performance with different number
of hidden states is shown in Fig. 3, when K= 2 Gaussian
components are used in GMM, and the performance with
different number of components in GMM is given in Fig.
4, when Q= 2 hidden states are used. The results in Fig.
3 and Fig. 4 illustrate that more hidden states and Gaus-
sian components used in HMM would increase the inference
performance due to the growing capacity of HMMs. How-
ever, the complex HMM models need more training data for
model parameters estimation and the computational com-
plexity would increase. The results also show that we are
able to obtain more than 90% accuracy to classify 12 events
at the end of the heating process (at the 40-th frame).
10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Frame
Averaged Classification Accurancy
Q = 2
Q = 3
Q = 4
Figure 3: The online inference performance with dif-
ferent number of hidden states in each HMM: Q= 2,
Q= 3, and Q= 4, when two components GMMs are
used (K= 2).
10 20 30 40 50 60 70 80 90 100
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Time Frame
Averaged Classification Accurancy
K = 1
K = 2
K = 3
Figure 4: The online inference performance with dif-
ferent number of Gaussian components in the obser-
vation distribution of each HMM: K= 1,K= 2, and
K= 3, when two hidden states are used (Q= 2).
5.3 Discussion
The Fog Computing architecture has significant advan-
tages over the Cloud Computing architecture for smart city
monitoring. First, the distributed computing and storage
Figure 5: The comparisons of the amount of data transmitted to the Cloud and the response time for
hazardous events within three different architectures: (a). The amount of data that are sent to the Cloud
per second; (b). The response time for hazardous events, when the Internet bandwidth is 1Mb/s; (c). The
response time for hazardous events with different Internet bandwidths. The log values in the y-axis are used
to clearly illustrate the comparisons.
nodes of Fog Computing ideally suited to support the mas-
sive numbers of sensors distributed throughout a city to
monitor infrastructure and environmental parameters. If
Cloud Computing alone is used for this task, huge amounts
of data will need to be transmitted to data centers, neces-
sitating massive communication bandwidth and power con-
sumption. Specifically, suppose that we use current sensing
setup with 1cm spatial-resolution and 0.5s time-resolution,
and that each edge device covers 10 meters pipeline and each
computing node connects 5 edge devices. Considering the
total pipeline length Lranging from 10km to 50km, we com-
pare the size of data that needs to be sent to the Cloud per
second in Fig. 5(a) for the following three cases: our cur-
rent Fog Computing architecture with layers 2 and 3, the
Fog Computing architecture with only layer 3 by removing
the computing tasks at layer 2 to the Cloud, and the tradi-
tional Cloud Computing architecture in which both comput-
ing tasks at layers 2 and 3 are executed at Cloud. To clearly
illustrate the difference among these three architectures, we
plot log values of data size. The results in Fig. 5(a) show
that using Fog Computing, the data transmitted is about
0.02% of the total size, significantly reducing transmission
bandwidth and power consumption.
Second, Fog Computing supports real-time interactions.
Because of high burdens on data transmission, Cloud Com-
puting fails to provide real-time control. To quantify the re-
sponse time for hazardous events under the above three com-
puting architectures, we assume that the execution speed in
computing node is 1GIPS, and we omit the memory access
time for simplifying our analysis. The comparison of re-
sponse time for these three architectures is shown in Fig.
5(b), when the Internet bandwidth connecting to the Cloud
is 1Mb/s. It is seen that the response time is dominated by
the data transmission in Cloud Computing. Fig. 5(c) also
shows the response time when different Internet bandwidths
are considered.
As shown in Fig. 1, different levels of latency of response
can be provided in the Fog computing, which is distinct from
the batch processing of Cloud Computing. These results il-
lustrate that Fog Computing addresses the big data anal-
ysis challenge by distributing computing tasks to the edge
devices and computing nodes at the edge of network, thus
offering optimal responses to changes in city environment.
6. CONCLUSIONS
In this paper, we introduce a hierarchical Fog Computing
architecture for big data analysis in smart cities. In contrast
to the Cloud, the Fog Computing parallelizes data process-
ing at the edge of network, which satisfies the requirements
of location awareness and low latency. The multi-layer Fog
computing architecture is able to support quick response
at neighborhood-wide, community-wide and city-wide lev-
els, providing high computing performance and intelligence
in future smart cities. We further enhance the“smartness”of
city infrastructure by employing advanced machine learning
algorithms across all system layers. To verify the effective-
ness of this architecture, we have implemented a prototypical
system for smart pipeline monitoring. A sequential learning
method, hidden Markov model, was successfully used for
hazardous event detection to monitor pipeline safety. These
observed performance of the hierarchical Fog Computing ar-
chitecture indicates its substantial potential as a method of
future smart city monitoring and control.
