Internet of Things (IoT) can be designed by various approaches with optimistic technology choices. This paper focuses on comparing recent studies on architectural choices and communication approaches for IoT Systems. Understanding Goals of an IoT system and inventing a general prototype for general IoT solutions is uniquely challenging. Existing research prototypes provide us information about IoT systems and their challenges. Existing architectures and communication approaches such as such as Service Oriented Architecture (SOA), Instant Messaging (XMPP) and Web-Sockets Service can be used to develop a general IoT System prototype. SOA provides centralized/decentralized IoT systems. Instant Message services such as XMPP can be used to build distributed and secure IoT platforms. Web-sockets also used to build scalable IoT systems. Overall the choice depends on IoT system Goal and limitations. Intelligent IoT (IIoT) Systems can be seen as decision making system. IoT systems can be built on Cloud infrastructures With Sensor Event as a Service (SEaaS) - Cloud Sensor networks can enable applications to access on-demand real-time sensor data. A generic IoT platform can be built and extended to newer applications and platforms.

1. Intelligent Internet of Things (IIoT): System
Architectures and Communications
Raghunath Nandyala
raghu.nandy(at)gmail.com
Abstract— Internet of Things (IoT) can be designed by
various approaches with optimistic technology choices. This
paper focuses on comparing recent studies on architectural
choices and communication approaches for IoT Systems.
Understanding Goals of an IoT system and inventing a
general prototype for general IoT solutions is unique
challenging. Existing research prototypes provide us
information about IoT systems and their challenges.
Existing architectures and communication approaches such
as such as Service Oriented Architecture (SOA), Instant
Messaging (XMPP) and Web- Sockets Service can be used
to develop a general IoT System prototype. SOA provides
centralized/decentralized IoT systems. Instant Message
services such as XMPP can be used to build distributed and
secure IoT platforms. Web-sockets also used to build
scalable IoT systems. Over all the choice depends on IoT
system Goal and limitations. Intelligent IoT (IIoT) Systems
can be seen as decision making system. IoT systems can be
built on Cloud infrastructures With Sensor Event as a
Service (SEaaS) - Cloud Sensor networks can enable
applications to access on demand real-time sensor data. A
generic IoT platform can be built and extended to a newer
applications and platforms.
Keywords—IoT Goal; Application layer; Cloud Computing;
XMPP; Web-scoket; RESFful; Decission System; Scalability;
Service Oriented Architecture (SOA), Resource Oriented
Architecture(ROA), Infrastructure as a Service(IaaS), Software as a
Service(SaaS), PaaS, SEaaS, Staas, Big Data, Sensor Observatory
Services (SOS), Sensor Event as a Service(SEaaS), Smart-Whatever,
IIOT
I. INTRODUCTION
Internet of Things is a general subject describing sensor
networks, mobile networks, and electronic device networks. It’s
not about a single sensor type or communication technique.
Internet of Things need to be considered a complete system,
which composes of individual unites that can communicate
together. It is a complete end to end solution for solving real
world problems.
IoT Systems consists of various types of nodes. Each node
has its own purpose. For example, sensors, network routers,
servers. Sensors cannot perform heavy computations. Sensors
might be placed in ad hoc places with limited resources. Sensor
nodes are very limited to compute and direct the decisions. The
centralized servers can derive decisions based on the analysis
of small to large volumes of data produced by sensor nodes.
IoT nodes can be categorized into three types based on
their resources and computation power. [1]
Figure 1: IoT Nodes Categorized based on Resources
 Resource-rich: Can host software architectures
(Example : PC, Servers)
 Resource-constraint: Can hold limited computation
capabilities (Example : Smart Phones)
 Simple devices: Very limited resource such as
energy, computation power (Example: Sensors)
By gathering the data from various node networks the
centralized systems can compute a set of automated decisions.
Cloud Computing enables applications to depend on on-
demand computing resources to handle heavy data loads and
analysis logics (such as ‘Big data’) [2].
