Edge applications. Cloud Computing has been seen as

Edge Mesh- A new
computational Paradigm in Internet of Things

Rinta Antony, Dr. Murali. P

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Computer Science
Engineering Department
Adi Shankara Institute of Engineering and Technology

Kalady, Ernakulam

 

Abstract— For the past few years Internet of
Things has been revolutionising our daily lives by connecting everything around
us. It is the network of physical
devices, vehicles, home appliances, and other items embedded with electronics,
software, sensors, actuators, and network connectivity which enable these
objects to connect and exchange data. Currently
most interaction between the IoT devices and the back-end servers is done
through large scale cloud data centres. However, with the exponential growth of
IoT devices and the amount of data they produce, communication between “things”
and cloud will be costly, inef?cient, and in some cases infeasible. In this
paper, we have proposed a new computing paradigm, named Edge Mesh, which
distributes the decision-making tasks among Edge devices within the network
instead of sending all the data to a centralized server. All the computation
tasks and data are shared using a mesh network of Edge devices and routers.
Edge Mesh provides many bene?ts including distributed processing, low latency,
fault tolerance, better scalability, better security and privacy, etc. These
bene?ts are useful for critical applications which require higher reliability,
real-time processing, mobility support, and context awareness. We ?rst give an
overview of existing computing paradigms to establish the motivation behind Edge
Mesh. Then, we describe in detail about the Edge Mesh computing paradigm
including the proposed software framework, research challenges, and bene?ts of
Edge Mesh.

 

Keywords: Fog Computing, Mesh Network, Cloud Computation.

I.      Introduction

The Internet of Things (IoT) is likely
to be incorporated into our daily life, in areas such as transportation,
healthcare, industrial automation, smart home, and emergency response. The IoT
enables things to see and sense the environment, to make coordinated decisions,
and to perform tasks based on these observations 1. In order to realize the
full bene?ts of the IoT, it will be necessary to provide suf?cient networking
and computing infrastructure to support low latency and fast response times for
IoT applications. Cloud Computing has been seen as the main enabler for IoT applications
with its ample storage and processing capacity. Nonetheless, being far from
end-users, cloud-supported IoT systems face several challenges including high
response time, heavy load on cloud servers and lack of global mobility.

In the era of upcoming high data
requirement applications, it may be inef?cient to send the extraordinarily
large amount of to the cloud, due to the high cost of communication bandwidth,
and due to the high redundancy of data. Instead of moving data to the cloud, it
may be more ef?cient to move the applications and processing capabilities
closer to the data produced by the IoT. This concept is referred to as fog
computing.

Edge Mesh proposes the idea of using
Edge devices to enable distributed intelligence in IoT. In 2, distributed
intelligence is de?ned as “cooperation between devices, intermediate
communication infrastructures (local networks, access networks, global
networks) and/or cloud systems in order to optimally support IoT communication
and IoT applications”. They consider that distributed intelligence involves
both processing and networking elements. We consider distributed intelligence
in a broader perspective which involves everything from data analytics and
networking to other functionalities such as data management, device management,
resource management, service management, orchestration, etc. There are many
research questions to be answered to develop Edge Mesh such as: How to de?ne
the network and computing model? How to distribute processing of data? How to
jointly optimize communication and computation? Etc.

II.   CLOUD COMPUTING

Cloud computing can be defined as the practice of using a
network of remote servers hosted on the Internet to store, manage, and process
data, rather than a local server or a personal computer. IoT devices are resource constraint which
limits their applicability; on the other hand, Cloud has abundant resources.
Therefore, IoT can make use of resources in the cloud to make up for its
limited resources 4. Cloud can bene?t IoT in many ways including
communication, computation, and storage. Data collected by IoT devices can be
stored in cloud and processed in a low-cost and effective manner. Cloud is
especially useful for IoT applications that are computation intensive and/or
use data driven processing 4. Cloud computing paradigm is heavily dependent
on Internet connectivity. Due to intermittent network connectivity, network
latency becomes high which is not suitable for applications with the real-time
requirement. As the amount of data being generated by IoT devices is becoming
huge, it is very dif?cult to send all the data to Cloud due to limited
bandwidth constraint. There is also a major issue of security and privacy as
data travels along intermediate networks which can be prone to attacks and if
the data is stored at public cloud then chances of unwanted access and/or
compromise becomes higher. Since Cloud data centers are usually located at a
faraway place, latency is higher which means Cloud computing paradigm is not
effective for an application that requires mobility support.

