The oneM2M experience in a Smart Port architecture€¦ · in a Smart Port architecture. North Tyrrhenian Sea Port System Authority (AdSP-MTS) & National Inter-University Consortium

North Tyrrhenian Sea Port System Authority (AdSP-MTS) & National Inter-University Consortium for Telecomunications (CNIT) Page 1Page 1

Speaker:BEng. Antonino Cuillo

[email protected]

The oneM2M experiencein a Smart Port architecture

North Tyrrhenian Sea Port System Authority (AdSP-MTS) & National Inter-University Consortium for Telecomunications (CNIT) Page 2

The oneM2M experience in a Smart Port architecture

o Moni.C.A.: Monitoring and Control Architecture of the Port System• Moni.C.A. Architecture

• Moni.C.A. Web Interface

o The oneM2M platform enabling the Internet of Things• oneM2M Platform Core

• oneM2M Platform Web Interface

o The connected Devices• Weather Stations

• Smart cameras for parking occupancy revelance

• Bathimetry sensors

• Seaside surveillance System (ROBORDER)

o The enabled Services• Weather dashboard

• Parking occupancy

• Bathimetry

• ROBORDER dashboard


«Moni.C.A.: Monitoring and Control Architecture of the Port System»


Moni.C.A.: Monitoring and Control Architecture of the Port System

The Port System Monitoring and Control Architecture (Moni.C.A.) is a complex, integrated and interoperable ICT Info-structure with the purpose of monitoring, controlling and integrating all the activities related to the port’s life.

It consists of three layers:– IaaS: Infrastructure as a Service (networks, computers, resident equipment and

interconnected smart objects);

– PaaS: Platform as a Service (a Service Oriented Middleware featuring oneM2M standard platform for the Internet of Things and non-IoT platforms and databases);

– SaaS: Software as a Service (final user applications for the port communities).

The PaaS layer is the Moni.C.A. Standard Platform. The main part of it consists of a ETSI oneM2M standard platform that provides common services functions (e.g. registration, data management, subscription/notification, security) as middleware to IoT applications of different service domains, ensuring interoperability and supporting the M2M communications. It’s based on Mobius, a open source IoT service platform complying with oneM2M specifications, provided by OCEAN, open alliance for IoT standard.

The other parts consists in all platforms and databases of the traditional digital port services (TPCS, etc.).


Moni.C.A.: Monitoring and Control Architecture of the Port System

Here is shown the Architecture Schema of the Monitoring System


«The oneM2M platform enabling the Internet of Things»


The oneM2M platform enabling the Internet of Things

Here is shown the Web interface that is used to interact with every single part of Moni.C.A. Standard Platform.



The core of the IoT platform is Mobius Yellow Turtle Server (v2.2.0). It enables the Internet of Things compliant with oneM2M standards.

A web interface was implemented to allow data viewing to human users. It is based on a Django framework and made available on internet by means of Nginx server



This web interface enables many services that interoperate with other components of the Saaslayer as shown below


«The connected Devices»


The connected Devices

In the last year we connected four kind of devices with the platform:

• Weather Stations

• Smart cameras for parking occupancy revelance

• Bathimetry sensors

• Seaside survelliance system (cameras + photonic radars) [ROBORDER]


The connected Devices

Every single devices have the proper way to interact with the world, the challenge is to integrate them with the IoT platform in a way compliant with oneM2M standards.

The common approch for us was to implement a storageapplication that makes the devices smart and compliant with the standards.


«The enabled Services»


The enabled Services

Weather Stations:

The storage application that interact with the weather station parse the data and send it to the platform with oneM2M standard semantic. We integrate two kind of different weather station that need two different parsingmethods.

Then the web application share the data in a human readble way.



Smart cameras for parking occupancy revelance:

A low-power open-source single-board computer is connected to the camera and elaborate the images. Itsends changes of occupancy to the server in JSON format, compliant with oneM2M standards.



Bathimetry sensors:

We installed a bathimetry sensor ontoa Livorno’s pilot corpsvessel to make a surveyover a portion of the port’s water. Data gathered was thensaved on oneM2M platform and used to filla provisionalGeographicalInformation System (GIS)



Seaside Surveillance System [ROBORDER]:

For the detection of seaside intrusionthis solution combine some photonicradar and some surveillance camera.

The photonic radar uses light to generate and detect radio frequency signals with better efficiency than current radars, while surveillance cameras support radar detections providing images of detected targets.


MIMO fused output


False Alarms

Target

Target

CFAR detection threshold

MIMO output compared with the CFAR thresholdAt each new radar timestamp it is important to associate in the proper ways the new incoming sensor measurements to the existing tracks. This can be do applying the Joint Probabilistic Data Association (JPDA) rule.

Target Detection and Tracking of a photonic radar:

The target detection block is necessary to decide whether a target (or more than one) is present in the defined search space. A wide class of detection algorithms satisfies the Constant False Alarm Rate (CFAR)property and various techniques do exist, which are designed to provide reliable detections under different operative conditions (e.g. non-homogeneous and non-stationary clutter with unknown power, closely spaced targets, masking caused by extended targets, etc…).

Target tracking strategies, typically based on the Kalman Filter (KF), must be applied to filter out false alarms and to estimate target trajectories from the available noisy data (i.e. target detections).

A kinematic model is assumed to characterize target movements, which is called state space model.

A sensor model is assumed to describe radar measurements, which is called measurement model.


THANK YOU

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The oneM2M experience in a Smart Port architecture€¦ · in a Smart Port architecture. North Tyrrhenian Sea Port System Authority (AdSP-MTS) & National Inter-University Consortium