A. Application Layer
In ISO Layer model of interconnectivity of computers,
Application layer stands on top of the stack. Application layer
consists of intelligence of the systems. Application developers
can concentrate on Application layer programs as primary and
rest of all the layer services will be abstracted.
Client-Server Model is a quite common architectural
pattern that is been used in the Software Industry. Many
standardizations, frameworks, prototypes are available. Service
Oriented Architectures (SOA) can be used heterogeneous
system communication possible through simple text based
communication.
II. RESEARH MOTIVATION/ISSUES
Current research is to understand and compare existing
research publications on IoT Architectures. With this research,
Simple
Devices
Resource
Constraint
Nodes
Resource Rich
Node

2. we can produce a high level understanding and implementation
knowledge.
A. IoT Goals:
Many fields (such as Medical, Home, Mobile and
Military) has applications that they can be implement using
IoT approach. The main goal of IoT systems is to automate
the decision making. Understanding Goals for the IoT
systems is a crucial to the system developers. IoT is mainly
an automated decision making systems comprises of
feedback mechanism where the system influenced by
environmental (external), internal, user factors. Figure 9
B. IoT Logic Componets and Simple Units:
IoT System nodes can be categorized into following
based on their purpose. Logical components come under
Resource rich or Resource-constraint nodes.
1) Decission Makers: Resource-rich Nodes
Decission makers can be treat as the brain of the IoT
system. It holds control subsystems and analysis componets.
a) Servers IoT Logic Components: Resource-rich
Nodes
Resource rich nodes which can run in Cloud with
automated scalablility. These components should be
100% available at any point of time.
b) Middle level IoT Logic Components: Resource-
constaint Nodes
Resource constaint nodes which take moderate
decissions. For example Smart phones or Smart
remotes have CPU with small computation power.
They are constrained by their capacity and availability.
2) Analysis Producers: Resource-rich Nodes
Analysis components produce analysis for a given set
of inputs. The inputs can be of any size. Big data analytics
or simple if condition also come under this components.
Example: Server IoT Analysis Componnet
3) Network Units: Simple Devices
Network units take care of data transpission. Example:
Network Routers
4) Actors: Simple Devices
Mechanical or Digital IoT Units which can perform
actions. Example: Unit to close the door.
5) Observers: Simple Devices
Simple devices which can observer the environment
and transmit the data to nearest network units. This devices can
not compute logic. They are very limited with resource and ad
hocly installed. Example: Thermal Sensors, Motion Sensors,
GPS Units.
C. Application Layer:
Application layer focus is to mainly concentrating on
develop a general prototype software model applicable to any
IoT implementation. Each IoT scenario has different set of
goals. For example, Smart Homes and Smart Shopping mall
may be have different IoT goals but the core infrastructure
could be the same. Certain level of abstraction can help to build
prototype models that can be applicable to any IoT
infrastructure.
Figure 2: Internet of Things, Sensing and Cloud Computing [2]
With appropriate technology choice, IoT systems can be
build more manageable and easy to enhance.
Figure 2: Developing a small or huge IoT infrastructures
require many things under consideration. Economical,
manageability, Security, Actability. This figure shows various
considerations for Cloud bases Internet of Things.
Following are the few challenges to be considered to
design large scale IoT systems. [3]
1. Heterogeneous things come together and interact with
each other
2. Unifying and standardizing the communication
protocols
3. Adoption of current and future market needs
4. Intelligence in architecture level
III. RELATED WORK AND COMPARISIONS
A. S³OiA (Smart Spaces and Smart Objects interoperability
Architecture)
This is a general purpose IoT architecture suitable for many
applications. The main idea is to use Service oriented
architecture (SOA-RESTful) to build an intelligent IoT
systems. This common architecture also be applicable to SOA-
Centralized or ROA-decentralized (Resource Oriented
Architecture) systems. This system follows Triple Space
computing Paradigm to extend to more semantic approach in
which the communication happens “subject predicate object”.
