Day 5 - 26 July

The 6TiSCH Architecture for Industrial IoT Applications

Prof. Giuseppe Anastasi

Outline:

• Introduction

• Industrial Applications

• 6TiSCH Architecture for IIoT

  • Protocol stack

  • Scheduling

  • Performance

• Open issues

Open

LLN= Low Power and Lossy Network

• Constrained Nodes

• Low power communication

• Unreliable links

Wireless Sensor Network (WSN) is a good example of LLN.

Usually the communication in such networks is multi-hop.

IETF Architecture

Open

• Protocol stack built around the IPv6 protocol

• The IETF architecture assumes the IEEE 802.15.4 MAC protocol

• 6LOWPAN is Adaptation Layer to allow the transmission of IPv6 datagram on a IEEE 802.15.4 frame

• CoAP is the application layer

• RPL Routing Protodol

Industrial Applications of IoT:

• Real-time monitoring for telemetry

• Remote System Control

• Remote Control of Industrial Machinery

We focus on the second class → wireless networks

IEEE 802.15.4 CSMA/CA MAC:
Reliability and scalability issues
Unbounded latency
No guaranteed bandwidth
No built-in frequency hopping technique

How to Manage Industrial Applications? → 5G Communication

16 different channels can be used.

6TOP is a New sub-layer for integrating higher IETF layers with IEEE 802.15.4e TSCH.

Centralized Scheduling:

Path Computation Element (PCE)

• Collects

  • network state information

  • traffic requirements from all nodes

• Builds

  • Communication schedule

• Installs

  • The schedule on the network

→ It happens one time

Adaptive MUlti-hop Scheduling (AMUS): Resources are reserved along the route, for each set of end-to-end links.

Decentralized Scheduling:

• No central entity

  • Schedule computed by each node

  • based on local, partial information exchanged with its neighbors

• The overall schedule is typically non optimal

• Limited Overhead

  • Suitable for energy-constrained nodes

Negotiation may include additonal overhead, delay, security attacks → we can remove the negotiation and replace the decentralized distributed scheduling with decentralized autonomous scheduling.

ALICE is one algorithm of this scheduling approach.

Integration of IoT devices into Cloud computing platforms: methods and practical examples

Prof. Carlo Vallati

Market of today is full of vertical systems, which are designed to serve one single purpose, operating in isolation or over limited cooperation.

However, horizontal approach has converged infrastructure and unified sensing and actuating infrastructure that supports multiple applications.

Direct Cloud Integration:

• Horizontal IoT is already available

• Almost all Cloud provides offer IoT support, through integration of devices in their cloud:

  • Amazon WS, Microsoft Azure, Google Cloud, IBM, etc.

• They all adopt a common and interoperable protocol that has been there for long: MQTT

MQTT:

• MQTT is a publish/subscribe messaging protocol designed or lightweight M2M communications

• MQTT has a client/server model, where every sensor is a client and connects to a server, known as a broker, over TCP

MQTT-based Cloud Platforms:

• An MQTT broker is instantiated in the cloud

• Sensors (MQTT Clients) publish on pre-defined topics

• Other sensors or other modules of the cloud platform subscribe to the topics

How things connect?

An initial skeleton of the code to program the sensors is usually provided for a set of popular boards by any cloud provider.

Pros of this architecture:

• Rapid and simple deployment

• It does not require the installation of a new infrastructure

• It scales with Cloud infrastructure

Cons:

• Cloud is always involved:
  – Low latency applications are not supported
  – Persistent connectivity
  – Machine-to-Machine interactions not possible

• WiFi was not designed with IoT in mind:
  – Energy consuming (no battery powered devices)
  – Coverage equal to the radius of Access Point/Router
  
  

It looks like a good idea to come back at the old pre-cloud approach, where data is collected and analyzed locally.

The solution is the extended Cloud architecture called “Fog Computing” or “Edge Computing”, it is composed of nodes installed in proximity of sensor, this layer allows the execution of a local application logic and data analysis.

Virtualization Technologies

Other wireless technology like LoRaWAN are used.

The solution is the Web of Things: it fits well in this architecture:

Each sensor provides an interface to expose their services (e.g. an information, a function) to applications.
The interface exposed by the devices is invoked directly by applications when needed.

The operations are performed in the same way, from the gateway or from the cloud.
IPv6 with its large addressing space will allow devices to be directly reachable.

Electromagnetic Information Security for IoT devices

Prof. Agostino Monorchio

MPG is the best one.

SE allows to know whether the shielding is enough or not.

For perfect shield, this number is infinite

Multiple reflections are not good, because even if the signals are attenuated, at each time there will be a small ration leaving the metal (leaking)

A perfect shield has a (0 conductivity)? → not sure I heard well

The waves resulting from a boat are used as example to EM going from antenna in near and far field difference,

If we cut the metal in orthogonal direction to EM field, so the aperture starts to radiate → antenna

The radiation effect of both situations is the same

We should avoid to open long aperture due to the problem mentioned above. → use small aperture

Advanced Phased Arrays for Communications and Wireless Power Transfer in Industrial Scenarios → continued

Prof. Giuliano Manara

Focusing: from optics to microwaves

After FF region, the wave is seen as a plane wave.

The maximum of the power is not on the focal point → there is a focal shift, it happens in the depth of focus (DoF) range

this red region forms an ellipsoid:

By including a correction for the quadratic phase:

to get a sharper maximum pulse we can also play with the amplitude and not just the phase.

New trends in the internet of autonomous vehicles

Prof. Sergio Saponara

Trends in smart vehicles and ITS:

• Improving safety (1.25M killed people/year worldwide, 3.3K/year in Italy)

• Reducing CO2

• Improving city life conditions with less pollution/traffic-jam

• Improving user experience

• High economic value

Motivations and market for ADAS:

ADAS can be applied for many functions:
Forward/Rear Collision Warning (FCW/RCW), Adaptive Cruise Control (ACC) Autonomous Emergency Braking (AEB) , Lane Departure Warning (LDW) Lane Change
Assist (LCA), Traffic or road Signs Recognition (TSR)

Origin of autonomous vehicle: from military to civil applications

In this scenario the cars should have the on-board units (OBU), which do not exist in all cars, without it cars cannot communicate.

Technology developed for smart phones cannot be used is same way with autonomous cars and other critical applications.

Automotive cybersecurity: a real challenge

Exposure to cyber attacks:
• Vehicle hack
• Data tampering
• Denial of Service

non-repudiation: being able to track the logs and everything

without respecting security consideration, IoT becomes an nightmare, even if they add overhead