Fog computing and data concurrency

Study of Fog Computing and Data Concurrency in
Internet of Things
Priyanka Goswami
Electrical and Computer Engineering
University of Arizona
Tucson, USA
priyankag@email.arizona.edu
Abstract—Internet of Things (IoT) was first explored in the
1980s, but even today there are many new concepts and
technologies that are coming up in this field. This is especially
because of the increase in smart phones and mobile devices like
smart watches, wearable medical devices, smart cars and the
emergence of smart cities. Also with increasing network speed
and availability of storage space, IoT has become available to the
masses and this has lead to an increased interest in this field.
Because of this sudden growth in devices, there is need system
which can store, process and analyze the big data that is
generated, also guaranteeing of privacy, security, low latency and
data integrity. This has lead to the combining of IoT with Cloud
Computing technologies, which provides virtually unlimited
storage and computing power. But with the system of a
centralized cloud space, the issues of latency, geographical
distribution and mobility of devices arise, and to address this,
research has started to integrate principle of Distributed
Computing Systems with IoT and Cloud, thus leading to the idea
of Fog Computing. Fog Computing, basically uses localized
clouds for storing and processing data, rather than a single
central cloud, thus addressing the above issues. This is beneficial
for IoT applications were time is a critical factor like smart
vehicles, aviation sector and smart healthcare services like
wearable heart monitor and tele-surgery. Fog faces the same
issues that are there in Distributed Computing like data
replication, retrieval and data concurrency. In the paper, all the
above topics are discussed and reviewed. Also an analysis of
different data concurrency techniques, their principle and the
some recent developments in that area is discussed.
Keywords—IoT; Distributed Computing;latency; mobile; cloud;
fog;computing;latency;concurrency;sensor;services
I. INTRODUCTION
Internet of Things was first explored by Mark Weiser who
defined it as invisible computers, which operate everyday
things like thermostats, switches and stereos, being connected
by an omnipresent network, and used the term “Ubiquitous
Computers” [1]. “Internet of Things” (IoT), first used by Peter
T. Lewis in 1985, can be defined as the connection of physical
devices like sensors and actuators through the internetwork/
internet, thus enabling them to store and exchange data. This
implies that physical entities become a part of the virtual
world, but can be controlled remotely and act as physical
access points to the Internet [2]. The physical devices are
called cyber – physical systems (CPS). The advancement in
IoT, in recent years can be contributed to developments in
smart sensors, communication technologies, RFID,
improvement in bandwidth and network speed, decreased cost
and size of storage devices and building of data centers, which
can store the data being generated. There are many areas in
which IoT can be used to improve the quality of life like
transportation, healthcare, industries and manufacturing
plants, disaster management, business as well as home
applications. There is already advancement in the area of
integrating homes with smart sensors, and using IoT for doing
various tasks like temperature control, laundry and cooking,
controlling lighting and entertainment systems. IoT is being
further integrated into everyday life with the development of
smart cars and smart cities. It is estimated that by 2020, more
than 50 billion devices will be connected to the internet, which
exceeds event the human population [3].
The IoT works on the same principle as the original
internet, which is connecting nodes through a network, with
data being stored and exchanged, but in IoT instead of servers
and computers, sensors and actuators act as the nodes. And
here is where the difference comes between the traditional
internet and IoT – sensors and actuators don’t have the same
computation or storage capacity, as that of servers and
computers. Additionally, IoT is an amalgamation of different
technologies, which help in providing the following
capabilities:
 Communication and Cooperation
 Locating, Addressing and Indentifying physical objects
 Sensing and Actuating
 Data storage and information processing
 User Interface
 Security and Privacy
 Quality of Service
Although the IoT has numerous applications, there are
many challenges that need to be addressed, before it can be
available on a mass scale and at low cost. Some of the
challenges are described below:
A. Scalability
Compared to the “Internet of Computers”, “Internet of
Things” has a much larger scale and need more cooperation
and data storage and processing capabilities. Today’s Internet
Routing System connects more than 3 billion people using
more than half million IP addresses and about 50,000

administered networks [4]. This will be higher for IoT since,
besides computers and smart phones, multiple sensors and
actuators will also be connected to each other, generating large
amount of data, in the real time, which needs to be stored and
processed at a very high speed to provide services to
customers. Also most of the network protocols designed today
is for devices having adequate computational and memory
resources [4]. Hence to solve scalability challenges, there is a
need of designing fast, adaptive network protocols,
considering that now the devices will be in motion.
