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

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

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]

Internet of Things (IoT)

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

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)

Concept of Smart University

Concept of Smart University

Proposed model University labs controlled Cisco, Games, Electronics, and Cyber-Security Wireless connectivity 802.11 Wi-Fi protocol Data communication through wired network Servers utilised within network User, Web, Mail, and BMS Connection to the ISP and cloud storage

Proposed model

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 802.11 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

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

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 0.135 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.

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.

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.

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.

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

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.

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.

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

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

Thank you for your time Any Questions?

Appendix

Appendix