Fog security

The Fog Computing Paradigm: Scenarios and
Security Issues
Ivan Stojmenovic
SIT, Deakin University, Burwood, Australia
and
SEECS, University of Ottawa, Canada
Email: stojmenovic@gmail.com
Sheng Wen
School of Information Technology,
Deakin University,
220 Burwood Highway, Burwood, VIC, 3125, Australia
Email: wesheng@deakin.edu.au
Abstract—Fog Computing is a paradigm that extends Cloud
computing and services to the edge of the network. Similar
to Cloud, Fog provides data, compute, storage, and application
services to end-users. In this article, we elaborate the motivation
and advantages of Fog computing, and analyse its applications
in a series of real scenarios, such as Smart Grid, smart traffic
lights in vehicular networks and software defined networks. We
discuss the state-of-the-art of Fog computing and similar work
under the same umbrella. Security and privacy issues are further
disclosed according to current Fog computing paradigm. As an
example, we study a typical attack, man-in-the-middle attack,
for the discussion of security in Fog computing. We investigate
the stealthy features of this attack by examining its CPU and
memory consumption on Fog device.
Index Terms—Fog Computing, Cloud Computing, Internet of
Things, Software Defined Networks.
I. INTRODUCTION
CISCO recently delivered the vision of fog computing
to enable applications on billions of connected devices,
already connected in the Internet of Things (IoT), to run
directly at the network edge [1]. Customers can develop,
manage and run software applications on Cisco IOx framework
of networked devices, including hardened routers, switches
and IP video cameras. Cisco IOx brings the open source Linux
and Cisco IOS network operating system together in a single
networked device (initially in routers). The open application
environment encourages more developers to bring their own
applications and connectivity interfaces at the edge of the
network. Regardless of Cisco’s practices, we first answer the
questions of what the Fog computing is and what are the
differences between Fog and Cloud.
In Fog computing, services can be hosted at end devices
such as set-top-boxes or access points. The infrastructure of
this new distributed computing allows applications to run as
close as possible to sensed actionable and massive data, com-
ing out of people, processes and thing. Such Fog computing
concept, actually a Cloud computing close to the ‘ground’,
creates automated response that drives the value.
Both Cloud and Fog provide data, computation, storage
and application services to end-users. However, Fog can be
distinguished from Cloud by its proximity to end-users, the
dense geographical distribution and its support for mobility
[2]. We adopt a simple three level hierarchy as in Figure 1.
Cloud
Fog
Core
Edge
Locations
Fig. 1. Fog between edge and cloud.
In this framework, each smart thing is attached to one of Fog
devices. Fog devices could be interconnected and each of them
is linked to the Cloud.
In this article, we take a close look at the Fog computing
paradigm. The goal of this research is to investigate Fog
computing advantages for services in several domains, such as
Smart Grid, wireless sensor networks, Internet of Things (IoT)
and software defined networks (SDNs). We examine the state-
of-the-art and disclose some general issues in Fog computing
including security, privacy, trust, and service migration among
Fog devices and between Fog and Cloud. We finally conclude
this article with discussion of future work.
II. WHY DO WE NEED FOG?
In the past few years, Cloud computing has provided many
opportunities for enterprises by offering their customers a
range of computing services. Current “pay-as-you-go” Cloud
computing model becomes an efficient alternative to owning
and managing private data centres for customers facing Web
applications and batch processing [3]. Cloud computing frees
the enterprises and their end users from the specification of
many details, such as storage resources, computation limitation
and network communication cost. However, this bliss becomes
Proceedings of the 2014 Federated Conference on
Computer Science and Information Systems pp. 1–8
DOI: 10.15439/2014F503
ACSIS, Vol. 2
978-83-60810-58-3/$25.00 c 2014, IEEE 1

Fig. 2. Fog computing in smart grid.
a problem for latency-sensitive applications, which require
nodes in the vicinity to meet their delay requirements [2].
When techniques and devices of IoT are getting more involved
in people’s life, current Cloud computing paradigm can hardly
satisfy their requirements of mobility support, location aware-
ness and low latency.
Fog computing is proposed to address the above problem
[1]. As Fog computing is implemented at the edge of the
network, it provides low latency, location awareness, and
improves quality-of-services (QoS) for streaming and real
time applications. Typical examples include industrial automa-
tion, transportation, and networks of sensors and actuators.