B. Software and Hardware Development
This includes software and hardware availability, such that
service can be provided in a seamless fashion, anytime and
anywhere. Software design should be such that services can be
provided to multiple users, at different geographical locations,
all accessing same or different data, considering the fact the
devices may be stationery or in motion. Also the way in which
the software will handle the large amounts of data and process
it in a sensible manner will have a huge role in the quality of
service provided. Programs should also have the capability to
identify invalid data, which can cause smart devices to
function incorrectly, and this critical especially for
applications like medical and smart vehicle applications.
There is also need for correctly configuring systems having a
wide variety of devices interacting with each other in a
complex manner and configuring them such that they are
always functioning in a proper sequence. Also the codes
running on these devices should have self diagnosing and
debugging capabilities. Recent development in Software
Defined Networking (SDNs), Human Computer Interaction
(HCI), Machine Learning and Machine to Machine (M2M)
technology, has helped in addressing some of the issues.
Hardware capability means that all devices (sensors and
actuators) should be compatible to the different functionalities
and protocols, like IPv6, Bluetooth, ZigBee, 6LoWPAN,
LTE Cat, NFC, RFID, etc [5].
C. Synchronisation between devices
IoT devices not only communicate with each other and the
cloud but also need to be synchronised in real time to function
in a seamless manner. For example in smart transportation,
there has to be real time update and exchange of information
between vehicles, roads, traffic signals, navigation devices and
also authorities, and this will require a robust and omnipresent
timing system to ensure availability and integrity of data [6].
Other such time critical applications include medical
applications like tele surgery, financial and banking systems,
etc. The Simple Networking Time Protocol (SNTP) can be
used for synchronising devices and has a 64 bit timestamp
providing a higher precision compared to NTP, which is used
in PCs for time synchronisation. But for critical applications
of IoT like in transportation or medicine, higher precision is
required.
D.Security Challenges
Since IoT will be used in may critical applications, there is
need for state of the art security, to ensure there is no
corruption in the system, and user privacy is maintained.
Because of the complex and diverse components there are
many challenges to ensuring proper security cover for all
devices. To reduce cost, many IoT devices may not have the
adequate security, thus compromising even the other devices
that interact with it.
Additionally, providing a unified firmware and updating
all devices at once is a big challenge because of the large no.
of them. Also many applications are open source softwares
that are susceptible to attacks and malware. Also since all
devices are connected to each other, it is difficult to isolate the
device that was the source of the attack and take corrective
measure.
E.Data generation, storage and analysis
One major aspect of IoT is the amount of data being
continuously generated by the devices and the resources
required to store and analyse the data to get useful information
from it, which can be used for training and machine learning.
This is important as there may be lot of false information and
false positives. Another important aspect is data association,
i.e. after data collection the subsequent inferences are
associated with the correct devices [8]. Additionally storage
and usage of the data will require access permissions and
addressing privacy issues, security, openness, fast access and
robustness.
Some of the main areas of research in IoT are big data
analysis, robustness, security and privacy, machine to machine
interface, synchronization and timing of devices, human – in –
the-loop and meeting software and hardware expectations and
all these are targeted to solve the above described challenges.
Also another interesting research area in IoT is “Distributed
Systems and IoT”, which aims in connecting the physical
items (which represent the access points to Internet services)
through a geographically distributed system that can integrate
the technologies like mobile, cloud and this is being
implemented in Fog Computing [9].
