This talk brings one of the major problems of IoT: scalability. And yet, besides putting the problem, here we also present our solution, which uses cloud elasticity to support the IoT demand.

1. Internet of Things Scalability:
Analyzing the Bottlenecks
and Proposing Alternatives
Márcio Miguel Gomes
Rodrigo da Rosa Righi
Cristiano André da Costa
Applied Computing Graduate Program
Universidade do Vale do Rio dos Sinos - Unisinos – Brazil
Corresponding address: cac@unisinos.br

2. 2
Agenda
• Introduction
• Theoretical basis
• Research question
• Related works
• Methodology and justification
• Proposed model
• Proposed algorithms
• Conclusion and Future Works

3. 3
Introduction
• Internet of Things - IoT
• Objects, animals or people equipped with unique
identifiers
• Ability to automatically transfer data over a network
• Without the need for human intervention

4. 4
Application of IoT on health area
Internet
Healthcare
server
Caregiver
or
physician
Emergency
services
or
Medical
researcher
Database
)))
)))
)))
Information
Assessment,
assistance,
treatment
Inertial
sensor
Pulse
and
blood
pressure
sensor
Oximetry
Sensor
Source: adaptated from Jiang et al (2008)

5. 5
Perspectives
• Study by IDC (International Data Corporation) - 2013
• Digital universe is doubling in size every two years (4.4
trillion gigabytes in 2013)
• Might be multiplied by 10 to 2020 (44 trillion gigabytes in
just 7 years)
• BRICs with the largest volume of data in 2020
• 30 billion devices connected to the Internet in 2020

6. 6
Typical architecture of an RFID system
Users
and
Applications
Data
Storage
RFID
Middlewares
and
Local
Applications
RFID
Readers
Antennas
RFID
Tags

7. 7
Theoretical Basis
• RFID Middleware
– Mediation of communication
between business systems
and RFID hardware
infrastructure
– Collecting, filtering,
aggregation, storage and
availability of data in a
standardized way
Source: Al Jaroodi, Aziz and Mohamed (2009)
Service
Management
Data
Management
Device
Management
Typical structure of
RFID Middlewares

8. 8
Theoretical Basis
• EPCglobal Architecture
Framework
• Set of interrelated standards for
hardware, software and data
interfaces
• LLRP – Low Level Reader
Protocol
• ALE – Application Level Events
• EPCIS – Electronic Product
Code Information Services
Source: http://www.gs1.org/gsmp/kc/epcglobal

9. 9
Research Question
• How would be a computer architecture and algorithms
for managing scalability of an Internet of Things
EPCglobal middleware, in order to guarantee the
performance from the dynamic demand of applications
and RFID sensors?

10. 10
Related Work
• Study of RFID middlewares
• Listing the most important features, applications, and used
technologies for identifying how they manage load balancing and
scalability
Middleware
MARM
Fosstrak
WinRFID
Hybrid
RF2ID
LIT
REFiLL
Scalability
Multi-agents
system
Dedicated server,
simulation mode
and embedded in
RFID reader
Distributed
modules
Peer-to-
peer multi-
ring
network
Virtual paths
between virtual
and physical
readers
Readers
management
interface
Light
programmable
framework
Load Balance
Not
addressed
Readers
subscription
Not
addressed
Peer-to-
peer
systems
Path
management
State-based
execution
model
Not addressed
EPCglobal
No
Yes
No
No
No
Yes
Yes
Comparison between RFID middlewares

11. 11
Choosing the RFID middleware
• EPCglobal compliant
• Application for general use
• Availability of access to the source code
• Possibility of modular deployment, in a distributed way
• Chosen middleware: Fosstrak

12. 12
Methodology and Justification
• MIB: Micro Benchmark for Evaluating Internet of Things
Middlewares
Source: Developed by the author

13. 13
Methodology and Justification
• Is there any situation of system failure?
• What is the relation between the applied load and
resource consumption?
• What is the system behavior when it reaches CPU
usage, network or memory limits?
• Is it possible to identify overload or underutilization
thresholds with this assessment methodology?

14. 14
Methodology and Justification
• Applying MIB in Fosstrak: this work focus in the current
model and in the future in the proposed model
• RFID data load: 4 readers with 0, 1 or 4 active tags,
resulting in 0, 4 or 16 data per cycle
• Parallel queries load: 1 to 512 threads (20 to 29 requests)
• Serial queries load: 1 to 16 queries (20 to 24 requests)

15. 15
Methodology and Justification
ALE module average behavior – Current model

16. 16
Methodology and Justification
EPCIS module average behavior – Current model

17. 17
Proposed Model
Current model Proposed model
Nuvem
User
Applications
App
1 App
2 App
“n”
ALE
multicore
multithread
RFID
Reader
1 RFID
Reader
2 RFID
Reader
“n”
NoSQL
P2P
Database
EPCIS
in
a
cloud
Capture
Interface
(HTTP)
o
o
oVM VM
Query
Interface
(SOAP)
o
o
oVM VM
Capturing
Applications
App
1 App
2 App
“n”

18. 18
Proposed Model
• Parallel processing for the ALE Module (multithreaded
and multicore)
• Split EPCIS module to meet different demands (reading
and writing operations)
• Scalability and elasticity of EPCIS module (scalability
manager and virtual machines and templates)
• High availability and fault tolerance for the database
(NoSQL P2P)

19. 19
Proposed Algorithms

20. 20
Proposed Algorithms

21. 21
Conclusion and Future Works
• Fosstrak presented a good scalability, although the
results demonstrated that a higher load can present
some performance issues.
• Opens the possibility of using multiple servers
• Future works include the implementation of the proposed
algorithms and further evaluation using MIB
methodology

22. Internet of Things Scalability:
Analyzing the Bottlenecks
and Proposing Alternatives
Márcio Miguel Gomes
Rodrigo da Rosa Righi
Cristiano André da Costa
Applied Computing Graduate Program
Universidade do Vale do Rio dos Sinos - Unisinos – Brazil
Corresponding address: cac@unisinos.br