III. FOG COMPUTING

Cloud computing paradigm suffers from
four major issues discussed above, i.e. latency, security, privacy, and
mobility, which has motivated researchers to propose a Fog Computing paradigm.
Open Fog Consortium de?nes Fog Computing as “a system level horizontal architecture
that distributes resources and services of computing, storage, control and
networking anywhere along the continuum from cloud to Thing” 5. Fog Computing
is a distributed paradigm that provides characteristics such as low latency,
real-time interaction, distributed analytics, context awareness, geographical
distribution, mobility support, which are not supported by centralized Cloud
computing paradigm 6. Fog Computing shares many similarities with Edge
Computing paradigm as both allow computation closer to devices that produce data78.Fog
computing focuses on infrastructure perspective while Edge computing focuses on
things perspective 8. There is another similar paradigm called Mobile Edge
Computing, which was proposed by ETSI as a platform that pushes cloud computing
capabilities closer to mobile devices in radio access networks (RAN) 9. There
are many challenging issues that need to be resolved to realize the full
potential of fog computing paradigm. These issues are related to fog
networking, Quality of service, interfacing and programming, computation
of?oading, accounting, billing, monitoring, provisioning and resource management,
and security and privacy 9. IoT integrates different application domains which
can have varying requirements. Fog computing and cloud computing paradigms both
provide different bene?ts but they can work complementary with each other to
satisfy multiple applications requirements 6. Currently researchers are working
to integrate Fog and Cloud computing paradigms 10 11.

 

IV.EDGE MESH

In this section, we give details about the
Edge Mesh computing paradigm, which aims to enable cooperation between
different types of devices and enable distributed intelligence in IoT. Edge
Mesh can be de?ned as a computing paradigm that uses mesh network of Edge
devices and routers to enable distributed decision-making within the network.

Components of
Edge Mesh:

1)        
End Devices: End devices are
those devices which have the capacity to sense the surrounding and change it
based on the requirement. End devices are responsible for sensing and
actuation. Devices in a smart home such as camera, lights, thermostat, etc. are
some examples of End devices.

2)        
 Edge devices: It is any computing or networking
resource residing between data sources and Cloud-based data center. In our
case, we consider Edge devices as those devices which are either connected to
end devices or to Cloud. These devices are responsible for decision-making and
enabling interaction between End devices. Any device that can be used for
processing and enables connection between different end devices can be used as
Edge device.

3) Routers: Routers are used for relaying data between edge devices.
Their function is just to route the data. Routers are not used for processing
or enabling decision-making like Edge Devices. Routers and Edge Devices
together form a mesh network which is used for sharing computation and data
among Edge devices. 4) Cloud: Cloud provides abundant computing resources
including networks, storage, processing, application, services, etc.
Traditional IoT systems use a centralized Cloud server for enabling
decision-making. However, in the case of Edge Mesh, major decision-making is
done by Edge devices instead of Cloud. Cloud is integrated with other devices
only to be utilized for very speci?c application requirements that cannot be
met using Edge devices.

 

TABLE I
Comparison between traditional and edge mesh network

Traditional Mesh
Network

Edge Mesh

Distribute data from one node to another within the mesh network.

Used for
distributing data within Edge Mesh as well as enabling interaction between
other devices including end devices and Cloud

Nodes in traditional mesh network just make routing decisions.

Besides routing, Edge devices in Edge Mesh make decisions for
different computation tasks including processing, storage, networking etc.

Nodes in traditional mesh do not make decisions regarding end
devices

Edge devices make decisions regarding interaction between end
devices.

Nodes in traditional mesh are not responsible for managing data
shared between end devices

Edge devices in Edge Mesh are responsible for managing data shared
between end devices

 

IV. BENEFITS OF EDGE MESH

Bene?ts of Edge Mesh The main
objective of Edge Mesh is to enable distributed intelligence which helps Edge
Mesh to provide bene?ts associated with distributed computing systems. Such
bene?ts include fault tolerance, better scalability, and ef?cient performance
due to the distribution of load. There are some other bene?ts provided by Edge
Mesh by the virtue that it integrates characteristics from three different
computing paradigms, i.e. Cloud Computing Fog Computing, and Cooperative
Computing. Edge Mesh provides the best features of the three computing
paradigms. The bene?ts provided by such integration include low latency, better
services, and higher security and privacy.

