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Public data should be made available to everyone and the basic services (that replace the existing practices) should be free to everyone; it doesn’t mean if we replace a bus time table with a smart phone app then who pays a premium should access it.
Helen to outline how TIHM works Trevor will finish slide by talking about his experience of the system as a carer.
Lecture 8: IoT System Models and Applications
Lecture 8: IoT System Models and
EEEM048/COM3023- Internet of Things
Prof. Payam Barnaghi
Centre for Vision, Speech and Signal Processing (CVSSP)
Electrical and Electronic Engineering Department
University of Surrey
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
− The same way, you can’t simply cluster, group lon/lat data using classical
methods that use Euclidian distance (e.g. k-means clustering)
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
− The Geohash algorithm can represent geographic regions in a
− A geohash is represented as a string:
− e.g. (-25.382708 and -49.265506) can be represented as:
− Or http://geohash.org/6gkzwgjzn820
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.
Table source: https://en.wikipedia.org/wiki/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
Example of similar locations
− (51.236127 -0.574036) - Guildford
− (51.243113 -0.590343) – University of Surrey
− (51.243603 -0.587994) – BA (Arthur C Clark) Building
Prefix similarity can be used to find close locations;
But it cannot be directly converted to a metric distance measure.
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:
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
− 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.
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.
Calculate distance between two
− 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
− haversine’ formula is used to calculate the great-circle distance between
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();
- 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.
−The IoT and M2M?
−IoT is a more generic term; M2M focuses on devices and
−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).
Some of the IoT Application areas
− Industrial automation
− Smart homes
− Automotive (navigation, traffic control, vehicle safety, fleet
− Smart cities (city automation, intelligent parking, intelligent
transport systems, air quality and pollution monitoring, etc.)
− Environmental monitoring
− Smart grid and smart metering
Types of applications
− Event detection
− Nodes report events and occurrences
− Anomaly and outlier detection
− Collaboration of nearby and/or remote sensors to detect more
− 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)
Types of applications
− Approximation and edge detection
− Detecting how a physical value (e.g. temperature) changes one place to
− 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
− This can be generalised to finding “edges” in different boundaries such as
space and time.
− 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.
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
Physical Environment/ Things
− 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).
− Fault tolerance
− Reliability and dependability
− What happens if a node runs out of power or gets damaged or losses
− Redundant deployment
− 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.
− How efficiently large number of nodes/Things can be supported.
− How efficiently the system can respond to large number of events, requests,
− Number of nodes per unit area
− Planning for an application to see if support for change and dynamic updates
− 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)
− Multi-hop wireless communications
− Transmission range can be short and in some cases multi-hop communication is
− Energy efficient operations
− To save energy and/or increase the lifetime of the network/services.
− 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
What is special about IoT applications?
− Different applications and various requirements
− Interaction and deployment in uncontrolled or less controlled
− 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.
− Cities account for 75% of green house emissions, while only occupying 2% of
− It is expected that the amount of people living in urban areas will double until
− By 2015, 1.2 billion cars will be on the road–making 1 car per 6 person.
− More space required
− Management of resources and infra structure waste, transportation, …
− Climate change
− Crisis management
Adapted from: Smart Cities and Internet of Things, Oliver Haubensak ETH-MTEC, ETHZ, May 2011.
Designing IoT applications for smart cities
Source LA Times, http://documents.latimes.com/la-2013/
Future cities: a view from 1998
Importance of designing for real
problems and challenges
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
IoT environments are usually dynamic and (near-) real-time
Off-line Data analytics
Data analytics in dynamic environments
Image sources: ABC Australia and 2dolphins.com
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
#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.
#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.
Data Quality Explorer
Source: The CityPulse project by the University of Applied Sciences Osnabrück
Video demo: https://www.youtube.com/watch?v=Yc1jiB5zdfE
− A large farm can exhibit wide spatial diversity in soil types,
nutrient content, water/moisture, and other important
− 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
Source: Dargie and Poellabauer, Fundamentials of Wirless Sensor Networks: Theory and Practice, Wiley, 2010
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;
TIHM Integrated View (iView)
monitored by blend of
Working closely with
Alzheimer’s Society, NHS,
social services and police
Freeing up clinicians
to focus on
Application requirements in IoT
− Service reliability, security, privacy, trust, mobility, lower power
consumption, lower delays;
− Mobility, real-time interactions, quality of services, location tracking;
− Smart cities
− Reliability, fault tolerance, delay tolerance;
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
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
-What are the key issues that should
be considered in the design?
-What type of in-network processes
can be done?
− 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: