Internet of thing (IoT and cloud convergence opportunitis and challenges
1. IoT and Cloud Convergence:
Opportunities and Challenges
A. Rahim Biswas Create-Net, Italy
2. Outline
IoT and Cloud vision and requirements
Convergence approach’s
Example of existing platform
Emerging challenges of IoT/Cloud
3. "Cloud computing a new business model and
management (e.g. data and device) paradigm of
Internet of thing"
Statements
”IoT is to enlarge the opportunities of cloud
service provisioning "
4. IoT is the vision of future connected physical and
virtual world, several billions of heterogeneous
devices/objects and big data, to provide smart
and personalize services to users
Connectivity, convergence and intelligent
everywhere
What is IoT?
5. A real-time online platform that
Dynamically management IoT data/objects and
Provide connectivity of the diverse
heterogeneous objects, considering the
interoperability issues
Deriving the value (useful information and
knowledge) from this connection and large
volume of IoT data
Major IoT platform requirements
7. Ubiquitous accessibility and connectivity,
facilitation of maximum accessibility as well as
connectivity of the diverse heterogeneous
objects/services and various volumes of users
including mobility
Dynamic management/orchestration of users,
billions of devices as well as massive amount of
data produced by those connected devices
IoT platform functionalities (1/2)
8. Maximum resources utilization, enabling of
sharing of IoT resources (objects,
applications, platforms)
Personalization of users and services,
providing services based on users preference
and requirements including real-world
context
IoT platform functionalities (2/2)
9. Dynamic resources sharing platform
Offers scalable, elasticity resources and
services management
Location independent can be access from any
where
Reliable and easy access of the services
Large amount of computing and storage
resources
It is also more homogeneous
What Cloud offers?
10. For IoT to provide cloud computing
functionalities, to support on realization of
IoT vision
For Cloud, IoT to provide huge opportunities
for cloud services
Convergence of IoT and Cloud
11. Cloud-centric IoT (Bring IoT functionalities in
Cloud)
IoT-Centric Cloud (Bring Cloud functionalities
in IoT
Convergence approach’s
12. Bring IoT data in the cloud
Processing and computing the data and
deploy management tools in cloud
This approach this good if service are
provided among objects located in multiple
location
Giving several example (market platform,
project platform..)
Cloud-centric IoT
13. ThingWorx IoT software Platform
Providing IoT service for SMEs, industries, business chain
It provides complete application, run-time and intelligent environment
ThingWorx IoT Software Platfrom
14. Carriots platform as a service design for IoT and M2M
application
Carriots Cloud platform
16. SensorCloud platform
MicroStrain’s SensorCloud™ is a unique sensor data storage,
visualization and remote management platform that leverages
powerful cloud computing technologies to provide excellent
data scalability, rapid visualization, and user programmable
analysis.
17. Cloud-centric IoT platform
IoT Cloud Platform
hosting databases applicationspartners SI
All devicesOur managed
devices
your devices
Simple, scalable, robust, resilient, trustful & secure17
FBConsulting,OneM2Mpresentation;BasedonETSISmartCityWS
June2013presentationM.Arndt,ORANGE.
Cognitive
capability
18. IoT infrastructure will provide the opportunities
to take services, workloads, applications and
large amounts of data and deliver it all to the
edge of the network.
Processing and storage of data close to
users/near to sources
To distribute data to move it closer to the end-users to
eliminate latency, numerous hop, and support mobile
computing and data streaming
Creating dense geographical distribution
IoT- Centric Cloud (1/2)
19. This approach are useful when service is
provisioned from the data coming from same
location
Supporting end-users security
Data process and service execute locally
(distributed cloud processing, sub-work flow,
data aggregation locally)
IoT- Centric Cloud (2/2)
21. Real-time data processing and service provisioning
“Big data” storage and processing for real-time services
un-structure and semi-structure data coming from distributed
sources and requirements to provide real-time/near real-time
services
More dynamic configuration/ resources
management/orchestration/automated provisioning of
resources from/to IoT
Considering performance targets/constraints
Offloading from clients/hosts to cloud
At design and run time
Including mobile users/apps
Dynamic metering when IoT devices are shared
Emerging IoT-Cloud challenges (1/3)
22. More distributed processing and storage of the massive data
as well as cloud functionalities
Decentralized (and infrastructure-less) clouds
Processing capabilities and data positioned closer to users
Migration of servers to follow mobile users
Virtualization of IoT devices: Access to advanced
resources/specialized hardware, including GPUs, sensors, etc.