Moreover, this new infrastructure supports heterogeneity as
Fog devices include end-user devices, access points, edge
routers and switches. The Fog paradigm is well positioned for
real time big data analytics, supports densely distributed data
collection points, and provides advantages in entertainment,
advertising, personal computing and other applications.
III. WHAT CAN WE DO WITH FOG?
We elaborate on the role of Fog computing in the following
six motivating scenarios. The advantages of Fog computing
satisfy the requirements of applications in these scenarios.
Smart Grid: Energy load balancing applications may run on
network edge devices, such as smart meters and micro-grids
[4]. Based on energy demand, availability and the lowest price,
these devices automatically switch to alternative energies like
solar and wind. As shown in Figure 2, Fog collectors at the
edge process the data generated by grid sensors and devices,
and issue control commands to the actuators [2]. They also
filter the data to be consumed locally, and send the rest
to the higher tiers for visualization, real-time reports and
transactional analytics. Fog supports ephemeral storage at the
lowest tier to semi-permanent storage at the highest tier. Global
coverage is provided by the Cloud with business intelligence
analytics.
Fig. 3. Fog computing in smart traffic lights and connected vehicles.
Smart Traffic Lights and Connected Vehicles: Video cam-
era that senses an ambulance flashing lights can automatically
change street lights to open lanes for the vehicle to pass
through traffic. Smart street lights interact locally with sensors
and detect presence of pedestrian and bikers, and measure
the distance and speed of approaching vehicles. As shown in
Figure 3, intelligent lighting turns on once a sensor identifies
movement and switches off as traffic passes. Neighbouring
smart lights serving as Fog devices coordinate to create green
traffic wave and send warning signals to approaching vehicles
[2]. Wireless access points like WiFi, 3G, road-side units and
smart traffic lights are deployed along the roads. Vehicles-to-
Vehicle, vehicle to access points, and access points to access
points interactions enrich the application of this scenario.
Wireless Sensor and Actuator Networks: Traditional wire-
less sensor networks fall short in applications that go beyond
sensing and tracking, but require actuators to exert physical
actions like opening, closing or even carrying sensors [2]. In
this scenario, actuators serving as Fog devices can control the
measurement process itself, the stability and the oscillatory
behaviours by creating a closed-loop system. For example,
in the scenario of self-maintaining trains, sensor monitoring
on a train’s ball-bearing can detect heat levels, allowing
applications to send an automatic alert to the train operator
to stop the train at next station for emergency maintenance
and avoid potential derailment. In lifesaving air vents scenario,
sensors on vents monitor air conditions flowing in and out of
mines and automatically change air-flow if conditions become
dangerous to miners.
Decentralized Smart Building Control: The applications
of this scenario are facilitated by wireless sensors deployed
to measure temperature, humidity, or levels of various gases
in the building atmosphere. In this case, information can be
exchanged among all sensors in a floor, and their readings can
be combined to form reliable measurements. Sensors will use
distributed decision making and activation at Fog devices to
2 PROCEEDINGS OF THE FEDCSIS. WARSAW, 2014

Fig. 4. Fog computing in SDN in vehicular networks [6].
react to data. The system components may then work together
to lower the temperature, inject fresh air or open windows. Air
conditioners can remove moisture from the air or increase the
humidity. Sensors can also trace and react to movements (e.g,
by turning light on or off). Fog devices could be assigned at
each floor and could collaborate on higher level of actuation.
With Fog computing applied in this scenario, smart buildings
can maintain their fabric, external and internal environments
to conserve energy, water and other resources.
IoT and Cyber-physical systems (CPSs): Fog computing
based systems are becoming an important class of IoT and
CPSs. Based on the traditional information carriers including
Internet and telecommunication network, IoT is a network
that can interconnect ordinary physical objects with identified
addresses [5]. CPSs feature a tight combination of the system’s
computational and physical elements. CPSs also coordinate
the integration of computer and information centric physical
and engineered systems. IoT and CPSs promise to transform
our world with new relationships between computer-based
control and communication systems, engineered systems and
physical reality. Fog computing in this scenario is built on the
concepts of embedded systems in which software programs
and computers are embedded in devices for reasons other than
computation alone. Examples of the devices include toys, cars,
medical devices and machinery. The goal is to integrate the
abstractions and precision of software and networking with the
dynamics, uncertainty and noise in the physical environment.
Using the emerging knowledge, principles and methods of
CPSs, we will be able to develop new generations of intelligent
medical devices and systems, ‘smart’ highways, buildings,
factories, agricultural and robotic systems.