1)      
Fault
Tolerance: Edge Mesh provides fault tolerance in terms of both communication
and computation. Since a mesh network is used for distributing data among
different devices, it provides many redundant connections. In the case of
failure of a device in the communication path, other paths can be used for
distributing data. Edge Mesh also provides redundancy for computation tasks.
The responsibility of any computation task lies on multiple Edge Devices that
cooperate with each other, therefore, failure of a single device does not
jeopardize the whole system.

2)      
Scalability:
Scalability is an important requirement for IoT systems as the number of
devices will continue to increase in the coming future. A computing paradigm that
relies on the centralized server for computation tasks cannot be scaled up.
Edge Mesh, on the other hand, has been proposed to enable distributed
intelligence which makes it suitable for IoT applications. Edge Mesh is
distributed so all the data is not sent to a single Edge device. The data is
sent to multiple Edge devices which can then share data so the communication
bottleneck issue is resolved due to the distributed nature of the system.

3)      
Load
Distribution: Computation tasks can be of?oaded to other Edge devices which
speed up the processing time. A single Edge device is not overloaded which
usually leads to better performance. Edge Mesh distributes the load among Edge
devices which leads to better response time, reduced makespan, and higher
throughput. Distribution of load also makes the systems more ?exible, i.e. in
the case of device failure, other devices can share the load of failed device.
IoT systems are dynamic, as devices can be mobile, added, removed, or changed
in con?guration. Edge Mesh can adjust to such changes as Edge devices can
cooperate with each other.

4)      
Low
Latency: Many IoT applications such as healthcare, video analytics, autonomous
vehicles, traf?c management systems, emergency response systems, smart parking,
etc. have low latency requirement. Cloud computing paradigm is not ef?cient
enough to be used for these time-critical applications. A large portion of the
time is consumed to transfer the data to and from a remote server which does
all processing tasks. Edge Mesh uses local Edge devices which can perform
computation tasks and share data within the required deadline.

V.   
APPLICATIONS

 
        The application scenarios
discussed in this section are related to three different application domains,
Smart Home, Intelligent Transportation System, and Healthcare. These
application scenarios help in illustrating the bene?ts of Edge Mesh and give an
understanding of scenarios where Edge Mesh computing paradigm can be of
signi?cant use. Smart Home has been one of the oldest
application domains of Internet of Things. The main objective of Smart Home is
to improve the comfort level, security, and safety of people inside the home
while considering energy conservation and cost into account. Intelligent transportation
system (ITS), also referred to as Internet of Vehicles (IoV), is another important
application domain of IoT. The technological advancement in sensor technologies
and vehicular communication technologies which is a communication protocol to
enable data exchange between high-speed vehicles and between vehicles and
roadside infrastructure units, has enabled exciting applications for ITS
domain. Google and other big corporations are now working on autonomous and
connected vehicles to improve road safety, traf?c ef?ciency, and enable other
services such as intelligent parking, accident prevention, collision warning,
etc.

 

VI. OPEN CHALLENGES

The two main characteristics of Edge
Mesh are that it uses distributed computation and integrates characteristics
from different computing paradigms. However, there are various open challenges
in it, Edge Mesh should support communication between different types of
devices such as Edge device, End device, routers, and Cloud. The data should not
only be shared between different types of devices but must be understood by the
devices. Communication protocols used in IoT suffer from low data rate,
frequent packet losses which make it dif?cult to achieve reliable
communication. Edge Mesh enables distributed intelligence;
however, where the intelligence should be placed is a major research question.
There are many other research questions too including, How the devices
cooperate with each other? Which devices should share data? How do devices
decide which data to be shared? Who decides the distribution of tasks among
devices? Which factors determine the distribution of tasks? etc. The
computation tasks are usually distributed among Edge devices, but Cloud can
also be used for big data analytics on large historical data. So, the tasks can
be distributed among different devices depending on application requirement and
resources available on the devices. It is challenging to determine how the
tasks must be distributed among Edge devices as it requires joint optimization
of computation and communication. Edge devices need to take care of many issues
including access control, resource allocation, QoS, security, data conversion,
data management, etc. This requires Edge devices to be robust and ?exible. It
is challenging to manage all the tasks simultaneously using Edge devices as
these devices are heterogeneous, resource constraint, and distributed. The
challenges in implementing Edge Mesh are a combination of many factors
including wireless distributed computing issues, IoT related challenges,
embedded device constraints, software implementation issues, theoretical
modelling limitations, algorithmic challenges, issues related to distributed
data analytics, etc.