[3] Depending on dynamic SOA principles (loosely coupling,
late binding and promote the service reuse) the core architecture
divided into following groups:
1) Device and Service Discovery
Group of modules which helps abstracting and integrating
with the device communication. For example DPWS
(Devices Profile for Web Services) device has common
communication for any type of underlaying UPnP consumer
appliences with a simple sensor.
Internet
of
Things
Security
PaaS
Sensors
IaaS
Storage(StaaS)
Access
Mechanism

3. 2) Semantic Triple Spaces and Web Service Exposition
Group of models throw which a semantic triplesapce-based
distributed computing to universal system. This system is
exposed to web based API that can share knowledge among
the groups of similar and context-aware nodes.
3) Service Repository and Dependencies Resolution
These groups manages the services with in Smart Space
and resolve the dependency. It provides, event management
model based on publisher and subscriber paradigm. In the
Figure 3: Dependency Management (a. Local, b. Remote) [3]
Figure 3 Smart Space A and Smart Space B are located
remotely. Common dependency manager resolves remote
dependency references.
4) Interaction Interface
This group of modules facilitate human Smart Space
interactions.
5) Composition, Fault Tolerance and Distant
Dependencies
This modules deal with composition and orchestration where
recovery and fault tolerance can be achieved.
B. Cloud Computing IoT
Cloud computing enables IoT as distributed, scalable
systems. Moreover it will become simple and economical
to procure infrastructure. Companies need not to procure
high processing servers directly, instead they can use
Cloud infrastructures on demand basis. Cloud services can
be categorized into following [2]
a) Infrastructure as a Service (IaaS)
IaaS Cloud providers provide virtual
infrastructure for computing power. This make it
simpler to use on demand cloud infrastructure to run
IoT servers. Amazon AWS, Windows Azure, Google
Cloud are few providers in the market.
b) Paltform as a Servcice(PaaS)
Platforms where developers can build
software such as web applications can become easy. It
differ from IaaS where IaaS provide Linux or
Windows based virtual environments. PaaS is more of
web application containers.
c) Storage as a Service (StaaS)
Cloud providers can also provide infinite
storage for hosting files, analysis reports and virtual
environments.
d) SEaaS (Sensor Event as a Service)
Sensor Cloud infrastructure is an extended
cloud computing capabilities, where sensors can be
provides as IT resources on demand basis. Sensor
owners can outsource their infrastructure and data to
other application providers. In IoT sensor network
establishment is very costly process. SEaaS makes it
very economical and flexible.
Every sensor will be identified by an IP address.
Applications receive sensor data through the specific
vendor channels. And each sensor can be utilized by
more than one application.
Figure 4. Prototype model of Cloud supporting Internet of things [2]
Figure 4 shows a simple prototype for server
client based Cloud infrastructure for Smart Home
appliances. In this example the cloud infrastructure
has there servers and database. Home has four sensors
connected and managed by the IoT servers. In this
solutions the sensor network can be procured by
SEaaS.
C. Security
Security is a very subtle and nontrivial feature of IoT
systems. Devices can be authenticated to the central system
and then operate based on their roles and responsibilities.
VIRTUS Middleware [1] is an IoT system built by adopting
existing Instant Messaging protocols such as XMPP or
JMS. This approach can overcome the overheads of SOA.
Implicit security features can become added advantage to
use such architectures. Large number of online chatting
applications (Such as Google Talk) providing services for
millions of users. XMPP capabilities provide VIRTUS
Middleware, a seamless communication layer to support
operations.

4. Figure 5: Simple Devices Management Principle [1]
1) Extensible Messaging and Presence Protocol (XMPP):
XMPP is fuly decentralized. XMPP Sepcification
provides Transport Layer Security (TLS). The
communication happens using encripted XML streams.
The relevent extentons are security, authentication,
privacy and access control. Specification released under
RFC 6120 [4].
2) Distributed XMPP Setup
Figure 6: VIRTUS Middleware Multi-instances VIRTUS architecture [1]
Figure 6 shows the distributed XMPP server overview.