Software Defined Networks (SDN): As shown in Figure 4,
Fog computing framework can be applied to implement the
SDN concept for vehicular networks. SDN is an emergent
computing and networking paradigm, and became one of the
most popular topics in IT industry [7]. It separates control
and data communication layers. Control is done at a central-
ized server, and nodes follow communication path decided
by the server. The centralized server may need distributed
implementation. SDN concept was studied in WLAN, wireless
sensor and mesh networks, but they do not involve multi-
hop wireless communication, multi-hop routing. Moreover,
there is no communication between peers in this scenario.
SDN concept together with Fog computing will resolve the
main issues in vehicular networks, intermittent connectivity,
collisions and high packet loss rate, by augmenting vehicle-
to-vehicle with vehicle-to-infrastructure communications and
centralized control. SDN concept for vehicular networks is
first proposed in [6].
IV. STATE-OF-THE-ART
A total of eight articles were identified on the concept of
Fog computing [1], [2], [8], [9], [10], [11], [12], [13], [14].
There are some other concepts, not declared as Fog computing,
fall under the same umbrella. We will also discuss these work
in the subsection of similar work.
A. Related Work
K. Hong et al. proposed mobile Fog in [11]. This is a
high level programming model for geo-spatially distributed,
large-scale and latency-sensitive future Internet applications.
Following the logical structure shown in Figure 1, low-latency
processing occurs near the edge while latency-tolerant large-
scope aggregation is performed on powerful resources in the
core of the network (normally the Cloud). Mobile Fog consists
of a set of event handlers and functions that an application
can call. Mobile Fog model is not presented as generic
model, but is built for particular application, while leaving
out functions that deal with technical challenges of involved
image processing primitives. Fog computing approach reduces
latency and network traffic.
B. Ottenwalder et al. presented a placement and migration
method for Cloud and Fog resources providers [13]. It ensures
application-defined end-to-end latency restrictions and reduces
the network utilization by planning the migration ahead of
time. They also show how the application knowledge of the
complex event processing system can be used to reduce the
required bandwidth of virtual machines during their migration.
Network intensive operators are placed on distributed Fog
devices while computationally intensive operators are in the
Cloud. Migration costs are amortized by selecting migration
targets that ensure a low expected network utilization for a
sufficiently long time. This work does not optimize workload
mobility because Fog devices are also able to carry compu-
tationally intensive tasks. It also does not optimize the size
of control information or mobility overhead, and does not
describe network control policies for finding optimal paths for
different applications.
In [11], K. Hong et al. proposed an opportunistic spatio-
temporal event processing system that uses prediction-based
continuous query handling. Their system predicts future query
regions for moving consumers and starts processing events
early so that the live situational information is available when
IVAN STOJMENOVIC, SHENG WEN: THE FOG COMPUTING PARADIGM 3

the consumer reaches the future location. Historical events for
a location are processed before the mobile user arrives at that
location. Live event processing begins at the moment the user
arrives. To mitigate large speed of mobile user, authors propose
using parallel resources to enable pipeline processing of future
locations in several time steps looking ahead. Further, they
proposed taking several predictions for each time step and
opportunistically compute the events for all of those locations.
When the user arrives at that time, the prediction among those
that is closest to truth will be selected and its events returned.
J. Zhu et al. applied existing methods for web optimization
in a novel manner [14]. Within Fog computing context, these
methods can be combined with unique knowledge that is only
available at the Fog devices. More dynamic adaptation to the
user’s conditions can also be accomplished with network edge
specific knowledge. As a result, a user’s Web page rendering
performance is improved beyond that achieved by simply
applying those methods at the Web server.
In the mobile Cloud concept [12], pervasive mobile devices
share their heterogeneous resources and support services.
Neighbouring nodes in a local network form a group called a
local Cloud. Nodes share their resources with other nodes in
the same local Cloud. A local resource coordinator serving as
Fog device is elected from the nodes in each local Cloud. The
work [12] proposed an architecture and mathematical frame-
work for heterogeneous resource sharing based on the key idea
of service-oriented utility functions. Normally heterogeneous
resources are quantified in disparate scales, such as power,
bandwidth and latency. However, authors in [12] present a
unified framework where all these quantities are equivalently
mapped to “time” resources. They formulate optimization
problems for maximizing the sum and product of the utility
functions, and solve them via convex optimization approaches.
The work [10] first reviews the reliability requirements of
Smart Grid, Cloud, and sensors and actuators. This work then
combines them towards reliable Fog computing. However, it
only concludes that building Fog computing based projects
is challenging and does not offer any novel concept for
the reliability of the network of smart devices in the Fog
computing paradigm.
