Internet of things explained in detail with storage, service and security.

1. Internet of Things
and Web Technology
C.K.Pithawala College Of
Engineering and Technology

2. TOPICS
• The internet of things today
• Time for convergence
• Towards IoT universe
• Internet Of Things Vision
• IoT Strategic Research and Innovation direction
• IoT Application
• Future Internet Technologies
• Infrastructure
• Network and Communication
• Process
• Data Management
• Security, Privacy and Trust
• Device level energy issues
• IoT Related Standardization
• Recommendation on Research Topics

3. The internet of things today
• What is iot?
The Internet of Things (IoT) is the
network of physical objects—devices,
vehicles, buildings and other items
embedded with electronics, software,
sensors, and network connectivity—that
enables these objects to collect and
exchange data.

5. What can be done with IoT?
 The possibilities are endless, but as devices start to
communicate with each other through the web,
several applications can be implemented:
 Smart Parking
 Smart Buildings / Offices / Houses
 Pollution Detection
 Detection of Explosive and Hazardous Gases in the
Industry
 Traffic Congestion Monitoring
 Smart & Adaptive Logistics Based on Real Time Data
 Vital Signs Detection and Medical Monitoring

6. CHARACTERISTICS
The fundamental characteristics of the IoT are
as follows:
1. Interconnectivity
2. Heterogeneity
3. Things related services
4. Dynamic changes
5. Enormous scale
6. Connectivity
7. Safety

7. Components of IoT
Internet
of
Things
Control
units
Sensor
Communi
cation
modules
Power
sources
Technology
& protocols

8. Advantages of IoT:
1. Improved customer communication
2. Support for technology optimization
3. Support wide range of data
collection
4. Reduced waste
5. Save time

9. Disadvantages of IoT:
1. Loss of privacy & security
2. Flexibility
3. Complexity
4. Compatibility

10. Time for convergence

11. The following will likely provide the
foundation for a step forward
to the Internet of Things:
1. Coherence of object capabilities and
behaviour
2. Coherence of application interactivity
3. Coherence of corresponding technology
approaches
4. Coherence of real and virtual worlds

12. Towards IoT universe
The forthcoming Internet of Things related research in the scope of Horizon
2020 and corresponding national research programs will address the matters,
challenges from a societal and policy perspective remain equally important, in
particular the following:
1. Fostering of a consistent, interoperable and accessible Internet of Things
across sectors, including standardisation.
2. Directing effort and attention to important societal application areas such as
health and environment, including focus on low energy consumption.
3. Offering orientation on security, privacy, trust and ethical aspects in the
scope of current legislation and development of robust and future-proof
general data protection rules.
4. Providing resources like spectrum allowing pan-European service provision
and removal of barriers such as roaming.
5. Maintaining the Internet of Things as an important subject for international
cooperation both for sharing best practises and developing coherent
strategies.

13. Internet Of Things Vision
Era of internet of things

14. Internet Of Things Vision
• The Internet-of-Things is emerging as one of the
major trends shaping the development of
technologies.
• The shift from an Internet used for
interconnecting end-user devices to an Internet
used for interconnecting physical objects that
communicate with each other and/or with humans
in order to offer a given service.
• From a conceptual standpoint, the IoT builds on
three pillars, related to the ability of smart objects
to:
(i) be identifiable (anything identifies itself),
(ii) to communicate (anything communicates)
(iii) to interact (anything interacts)

15. 1) Devices heterogeneity.
2) Scalability.
3) Emergency optimized solution.
4) Self organizing capabilities.
5) Data management.
Internet Of Things Vision

16. Research Challenges: cities

17. • Smart Parking: Monitoring of parking spaces availability in
the city.
• Structural health: Monitoring of vibrations and material
conditions in buildings, bridges and historical monuments.
• Noise Urban Maps: Sound monitoring in bar areas and
centric zones in real time.
• Traffic Congestion: Monitoring of vehicles and pedestrian
levels to optimize driving and walking routes.
• Smart Lightning: Intelligent and weather adaptive lighting
in street lights.
• Waste Management: Detection of rubbish levels in
containers to optimize the trash collection routes.
• Intelligent Transportation Systems: Smart Roads and
Intelligent Highways with warning messages and
diversions according to climate conditions and unexpected
events like accidents or traffic jams.
Research Challenges: cities

18. Residential Building System

19. Research Challenges:
Environment
• Forest Fire Detection: Monitoring of
combustion gases and preemptive fire
conditions to define alert zones.
• Air Pollution: Control of CO 2 emissions of
factories, pollution emitted by cars and toxic
gases generated in farms.
• Landslide and Avalanche Prevention:
Monitoring of soil moisture, vibrations and
earth density to detect dangerous patterns in
land conditions.
• Earthquake Early Detection: Distributed
control in specific places of tremors.