Emerging business model: Data-incentive cloud-based
applications
Considering data migration issues and avoiding lock-in
Emerging IoT-Cloud challenges (2/3)
23. Portability of the services
Users experience creation and enablement
Interoperability: Interoperability between cloud/IoT services
and infrastructure
Accountability- Services and data hosted and executed
across borders
Enabling reliable and real-time communication from objects
to applications and vice-versa;
Emerging IoT-Cloud Challenges (3/3)
25. Thank you very much
Questions
abdur.rahim@create-net.org
Notas do Editor
It is about to drive the relation between the objects and thing which did not explore
The outline of the presentation is
To show the internet of thing is about richer connection
Connection to one objects are growing and growing….
As like as economical message transition and connection boster the economy
To compare the how this connect is growing …
To assess the value of connection …
Understand and robustness of each of the connection…
It is about big data….
These data need to process…. And need powere…
Concept of local cloudl and global cloud
To provide services based on the uses requirements and users centric, IoT infrastructure is a BIG cloud of ubiquitous devices and objects, in where objects/devices are connected to provide user centric application and services.
These devices are heterogeneous in nature and are location specific.
The IoT deployed for certain purpose, difficult to re-use and is resources constraint and costly to deployed.
Ubiquitous accessibility (i.e., more business opportunities)
Facilitation of maximal accessibility of objects/services through commonly agreed APIs and standards;
Scalable, reliable dynamic management of massive amounts of data ("big data") produced by a huge number of diverse IoT connected devices
Reliability (e.g., for handing context/policy changes and accomplishing trust from the parts of the users)
Scalability (e.g., as various volumes of users, resources and data may be involved in service provision)
Ubiquitous accessibility (i.e., more business opportunities)
Facilitation of maximal accessibility of objects/services through commonly agreed APIs and standards;
Scalable, reliable dynamic management of massive amounts of data ("big data") produced by a huge number of diverse IoT connected devices
Reliability (e.g., for handing context/policy changes and accomplishing trust from the parts of the users)
Scalability (e.g., as various volumes of users, resources and data may be involved in service provision)
Most of the IoT platform provide connectivity service…
This approach are useful when service is provisioned from the data coming from same location
Data process and service execute locally (distributed cloud processing, sub-work flow, data aggregation locally)
This approach are useful when service is provisioned from the data coming from same location
Data process and service execute locally (distributed cloud processing, sub-work flow, data aggregation locally)
Distributed system
Local and global cloud
Processing data near source
Execute service near users
Real-time data processing and service provisioning:
IoT devices are sources of data, they will produce massive of data, so the cloud crumble under the data weight.
Need real-time processing of the data
Distribution of cloud functionalities
Due to nature of IoT it is more distributed. So some of the functionalities should be distributed over different cloud and but not only in the cloud but also edge of the IoT. So some of the cloud functionalities could be done as the devices level.
More Distribution: Decentralised (and infrastructure-less) clouds
Processing capabilities and data positioned closer to users
Migration of servers to follow mobile users
Real-time service: IoT devices are sources of data, they will produce massive of data, so the cloud crumble under the data weight.
The sensor are resources constraint devices, in where it is hard to process the massive data and provide zero tolerant services
need a new approach on scalable and distributed managing and processing of storage of massive data
Need real-time processing of the data
More dynamic and distribution: Due to nature of IoT it is more distributed. So some of the functionalities should be distributed over different cloud and but not only in the cloud but also edge of the IoT. So some of the cloud functionalities could be done as the devices level.
More dynamic: Dynamic configuration/automated provisioning/orchestration
Considering performance targets/constraints
Offloading from clients/hosts to cloud
At design and run time
Including mobile users/apps
The sensor are resources constraint devices, in where it is hard to process the massive data and provide zero tolerant services
Real-time data processing and service provisioning:
IoT devices are sources of data, they will produce massive of data, so the cloud crumble under the data weight.
Need real-time processing of the data
Distribution of cloud functionalities
Due to nature of IoT it is more distributed. So some of the functionalities should be distributed over different cloud and but not only in the cloud but also edge of the IoT. So some of the cloud functionalities could be done as the devices level.
More Distribution: Decentralised (and infrastructure-less) clouds
Processing capabilities and data positioned closer to users
Migration of servers to follow mobile users
Real-time service: IoT devices are sources of data, they will produce massive of data, so the cloud crumble under the data weight.
The sensor are resources constraint devices, in where it is hard to process the massive data and provide zero tolerant services
need a new approach on scalable and distributed managing and processing of storage of massive data
Need real-time processing of the data
More dynamic and distribution: Due to nature of IoT it is more distributed. So some of the functionalities should be distributed over different cloud and but not only in the cloud but also edge of the IoT. So some of the cloud functionalities could be done as the devices level.
More dynamic: Dynamic configuration/automated provisioning/orchestration
Considering performance targets/constraints
Offloading from clients/hosts to cloud
At design and run time
Including mobile users/apps
The sensor are resources constraint devices, in where it is hard to process the massive data and provide zero tolerant services