There are local networks connected to a global XMPP Server.
Local XMPP servers handle the local communication, and the
level of security is low. Bundle gateways proved interface to
Global XMPP Server so that other middleware instance. With
this approach, the distributed XMPP servers handle multiple
user communications. For IoT each sensor node is like a user in
XMPP network, and can communicate with other and global
XMPP server.
The main advantage of using VIRTUS architecture is that
XMPP communication servers and protocols are available and
inbuilt Transport layer security.
D. Message Transferring Architecture using Web-Sockets
A dedicated server holds Web-Sockets from various
applications and devices, and act as transfer communication
points. Once the connection established then data can be
exchanged without latency. Web-Socket specification released
under RFC 6455 for browser based HTTP connections. [5]
In Web-Sockets every device identified by unique identifier
with respect to every connection. And Web-Socket servers
maintains the key value pairs of id and connections. At any
point in time, server can transmit data to any connection.
Each server can hold finite number of live device
connections. Web-Sockets systems can be scalable to many
device. Following are the two architectural approaches to
achieve Web-Socket scalability. In the following architectural
explanation we considered a Smart Home device control
system. Multiple devices controlled monitored and controlled
by central Web-socket system.
3) Mediation Architecture
Figure 7: In the mediation architecture, there is a single
mediation server which can open connection and
delegate the communication to one of the Web-Socket
servers. All the device ids are persisted in Database.
Figure 7: Web-Socket Mediation Architecture and Sequence Diagram
for IoT [6]
4) Forward Architecture
Figure 8: In the following example, all the devices connected to
multiple Web Socket servers. Web socket server owns a set of
devices’ connections. If the connection is not found in the local
server map, then the server forwards to the corresponding Web-
Socket server which holds the connection. In this architecture
also the database is been used to persist the device ids.

5. Figure 8: Web-Socket Forwarding Architecture and Sequence
Diagram for IoT [6]
E. Intelligent IoT (IIoT): Decision Making System
Figure 9: IoT General Decision System (a novel interaction paradigm)
[3]
When we design IoT Systems, first question to ask is
“what is the goal that we are going to achieve by using the
system?” The goal is the application of the IoT system. It
can be a Smart Home, Smart Car or Smart – Whatever. The
smartness of a system is that the intelligence of the system.
An intelligent IoT (IIOT) system can take automated
decision based on user inputs, external factors, sensor data,
other systems and external information sources. For that
IoT system need to use small to high volume data
processing servers.
5) Analysis Producers : Big Data
Big Data is defined as a collection of complex data sets
that are difficult to process with the available data
management tools. Big data can grow in three dimensions-
velocity, variety and volume. [2]. Big Data tasks produce a
set of system wide conditions. Big Data systems cannot
make decisions. They run on Resource rich servers. System
learning, fault tolerance, scalable features are available in
Big Data. Cloud infrastructures provide dedicated services
for Big Data analytics.
6) Automatic Desission making
Intelligent IoT (IIoT) systems have high volumes of
data sets with historical and real time information. Using
Big Data analytical system as thinking brain, IoT systems
can take decisions. After running analysis on current or
historical sensor data sets Big Data systems raise
conditions. Depending on these raised conditions IoT
system central servers can send action commands to actor
nodes. [2] Upon the central servers command Actors
perform the actions. Observers (Sensors) can report back
to the central servers about the status of the action. In most
of the scenarios human interactions will not be there. These
intelligent systems designed for orchestration, and fault
tolerance.
7) IIoT Organization
IIoT Organization is the proposed solution for
artificial intelligent IoT system that can automate the
decision making. In general the system process can be
represented in six stages. Where each stage can looped and
produce high volumes of data. This System can be
extended for many sensor nodes and actor nodes.
Figure 10: IIoT Organization
Following are the data flow sequences from one node
to another node.