The paper will focus on the integration of cloud computing
with IoT and how it helps in addressing the challenge of “data
generation, storage and analysis”. The paper will also explore
the use of Fog Computing, which is the use of Distributed
Computing for providing application services like data
analysis and security in IoT, with the focus being on Data
Concurrency, along with the challenges and possible solutions
using some real world applications as examples
II. CLOUD COMPUTING IN IOT
The NIST defines Cloud Computing as: “A model for
enabling ubiquitous, convenient, on-demand network access to

a shared pool of configurable computing resources (e.g.,
networks, servers, storage applications and services) that can
be rapidly provisioned and released with minimum
management effort or service provider interaction [10].
According to the Cisco Global Cloud Index (2014-2019),
by the end of 2019 there will be quadruple rise in the global
cloud traffic, and this can be clearly seen in Figure.1 [11]. But
the growth may be more because of the increases in no. of
devices that will be connected through the internet. And with
integration of Cloud and IoT, the amount of data being
generated will increase substantially thus causing the traffic to
increase more than that predicted. This will also cause the
Cloud traffic to be more than the data center traffic.
Figure.1. Graph showing the increase in the cloud traffic for
years 2014 – 19 [Source:Cisco Global Cloud Index 2014]
Cloud computing and IoT, although different technologies,
can be merged to solve many of the challenges described
above especially data generation, storage and analysis,
scalability and security. This is being referred as the CloudIoT
paradigm [12]. In IoT, every device will have an IP and are on
IP connected network, which means that exchanging sensor
data with every device will be a computationally and
economically expensive process and Cloud can provide a
convenient and cost effective solution. Features of Cloud like
fault tolerance, virtually unlimited storage and computational
ability means to manage and analyse big data, compatibility
with TCP/IP and other common protocols for service delivery
and location independent access to data. Additionally it can
also provide services like Saas (Sensing as a Service), SAaaS
(Aensing and Actuation as a Service), DBaaS (DataBase as
aService), IPMaaS (Identity and Policy Management as a
Service), etc [12].
Sensor cloud is an infrastructure providing large scale
integration of sensor networks, with sensing applications and
cloud computing, thus allowing collection and processing of
data from different sensor networks along with data sharing
among users and applications running on the cloud. Sensor
cloud can be defined as a heterogeneous computing
environment that is spread over a large geographical area and
combines different wireless sensor networks and provide SaaS
to the user [13]. The nodes of the Sensor network consist of
three parts [14]:
 Sensing
 Processing
 Communication
The sensor cloud is basically an extended version of cloud
computing, but is used for managing sensors (which includes
their identification, data generated, data storage, processing
and analysis), connected through the wireless network. With
the cloud providing extensive storage and processing capacity,
it enables collecting the large amount of sensor data by linking
the cloud and Wireless Sensor Network through the sensor
gateway and cloud gateway [15]. The process is as follows:
 Sensor gateway collects and compresses the data it gets
from the WSN nodes before transmitting it to the cloud
gateway.
 Cloud gateway on getting the compressed data,
decompresses and stores it
For sensor cloud, the operation can be divided in two
phases:
a. Creating the service catalogue [17], which includes the list
of services and the specifications like network speed, types of
sensors and data availability, from which templates can be
created to meet the specific requirements of the user. Here the
sensors are not actual physical entities but virtual sensors on a
cloud computing platform, making them dynamic and
geographically independent. Thus users can utilise the sensors
using come set functions and need not consider their actual
physical locations [16].
b. Service providing phase [17], in which the user requests for
service based on the templates generated in the first phase.
Once the provider generates the service, the user is
acknowledged about it and given full access and control. Once
finished with the task, the service instance is released (the
virtual sensors ceases to exist) and the user is billed for it [16].
The sensor cloud combined with IoT is used for many
applications like real time health monitoring, telemedicine,
traffic monitoring and control in smart cities, controlling
energy consumptions and operating devices in smart homes,
agriculture and environment monitoring, and many more.
Although the classic cloud computing model is an efficient
option for IoT applications, as compared to large data centers,
it may be inadequate to meet the requirements of some
latency-sensitive applications that need the sensor nodes to be
nearby, to meet the time delay requirements and also the
mobility and geologically distributed aspect of IoT [11].