B. Similar Work
BETaaS [15] proposed replacing Cloud as the residen-
t for machine-to-machine applications by ‘local Cloud’ of
gateways. The ‘local Cloud’ is composed of devices that
provide smart things with connectivity to the Internet, such as
smart phones, home routers and road-side units. This enables
applications that are limited in time and space to require simple
and repetitive interactions. It also enables the applications to
respond in consistent manner.
Demand Response Management (DRM) is a key component
in the smart grid to effectively reduce power generation costs
and user bills. The work [16] addressed the DRM problem
in a network of multiple utility companies and consumers
where every entity is concerned about maximizing its own
benefit. In their model, utility companies communicate with
each other, while users receive price information from utility
companies and transmit their demand to them. They propose
a Stackelberg game [17] between utility companies and end-
users to maximize the revenue of each utility company and
the payoff of each user. Stackelberg equilibrium of the game
has a unique solution. They develop a distributed algorithm
which converges to the equilibrium with only local information
available for both utility companies and end-users. Utility
companies play a non-cooperative game. They inform users
whenever they change price, and users then update their
demand vectors and inform utility companies. This iterates
until convergence. The main drawback of this algorithm is a
significant communication overhead between users and utility
companies. Though DRM helps to facilitate the reliability of
power supply, the smart grid can be susceptible to privacy and
security issues because of communication links between the
utility companies and the consumers. They study the impact of
an attacker who can manipulate the price information from the
utility companies, and propose a scheme based on the concept
of shared reserve power to improve the grid reliability and
ensure its dependability.
The work [18] investigated how energy consumption may
be optimized by taking into consideration the interaction
between both parties. The energy price model is a function
of total energy consumption. The objective function optimizes
the difference between the value and cost of energy. The
power supplier pulls consumers in a round-robin fashion,
and provides them with energy price parameter and current
consumption summary vector. Each user then optimizes his
own schedule and reports it to the supplier, which in turn
updates its energy price parameter before pulling the next
consumers. This interaction between the power company and
its consumers is modelled through a two-step centralized
game, based on which the work [18] proposed the Game-
Theoretic Energy Schedule (GTES) method. The objective of
the GTES method is to reduce the peak to average power ratio
by optimizing the users energy schedules.
The closest work for SDN in vehicular networks are several
implementations in wireless sensor network and mesh net-
works [19], [20]. Moreover, B. Zhou et al. studied adaptive
traffic light control for smoothing vehicles’ travel and maxi-
mizing the traffic throughout for both single and multiple lanes
[21], [22]. In addition, the work [23] proposed a three-tier
structure for traffic light control. First, an electronic toll collec-
tion (ETC) system is employed for collecting road traffic flow
data and calculating the recommended speed. Second, radio
antennas are installed near the traffic lights. Third, road traffic
flow information can be obtained by wireless communication
between the antennas and ETC devices. A branch-and-bound-
based real-time traffic light control algorithm is designed to
smooth vehicles’ travels.
V. SECURITY AND PRIVACY IN FOG COMPUTING
Security and privacy issues were not studied in the context
of fog computing. They were studied in the context of s-
mart grids [24] and machine-to-machine communications [25].

Cloud
PDA
PC
Phone
Gateway
(Fog Devices)
TD-SCDMA /
WCDMA /
CDMA2000
WLAN
802.11 b/g
Fig. 5. A scenario for a man-in-the-middle attack towards Fog.
There are security solutions for Cloud computing. However,
they may not suit for Fog computing because Fog devices
work at the edge of networks. The working surroundings of
Fog devices will face with many threats which do not exist in
well managed Cloud. In this section, we discuss the security
and privacy issues in Fog Computing.
A. Security Issues
The main security issues are authentication at different
levels of gateways as well as (in case of smart grids) at the
smart meters installed in the consumer’s home. Each smart
meter and smart appliance has an IP address. A malicious
user can either tamper with its own smart meter, report false
readings, or spoof IP addresses. There are some solutions
for the authentication problem. The work [26] elaborated
public key infrastructure (PKI) based solutions which involve
multicast authentication. Some authentication techniques using
Diffie-Hellman key exchange have been discussed in [27].
Smart meters encrypt the data and send to the Fog device, such
as a home-area network (HAN) gateway. HAN then decrypts
the data, aggregates the results and then passes them forward.