20. Research Challenges: Water
• Water Quality: Study of water suitability in
rivers and the sea for fauna and eligibility
for drinkable use.
• Water Leakages: Detection of liquid
presence outside tanks and pressure
variations along pipes.
• River Floods: Monitoring of water level
variations in rivers, dams and reservoirs.

21. Research Challenges: Energy Smart
Grid, Smart Metering
• Smart Grid: Energy consumption monitoring
and management.
• Tank level: Monitoring of water, oil and gas
levels in storage tanks and cisterns.
• Photovoltaic Installations: Monitoring and
optimization of performance in solar energy
plants.
• Water Flow: Measurement of water pressure
in water transportation systems.
• Silos Stock Calculation: Measurement of
emptiness level and weight of the goods.

22. Applications of IOT
• Smart devices or “Connected devices ” as commonly called as, are
designed in such a way that they capture and utilize every bit of data
which you share or use in everyday life. And these devices will use
this data to interact with you on daily basis and complete tasks.
• This new wave of connectivity is going beyond laptops and
smartphones, it’s going towards connected cars, smart homes,
connected wearables, smart cities and connected healthcare.
Basically a connected life. According to Gartner report, by 2020
connected devices across all technologies will reach to 20.6 billion.

23. Applications of IOT
Smart home
• It involves the control and automation of lighting, heating (such
as smart thermostats), ventilation, air conditioning (HVAC), and
security, as well as home appliances such as washer/dryers, ovens
or refrigerators/freezers.
• Wi-Fi is often used for remote monitoring and control. Home
devices, when remotely monitored and controlled via the
Internet, are an important constituent of the Internet of Things.
• Modern systems generally consist of switches and sensors
connected to a central hub sometimes called a "gateway" from
which the system is controlled with a user interface that is
interacted either with a wall-mounted terminal, mobile phone
software, tablet computer or a web interface, often but not
always via Internet cloud services.

24. Applications of IOT
Smart home
• While there are many competing vendors, there are very few world-wide
accepted industry standards and the smart home space is heavily
fragmented. Popular communications protocol for products
include X10, Ethernet, RS-485, 6LoWPAN, Bluetooth LE (BLE), ZigBee and Z-
Wave, or other proprietary protocols all of which are incompatible with each
other. Manufacturers often prevent independent implementations by
withholding documentation and by litigation.
• The home automation market was worth US$5.77 billion in 2015, predicted
to have a market value over US$10 billion by the year 2020.
Internet enabled cat feeder
CITIB-AMX control panelRoom control unit

25. Applications of IOT
Wearables
• You'll find wearable technology for every level of fitness, whether
you want to monitor everyday activity, start a fitness program or
train for an athletic competition. And when you pair your
wearable tech device with a compatible app, it's easy to set
fitness goals and log your progress. Many activity trackers are
worn on your wrist, and you'll find a variety of styles that look like
bracelets or watches.
Smartwatch Options
A popular wearable technology option is the smartwatch. These stylish
yet functional devices allow you to conveniently and discreetly manage
your digital life. Smartwatches sync with your iPhone or android phone
and can even double as activity trackers. Options like the Apple
watch deliver alerts, notifications and apps to your wrist. Browse a
variety styles and brands to find the best smartwatch option to fit your
lifestyle.