7. ACKNOWLEDGEMENT
The authors are grateful to the anonymous reviewers for
providing comments and suggestions that improved the qual-
ity of the paper. This research is supported in part by NSF
grants CCF-1439011 and CCF-1421823. Any opinions, find-
ings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily
reflect the views of the National Science Foundation.
8. REFERENCES
[1] Smart Cities. http://www.ibm.com/smarterplanet/us/
en/smarter cities/overview/. Accessed: 2015-07-26.
[2] Smart Cities USA.
http://smartamerica.org/teams/smart-cities-usa/.
Accessed: 2015-07-26.
[3] R. Alzbutas, T. Ieˇsmantas, M. Povilaitis, and
J. Vitkut˙e. Risk and uncertainty analysis of gas
pipeline failure and gas combustion consequence.
Stochastic Environmental Research and Risk
Assessment, 28(6):1431–1446, 2014.
[4] B. Anifowose, D. Lawler, D. Horst, and L. Chapman.
Evaluating interdiction of oil pipelines at river
crossings using environmental impact assessments.
Area, 46(1):4–17, 2014.
[5] F. Bonomi, R. Milito, P. Natarajan, and J. Zhu. Fog
computing: A platform for internet of things and
analytics. In Big Data and Internet of Things: A
Roadmap for Smart Environments, pages 169–186.
2014.
[6] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli. Fog
computing and its role in the internet of things. In
Proceedings of the first edition of the MCC workshop
on Mobile cloud computing, pages 13–16, 2012.
[7] M. Castro, A. J. Jara, and A. F. Skarmeta. Smart
lighting solutions for smart cities. In International
Conference on Advanced Information Networking and
Applications Workshops (WAINA), pages 1374–1379,
2013.
[8] Z. Chen, G. Hefferman, and T. Wei. Multiplexed oil
level meter using a thin core fiber cladding mode
exciter. IEEE Photonics Technology Letters, (99):1–1,
2015.
[9] Z. Chen, Y. Zeng, G. Hefferman, and Y. Sun. Fiberid:
molecular-level secret for identification of things. In
IEEE International Workshop on Information
Forensics and Security (WIFS), pages 84–88, Dec
2014.
[10] S. Dey, A. Chakraborty, S. Naskar, and P. Misra.
Smart city surveillance: Leveraging benefits of cloud
data stores. In IEEE Conference on Local Computer
Networks Workshops, pages 868 – 876.
[11] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern
classification. John Wiley & Sons, 2012.
[12] M. Froggatt and J. Moore. High-spatial-resolution
distributed strain measurement in optical fiber with
Rayleigh scatter. Applied Optics, 37(10):1735–1740,
1998.
[13] J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami.
Internet of Things (IoT): A vision, architectural
elements, and future directions. Future Generation
Computer Systems, 29(7):1645–1660, 2013.
[14] G. Hefferman, Z. Chen, L. Yuan, and T. Wei.
Phase-shifted terahertz fiber bragg grating for strain
sensing with large dynamic range. IEEE Photonics
Technology Letters, 27(15):1649–1652, Aug 2015.
[15] J. Kane, B. Tang, Z. Chen, J. Yan, T. Wei, H. He, and
Q. Yang. Reflex-Tree: A biologically inspired parallel
architecture for future smart cities. In International
Conference on Parallel Processing (ICPP), 2015.
[16] K. Su, J. Li, and H. Fu. Smart city and the
applications. In International Conference on
Electronics, Communications and Control (ICECC),
pages 1028–1031, 2011.
[17] B. Tang and H. He. ENN: Extended nearest neighbor
method for multivariate pattern classification. IEEE
Computational Intelligence Magazine (CIM),
10(3):52–60, 2015.
[18] B. Tang, H. He, Q. Ding, and S. Kay. A parametric
classification rule based on the exponentially
embedded family. IEEE Transactions on Neural
Networks and Learning Systems, 26(2):367–377, Feb
2015.
[19] B. Tang, S. Khokhar, and R. Gupta. Turn prediction
at generalized intersections. In IEEE Intelligent
Vehicles Symposium (IV), pages 1399–1404, 2015.
[20] S. Yamamoto, S. Matsumoto, and M. Nakamura.
Using cloud technologies for large-scale house data in
smart city. In IEEE 4th International Conference on
Cloud Computing Technology and Science, pages
141–148, 2012.
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