Cloud Based IoT Applications Prof. Antonio Puliafito [email protected] Mobile Crowdsensing, Social and Big Data as Innovation Enablers for Future Internet.

Cloud Based IoT Applications

Prof. Antonio [email protected]

Mobile Crowdsensing, Social and Big Data as Innovation Enablers for Future Internet Cloud-based Architectures and Services

Athens - March 18, 2014

“Biggest Paradigm Shift in 20 years”

“Game Changers”

“Tremendous Cost Cutting”

“Just On” “Pay As You

Go”

Cloud Computing is …

Hype cycle for Cloud Computing

Source: Gartner, Hype Cycle for Cloud Computing, 2011 David Mitchell Smith Publication Date: 27 July 2011 ID Number: G00214915 © 2011

Cloud Services Beyond the IT Industry

Cloud Applications

(Apps-as-a-service)

Cloud(Application)

Platforms

Cloud Infrastructur

e(Infrastructure-as-a-

Service)

App Dev/Test

App DeployThe IT

Industry’sCloud Services

Cloud Business

(Process-as-a-service)

Every Other Industries’Cloud Services

Source: IDC Executive Telebriefing29 September 2009

Why Cloud Computing?• Cloud computing brings a new level of efficiency and

economy to delivering IT resources on demand just like public facilities and it opens up new business models and market opportunities.

• It offers more than a “pay-per-use” model. The major trends are:▫ IT Efficiency. Companies are minimizing costs, converting them

from capital expenses to operating expenses through technologies such as virtualization (i.e. an enterprise do not have to buy expensive equipments to build its business but can build its infrastructure compousing services).

▫ Business Agility. Companies are maximizing return using IT as a competitive weapon through the rapid time to market, by mean of integrated application stacks, instant machine image deployment, and parallel programming

Virtualization• Virtualization is the main technology behind the clouds.• It allows servers, storage devices, and other hardware to be treated

as a pool of resources rather than discrete systems.• These resources can be allocated on-demand.• It allows also to exploit and migrate resources, regardless of the

underlying real physical infrastructure.• Virtualization solves several core challenges of datacenter managers

and delivers specific advantages, including:▫ Higher utilization rates▫ Resource consolidation▫ Lower power usage/costs▫ Space savings▫ Disaster recovery/business continuity▫ Reduced operations costs

WSNs/mobiles: towards the IoT

•smart things get linked through the Net

•IoT/Future Internet, the current trends underlying the ubiquitous convergence of devices and Web

Next frontiers for IoT

•semantic enablement•autonomous cooperation

• Abstraction• Virtualization• ManagementKey features

Cloud towards sensing● Widespread availability of cheap sensing devices● On-board components built into a wide range of systems (e.g.,

smartphones, indash units, body sensor networks...)● Advances in pervasive computing techniques

❑Many application scenarios: healthcare, smart city, domotic, traffic assistant, ... ❑Many concepts, standards and projects:

⚫ Internet of Things (IoT)⚫ OGC Sensor Web Enablement (SWE),W3C Semantic Sensor Networks (SSN),...⚫ SIMONE(Sistema Integrato per il MONitoraggio della produzione di Energia elettrica), DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies), CASA (Center for Collaborative Adaptive Sensing of the Atmosphere), Portolan Network Sensing Architecture,...

Towards a sensing Cloud: a new take

▫Geographic SN

▫IaaS-like, on-demand service provisioning of (virtual) sensing and actuation resources

▫Device-driven approach, i.e. provisioning of actual (even if virtual) sensing resources – vs data-driven i.e. SaaS-like

▫Basic functionalities:

⚫Abstraction, virtualisation, customisation of sensing resources⚫Enrolment, management of contributing nodes (static and mobiles – SN and smart devices)⚫On-demand provisioning of virtual sensing resources: discovery, monitoring, management, etc.

New challenges

Heterogeneous sensing environments• smart sensors/actuators• embedded systems• sensor networks• ...

Uniform management of resources• data monitoring• application deployment• remote system control• ...