Additionally, another problem faced in cloud computing is
security and privacy, since being connected through the
Internet, user requests, data transmission and system response
have to traverse through large no. of intermediate network
nodes, thus increasing the vulnerability to attack [18].
Additionally, with user data being stored in public cloud, there

is a risk of its integrity being compromised and there is no
assurance of complete confidentiality [18]. To address this, a
new platform has been developed, known as “Edge of the
Network Computing” or “Fog Computing” [19].
III. FOG COMPUTING
Fog computing is an extension of the classical concept of
cloud computing and was introduced by Cisco to ease wireless
data transfer to distributed devices which form part of the IoT
[20].The reason for naming it as “fog” is because in
meteorological terms, fog means “a cloud that is close to the
ground” (with data source being analogous to the ground).
Cisco defines Fog Computing as follows: “A paradigm that
extends Cloud Computing and services to the edge of the
network” [20]. Another definition of Fog Computing, also
known as “Fog Networking” or “Fogging” [20], is a
decentralized infrastructure in which computing resources and
application services are distributed in the most logical and
efficient manner between the point where data is generated to
the cloud, where the data is stored, with the main goal being
reducing the amount of transportation required for the data to
get stored, processed and analysed [21]. Hence, from the
above definitions, it can be said that fog can be defined as
using the concept of distributed computing systems for data
transfer and management (storage, processing and analysis) in
IoT. In this environment, the sensor data is mostly processed
in a data hub, which can be in a smart mobile device or at the
edge of network, like a smart router, thus reducing the amount
of bandwidth and time required for sending a data bundle [21].
This helps in reducing latency and is important in IoT
especially for applications like smart vehicles or controlling
traffic, where the delay in communication can cause
circumstances that can harm humans.
Some of the main characteristics of fog computing,
because of which it is being widely employed for data
transmission in IoT, are [18]:
 Highly virtualized and providing end-to-end services
between sensors and cloud computing data centers
 Location awareness and low latency
 Targeting IoT applications which need widely distributed
deployment and geographical distribution. This is also
ensured by using large number of nodes as seen in sensor
networks
 Supports mobility, which is an important aspect of most of
IoT based services.
 Supports real time interaction, over batch processing, thus
giving a more recent version of the data. This helps in
improving user experience and quality of service
 Predominantly wireless and heterogeneous and hence can
be deployed in a wide variety of environments
 Supports real time analysis and interaction with the cloud
and hence help in processing data close to the point of
generation.
Figure.2 shows a basic architecture of the fog computing
[22]. The bottom layer of consists of all IoT devices (sensors
like thermostats, pressure sensors, controllers/actuators like
switches, routers etc.) that generate or use the data. The next
layer consists of devices with computing, storage and network
connectivity known as fog nodes or fog servers and forms the
core of fog network. The data generated by IoT devices are
analyzed in this layer, with the fog nodes being close to where
the data is collected, which ensures that there is minimum
latency and sensitive data is kept within the network [22]. The
last layer consists of the cloud based data center, which
basically collects data from all the fog nodes and also analyses
data that do not have hard time limits.
Figure.2 Architecture of Fog Network
In fog the most time sensitive data will be processed on the
node closest to the point of generation, while data that can be
delayed by few seconds or minutes will be analyzed on
aggregation of nodes and the data that doesn’t have time
constraints will be passed on to the cloud [22].
From the above discussion, it can be seen that with the
increase in no. of IoT devices, it is not efficient or convenient
to send all the generated data to the cloud. This will not only
consume large amounts of bandwidth and time, but would not
also be cost efficient and reduce the quality of service. Hence,
using fog computing, these challenges can be overcome. Fog
provides the benefits of distributed computing, with the data
being collected and processed on nodes that are
geographically spread, with the main aim being that the data
should have minimum transportation. This is required for
many of time critical IoT applications like transport,
healthcare and patient monitoring and environment
monitoring. It also helps solving the issue of security and
privacy, with the chances of data being prone to attack very
less compared to that in cloud. Additionally it is more
economically efficient compared to using only cloud, thus
increasing the user base of IoT.