Intrusion detection techniques can also be applied in Fog
computing [28]. Intrusion in smart grids can be detected
using either a signature-based method in which the patterns
of behaviour are observed and checked against an already
existing database of possible misbehaviours. Intrusion can also
be captured by using an anomaly-based method in which
an observed behaviour is compared with expected behaviour
to check if there is a deviation. The work [29] develops
an algorithm that monitors power flow results and detects
anomalies in the input values that could have been modified
by attacks. The algorithm detects intrusion by using principal
component analysis to separate power flow variability into
regular and irregular subspaces.
B. An Example: Man-in-the-Middle Attack
Man-in-the-middle attack has potential to become a typical
attack in Fog computing. In this subsection, we take man-
in-the-middle attack as an example to expose the security
problems in Fog computing. In this attack, gateways serving
Linux IP Stack
Hook
process
AMR H.263 H.324M
IP_INPUT IP_OUTPUT
Attacker
Victims
Fig. 6. A system design of man-in-the-middle-attack in Fog.
as Fog devices may be compromised or replaced by fake ones
[30]. Examples are KFC or Star Bar customers connecting
to malicious access points which provide deceptive SSID as
public legitimate ones. Private communication of victims will
be hijacked once the attackers take the control of gateways.
1) Environment Settings of Stealth Test: Man-in-the-middle
attack can be very stealthy in Fog computing paradigm. This
type of attack will consume only a small amount of resources
in Fog devices, such as negligible CPU utilization and mem-
ory consumption. Therefore, traditional anomaly detection
methods can hardly expose man-in-the-middle attack without
noticeable features of this attack collected from the Fog. In
order to examine how stealthy the man-in-the-middle attack
can be, we implement an attack environment shown in Figure
5. In this scenario, a 3G user sends a video call to a WLAN
user. Since the man-in-the-middle attack requires to control
the communication between the 3G user and the WLAN user,
the key of this attack is to compromise the gateway which
serves as the Fog device.
Two steps are needed to realize the man-in-the-middle attack
for the stealth test. First, we need to compromise the gateway,
and second, we insert malicious code into the compromised
system. For susceptible gateways, we can either refresh the
ROM of a normal gateway or place a fake active point in
PC Phone
Compromised
Gateway
TD-SCDMA /
WCDMA /
CDMA2000
WLAN
802.11 b/g
AttackerA
(4) (1)
(3) (2)
Fig. 7. The hijacked communication in Fog (e.g. from phone to PC).

Fig. 8. Memory Consuming of man-in-the-middle-attack in Fog.
Fig. 9. CPU consuming of man-in-the-middle-attack in Fog.
the environment. Both methods can be easily implemented in
the real world, such as in the KFC or Star Bar environments.
In our experiment, we choose the former and use Broadcom
BCM5354 as the gateway [31]. This device has a high-
performance MIPS32 processor, IEEE 802.11 b/g MAC/PHY
and USB2.0 controller. Video communication is set up on
BCM5354 between a 3G mobile phone and a laptop which
adopts Wifi for connection. We refresh the ROM of BCM4354
and update its system to the open-source Linux kernel 2.4.
In order to hijack and replay victims’ video communication,
we insert a hook program into the TCP/IP stack of the
compromised system. Hook is a technique of inserting code
into a system call in order to alter it [32]. The typical hook
works by replacing the function pointer to the call with its own,
then once it is done doing its processing, it will then call the
original function pointer. The system structure is implemented
in Figure 6. We further employ the relevant APIs and data
structures in the system to control the gateway device, such as
boot strap, diagnostics and initialization code. The IP packets
from WLAN will be transferred to and processed in 3G related
modules. We plug a 3G USB modem on BCM5354 device,
on which we implement H.324M for video and audio tunnel
with 3G CS. H.263 and AMR functions are also implemented
as the video and audio codec modules in the system.
2) Work Flow of Man-in-the-Middle Attack: The communi-
cation between 3G and WLAN needs a gateway to translate the
data of different protocols into the suitable formats. Therefore,
all the communication data will firstly arrive at the gateway
and then be forwarded to other receivers.
In our experiment, the man-in-the-middle attack is divided
into four steps. We illustrate the hijacked communication from
3G to WLAN in Figure 7. In the first two steps, the embedded
hook process of the gateway redirects the data received from
the 3G user to the attacker. The attacker replays or modifies
the data of the communication at his or her own computer,
and then send the data back to the gateway. In the final step,
the gateway forwards the data from the attacker to the WLAN
user. In fact, the communication from the WLAN user will also
be redirected to the attacker at first, and then be forwarded by
the hook in the gateway to the 3G user. We can see clearly
from Figure 7 that the attacker can monitor and modify the
data sent from the 3G user to the WLAN user in the ‘middle’
of the communication.
3) Results of Stealth Test: Traditional anomaly detection
techniques rely on the deviation of current communication
from the features of normal communication. These features
include memory consumption, CPU utilization, bandwidth
usage, etc. Therefore, to study the stealth of man-in-the-
middle attack, we examine the memory consumption and the
CPU utilization of gateway during the attack. If man-in-the-
middle attack does not greatly change the features of the
communication, it can be proofed to be a stealthy attack. For
simplicity, we assume the attacker will only replay the data at
his or her own computer but will not modify the data.
Firstly, we compare the memory utilization of gateway
before and after a video call tunnel is built in our experiment.

The results are shown in Figure 8, and the red line in
plots indicates the average amount of memory consumption.
We can see clearly that man-in-the-middle attack does not
largely influence the video communication. In Figure 8(A),
the average value is 15232 K Bytes, while after we build the
video tunnel on gateway, the memory consumption reaches
15324.8 K Bytes in Figure 8(B). Secondly, we show the CPU
consumption of gateway in Figure 9. Based on the results
in Figure 9, we can also see that man-in-the-middle attack
does not largely influence the video communication. In the
Figure 8(A), the average value is 16.6704%, while after the
video tunnel is built, the CPU consumption reaches 17.9260%.
We therefore conclude that man-in-the-middle attack can be
very stealthy in Fog computing because of the negligible
increases in both memory consumption and CPU utilization
in our experiments.
Man-in-the-middle attack is simple to launch but difficult
to be addressed. In the real world, it is difficult to protect Fog
devices from compromise as the places for the deployment
of Fog devices are normally out of religious surveillance.
Encrypted communication techniques may also not protect
users from this attack since attackers can set up a legitimate
terminal and replay the communication without decryption.
Particularly, complex encryption and decryption techniques
may not be suitable for some scenarios. For example, the
encryption and decryption techniques will consume lots of
battery power in 3G mobile phones. In fact, this attack is
not limited to the scenario of our experiment environment.
We can find many applications running in Fog computing
are susceptible to man-in-the-middle attack. For example,
many Internet users communicate with each other using MSN
(Windows Live Massager). The communication data of MSN
is normally not encrypted and can be modified in the ‘middle’.
Future work is needed to address the man-in-the-middle attack
in Fog computing.
C. Privacy Issues
In smart grids, privacy issues deal with hiding details, such
as what appliance was used at what time, while allowing
correct summary information for accurate charging. R. Lu et
al. described an efficient and privacy-preserving aggregation
scheme for smart grid communications [33]. It uses a super-
increasing sequence to structure multi-dimensional data and
encrypt the structured data by the homomorphic cryptogram
technique. A homomorphic function takes as input the encrypt-
ed data from the smart meters and produces an encryption
of the aggregated result. The Fog device cannot decrypt the
readings from the smart meter and tamper with them. This
ensures the privacy of the data collected by smart meters,
but does not guarantee that the Fog device transmits the
correct report to the other gateways. For data communications
from user to smart grid operation center, data aggregation is
performed directly on cipher-text at local gateways without
decryption, and the aggregation result of the original data can
be obtained at the operation center [33]. Authentication cost
is reduced by a batch verification technique.
VI. CONCLUSIONS AND FUTURE WORK
We investigate Fog computing advantages for services in
several domains, and provide the analysis of the state-of-the-
art and security issues in current paradigm. Based on the work
of this paper, some innovations in compute and storage may be
inspired in the future to handle data intensive services based
on the interplay between Fog and Cloud.
Future work will expand on the Fog computing paradigm in
Smart Grid. In this scenario, two models for Fog devices can
be developed. Independent Fog devices consult directly with
the Cloud for periodic updates on price and demands, while
interconnected Fog devices may consult each other, and create
coalitions for further enhancements.
Next, Fog computing based SDN in vehicular networks will
receive due attention. For instance, an optimal scheduling in
one communication period, expanded toward all communica-
tion periods, has been elaborated in [6]. Traffic light control
can also be assisted by the Fog computing concept. Finally,
mobility between Fog nodes, and between Fog and Cloud, can
be investigated. Unlike traditional data centres, Fog devices
are geographically distributed over heterogeneous platforms.
Service mobility across platforms needs to be optimized.
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