1. Smart University utilising the concept of the Internet of Things (IoT) Simon Downes BSc MBCS Carlene Campbell March 2018

2. Introduction Overview of the Internet of Things (IoT)
Concepts and related works
Student count
Managing the environment
Proposed network architecture model
Simulation results
Application layer
Transport layer
Mac layer
Conclusion
Future research

3. Internet of Things (IoT)?
The ‘Internet of Things’, or IoT, is made up of ‘smart’ objects such as mobile phones, tablets, alarm systems, home appliances, and industrial machines. These devices are connected to the internet and each other in a continuous state through an internet of networks.
(N. Bari et all, 2013)[1]

4. Internet of Things (IoT)

5. Internet of Things (IoT)
S-E-N-S-E
Internet of Things
The Internet
Sensing
Sensors (things) attached to network (e.g. temperature, pressure, acceleration)
Greater amounts of data generated by things than by people
Efficient
Utilises intelligence to manual processes (e.g. reduction of power on hot days)
Encompasses the internets production gains to things, not just people
Networked
Communication between objects / things to the network (e.g. thermostats, cars, watches)
Information moves from the cloud to the network’s edge (‘fog’ computing)
Specialised
Customises technology and processes to precise assignments (e.g. healthcare, education, and industry)
Devices such as PCs, smartphones, and laptops have a broad reach, IoT however can be defined as fragmented.
Everywhere
Deployed ubiquitously (e.g. body, cars, homes, businesses, and buildings)
World presence, resulting in more devices and higher security risks

6. Concept of Smart University
Student occupancy and monitoring
Radio frequency Identification (RFID)
Face / motion detection camera
Environmental control
Temperature, humidity, and air conditioner (AC)
Lighting and lux
Blind and window automation
Building management system (BMS)

7. Concept of Smart University

8. Concept of Smart University

9. Proposed model University labs controlled
Cisco, Games, Electronics, and Cyber-Security
Wireless connectivity Wi-Fi protocol
Data communication through wired network
Servers utilised within network
User, Web, Mail, and BMS
Connection to the ISP and cloud storage

10. Proposed model

11. Testing and Results
Two connections were configured on QualNet software to simulate connectivity in each lab.
Packets forwarded by routers
MAC and PHY protocols utilise IEEE
IPv4 addressing scheme
AODV as routing protocol
UDP and TCP represent the client
500 packets of data transmitted across the network
Application, MAC, and transport layers graphically displayed

12. Unicast session start
Looking at the results from figure 6, it shows that all the nodes associated with the UDP client will start up efficiently within one second of having any traffic. This is vital in a wireless sensor network due to the changes in conditions as defined in the management software

13. Unicast jitter
An important factor in a network is jitter; this test is identify in figure 8. How much of a delay will there be prior to receiving a packet of data. Clearly, on nodes 14, 23 and 25 the delay is almost non-existent, however looking closely at node 8 having the delay of almost seconds could cause substantial downtime or potential poor operation of the equipment or sensor. A possible solution to this could be to ensure that the traffic is prioritised across the network.

14. Unicast received throughput
When reviewing the results from the received throughput it is important to identify that this will highlight the performance of the proposed network and as to how long it would take to receive data sent across the network. In figure 9, node, 25 has the highest bits per sec rate and is capable of handling large amounts of data, however, node 8 will take a substantial time to receive large data traffic. It would be important to look into the placement within the network of these two nodes and possibly look at maybe splitting the channels that these nodes operate on to create a faster network.

15. Unicast overhead packets sent
The results from figure 13 show that only four nodes are sending large amounts of data across the network. Potentially this could result in some loss, delay and queueing. In figure 12, it shows that the corresponding nodes in each lab receive high data; again, this could demonstrate on the simulation that there has been queuing and delays across the network.

16. Unicast overhead packets received
The results from figure 13 show that only four nodes are sending large amounts of data across the network. Potentially this could result in some loss, delay and queueing. In figure 12, it shows that the corresponding nodes in each lab receive high data; again, this could demonstrate on the simulation that there has been queuing and delays across the network.

17. UDP Simulation
The time interval between packet generations is exponential
The packet lengths are also exponential
The average value of the packet lengths constant during the simulation
Each connection should generate 500 packets
One simulation “No Fade Model”
Second simulation “With Fade” set to Rayleigh

18. UDP packet delay
The results from this graph show that node 4 has a significant delay when you introduce the Fade Model as this simulates real-world environments from geographical, weather and building structures etc. this clearly shows that node 4 will require careful location within the classroom and maybe even its own dedicated channel to ensure the QoS is achieved.

19. UDP server throughput
Comparing the results from this simulation it has shown that there is little difference between no fade model and with fade, again node 4 appears to perform slightly better, this is in part due to its location within the network. To improve the testbed for future modeling larger amounts of data and more traffic would be required thus simulating a more conclusive real-world scenario.

20. Conclusion IoTs impact within society
Home, industry, personal, and mobile devices
Smart cities, grids, and vehicles
Smart universities are achievable
Location identification is vital to success to prevent data loss, delay, and re-transmission of data
Planning of network utilising simulation software will be a major component
Simulation has drawbacks as it doesn’t allow for user error or interference

21. Future research Review of findings against real-world scenarios
Implementation of physical testbed
Comparison of simulation results against the physical
Identify possible further applications for the sensor network other than educational
Proposal for PHD study into WSN and IoT technologies

22. Thank you for your time
Any Questions?

23. Appendix

24. Appendix