 Stage 1: Receive Data from Sensor 1
 Stage 2: Central IIoT Server submits the received data
and instructions to Big Data Analyzer
 Stage 3: Big Data retrieves corresponding historical
data from database archives
 Stage 4: After computing Big Data Analysis submits
the conditions to the Central IIoT server.
 Stage 5: After evaluating the conditions, the central
server submits the command to Actor 2.
 Stage 6: Sensor 3 submits the observations on Actor
2 actions for the previous command.

6. B. Sensor Cloud Infrastructure
This architecture uses extended cloud computing
infrastructure to procure and manage sensor networks on
demand. There are few challenges identified in the Sensor
infrastructures. [2]
 Complex Event processing & Management
 Massive Scale & real-time data processing
 Large scale computing framework
In Cloud Sensor Event as a Service (SEaaS), the vast
number of sensor owners can publish their sensor real-time data
to outside applications. The sensor network establishment is a
very expensive setup that every company cannot afford. SEaaS
approach is economical, scalable and manageable for
application developers.
1) Applications: AIR quality stations
There is an example web services domain 52North.org
which provides RESTful web services from their AIR
pollution stations. Their Senor Observatory Services (SOS)
produce data that can be used in any applications. [7]
Figure 11: Sensor Observation Service (SOS): AIR quality stations [7]
Figure 11: Shows the European sensor establishments of
52North Air quality stations.
Figure 12: SOS RESTful Extension [7]
And Figure 12 shows SOA web service end points for the SOS
resources such as observations, capabilities, offerings, sensors,
and features.
IV. DISCUSSION
Why do IoT systems need to follow Architectures? Which layer
does it come?
Considering and comparing standardized architecture
patterns can help us to build better systems. Without
architectural awareness understanding IoT is difficult. This
Architectures can come under Application, Transport layers,
may also influenced by Network layer, Data link layer and
Physical layers.
How to decide which architecture is suitable for a specific
application?
Architectural choice is an initial decision made by
companies for developing IoT systems. Sensor nodes,
capabilities, security, level of intelligence are few
considerations for architectural choices.
V. CONCLUSION
In conclusion, IoT is a general idea of System of things
connected and communicated together and make decisions.
Designing an intelligent universal architecture can help
standardization of IoT systems for any Goal. Communication
techniques such as SOA, XMPP and Web-sockets has their own
approaches to facilitate communication between simple nodes
to central server. Security, Scalability, Depending on data
frequency and type communication technique can be chosen for
optimistic resource use. An intelligent IoT (IIOT) system will
backed by a Big Data analytical system. Cloud Computing
approaches further extend the scalability of the IoT systems and
ease their management.
VI. REFERENCES
[1] Davide Conzon, Thomas Bolognesi, Paolo Brizzi, Antonio
Lotito, Riccardo Tomasi and Maurizio A. Spirito, "The
VIRTUS Middleware: an XMPP based architecture for secure
IoT communications," IEEE, 2012.
[2] P. R. B. B, P. Saluja, N. Sharma, Ankit Mittal and S. V.
Sharma, "Cloud Computing for Internet of Things & Sensing
Based Applications," IEEE, 2012.
[3] Mario Vega-Barbas, Diego Casado-Mansilla, Miguel A.
Valero, Diego Lopez, Jose Bravo and Francisco Florez, "Smart
Spaces and Smart Objects interoperability Architecture
(S³OiA)," IEEE, 2012.
[4] P. Saint-Andre, "RFC 6120 - Extensible Messaging and
Presence Protocol (XMPP): Core," Internet Engineering Task
Force (IETF), 2011.
[5] I. Fette, "RFC 6455 - The WebSocket Protocol," Internet
Engineering Task Force, 2011.
[6] Hiroshi Kawazoe, Daisuke Ajitomi and Keisuke Minami,
"Large-scale and Real-time Remote Control Architecture for
Home Appliances," IEEE, 2014.
[7] 52north.org, "Demo of the ArcGIS Server SOS Extension,"
2013. [Online]. Available:
http://52north.org/communities/sensorweb/sosSOE/demo.html.