26. Applications of IOT
Wearables
Usage
Wearable technology usage can be categorized into two major categories;
• personal usage
• business usage4
Whether for personal or business use, wearable tech gadgets are primarily
used for any one of the following functions;
• As a fashion statement
• As a fitness tracker
• As a treatment for hearing impairments
• As a sport tracker
• To synchronize data and communication from other gadgets
• For specific health issue monitoring, for example stress management
• As navigation tools
• As media devices
• As communication gadgets

27. Applications of IOT
Smart City
• Smart city spans a wide variety of use cases, from traffic management
to water distribution, to waste management, urban security and
environmental monitoring. Its popularity is fueled by the fact that
many Smart City solutions promise to alleviate real pains of people
living in cities these days. IoT solutions in the area of Smart City solve
traffic congestion problems, reduce noise and pollution and help make
cities safer.
• A smart city utilizes IoT sensors, actuators and technology to connect
components across the city, and it impacts every layer of a city, from
underneath the streets, to the air that citizens are breathing. Data
from all segments is analyzed, and patterns are derived from the
collected data.

28. Applications of IOT
Smart City
• Several concepts of the Smart city rely heavily on the use of technology; a
technological Smart City is not just one concept but there are different
combinations of technological infrastructure that build a concept of smart
city.
• Digital city: it combines service oriented infrastructure, innovation services and
communication infrastructure
• Virtual city: In these kinds of cities functions are implemented in a cyberspace; it
includes the notion of hybrid city, which consists of a reality with real citizens and
entities and a parallel virtual city of real entities and people.
• Information city: It collects local information and delivered them to the public
portal; In that city, many inhabitants are able to live and even work on the Internet
because they could obtain every information through IT infrastructures, thanks to
the sharing information method among citizens themselves.
• Intelligent city: it involves function as research or technological innovation to
support learning and innovation procedure.
• Ubiquitous city (U-city): It creates an environment that connect citizens to any
services through any device.

30. Applications of IOT
Smart grids
• A smart grid is an electrical grid which includes a variety of
operational and energy measures including smart meters, smart
appliances, renewable energy resources, and energy efficient
resources. Electronic power conditioning and control of the
production and distribution of electricity are important aspects of
the smart grid.

31. Applications of IOT
Smart grids
Features of the smart grid:
 Reliability
 Flexibility in network topology
 Efficiency
 Load adjustment/Load
balancing
 Peak curtailment/leveling and
time of use pricing
 Sustainability
 Market-enabling
 Demand response support
 Platform for advanced services
 Provision megabits, control
power with kilobits, sell the rest
Technologies:
 Smart meters
 Phasor measurement units
 Smart power generation using
advanced components
 wind turbines
 solar cells
 Power system automation

32. Applications of IOT
Smart retail
• Automated retail is the category of self-service, standalone
kiosks in heavily trafficked locations such as airports, malls and
resorts, and convenience store's.
• Consumers select products using a touchscreen interface, pay
for purchases using a credit or debit card and then the product
is dispensed, sometimes via an internal robotic arm in the
machine.
• Smartphones will be the way for retailers to remain connected
with their consumers even out of store. Interacting through
Smartphones and using Beacon technology can help retailers
serve their consumers better. They can also track consumers
path through a store and improve store layout and place
premium products in high traffic areas.

34. Applications of IOT
Smart Agriculture
• Smart Farming should provide the farmer with added value in the
form of better decision making or more efficient exploitation
operations and management. In this sense, smart farming is
strongly related, to three interconnected technology fields
addressed by Smart Network:
o Management Information Systems: Planned systems for
collecting, processing, storing, and disseminating data in the form
needed to carry out a farm’s operations and functions.
o Precision Agriculture: Management of spatial and temporal
variability to improve economic returns following the use of
inputs and reduce environmental impact.
o Agricultural automation and robotics: The process of applying
robotics, automatic control and artificial intelligence techniques
at all levels of agricultural production, including farmbots and
farm drones.

35. Applications of IOT
Smart Agriculture

36. Applications of IOT
Smart Healthcare
• Smart systems are critical in driving innovations in the field of
medical technology, as they provide the basis for information-based
care and cure.
• The integration of micro sensors and micro-actuators in products will
provide the healthcare professional to better treat and take care of
patients in the hospital and at home.
• The seamless linking of microsystems to a telemetric and tele
diagnostic infrastructure will significantly reduce response time, and
simultaneously contribute to containing public healthcare costs
• Successful new products require joint technological development,
and clinical development & validation (and business model
innovation)
• Multidisciplinary collaboration across industries and with multiple
academic partners (including those with access to clinical
applications) is key

37. Applications of IOT
Smart Healthcare

38. Applications of IOT
Smart Vehicles
Target users
•Automotive
•Security & insurance
•Transport & infrastructure
companies
•Administration/
governments
Opportunity areas
•Autonomous vehicles
•Connected bus-stops
•Connected trucks
•Connected cars
Intelligent transportation systems (ITS) are advanced
applications which, without embodying intelligence as such, aim to
provide innovative services relating to different modes of transport
and traffic management and enable various users to be better
informed and make safer, more coordinated, and 'smarter' use of
transport networks. They are considered a part of the Internet of
things.

