iot

1. LUPA
GA SEMPAT
GA TELITI
TERBURU BURU
Heinrich dalam bukunya berjudul "The Origin of Accident", menempatkan unsafe
act sebagai penyumbang terbesar kecelakaan kerja, yakni sebanyak 88%.
Anggapan unsafe act atau perilaku tidak aman sama halnya menganggap manusia sebagai
akar masalah.

2. Internet of Things
Evolusi
Internet

3. a proposed development of the Internet in which
everyday objects have network connectivity,
allowing them to send and receive data.

5. Why Internet of Things is Needed?
• IoT akan mengubah dan menyelamatkan hidup
• IoT akan mengubah industri yang ada dan
membuatnya menjadi baru.
• IoT akan menjadi hal yang biasa.

6. Benefits Internet of Things?
• Safety, Comfort, Efficiency
• Better Decision Making
• Revenue Generation

7. Threats Internet of Things?
• Security and Privacy
• Data and Complexity
• Business and IT Buy-in

8. Perangkat IoT (Embedded device)
• Sensor/Input
• CPU/komputer/single board
computer/microcontroller MCU(Arduino Uno
R3, Raspberry Pi, Intel Galileo dll)
• Sistem Operasi (Google Brillo, Windows 10 IOT
OS, ARM mbed OS, Huawei LiteOS, Intel
RealSense OS X)
• Jalur komunikasi (bluetooth, wifi, internet)
• Output

9. Embedded Operating System
• RIOT OS
• Windows 10 For IoT
• WindRiver VxWorks
• Google Brillo
• ARM Mbed OS
• Embedded Apple iOS And OS X
• Nucleus RTOS
• Green Hills Integrity

10. How the Internet of Things Works?

11. What is an IoT Platform?
The IoT platform is a suite of components that
enable: Deployment of applications that
monitor, manage, and control connected
devices. Remote data collection from connected
devices. Independent and secure connectivity
between devices. Device/sensor management.

13. The 8 components of an IoT
Application Enablement Platform
(Source: IoT Analytics)
Connectivity & normalization: brings different protocols and different data
formats into one “software” interface ensuring accurate data streaming and
interaction with all devices.
Device management: ensures the connected “things” are working properly,
seamlessly running patches and updates for software and applications
running on the device or edge gateways.
Database: scalable storage of device data brings the requirements for hybrid
cloud-based databases to a new level in terms of data volume, variety,
velocity and veracity.
Processing & action management: brings data to life with rule-based event-
action-triggers enabling execution of “smart” actions based on specific sensor
data.

14. Analytics: performs a range of complex analysis from basic data clustering
and deep machine learning to predictive analytics extracting the most value
out of the IoT data-stream.
Visualization: enables humans to see patterns and observe trends from
visualization dashboards where data is vividly portrayed through line-,
stacked-, or pie charts, 2D- or even 3D-models.
Additional tools: allow IoT developers prototype, test and market the IoT use
case creating platform ecosystem apps for visualizing, managing and
controlling connected devices.
External interfaces: integrate with 3rd-party systems and the rest of the
wider IT-ecosystem via built-in application programming interfaces (API),
software development kits (SDK), and gateways.

16. Different entry strategies into the IoT
Platform market
• Organic bottom-up approach: Starting with the
connectivity part and building out platform features from
the bottom-up (e.g., Ayla Networks)
• Organic top-down approach: Starting with the analytics
part and building out platform features from the top-down
(e.g., IBM IoT Foundation)
• Partnership approach: Striking alliances to offer the full
package (e.g., GE Predix & PTC Thingworx)
• M&A approach: Targeted acquisitions (e.g., Amazon –
2lemetry) or contenders performing strategic mergers (e.g.,
Nokia & Alcatel-Lucent)
• Investment approach: Tactical investments throughout the
IoT ecosystem (e.g., Cisco).

17. IoT Software Platform Device management? Integration Security
Protocols for data
collection
Types of analytics Support for visualizations?
2lemetry - IoT Analytics
Platform**
Yes
Salesforce, Heroku,
ThingWorx APIs
Link Encryption (SSL),
Standards ( ISO 27001,
SAS70 Type II audit)
MQTT, CoAP,
STOMP,M3DA
Real-time analytics (Apache
Storm)
No
Appcelerator No REST API
Link Encryption (SSL, IPsec,
AES-256)
MQTT, HTTP
Real-time analytics
(Titanium [1])
Yes (Titanium UI
Dashboard)
AWS IoT platform Yes REST API
Link Encryption
(TLS), Authentication
(SigV4, X.509)
MQTT, HTTP1.1
Real-time analytics (Rules
Engine, Amazon Kinesis,
AWS Lambda)
Yes (AWS IoT Dashboard)
Bosch IoT Suite - MDM IoT
Platform
Yes REST API *Unknown
MQTT, CoAP,
AMQP,STOMP
*Unknown
Yes (User Interface
Integrator)
Ericsson Device Connection
Platform (DCP) - MDM IoT
Platform
Yes REST API
Link Encryption
(SSL/TSL),Authentication
(SIM based)
CoAP *Unknown No
EVRYTHNG - IoT Smart
Products Platform
No REST API Link Encryption (SSL)
MQTT,CoAP,
WebSockets
Real-time analytics (Rules
Engine)
Yes (EVRYTHNG IoT
Dashboard)
IBM IoT Foundation Device
Cloud
Yes REST and Real-time APIs
Link Encryption ( TLS),
Authentication (IBM Cloud
SSO), Identity management
(LDAP)
MQTT, HTTPS
Real-time analytics (IBM
IoT Real-Time Insights)
Yes (Web portal)
ParStream - IoT Analytics
Platform***
No R, UDX API *Unknown MQTT
Real-time analytics, Batch
analytics (ParStream DB)
Yes (ParStream
Management Console)
PLAT.ONE - end-to-end IoT
and M2M application
platform
Yes REST API
Link Encryption (SSL),
Identity Management
(LDAP)
MQTT, SNMP *Unknown
Yes (Management Console
for application
enablement, data
management, and device
management)
ThingWorx - MDM IoT
Platform
Yes REST API
Standards (ISO 27001),
Identity Management
(LDAP)
MQTT, AMQP, XMPP,
CoAP, DDS, WebSockets
Predictive
analytics(ThingWorx
Machine Learning), Real-
time analytics (ParStream
DB)
Yes (ThingWorx SQUEAL)
Xively- PaaS enterprise IoT
platform
No REST API Link Encryption (SSL/TSL)
HTTP, HTTPS, Sockets/
Websocket, MQTT
*Unknown Yes (Management console)

18. IoT standardization components

19. IoT standardization components
• Platform: This part includes the form and design of the
products (UI/UX), analytics tools used to deal with the
massive volume of data streaming from all products in a
secure way, and scalability which means that wide adoption of
protocols like IPv6 in all vertical and horizontal markets is
needed.
• Connectivity: This phase includes all parts of the consumer's
day and night routine, from using wearables, smart cars,
smart homes, and in the big scheme, smart cities. From the
business prospective we have connectivity using IIoT
(Industrial Internet of Things) where M2M communications
dominate the field.

20. IoT standardization components
• Business Model: The bottom line is a big motivation for
starting, investing in, and operating any business; without a
sound and solid business model for IoT we will have another
bubble , this model must satisfied all the requirements for all
kinds of e-commerce; vertical markets, horizontal markets and
consumer markets. But this category is always a victim of
regulatory and legal scrutiny.
• Killer Applications: In this category there are three functions
needed to have killer applications: control "things", collect
"data", and analyze "data". IoT needs killer applications to
drive the business model using a unified platform.

21. IoT implementation components

22. IoT implementation components
• Sensors There two types of sensor: active sensors and passive
sensors. The driving forces for using sensors in IoT today are
new trends in technology that have made sensors cheaper,
smarter and smaller. The challenges facing IoT sensors are:
power consumption, security, and interoperability.

23. • Networks The second component of IoT implementation is to
transmit the signals collected by sensors over networks with
all the different components of a typical network including
routers, bridges in different topologies. Connecting the
different parts of networks to the sensors can be done by
different technologies including Wi-Fi, Bluetooth, Low Power
Wi-Fi, WiMAX, regular Ethernet, Long Term Evolution (LTE)
and the recent promising technology of Li-Fi (using light as a
medium of communication between the different parts of a
typical network including sensors).
The driving forces for widespread network adoption in IoT are
high data rate, low prices of data usage, virtualization (X -
Defined Network trends), XaaS concept (SaaS, PaaS, and IaaS),
and IPv6 deployment. But the challenges facing network
implementation in IoT are the enormous growth in the
number of connected devices, availability of network
coverage, security, and power consumption.

