1. 1
Lecture 8: IoT System Models and
Applications
EEEM048/COM3023- Internet of Things
Prof. Payam Barnaghi
Centre for Vision, Speech and Signal Processing (CVSSP)
Electrical and Electronic Engineering Department
University of Surrey
Autumn 2018

2. Spatial Data and IoT
− Most of the data in IoT applications is location dependent;
− Services could be also location-based;
− Location can be specified as:
− Names, labels
− Tags and semantic annotations
− GPS data - Longitude, Latitude (and Altitude)
− How can we define an area?
− Multiple points are required
− Simple Euclidian distance measure won’t work for longitude/latitude
(lon/lat) data
− The same way, you can’t simply cluster, group lon/lat data using classical
methods that use Euclidian distance (e.g. k-means clustering)

3. How to create location tags?
− GeoHashing is one way to do this;
− Geohash is a latitude/longitude geo-coding that was invented
by Gustavo Niemeyer.
− GeoHashing function can encods/decods (lat,lon) pairs in a
compact form.
− The Geohash algorithm can represent geographic regions in a
hierarchical structure.
− A geohash is represented as a string:
− e.g. (-25.382708 and -49.265506) can be represented as:
6gkzwgjzn820
− Or http://geohash.org/6gkzwgjzn820

4. How does it work?
− A Geohash is calculated by interleaving bits obtained from
latitude and longitude pairs and converting the bits to a string
using a Base-32 character map.
− Base-32:
Table source: https://en.wikipedia.org/wiki/Geohash

5. Geohash – example
5
ezs42
01101 11111 11000 00100 00010

6. GeoHash
− A Geohash string represents a fixed spatial bounding box.
− For example, the latitude and longitude coordinates of (-
25.382708 and -49.265506) fall within the Geohash bounding
box of "6gkzwgjzn820".
− Appending characters to the string would make it refer to
more precise geographical subsets of the original string.
− More information: http://geohash.org/site/tips.html#format

7. Example of similar locations
− (51.236127 -0.574036) - Guildford
− gcpe6zmbpfrd
− (51.243113 -0.590343) – University of Surrey
− gcped8d0u087
− (51.243603 -0.587994) – BA (Arthur C Clark) Building
− gcped8egdezy
gcpe6zmbpfrd
gcped8d0u087
gcped8egdezy

8. GeoHash
Prefix similarity can be used to find close locations;
But it cannot be directly converted to a metric distance measure.

9. GeoHashing – Location Codes
Image credit: Pramod Anantharam et al., Wright State University/University of Surrey;
Alternatively Grid boxes and tags can be
defined manually or using other different
techniques; here is an example:

10. Limitations of GeoHash
− Geohash algorithm can be used to find locations (e.g. points) that are
close to each other by checking prefix similarity of the GeoHash tags.
− However, the points that close to each other but located at two opposite
sides of the Equator line or for the nodes that fall on line of longitude (i.e.
Meridian points) can produce Geohash codes that have no common
prefix.
− Point close to North and South Poles can have very different geohashes (in
Norht and South Poles close areas can have different latitudes)
− Geohash also defines a Bounding Box; this can then result having locations
that are close but have different GeoHash codes.
− For better proximity searches, the surrounding eight geohashes of a
geohash should be calculated but this can make the proximity
searches more complicated.

11. How to calculate the distance between two
geohashes or locations
− If you have two geohashes, the first step is to decode them
into latitude and longitude values.
− Longitude varies between [-180 , +180]
− Latitude varies between [-90 , +90]
− The Geohash is based on the splitting the interval in 2 (for
each of the Longitude and Latitude) at each step and
take 0 for the right part and 1 for the left part.
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12. Calculate distance between two
Latitude/Longitude points
− There are a variety of calculations for lati­tude/longi­tude
points, with different formulae.
− The formulae to calculate distance between two Lat/Lon
points are often on the basis of a spherical earth (ignoring
ellipsoidal effects) – which is accurate enough for most of the
applications.
12
Source: http://www.movable-type.co.uk/scripts/latlong.html

13. Haversine’ formula
− haversine’ formula is used to calculate the great-circle distance between
two points
a = sin²( /2) +Δφ cos 1φ ⋅ cos 2φ ⋅ sin²( /2)Δλ
c = 2 atan2( √a, √(1 a) )⋅ −
d = R c⋅
where φ is latitude, λ is longitude, R is earth’s radius (mean radius = 6,371km);
1 =φ lat1.toRadians();
2 =φ lat2.toRadians();
= (Δφ lat2-lat1).toRadians();
= (Δλ lon2-lon1).toRadians();
13Source: http://www.movable-type.co.uk/scripts/latlong.html

14. Haversine’ formula
- Note that angles need to be in radians.
- In this module, you don’t need to remember these
formulae. The main formulae and also the required
conversion formula (e.g. degree to radian) will be
given in the exam.
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15. Haversine’ formula- sample code
15
Source: http://www.movable-type.co.uk/scripts/latlong.html

16. IoT Applications
−The IoT and M2M?
−IoT is a more generic term; M2M focuses on devices and
machine-to-machine communications;
−Sometimes the terms are used interchangeably; however,
M2M is mainly meant for automated interactions between
devices and IoT is an umbrella term for describing
technologies that allow real world data collection,
communication, processing and interactions (machines,
devices and human users).