40. Future internet technology
infrastructure
1. Cloud computing
2. IoT semantic technologies
3. Autonomy
4. Infrastructure

41. What is Cloud computing ?
• Cloud Computing is used to describe a new
class of network based computing that takes
place over the Internet,
– basically a step on from Utility Computing
– a collection/group of integrated and
networked hardware, software and
Internet infrastructure (called a platform).
– Using the Internet for communication and
transport provides hardware, software and
networking services to clients.

42. • These platforms
– hide the complexity and details of the underlying
infrastructure from users
– applications by providing very simple graphical
interface or API
– provides on demand services, that are always on,
anywhere, anytime and any place.
– Pay for use and as needed,
– elastic scale up and down in capacity and
functionalities
• The hardware and software services are available to
general public, enterprises, corporations and
businesses markets.
What is Cloud computing ?

43. Characteristics of cloud data
• A number of characteristics define cloud data,
applications services and infrastructure:
– Remotely hosted: Services or data are hosted on
remote infrastructure.
– Ubiquitous: Services or data are available from
anywhere.
– Commoditised: The result is a utility computing
model similar to traditional that of traditional
utilities, like gas and electricity - you pay for what
you would want!

45. • Shared pool of configurable computing resources
• On-demand network access
• Provisioned by the Service Provider

46. Characteristics of Cloud
Computing
Common Characteristics:
Low Cost Software
Virtualization Service Orientation
Advanced Security
Homogeneity
Massive Scale Resilient Computing
Geographic Distribution
Essential Characteristics:
Resource Pooling
Broad Network Access Rapid Elasticity
Measured Service
On Demand Self-Service

47. Characteristics of Cloud
Computing
• The “no-need-to-know” in terms of the underlying details of
infrastructure, applications interface with the infrastructure via the
APIs.
• The “flexibility and elasticity” allows these systems to scale up and
down at will
– utilizing the resources of all kinds
• The “pay as much as used and needed” type of utility computing and
the “always on!, anywhere and any place” type of network-based
computing.
• Cloud are transparent to users and applications, they can be built in
multiple ways
– branded products, proprietary open source, hardware or software,
or just off-the-shelf PCs.
• In general, they are built on clusters of PC servers and off-the-shelf
components plus Open Source software combined with in-house
applications and/or system software.

48. Cloud Computing Service
Layers

49. Advantages
• Lower computer costs
• Improved performance
• Reduced software costs
• Instant software updates
• Improved document format compatibility
• Unlimited storage capacity
• Increased data reliability
• Universal document access
• Latest version availability
• Easier group collaboration
• Device independence

50. Disadvantages
• Requires a constant Internet connection
• Does not work well with low-speed connections
• Features might be limited:
– For example, you can do a lot more with Microsoft
PowerPoint than with Google Presentation's web-
based offering
• Stored data might not be secure
• Stored data can be lost
• Scheduling is important with this
type of application

51. Semantics
• Semantics and Data
– Data with semantic annotations
– Provenance, quality of information
– Interpretable formats
– Links and interconnections
– Background knowledge, domain
information
– Hypotheses, expert knowledge
– Adaptable and context-aware solutions

52. Semantic technologies in the IoT
• Applying semantic technologies to IoT can
support:
– Interoperability
– effective data access and integration
– resource discovery
– reasoning and processing of data
– knowledge extraction (for automated
decision making and management)

53. Semantic modeling
• Lightweight:
– experiences show that a lightweight ontology model
that well balances expressiveness
– inference complexity is more likely to be widely
adopted and reused
– large number of IoT resources and huge amount of
data need efficient processing
• Compatibility:
– an ontology needs to be consistent with those well
designed,
– existing ontologies to ensure compatibility wherever
possible.
• Modularity:
– modular approach to facilitate ontology evolution,
– extension and integration with external ontologies.

54. • However, we should design
and use the semantics
carefully and consider the
constraints and dynamicity
of the IoT environments.

55. #1: Design for large-scale and provide tools
and APIs.
#2: Think of who will use the semantics and
how when you design your models.
#3: Provide means to update and change the
semantic annotations.
#4: Create tools for validation and
interoperability testing.
#5: Create taxonomies and vocabularies.
#6: Of course you can always create a better
model, but try to re-use existing ones as much
as you can.

56. #7: Link your data and descriptions to other existing
resources.
#8: Define rules and/or best practices for providing
the values for each attribute.
#9: Remember the widely used semantic
descriptions on the Web are simple ones like
FOAF.
#10: Semantics are only one part of the solution
and often not the end-product so the focus of the
design should be on creating effective methods,
tools and APIs to handle and process the
semantics.
Query methods, machine learning, reasoning and
data analysis techniques and methods should be
able to effectively use these semantics.

57. Semantics: services and application services models
and business process description models
Semantics: domain knowledge domain ontologies
and knowledge base
Semantics: devices, resources, and data description
models
Semantics: real world objects thing and entity
descriptions models
Securityprivacyandtrust

58. Semantic related issues
• The current IoT data communications often rely on binary
or syntactic data models which lack of providing machine
interpretable meanings to the data.
– Syntactic representation or in some cases XML-based
data
– Often no general agreement on annotating the data
• requires a pre-agreement between different parties
to be able to process and interpret the data
– Limited reasoning based on the content and context
data
– Limited interoperability in data and resource/device
description level
– Data integration and fusion issues
• Overall, we need semantic technologies in the IoT and
these play a key role in providing interoperability.

59. Autonomy
• There is still a lack of research on how to adopt and tailor existing
research on autonomic computing to the specific characteristic of
CPS,such as high dynamicity and distribution ,real time nature
,resource constraints and loss environments and .most existing
research in self aware Iot is lacking experimentation for validation.

60. • Autonomy in Iot can be realized by implementing
self-managing system
• Self management is the property of a system to
achieve management and maintenance of its
resources intrinsically and internally.
• managment and maintenance is realized through
many levels of decision making.
• management scope extends to access management
device management thus for self management
decision making in Iot should pertain to this scope of
Iot
• An autonomic computing system is required to be self
managing with minimum human interface.
Autonomy

61. Characteristics of AC(Autonomy
computing) system:
Self configuring
Self healing
Self optimizing
Self protecting

62. Infrastructure
• A category of cloud services which provides capability to
provision processing, storage, intra-cloud network connectivity
services, and other fundamental computing resources of the
cloud infrastructure.
• Iot refers to the set of devices and system that that
interconnected
real world sensors and actuators to the internet.
• Includes many different types of system such as
– Mobile devices
– Smart meters and objects
– Wearable device including clothing
– Health care implants
– Smart watch and fitness devices
– Internet connected automobile
– Home automation system, including thermostats, lighting and
home security

63. Basic IoT infrastructure

64. • Required diverse sensor and actuators.
• IoT devices and services should be able to
connect seamlessly and on a plug and play
basis how your device connects to the rest of
the world is a key consideration for IoT
products.
• To work with all feature of IoT different types
of application must run on it devices used in
IoT must supporting plug and play facilities
• Support to finding the things required
• An app may run anywhere including things
themselves.
Basic IoT infrastructure

65. Networks and Communication
and Processes
• Present communication technologies
span the globe in wireless and wired
networks and support global
communication by globally-accepted
communication standards.

66. Networks and Communication:
• Internet of things is an integrated part of
future internet including existing and evolving
internet and network developments.
• IOT allows communication among very
heterogeneous devices connected via a very
wide range of networks through the internet
infrastructure.

67. Networks and Communication:
• IOT devices and resources are any kind of
device connected to internet, from existing
devices, such as servers, laptops, personal
computers, to emerging devices such as smart
phones, smart meters, sensors, identification
readers and appliances.
• Capturing real world data, information and
knowledge and events is becoming increasingly
easier with sensor networks, social media
sharing, location based services and emerging
IOT applications.

