Application scenarios and real-world deployments for IoT and Smart Cities

IoT & Smart Cities Ph.D. School 2013 - September 16th - 21st, 2013 - Lerici, Italy
Application scenarios and
real-world deployments for
IoT and Smart Cities
Dr Màrius Montón
marius@worldsensing.com

IoT & Smart Cities Ph.D. School 2013 - September 16th - 21st, 2013 - Lerici, ItalyMàrius Montón2
Outline
1.Company Presentation
2.What do we want from you
3.Role in the company (Innovation Unit Presentation)
4.Product Presentation
a.Spider
b.FastPrk
c. Loadsensing
5.Actual Technology
6.Challenges
7.Conclusions

IoT & Smart Cities Ph.D. School 2013 - September 16th - 21st, 2013 - Lerici, ItalyMàrius Montón
About Worldsensing
2008: founded in Barcelona, Spain
2010: Initiates product commercialization
2013: International Expansion
Board:
Ignasi: CEO, Ph.D. in Geophysics; passionate cycler
Mischa: Board of Directors, Ph.D. in Telecomms; Orange, KCL; pianist
Xavi: Scientifc Advisor, Ph.D. in Computing; passionate cycler
Jordi: CTO, MSc in Computing; passionate rower
Location:
Barcelona: “Silicon Valley” of Mediterranean; main office
London: consultant, sales & technical interface
3

Low Power Wireless sensors are creating
business opportunities in Industrial sectors
Transportation Construction Oil & Gas
Current
Problems:
• Increasing congestion/More
people in cities
• Developed cities have financial
problems
• Cars in developing countries are
booming without control
Opportunities: • New revenue streams for
companies using technology
• Empower the cities and mayors
with the correct technology tools
• Lower infrastructure costs
• Very manual labor
• Tight regulation and
safety issues
• Inaccessible sites
• 24/7 inspections needed
• Increase safety and
comply with regulations
• Lower costs
• Optimize maintenance
cycles
• More demand for oil and
increasing cost of
exploration
• Long exploration cycles
and uncertainty
• Facing Shale Revolution
• Decreased exploration
cycles and times
• Identify new sources of
Oil & Gas
• Lower operational costs
4

DATABASE
Instrumented + Interconnected + Intelligence = Smarter Planet
Sensors
cloud
(ubiquitous)
data
(scalable)
application
(secure)
network
(reliability)
sensor
(efficiency)
Worldsensing
user
Our solutions relies on Low power wireless
sensors & managing large data volumes
Analytics OutputStoring
5

• Requirements
• Board of Directors
• Other BUs (market)
• Input
• New technologies
• Ideas!
• Academia
• Output
• Improvements to products
• New products
• Knowledge
Innovation Business Unit
6

Innovation Business Unit
• Requirements
• Board of Directors
• Other BUs (market)
• Input
• New technologies
• Ideas!
• Academia
• Output
• Improvements to products
• New products
• Knowledge
• Personnel
• 3 x management
• Technical Manager
• Engineering Manager
• Me!
• 2 PhD Students
• All engineers in WS!!!
• EU Projects
• CALIPSO
• RELYonIT
• SWAP
7

What Do We Want From You
Collaborate
Worldsensing: make our product better with your experience and our market knowledge
Innovate
Worldsensing: offers real market driven problems which need innovative solutions
Test your ideas
Real testbeds, real conditions, real applications
Get new projects
If you are interested contact me after the talk.
8

Worldsensing Solutions
9

What is FastPrk? –
Intelligent Parking Solution
1. Sensors used to manage
open parking spots
2. Client uses SmartPhone
App to identify open spots
3. Worldsensing software solution manages the parking of all cars
in the city and connects with payment and cities
10

Control Center Application
11

Becoming a global priority
12

Remote Monitoring
Cloud Memory
Forget Cables
Plug and Play
End-to-End
What exactly is Loadsensing? –
Monitor Construction Assets Remotely
13

Examples: Palau Sant Jordi, Barcelona
http://youtu.be/yexslRP8U0k
14

Device
Sensors read
by this device
Sensors
currently being
monitored
Data from the sensors and
readings
Status and comments about the
device
Use in the control centre
15

What is Spidernano?
Cableless seismic acquisition system
Capture large areas seismic vibration remotely without
wires and deliver it real time
Features
• Easy installation
• Real time
• Low cost (capex and Opex) and low power design
• Weather resistant
16

Spidernano – Problem
Setting the Scene:
resources: humanity is heavily relying on increasingly scarce oil (gas, minerals, etc)
exploration: geological sub-soil imaging with Millions of seismic acquisition sensors
Headache of Acquisition:
logistics: long/expensive cables; large trucks; inflexible rollout; weeks of work
staff: need of specialized staff able to rollout cables over rugged terrain
cost: very, very expensive overall acquisition exercises
inefficient: 70% of the oil is left behind because imaging and production too coarse
17

Setting the Scene:
cost:very, very expensive overall acquisition exercises
18

Setting the Scene:
cost:very, very expensive overall acquisition exercises
19

Actual technology
20

Tech WS – Loadsensing
• Datalogger for wireless monitoring
• Geotechnical
• Infrastructure
• Environmental
• Sensors
• Voltage / Current / Resistor
• Vibrating Wire
• Pulses
• Digital
• Sampling rate: 5 minutes – 1 day

Tech WS – FastPrk
• Car detection
• Magnetic sensor
• Fixed sampling rate ~0.2 Hz
• Filtering and decision algorithm
• Wireless communication
• Battery powered (>5 years)