VI. CONCLUSION

This paper proposes a new computing
paradigm, Edge Mesh, which focuses on enabling distributed intelligence in IoT.
Edge Mesh distributes the whole application into sub-tasks which are
distributed among Edge devices. Edge devices together with routers form a mesh
network which is responsible for many computation tasks such as storage,
processing, data sharing, etc. Edge Mesh tries to integrate best features from
Cloud computing, Fog computing, and cooperative computing to provide
multi-dimensional features. Many research challenges discussed in this paper
such as enabling meaningful exchange of data between heterogeneous devices, proposing
distributed security and privacy algorithms for resource constraint devices, enabling
integration of different computing paradigms, etc. are all open issues that
require more research efforts.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

1       
A. Al-Fuqaha, M. Guizani, M.
Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on
enabling technologies, protocols, and applications,” IEEE Communications
Surveys Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.

2       
C.-W. Tsai, C.-F. Lai, M.-C.
Chiang, L. T. Yang et al., “Data mining for internet of things: A survey.” IEEE
Communications Surveys and Tutorials, vol. 16, no. 1, pp. 77–97, 2014..

3       
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,
pp. 13–16, 2012M. Wegmuller, J. P. von der Weid, P. Oberson, and N. Gisin,
“High resolution fiber distributed measurements with coherent OFDR,” in Proc.
ECOC’00, 2000, paper 11.3.4, p. 109.

4       
A.
Botta, W. De Donato, V. Persico, and A. Pescap´e, “Integration of cloud
computing and internet of things: a survey,” Future Generation Computer
Systems, vol. 56, pp. 684–700, 2016.

5       
OpenFog, “De?nition of fog
computing,” http://www. openfogconsortium.org/resources/#de?nition-of-fog-computing,
accessed: 2017-05-14.

6       
F.Bonomi,R.Milito,J.Zhu,andS.Addepalli,”Fogcomputingand
its role in the internet of things,” in Proceedings of the ?rst edition of the
MCC workshop on Mobile cloud computing. ACM, 2012, pp. 13–16.

7       
 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. Springer, 2014,
pp. 169–186.

8       
W.Shi,J.Cao,Q.Zhang,Y.Li,andL.Xu,”Edgecomputing:Vision
and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646,
2016.

9       
S. Yi, C. Li, and Q. Li, “A survey
of fog computing: concepts, applications and issues,” in Proceedings of the
2015 Workshop on Mobile Big Data. ACM, 2015, pp. 37–42.

10    
M. Villari, M. Fazio, S.
Dustdar, O. Rana, and R. Ranjan, “Osmotic computing: A new paradigm for
edge/cloud integration,” IEEE Cloud Computing, vol. 3, no. 6, pp. 76–83, 2016.

11    
A.Munir,P.Kansakar,andS.U.Khan,”Ifciot:Integratedfogcloud
iot architectural paradigm for future internet of things,” arXiv preprint
arXiv:1701.08474, 2017. 22 T. H. Luan, L. Gao, Z. Li, Y. X

 

12    
Edge Mesh: A New Paradigm to
Enable Distributed Intelligence in Internet of Things ,Yuvraj Sahni, Jiannong
Cao, Fellow, IEEE, Shigeng Zhang, Member, IEEE, and Lei Yang, 15 August, 2017

10.1109/ACCESS.2017.2739804.

16   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. Springer, 2014,
pp. 169–186.

17 W.Shi,J.Cao,Q.Zhang,Y.Li,andL.Xu,”Edgecomputing:Vision
and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646,
2016. 18 Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “Mobile edge
computing: Survey and research outlook,” arXiv preprint arXiv:1701.01090, 2017.
19 S. Yi, C. Li, and Q. Li, “A survey of fog computing: concepts,
applications and issues,” in Proceedings of the 2015 Workshop on Mobile Big
Data. ACM, 2015, pp. 37–42. 20 M. Villari, M. Fazio, S. Dustdar, O. Rana, and
R. Ranjan, “Osmotic computing: A new paradigm for edge/cloud integration,” IEEE
Cloud Computing, vol. 3, no. 6, pp. 76–83, 2016. 21
A.Munir,P.Kansakar,andS.U.Khan,”Ifciot:Integratedfogcloud iot architectural
paradigm for future internet of things,” arXiv preprint arXiv:1701.08474, 2017.