68. Networks and Communication:
• Embedding real world information into networks,
services and applications is one of the aim of IOT
technology by using enabling technologies like wireless
sensor and actuator networks, IOT devices and RFID.
• The internet of things infrastructure allows
combinations of smart objects, sensor network
technologies, and human beings using different
communication protocols and realizes a dynamic
heterogeneous network that can be deployed also in
remote spaces.
• Network users will be humans, machines, things and
group of them.

69. Networks and Communication:
• Capabilities such as self-awareness, context
awareness and inter machine communication are
considered a high property for the IOT.
• New smart antennas that can be embedded in the
objects and made of new materials are the
communication means that will enable new
advanced communication systems on chip.
• Network users will be humans, machines, things
and group of them.

70. Communication Technology:
• Communication to enable information exchange
between “smart things/objects” and gateways
between those “smart things/objects” and internet.
• Communication with sensor for capturing and
representing the physical world in the digital world.
• Communication with actuators to perform action in
the physical world triggered in the digital world.

71. Communication Technology:
• Communication with distributed storage units for data
collection from sensors, identification and tracking
systems.
• Communication for interaction with humans in the
physical world.
• Communication and processing to provide data mining
and services.
• Communication for physical world localization and
tracking.

72. Process:
• The deployment of IOT technologies will
significantly impact and change the way
enterprises do business as well as interactions
between different parts of the society, affecting
many processes.
• The main benefits of IOT integration is that
processes become more adaptive to what is
actually happening in the real world.

73. • Processes become more adaptive after an IOT
integration.
• Data collection is based on the event or entity.
• When data is collected from the sensor or real time
data , integration processes happens.
• Such events can occur at any time in the process.
• Event occurrence probability is very low.
• How to react to a single event can depend on the
context.
• Example: the set of events that have been detected
previously.
Adaptive and Event-driven
Processes:

74. • When dealing with events coming from the physical
world, a degree of unreliability and uncertainty is
introduced into the processes.
• If decisions in a business process are to be taken
based on events that have some uncertainty
attached, it makes sense to associate each of these
events with some value for the quality of information.
• Data as well as resources are inherently unreliable.
• This is because of failure of the hosting device.
• Processing relying on such resources need to be able
to adapt to such situations.
• It is necessary to detect a failure.
• The quality of the generated reports should be
regularly audited for correctness.
Processes Dealing with
Unreliable Data:

75. Data Management
• Data management is to manage the data those are
collected from physical world.
• Data management is a crucial aspect in the Internet
of Things.
• When considering a world of objects
interconnected and constantly exchanging all types
of information, the volume of the generated data
and the processes involved in the handling of those
data become critical.
• There are many technologies and factors involved
in the “data management” within the IOT context.

76. Data Management
• Some of the most relevant concepts which enable
us to understand the challenges and
opportunities of data management are:
 Data Collection and Analysis
 Big Data
 Semantic Sensor Networking
 Virtual Sensors
 Complex Event Processing.

77. Data Collection and
Analysis(DCA)
• Data Collection and Analysis modules or capabilities are
the essential components of any IOT platform or system.
• The DCA module is part of the core layer of any IOT
platform. Some of the main functions of a DCA module
are:
 User/customer data storing:
Provides storage of the customer’s
information collected by sensors
 User data & operation modelling:
Allows the customer to create new sensor
data models to accommodate collected
information and the modelling of the
supported operations

78. Data Collection and
Analysis(DCA)
 On demand data access:
Provides APIs to access the collected data
 Device event
publish/subscribe/forwarding/notification:
Provides APIs to access the collected data
in real time conditions
 Customer rules/filtering:
Allows the customer to establish its own
filters and rules to correlate events

79. Data Collection and
Analysis(DCA)
 Customer task automation:
Provides the customer with the ability to manage
his automatic processes. Example: scheduled
platform originated data collection, …
 Customer workflows:
Allows the customer to create his own work flow to
process the incoming events from a device
 Multitenant structure:
Provides the structure to support multiple
organizations and reseller schemes.

80. Big Data
• Big data is about the processing and analysis of large data
repositories, so disproportionately large that it is impossible to
treat them with the conventional tools of analytical
databases.
• Big data requires exceptional technologies to efficiently process
large quantities of data within a tolerable amount of time.
• Technologies being applied to big data include
massively parallel processing (MPP) databases,
data-mining grids, distributed file systems, distributed
databases, cloud computing platforms, the Internet, and
scalable storage systems.
• These technologies are linked with many aspects derived from the
analysis of natural phenomena such as climate and seismic data to
environments such as health, safety or, of course, the business
environment.