Tech WS – Spider
• Real time acquisition
• 3 channels
• 24 bits per channel
• 1000 samples per second
• GPS Synchronization
• 10 us delay
• Store locally
• Low power
• 1 month battery life

Challenges
25

Challenges WS – Communications
• Ease sensor Deployment
• TSCH mesh networks may need hours to
be build
• Can't wait for minutes/hours to build mesh
network
• TSCH: Time slotted Channel Hopping

be build
network

be build
network
• Need tools for real-time monitoring and
deployment
• <Put your idea here>

• Reliability
• Interferences and no deterministic behavior.
• Need tools for real-time monitoring and
deployment

• Temperature variations affect TSCH
networks
• TSCH networks very sensitive to clock drift
due to temperature changes

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0
5
10
15
20
25
30
35
40
Temperature
Column C
Column D
Date
ºC
networks

• Latency can be unacceptable
• Multi-hop networks can have long latencies
• TSCH can have long latencies
Sensor time ~ 5 seconds
SlotTime up to 30 seconds
Multi-hop Time < 1 minute
Basestation/Internet < 2 seconds
Total 1.5 minutes

• Latency can be unacceptable
• Multi-hop networks can have long latencies
• TSCH can have long latencies
• Application unusable
Sensor time ~ 5 seconds
SlotTime up to 30 seconds
Multi-hop Time < 1 minute
Basestation/Internet < 2 seconds
Total 1.5 minutes

# Devices Cost
Nodes 1000 1000 x 200$
Repeaters 100 100 x 200$
Basestations 10 10 x 2000$
Total 1110 240,000$
• Cost can be very high
• Short Range => Repeaters
• Operational
• Exploitation

• Different protocols
• WSN own protocols (no interoperability)
• IEEE 802.15.4E, Contiki MAC, …
• IETF 6LoWPAN, IETF RPL, …
• CoAP
• Internet standard protocols (interoperability)
• IEEE 802.11, IEEE 802.3, …
• IP
• TCP, UDP
• HTTP, FTP, SMTP, …

• CoAP
• IEEE 802.11, IEEE 802.3, …
• IP
• TCP, UDP
• Basestation
• Translates between layers
• SW doing translation scales?
• Approach Layers
37

• CoAP
• IEEE 802.11, IEEE 802.3, …
• IP
• TCP, UDP
• Basestation
• Translates between layers
• SW doing translation scales?
• Approach Layers
38

• But, we don't want a mesh network, we use them because are (were?) the best option
R
R
Basestation

R
R
Basestation
40
• But, we don't want a mesh network, we use them because are (were?) the best option
• We “only” need a point-to-point communication
• Many-To-One communication
• Mesh network?

• More and more sensors
• City became more noisy (BT, WiFi, …)
• Less available channels
• Interferences

• More and more sensors
• City became more noisy (BT, WiFi, …)
• Less available channels
• Interferences
• Data loss!

Challenges WS – Data
• As sensors became cheaper, more sensors for
the same deployment.
• Palau Sant Jordi
• ~200 sensors now ~21Msamples/year

• ~1000 tomorrow? ~100MSamples/year
• ~10000 future? ~1000MSamples/year

• ~1000 tomorrow? ~100MSamples/year
• ~10000 future? ~1000MSamples/year
• Network Capacity
• Data aggregation?
• Processing-on-mote?

• Real Scenario - Moscow
• 1 month
• 5000 Sensors
• 3 MSamples

• Real Scenario - Moscow
• 1 month
• 5000 Sensors
• 3 MSamples
• Data retrieve
• Statistics
• BigData

• Data should be encrypted
• Fraud
• Privacy

• Fraud
• Privacy
• Same key for all motes?
• Force brute can break it ?
• Tempering device and reverse engineering for the key at the uC

• Fraud
• Privacy
• Same key for all motes?
• Force brute can break it ?
• Tempering device and reverse engineering for the key at the uC
• 1 device – 1 key
• Problems in server: thousands of pairs (deviceID, key)?
• Basestation decrypts?

Challenges WS – Detection
• Better Detection (~95% -> 100%)
• New Sensors
• Better Signal Processing
• Complex Decision Algorithms
• Less Batteries
• Low Power
• Energy harvesting

Challenges WS – Spider
• Market demands
• 1000’s of Spiders has to be connected wirelessly
• sending 1000 samples per second x 3 channels (24 bits each)
• in a 10km2 area
• and all perfectly synchronized (~ 10 us)
• and has to be low power
• <Put your GREAT idea here>

TestBed
54

Testbed
• Real Testbed for FastPrk
• Barcelona
55

Testbed
• Barcelona
• “Easy” access
56

Testbed
• Barcelona
• “Easy” access
• Not user critical
• CALIPSO will use it
57

Conclusions
58

Conclusions
• Usually Academia
• Optimal solution for a problem
• These problems are just a part of the reality
59

Conclusions
• Industry
• Different needs
• Hard to deeply understand details
• Sometimes optimal is not best
• Lot of Trade-offs
60

Conclusions
• Industry
• Different needs
• Hard to deeply understand details
• Sometimes optimal is not best
• Lot of Trade-offs
• Worlsensing
• Best of Academia
• Driven by Industry (market)
• Meet in the middle
• If you need a real challenge contact me!
61

End
Thank you for your attention
62

End
Thank you for your attention
And your Ideas!
63

Application scenarios and real-world deployments for IoT and Smart Cities

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