17. Some of the IoT Application areas
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− Industrial automation
− Smart homes
− eHealth
− Automotive (navigation, traffic control, vehicle safety, fleet
management, etc.)
− Smart cities (city automation, intelligent parking, intelligent
transport systems, air quality and pollution monitoring, etc.)
− Environmental monitoring
− Smart grid and smart metering
− …

18. Types of applications
− Event detection
− Nodes report events and occurrences
− Anomaly and outlier detection
− Collaboration of nearby and/or remote sensors to detect more
complicated events
− Pattern detection and anomaly detections in patterns
− Periodic monitoring and measurements, information
extraction and interactons
− Measuring and monitoring and reporting the data
− Monitoring and measurement can be triggered by an event
− Processing the collected information and implementing (automated)
interactions.
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19. Types of applications
− Approximation and edge detection
− Detecting how a physical value (e.g. temperature) changes one place to
another;
− This can be used to approximate spatial characteristics and map it to an area
− For example, in a forest fire, this can be used to approximate the border of
actual fire;
− This can be generalised to finding “edges” in different boundaries such as
space and time.
− Tracking
− An event source can be mobile;
− sensors can be used to monitor and track an object;
− Speed and direction of the object can be also estimated.
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20. Types of applications
− Control and feedback
− Using actuators to interact with the environment;
− Make a change and the sense and obtain feedback from the physical
environment.
20
Physical Environment/ Things
Actuator Sensor
Controller
actuation sensing
feedbackcommand

21. Characteristic requirements
− Types of services
− Interfaces and interaction models
− Autonomy of services
− Data processing and information extraction requirements
− Service network requirements
− Quality of service
− Delay and latency
− Quality of information
− Accuracy and quality measures of the functions (e.g. reliability and
accuracy of event detection).
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22. Characteristic requirements
− Fault tolerance
− Reliability and dependability
− What happens if a node runs out of power or gets damaged or losses
coverage
− Redundant deployment
− Lifetime
− Especially if the nodes rely on limited power
− Sometimes it is a trade-off between energy efficiency against the
quality of service
− Can be defined as the time that first node fails or runs out of energy;
or when x% of the nodes fail; or it can be defined as the time that the
observed “thing” is no longer covered.
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23. Characteristic requirements
− Scalability
− How efficiently large number of nodes/Things can be supported.
− How efficiently the system can respond to large number of events, requests,
traffic, etc.
− Density
− Number of nodes per unit area
− Programmability
− Planning for an application to see if support for change and dynamic updates
required.
− Maintainability
− Ability to adapt to the changes or to change operational parameters
− Or, in some applications ability to access and maintain or replace the nodes or
to re-configure them (remotely or locally)
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24. Required mechanisms
− Multi-hop wireless communications
− Transmission range can be short and in some cases multi-hop communication is
required.
− Energy efficient operations
− To save energy and/or increase the lifetime of the network/services.
− Auto-configuration
− Ability to configure (at least some of) the functional parameters automatically.
− Collaboration and in-network processing
− Several node collaborate
− Parts of the process is performed on the node and/or in the network.
− Data-centric solutions
− Conventional networks often focus on sending data between two specific nodes each
equipped with an address.
− Here what is important is data and the observations and measurements not the node
that provides it.
− Security, Trust and Privacy
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25. What is special about IoT applications?
− Different applications and various requirements
− Interaction and deployment in uncontrolled or less controlled
environments
− Heterogeneity and scale
− Energy and resource constraints
− Autonomous mechanisms that are often required; e.g. self-configurability
− Security and Privacy issues
− Data quality and data processing and analysis requirements
− Actuation, feedback and control loop to interact with physical
objects/environment over distributed networks.
− Mobility
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26. 26
Crowdsourced solutions

27. 27
Smart Cities
− Cities:
− Cities account for 75% of green house emissions, while only occupying 2% of
world surface.
− It is expected that the amount of people living in urban areas will double until
2050.
− By 2015, 1.2 billion cars will be on the road–making 1 car per 6 person.
− Challenge:
− More space required
− Management of resources and infra structure waste, transportation, …
− Climate change
− Competitiveness
− Crisis management
Adapted from: Smart Cities and Internet of Things, Oliver Haubensak ETH-MTEC, ETHZ, May 2011.