24. • Standards The third stage in the implementation process
involves the sum of all activities of handling, processing and
storing the data collected from the sensors. This aggregation
increases the value of data by increasing the scale, scope, and
frequency of data available for analysis. But aggregation can
only be achieved through the use of various standards
depending on the IoT application in use.
There are two types of standards relevant for the aggregation
process; technology standards (including network protocols,
communication protocols, and data-aggregation standards)
and regulatory standards (related to security and privacy of
data, among other issues). Challenges facing the adoption of
standards within IoT are: standards for handling unstructured
data, security and privacy issues in addition to regulatory
standards for data markets.

25. • Intelligent analysis The fourth stage in IoT implementation is
extracting insight from data for analysis. IoT analysis is driven
by cognitive technologies and the accompanying models that
facilitate the use of cognitive technologies. With advances in
cognitive technologies' ability to process varied forms of
information, vision and voice have also become usable, and
open the doors for in-depth understanding of the non-stop
streams of real-time data. Factors driving adoption of
intelligent analytics within the IoT include artificial
intelligence models, growth in crowdsourcing and open-
source analytics software, real-time data processing and
analysis. Challenges facing the adoption of analytics within
IoT; inaccurate analysis due to flaws in the data and/or model,
legacy systems' ability to analyze unstructured data, and
legacy systems' ability to manage real-time data.

26. • Intelligent actions Intelligent actions can be expressed as
M2M (Machine to Machine) and M2H (Machine to Human)
interfaces for example with all the advancement in UI and UX
technologies. Factors driving adoption of intelligent actions
within the IoT; lower machine prices, improved machine
functionality, machines "influencing" human actions through
behavioral-science rationale, and deep learning tools.
Challenges facing the adoption of intelligent actions within
IoT: machines' actions in unpredictable situations, information
security and privacy, machine interoperability, mean-reverting
human behaviors, and slow adoption of new technologies

27. Protokol
Protokol adalah sebuah aturan atau standar yang mengatur atau
mengijinkan terjadinya hubungan, komunikasi, dan perpindahan
data antara dua atau lebih titik komputer. Protokol dapat
diterapkan pada perangkat keras, perangkat lunak atau kombinasi
dari keduanya. Pada tingkatan yang terendah, protokol
mendefinisikan koneksi perangkat keras.

28. Fungsi Protokol
a. Fragmentasi dan Re-assembly
Pembagian informasi yang dikirim menjadi beberapa paket data dari sisi
pengirim. Jika telah sampai di penerima, paket data tersebut akan
digabungkan menjadi paket berita yang lengkap.
b. Enkapsulasi
Enkapsulasi (Encaptulation) adalah proses pengiriman data yang dilengkapi
dengan alamat, kode-kode koreksi, dan lain-lain.
c. Kontrol Konektivitas
Membangun hubungan komunikasi berupa pengiriman data dan mengakhiri
hubungan dari pengirim ke penerima.
d. Flow Control
Fungsi dari Flow Control adalah sebagai pengatur jalannya data dari pengirim
ke penerima.
e. Error Control
Tugasnya adalah mengontrol terjadinya kesalahan sewaktu data dikirimkan.
f. Pelayanan Transmisi
Fungsinya adalah memberikan pelayanan komunikasi data yang berhubungan
dengan prioritas dan keamanan data.

29. M2M
M2M adalah terminologi dari "Machine-to-Machine", merupakan
salah satu dari bidang kategori ICT (Information and
Communication Technology), yaitu penggabungan teknologi
komunikasi, komputer dan daya sehingga memungkinkan
terjadinya komunikasi jarak jauh antara manusia dan mesin
melalui interaksi fisik, kimia serta sistem dan proses biologi.
Sebagai tambahan, M2M merupakan aplikasi komputasi baru
dimana data dialirkan dari dan ke fisik serta biologi.

30. OneM2M
oneM2M adalah sebuah standar global terkemuka untuk
m2m(komunikasi machine to machine) dan juga IoT. Dibentuk
melalui gabungan beberapa standar organisasi untuk
mengembangkan suatu platform horizontal untuk pertukaran
dan berbagi data antara beberapa aplikasi. oneM2M membuat
distribusi software layer seperti sistem operasi, yang
memfasilitasi penggabungan dengan menyediakan kerangka
untuk interworking dengan teknologi yang berbeda. Inti dari 2
elemen terpenting dari oneM2M : menyediakan interworking
framework dan memungkinkan menggunakan kembali apa yang
sudah tersedia sebanyak mungkin yang bisa digunakan.