But as the complexity in IoT is increasing, and with the
merging of distributed computing (which is the underlying
concept of Fog Computing), the systems can face issues like
concurrency errors, deadlocks and resource starvation, which
can lead to reduction in security, data integrity and the overall
quality of service [23]. This issue will be especially
encountered at the top level of the fog network architecture,
which is the cloud based data center, where the data from all

nodes is finally stored. Hence existing concurrency control
techniques will have to be modified for application in IoT,
since here the rate and quantity of data generation is much
faster than simple PCs connected using internet. Additionally
geographical distribution and mobility of devices will also
have to be factored in. The next section will cover the issue of
concurrency at the cloud level in fog computing with the
possible solutions to resolve it.
IV. DATA CONCURRENCY IN FOG
Form the point of generation, the data within the IoT
system will have the following stages – collection at fog
nodes, filtering and processing, storage at the cloud and
retrieval whenever there is a query [24]. In IoT, the data
management system can be divided into two parts:
a. The front end that directly interacts with the sensors and
this is communication intensive, with large amount of data
being retrieved in response to queries [24]. This is the
storage units at the fog node level
b. The back end, which can be online or offline, and handles
mass storage and in-depth analysis of the data and this
handles only in depth queries, and has less communication
[24]. In fog network, this is the cloud.
Hence here, it has to be ensured that transaction
management is such that the data integrity is maintained by
guaranteeing the properties of Atomicity, Consistency,
Isolation and Durability (ACID), and it can be managed using
traditional techniques for bound spaces, but becomes complex
at a globalized scale [24]. And this is important because IoT is
continuously growing, with large no. of device being added to
the system every day.
“Data Concurrency” means that multiple users (devices)
can access data at the same time and also process it, while
“Data Consistency” means that each user can have a consistent
view of the data, with the changes being done on the data
visible to all the devices/ transactions [25]. Concurrency is
impacted by the following two scenarios:
a. When a process changing the data, doesn’t allow other
processes form accessing it [26].
b. When multiple processes try to change the data
simultaneously, but are not able to without compromising
data consistency [26].
Transactions can be classified into two types, based on
their execution time and the amount of data that they will
affect:
a. Online transactions: They have a short life span and affect
a small part of the data base. They have fast response times
and most of the transaction in IoT is of this type [27].
These transactions will use the data that is being analysed
and stored at the fog nodes. Example - Controlling the
light intensity in a smart home
b. Batch transactions: They have a longer life span and affect
a large portion of the data base [27]. In IoT these
transactions will generally run on the data stored in the
cloud. Example – Statistical analysis and application,
carrying out image processing and analysis on data
collected during environment monitoring.
Some of the main challenges that are faced by IoT
applications, especially if they run on the “edge of the
network” (fog) are as follows:
 Even if the service is only responsible for forwarding data,
through the routers to the cloud, there are many processes
simultaneously occurring in parallel, with the routers
having to handle multiple streams of data (from multiple
sensors or actuators) arriving at their interface, all at
different rates and from different locations [28]. To ensure
that the data integrity is maintained, there has to
concurrency control mechanism, but such that they do not
create latency, since the rate of new data generation is very
high, there should not be a scenario in which, by the time a
process is able to store data in the cloud a newer version of
the data is available and overwrites the old data. This will
cause data to lose its integrity and also create errors in
analysis.
 Another challenge is that the smart routers work
autonomously and with no user interface being directly
connected to it they are operated and serviced remotely,
via the same network through which the data is sent, and
so also has to be handled [28].
 The same concurrency handling mechanism/framework
cannot be used for all applications, because all the
applications are targeted to provide different types of
services [28]. For example, a framework used to handle
data concurrency for services targeted to operate smart
vehicles, cannot be used for service that cater to smart
homes or environment monitoring.
 In IoT the transaction serializability model cannot be
implemented because of the scale of transactions and the
need for no. of devices to be serviced simultaneously.
Reducing complexity of concurrency by building
application to operate in sequential manner will cause
latency as large no. of devices will require the same
services, causing large queues and time to service a
request. This cannot be tolerated for many time critical
applications.