81. Big Data
• Among the imminent research targets in this field are:
 Privacy. Big data systems must avoid any
suggestion that users and citizens in general
perceive that their privacy is being invaded.
 Integration of both relational and NoSQL
systems.
 More efficient indexing, search and processing
algorithms, allowing the extraction of results in
reduced time and, ideally, near to “real time”
scenarios.
 Optimized storage of data. Given the amount of
information that the new IOT world may
generate, it is essential to avoid that the storage
requirements and costs increase exponentially.

82. Semantic Sensor Networks and
Semantic Annotation of Data
• There are currently on-going efforts to define
ontologies and to create frameworks to apply
semantic Web technologies to sensor networks.
• The Semantic Sensor Web (SSW) proposes
annotating sensor data with spatial, temporal, and
thematic semantic metadata.
• This approach uses the current OGC and SWE
specifications and attempts to extend them with
semantic web technologies to provide enhanced
descriptions to facilitate access to sensor data.

83. Semantic Sensor Networks and
Semantic Annotation of Data
• In general , associating sensor and sensor network data
with other concepts (on the Web) and reasoning makes
the data information widely available for different
applications, front-end services and data consumers.
• The semantic description allow machines to interpret
links and relations between the different attributes of a
sensor description and also other data existing on the
Web or provided by other applications and resources.
• Utilizing and reasoning this information enables the
integration of the data on a wider scale, known as
networked knowledge.
• This machine-interpretable information (i.e. semantics)
is a key enabler for the semantic sensor networks.

84. Virtual Sensors
• A virtual sensor can be considered as a product of
spatial , temporal and/or thematic transformation
of raw or other virtual sensor producing data with
necessary provenance information attached to this
transformation.
• The data acquired by a set of sensors can be
collected ,processed according to an application-
provided aggregation function, and then perceived
as the reading of a single virtual sensor.
• The flow of information between real devices and
virtual sensors or actuators is presented in Figure.

85. Virtual Sensors

86. Virtual Sensors
• We follow that statement with this definition:
 A virtual sensor behaves just like a real sensor,
emitting time series data from a specified
geographic region with newly defined thematic
concepts or observations which the real sensors
may not have.
 A virtual sensor may not have any real sensor’s
physical properties such as manufacturer or battery
power information, but does have other properties,
such as: who created it; what methods are used,
and what original sensors it is based on.

87. Virtual Sensors
• The development of virtual sensors could be
approached following two different degrees of
complexity:
 The combination of a limited number of
related sensors or measurements to derive
new virtual data (usually done at the sensor
node or gateway level).
 The complex process of deriving virtual
information from a huge space of sensed
data (generally at the application level).

88. Complex Event Processing
• Virtual Sensors can be used to implement “single
sensors” from complex and multiple(actual) sensors
or various data sources ,thus providing a seamless
integration and processing of complex events in a
sensor (or Data Collection and Analysis) platform or
system.
• Complex event processing (CEP) is an emerging
network technology that creates actionable
,situational knowledge from distributed message-
based systems ,databases and applications in real
time or near real time.
• CEP can provide an organization with the capability
to define ,manage and predict events , situations,
exceptional conditions, opportunities and threats in
complex, heterogeneous networks.

89. Complex Event Processing
• CEP is a technology for extracting higher level
knowledge from situational information
abstracted from processing sensory information
and for low-latency filtering, correlating,
aggregating, and computing on real-world event
data.
• Most CEP solutions and concepts can be
classified into two main categories:

90. Complex Event Processing
 Computation-oriented CEP:
Focused on executing on-line algorithms as a
response to event data entering the system. A
simple example is to continuously calculate an
average based on data from the inbound
events
 Detection-oriented CEP:
Focused on detecting combinations of events
called event patterns or situations. A simple
example of detecting a situation is to look for a
specific sequence of events.

91. Security , privacy & Trust
• Security:
• As the IOT becomes a key element of the Future
Internet and a critical national/international
infrastructure, the need to provide adequate
security for the IOT infrastructure becomes ever
more important.
• Large-scale applications and services based on
the IOT are increasingly vulnerable to disruption
from attack or information theft.
• Advances are required in several areas to make
the IOT secure from those with malicious intent.