A framework able to abstract many types of HW and SW resources enabling

an integration between application requirements and sensors capabilities

Cloud computing

A Cloud Data provisioning system:• for capturing information from

the physical world• interacting with heterogeneous

devices and observation environments▫ HW and SW sensing

• able to store and manage huge amount of data

Cloud computing might be the GLUE for aggregating heterogeneous systems able to gather information from the environment Data

Consumers

Sensing Devices (SDs)

Sensor Networks (SNs)

• virtualization, it is the most suitable approach to guarantee the high level of interoperability. It allows a world-wide and cross-related range of services

From IaaS/DaaS to SAaaS

• traditional Infrastructure as a Service: computing and storage resources

• the next step, SAaaS: Sensing/Actuating resources as new infrastructure to be served according to the current Cloud paradigm

Volunteer approaches

•glueing in-advance (steady) and opportunistic participation

•enabling fully unassisted contributing node setup

•exploiting optional crowd-sourced content to build metadata, thus adding value and building new services

C@H architecture

A new computing paradigm merging Volunteer computing and Cloud computing allowing open computing service market.

PRIN 2008

Goal▫ acquire, integrate and compute heterogeneous data, from

various sensor networks (weather, seismic, volcanic, water, rain, car and marine traffic, environmental, etc.), in order to strengthen control and monitoring systems to provide useful data for the prevention and management of risk situations through services provided to citizens and businesses, both public and private.

Innovations▫ Cloud platform: develop a federated cloud management

systems▫ Security: technology for specifying/enabling security

features▫ Sensors and Cloud integration: define methods to grab data

and interact with sensors▫ Advanced Capabilities for Cloud-based Storage: support

delivery of data-intensive services securely, at the desired QoS, at competitive costs

▫ Data Mobility and Federation: enable comprehensive data migration and interoperability across remote locations

Facts▫ A 2-year project, started May 2013▫ €20.660 M (total budget all partners)

SIGMA: SISTEMA INTEGRATO DI SENSORI IN AMBIENTE CLOUD PER LA GESTIONE MULTIRISCHIO AVANZATA

Sensor Cloud: scenarios

Management of physical resource in a datacenter, providinguser accounting, Service Level Agreements (SLAs), Billings,...

Storage of information related to the physical and virtual resources, middleware working status and data for clients

Management of physical sensing resources hiding underneath technologies.

Data-driven approach

Sensor Web Enablement standard, defined by the Open Geospatial Consortium

❑SOS (Sensor Observation Service)❑ interface for requesting, filtering, and retrieving observations and sensor system information;

❑ SPS (Sensor Planning Service)❑ interface for requesting user-driven observations;❑ SAS (Sensor Alert Service) ❑interface for publishing and subscribing alerts from sensors.

❑SensorML❑models and XML schemas for describing sensors systems and processes;

❑O&M (Observation and Measurements)❑models and XML Schema for encoding observations from a sensor network;

Data-driven approach

C-SENSOR ArchitectureIt implements the SWE-SOS standard (Sensor Observation Service)▪request, filter, and retrieve observations and sensor system information

SE

DN

A D

B

It implements the SWE-SAS (Sensor Alert Service) and the SWE-SPS ((Sensor Planning Service) standards▪ provisioning of sensing information towards Data Consumers▪ actuation of remote Data Consumers directives

▪Inter host (inter cluster) Communication: p2p▪Zero configuration: ZeroConf▪Monitoring▪Advanced Security features▪XMPP based: host presence, open standard▪Fault Tolerance: no central point of failure

• Official CLEVER's web site http://clever.unime.it.

• The source code hosted on https://github.com/clever-unime

ST Microelectronic testbed

• Monitoring Energy consumption in an industrial site

• MeshNetics devices• FRER Q96U4 analyzer• WhereX middleware

Device-driven approach: SAaaSHigh Level Architecture

SAaaS: Hypervisor

SAaaS: Adapter

The Planning Agent (PA)

The main functionalities provided by the SAaaS PA are:

▫tuning of sampling parameters according to user-defined preferences⚫Extensible standards-compliant encoding of requests for tasks, and corresponding responses

▫scheduling of observations ⚫following a predefined schedule, or ⚫upon the occurrence of a particular event, or ⚫simply on user demand

▫exposing all underlying knobs to make them available for customers to operate on transparently

PA Android Implementation – SAaaS4Mobile

Discovery Task Listing Task Submission Results Retrieval

SAaaS killer application: Mobile CrowdSensing

Mobile Crowdsensing: current issues

• volunteer enrolment:

• requires out-of-band campaign (social network) to get attention

• involves user-initiated activity (website download) to begin contributing

• slow and unpredictable uptake

• app/service availability/reliability:

• degradation with node churn

• real-time info may translate into severe burden on resources (battery)

Mobile Crowdsensing: SAaaS possibities

• MCS app providers may leverage automatic management of SAaaS-enabled infrastructure:

• no need for targeted ads or direct interaction• (app) provider-initiated involvement workflow• uptake rates just limited by chosen area of interest and

widespread coverage of SAaaS contributors (and by willingness to pay/barter)

• in typical PaaS fashion:

• placing a platform layer over Cloud-enabled infrastructure• leaving no dependency (either explicit or strictly needed)

between the two levels

MCSaaS - MCS as a Service

Mobile Crowdsensing application: PotHole Detector

• based on two components:▫ an Android app running on volunteer-owned

mobiles▫ a Back-End system to collect data, and also filter,

analyze and mine it

▫ exploiting mobile-carrying volunteering commuters to detect and classify automatically road surface conditions

▫ combined sampling of:• acceleration data from on-board motion detection

sensors• geospatial coordinates as provided by the GPS

Arduino Yún

Arduino Yún

•The Yún distinguishes from other Arduino boards in that it can communicate with the

Linux distribution onboard, offering a powerful networked computer with the ease of

Arduino.

•The Atheros AR9331 processor supports a Linux distribution based on OpenWRT

named Linino.

•A MIPS GNU/Linux box for Arduino

•Combining the Linux OS with Arduino HW + certified WiFi 802.11n connectivity and use OpenWRT and Peer-to-Peer (AllJoyn) technology to customize your own project.

•Linino.org is a dog hunter-sponsored community project.

•An installation of Python is included with Linino, with which you can write applications or scripts.

•Linino uses REST for clients and servers. It is a software architecture that exposes various parts of the Arduino hardware through URLs.

A Common Language for theInternet of Everything

• The other great thing about the Yún is the integration with AllJoyn™

• AllJoyn™ is the open source project that lets the compatible smart things around us recognize each other and share resources and information across brands, networks, and operating systems.

• provides a software framework and set of Services that enable interoperability among connected products and software applications, across manufacturers, to create dynamic proximal networks.

• Originally developed by Qualcomm Innovation Center, Inc. (QCE), and now hosted on AllSeen Alliance.

• AllSeen Alliance is a Linux Foundation Collaborative Projects.

Referencehttp://www.alljoyn.orghttp://allseenalliance.orghttp://wiki.allseenalliance.orgAndrea Rocco Lotronto AllJoyn Framework

AllJoyn bus

• The most basic abstraction of the AllJoyn system is the AllJoyn bus. It provides a fast, lightweight way to move marshaled messages around the distributed system

What new experiences can AllJoyn enable?

Conclusions

• a novel approach: IaaS-like sensing Cloud through the SAaaS Device-driven vs Data-driven

• basic low-level mechanisms for interfacing with the device

• SAaaS4mobile: an Android implementation of the stack (Planning agent and SWE std.)

• first design and implementation steps into an IaaS-enabled platform

Future work

• SAaaS engineering of customisation, virtualisation, self-management and volunteer-Cloud features

• porting core SAaaS4mobile logic to other embedded platforms, e.g. typical WSN nodes

• extending MCSaaS with user preference profiles and sandboxing capabilities

• Cloud of Things, Things as a Service (TaaS) • semantically tagging devices

• Integration of CLEVER modules inside YUN

A journey of a thousand miles begins with a single

step

Lao - tzu

Antonio PuliafitoUniversity of [email protected]