28. Designing IoT applications for smart cities
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29. 29
Source LA Times, http://documents.latimes.com/la-2013/
Future cities: a view from 1998

30. Importance of designing for real
problems and challenges
30Source: http://robertluisrabello.com/denial/traffic-in-la/#gallery[default]/0/
Source: wikipedia

31. 31

32. IoT applications in smart cities
− Traffic management
− Waste management
− City transport
− Noise, air-quality control and monitoring
− Emergency services
− Security and safety
− Infrastructure management
− Elderly-care and social care
− Smart metering
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33. IoT environments are usually dynamic and (near-) real-time
33
Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com

34. Traffic control and sensing
− There are various sensing technologies that can be used
including: video, sonar, radar, inductive loops, bacons, etc.
− Some of these will need cable and some need to be installed
on lampposts, etc..
− Some others like inductive loops can be embedded into the
transportation road infrastructure.
− One application is congestion control by counting (and
estimating) the number of passing vehicles and their speed.
− Environmental factors can affect the sensors; for example,
video cameras are not very helpful during fog and heavy snow
or rain.
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35. #1: Design for large-scale and provide tools and APIs.
#2: Think of who will use the data and how, when you
design your models.
#3: Provide means to update and change the data
models and processing methods.
35
Smart Cities

36. #4: Design for different audience (data consumers,
developers, providers) and think about real impact and
sustainability of your solution.
#5: Specify (and encourage others to do the same) data
governance and privacy procedures, explain the
ownership and re-use rules, and give control to the
owners of the data.
36
Smart cities

37. 37

38. Users in control or losing control?
38
Image source: Julian Walker, Flicker

39. Smart City: IoT Application Examples

40. 40
Event Visualisation

41. A sample smart city application
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42. Data Quality Explorer
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Source: The CityPulse project by the University of Applied Sciences Osnabrück
Video demo: https://www.youtube.com/watch?v=Yc1jiB5zdfE

43. Precision Agriculture
− A large farm can exhibit wide spatial diversity in soil types,
nutrient content, water/moisture, and other important
factors.
− Sensors for monitoring temperature, humidity, acidity of soil.
− Monitoring for fungal disease; e.g. by monitoring humidity and
temperature conditions in the field as well as the wetness of
potato leaves the researchers can identify the risk of fungal
disease.
43
Source: Dargie and Poellabauer, Fundamentials of Wirless Sensor Networks: Theory and Practice, Wiley, 2010

44. Healthcare: IoT Application use-case scenarios

45. IoT for healthcare: Dementia use-case
 16,801 people with dementia in Surrey – set to rise to 19,000
by 2020 (estimated) - nationally 850,000 - estimated 1m by
2025 (Alzheimer’s Society);
 Estimated to cost £26bn p/a in the UK (Alzheimer’s Society):
health and social care (NHS and private) + unpaid care;
 IoT devices can provide data that can further analysed to
detect agitation, sleep, weight loss, and wandering – all have a
big impact on quality of life and wellbeing;

46. Technical Challenges
− Infrastructure
− Interoperability, integration
− Security
− Data governance
− Scalability

47. Device/Data interoperability
47

48. FIHR4TIHM
48

49. Gateway
Gateway
Data Analytics
Cloud systems
External systems
Possible links to
other networks
Gateway
Data-driven and patient
centered Healthcare
Applications

50. How does TIHM work?
Signals
Machine
Analysis
Human
Decision
Action
24/7

51. Personalised thresholds
51

52. Probabilistic machine learning models
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53. TIHM Integrated View (iView)
Integrated view
monitored by blend of
health professionals
Working closely with
Alzheimer’s Society, NHS,
social services and police
Freeing up clinicians
to focus on
treatment

54. 54
Application requirements in IoT
− Healthcare
− Service reliability, security, privacy, trust, mobility, lower power
consumption, lower delays;
− Automotive
− Mobility, real-time interactions, quality of services, location tracking;
− Smart cities
− Reliability, fault tolerance, delay tolerance;

55. Exercise : A use-case study
55Image source: Mahmoud Meribout, A Wireless Sensor Network-Based Infrastructure for Real-Time and Online Pipeline Inspection, IEEE SENSORS JOURNAL, VOL. 11, NO. 11, NOVEMBER
2011
This diagram shows a leak detection
System in a pipeline.
Work in groups and identify:
-What parameters can be measured
to detect a leak?
-What type of sensors can be used?
-What components can be added to
this diagram?
-What are the key issues that should
be considered in the design?
-What type of in-network processes
can be done?

56. Further reading
− If you are interested in more information about spatial data,
you may want to refer to (this won’t be part of the exam):
− Spatial Data on the Web Best Practices:
− https://www.w3.org/TR/sdw-bp/
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57. 57
Questions?