V. CONCURRENCY CONTROL
“Concurrency Control” can be defined as a method to
maintain the consistency and integrity of the data while there
is concurrent access to the database in a multi user database
management system and creates the illusion that each user is
executing alone on the dedicated system [30]. Sometimes
concurrency control may lead to deadlocks, due to which the
data may not be accessible to the user at any point of time.
Hence to resolve such “Deadlock” situations, there are three
basic types of methods that can be used:

 Time Stamp –A unique time based identifier is used to
identify if an entry if “stale”. Read and Write operations
are based on the timestamp and accordingly executed.
Generally used where the data access pattern is mostly
consistent and only used for transactional purpose [31].
 Two-Phase Locking (2PL) – This supports multiple reads
and single writes. There are two phases: the growing phase
in which transactions set the read/write locks and the
shrinking phase in which locks are released. A process can
keep a read lock on a data and if it needs to write then it is
converted to write lock. It is used where the load pattern is
changing continuously and randomly and can be used for
transaction and analysis function [31]. For IoT databases
(in cloud and fog nodes), this technique is used and there
have been various modifications to suit to the application
needs.
 Optimistic Concurrency Control – Probability of
conflicting transaction is assumed to be low. Transactions
are allowed to proceed with writing the data and are
assigned unique version numbers. Once all the transactions
are completed, a “prepare to commit” message is sent to
the main server and if there are no conflicts the data is
updated with a unique stamp. In recent years the OCC has
been modified into two schemes by Harder: Forward
Oriented Optimistic Control (FOOC) and Backward
Oriented Optimistic Control (BOOC). This technique can
be used where the pattern of data access is consistent, since
it assumes that transaction conflicts will not be there [31].
 Wait-Die and Wound-Wait Algorithm – This algorithm is
similar to the 2PL scheme, but differs in the way deadlocks
are handled. It implements a time stamp based mechanism
to resolve issues of concurrent writes, by assigning a
timestamp to every transaction, with older transactions
given priority over the new ones to put lock on the data
[32].
For IoT based on FOG and Cloud Computing, to resolve
concurrency issues, the above described traditional schemes
are being modified and extended to suit the needs of different
IoT applications. One such scheme is the Distributed Real
Time Database Concurrency Control Transaction (DRTDBS)
[33]. This is an improved version of the two phase locking-
high priority protocol, in which transactions are allowed to put
locks based on a priority assigned to them. In the DRTBDS,
the transactions are also assigned a deadline, with the priority
being it function [33]. Hence priority can be decided in
different ways like a transaction having an early release will
be assigned a higher priority, or a transaction having the
earliest deadline is given a priority. This will depend on the
type of application that is using the data. For example if we
consider smart vehicles using the data to determine if it has to
apply brakes, then the second scheme is preferable. This
modification to the 2PL scheme helps in incorporating the
“urgency of situation” factor in the concurrency control
mechanism [33].
Conclusion
With IoT becoming available to the masses, there is going
to be massive increase in the amount of data that is generated
by the device and that needs to be stored, processed and
analyzed. The integration of IoT, Cloud and Distributed
Computing, has lead to the concept of fog computing and this
makes it possible to employ IoT at large scale, while ensuring
Quality of Service. But with Fog, being a recent development,
there are many issues that have yet to be addressed like
security, privacy, data integrity, replication and retrieval and
data concurrency. From the papers reviewed, it can be
concluded that researchers are taking the basic techniques of
Data concurrency like the 2PL, Time Stamp and Optimistic
Concurrency Control techniques and modifying them based on
the requirements of specific applications. The Two Phase
Locking Technique is said to be more suited for IoT
applications because it can easily adapt to changing load
conditions. One modification of the 2PL mechanism is the
2PL-HP, which allows for tasks to be assigned priorities and
accordingly set read/write locks on data. The DRTDBS [33],
scheme uses task deadline and time taken to release the data as
criteria to set the priority in the 2PL-HP scheme. This scheme
can be used for applications having strict deadlines like smart
vehicles and IoT in transportation.
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