92. Privacy
• As much of the information in an IoT system may
be personal data, there is a requirement to
support anonymity and restrictive handling of
personal information.
• There are a number of privacy implications
arising from the ubiquity and pervasiveness of
IoT devices where further research is required,
including:

93. Privacy
• Preserving location privacy, where location can
be inferred from things associated with people.
• Prevention of personal information inference,
that individuals would wish to keep private,
through the observation of IOT-related
exchanges.
• Keeping information as local as possible using
decentralized computing and key management.
• Use of soft identities, where the real identity of
the user can be used to generate various soft
identities for specific applications.

94. Trust
• The trust framework needs to be able to deal
with humans and machines as users, i.e. it needs
to convey trust to humans and needs to be
robust enough to be used by machines without
denial of service.
• The development of trust frameworks that
address this requirement will require advances in
areas such as:

95. Trust
• Lightweight Public Key Infrastructures (PKI) as a
basis for trust management.
• Light weight key management systems to enable
trust relationships to be established.
• Quality of Information is a requirement for many
IOT-based systems where metadata can be used
to provide an assessment of the reliability of IoT
data.
• Decentralized and self-configuring systems as
alternatives to PKI for establishing trust

96. Device Technical Challenges
• One of the essential challenges in IoT is how to
interconnect “things” in an interoperable way
while taking into account the energy constraints,
knowing that the communication is the most
energy consuming task on devices.
1. Low Power Communication
Several low power communication
technologies have been proposed from different
standardization bodies.

97. Device Technical Challenges
• The most common ones are:
• IEEE802.15.4 has developed a low-cost , low-power
consumption, low complexity, low to medium range
communication standard
• Bluetooth low is the ultra-low power version of the
Bluetooth technology that is up to 15 times more
efficient than Bluetooth.
• Ultra-Wide Bandwidth (UWB) Technology is an
emerging technology in the IoT domain that
transmits signals across a much larger frequency
range than conventional systems
• RFID/NFC proposes a variety of standards to offer
contact less solutions.

98. Device Technical Challenges
2. Energy Harvesting
Energy harvesting (EH) must be chosen according
to the local environment.
• For outside or luminous indoor environments,
solar energy harvesting is the most appropriate
solution.
• The energy harvesting wireless sensor solution is
able to generate a signal from an extremely
small amount of energy.

99. Device Technical Challenges
3. Future Trends and Recommendations
• In the future, the number and types of IoT
devices will increase, therefore inter-operability
between devices will be essential. More
computation and yet less power and lower cost
requirements will have to be met. Technology
integration will be an enabler along with the
development of even lower power technology
and improvement of battery efficiency.

100. IoT Standardization cycle

101. IoT Related Standardization
• Standards mean is general common method , norms
and regulation , based on which some work must be
done.
• Standards can be official and binding (de jure) De facto
standards can be formed by companies or group of
companies which have come to market therefore used
methods.
• Standards play an important role in applying new
technologies.
• Standards are published documents that establish
specification and procedures designed to maximize the
reliability of materials.
• Standards address a range of issues , including but not
limited to various protocols to help product
functionality

102. Ides!
Project
approval
process
Develop
draft
standard
s
Sponsor
ballot
IEEE-SA
standards
board
approval
process
Publish
standards
Revise standard
Withdraw standardarchive
Max 4 years
Max
10
year
Standardization process

103. The Internet of Things (IoT)
Security Considerations for Higher
Education

104. Recommendations
Accommodate IoT with existing
practices:
– Policies, Procedures, & Standards
– Awareness Training
– Risk Management
– Vulnerability Management
– Forensics

105. Recommendations
• Plan for IoT growth:
– Additional types of logging, log storage: Can you
find the needle in the haystack?
– Increased network traffic: will your firewall / IDS
/ IPS be compatible and keep up?
– Increased demand for IP addresses both IPv4
and IPv6
– Increased network complexity – should these
devices be isolated or segmented?

106. Recommendations
• Strengthen partnerships with researchers,
vendors, and procurement department

107. Threat vs. Opportunity
• If misunderstood and misconfigured, IoT poses
risk to our data, privacy, and safety
• If understood and secured, IoT will enhance
communications, lifestyle, and delivery of
services
Threat
Opportunity