Performance of Wireless Access Points as a Function of Distance, Signal Quality and Number of Connections in a School Environment

Abstract:
With the popularity of wireless devices and the elimination of wired ethernet connections, the importance of strong reliable Wireless Local Area Network (WLAN) is crucial to the productivity of faculty and students. In a school environment with the high number of users, the wide area along with high concentration in certain areas results in challenges for wireless connection. In this report we are going to use Riverbed Modeler and the 802.11n wireless protocol to simulate these different typical school environments. Finally a comparison of the performance results will be analyzed to determine the optimal configuration for a typical university environment.

References:
[1] M. Gast, 802.11n: A Survival Guide, 1st ed. Sebastopol [etc.]: O'Reilly, 2012.
[2] D. Maraj and A. Maraj, "Performance analysis of WLAN 802.11g/n standards using OPNET (Riverbed) application", 2015 57th International Symposium ELMAR (ELMAR), 2015.
[3] P. Gaonkar, D. Tandur and G. Rafiq, "Range performance evaluation of IEEE 802.11n devices", 2015 IEEE International Conference on Industrial Technology (ICIT), 2015.
[4] F. Abinader, E. Almeida, S. Choudhury, V. Sousa, A. Cavalcante, F. Chaves, E. Tuomaala, R. Vieira and K. Doppler, "Performance Evaluation of IEEE 802.11n WLAN in Dense Deployment Scenarios", 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014.
[5] E. Kadir, A. Siswanto and A. Syukur, "Performance analysis of wireless LAN 802.11n standard for e-Learning", 2016 4th International Conference on Information and Communication Technology (ICoICT), 2016.

Netflix over LTE Content Distribution Network Optimization

Abstract:
In the United States and Canada alone, Netflix has north of 25 million users [1], accounting for over 30% of all downstream traffic in the US [2]. With so much traffic Netflix employs a system of servers that form a Content Distribution Network (CDN) from which the video “chunks” are cached and streamed to the user [3]. This is in an attempt to reduce the load on the network and improve the customer experience. With LTE providing throughput speeds similar to high speed internet access the demand for mobile live streamed HD video has increased [4]. We will investigate the performance of the LTE network for streaming Netflix movies to mobile devices and analyze how varying the proximity and design of Netflix's CDN, called Open Connect, impacts this performance [5]. In our analysis we will look at the bit rates of the transfers, loss of packets, and delays. We will be using Riverbed Modeler to create testing scenarios, in which we will change the number and location of the CDNs video servers servicing both stationary and moving mobile devices.

References:
[1] V. K. Adhikari, Y. Guo, F. Hao, M. Varvello, V. Hilt, M. Steiner, Z. Zhang, "Unreeling netflix: Understanding and improving multi-CDN movie delivery," in Proc. IEEE INFOCOM, Orlando, FL, 2012, pp. 1620-1628. doi: 10.1109/INFCOM.2012.6195531
[2] V. K. Adhikari, Y. Guo, F. Hao, V. Hilt, Z. Zhang, M. Varvello, M. Steiner, "Measurement Study of Netflix, Hulu, and a Tale of Three CDNs," IEEE/ACM Transactions on Networking, vol. 23, no. 6, pp. 1984-1997, Dec. 2015. doi: 10.1109/TNET.2014.2354262
[3] J. Summers, T. Brecht, D. Eager and A. Gutarin, "Characterizing the workload of a netflix streaming video server," in Proc. IEEE International Symposium on Workload Characterization (IISWC), Providence, RI, 2016, pp. 1-12. doi: 10.1109/IISWC.2016.7581265
[4] C.D. Cranor, M. Green, C. Kalmanek, D. Shur, S. Sibal, J.E. Van der Merwe, C.J. Sreenan, "Enhanced streaming services in a content distribution network," IEEE Internet Computing, vol. 5, no. 4, pp. 66-75, Jul/Aug 2001. doi: 10.1109/4236.939452
[5] T.Böttger, F. Cuadrado, G. Tyson, I. Castro, S. Uhlig, Open Connect Everywhere: A Glimpse at the Internet Ecosystem through the Lens of the Netflix CDN, eprint arXiv:1606.05519 [cs.NI], 2016, pp. 1-14.

Mobile Gaming on a 5G Network

Abstract:
We are long past the days where games were defined by titles such as Pong, Space Invaders, and Pac Man running on arcade machines the size of a refrigerator. The games of today provide a far richer experience in terms of graphics and intricacy. In addition, the computational power now packed into smart phones have enabled complex games to be run on a mobile platform you can hold in your hand.

The emergence of online multiplayer played a major role in the growth in popularity and culture within video games. As such, mobile game developers have sought out the creation of mobile online multiplayer games hoping to capitalize on their popularity. This has appeared to be a great success, with mobile multiplayer games being some of the top grossing games on most app markets. Further app development here seems inevitable, and this begs the question: How well can the existing 4G network support these games? Also, seeing the popularity and proliferation of this traffic, how well will future 5G networks support it?

To answer this question, we plan to analyze the performance and reliability of mobile online multiplayer games on a 5G network. The creation of 5G is focused on improving the shortcomings of 4G with the increase in number of users and demands of each particular user on a mobile network [1]. With 5G being a relatively new telecommunication standard we feel our analysis will give valuable insight into what mobile gamers can expect.

References:
[1] M. Krishna and J. Lloret Mauri, Advances in mobile computing and communications, 1st ed. Springer, pp. 2-4.
[2] J. Saldana and M. Suznjevic, "QoE and Latency Issues in Networked Games," Springer, Singapore, 2015.
[3] M. Gerla, D. Maggiorini, C. E. Palazzi and A. Bujari, "A survey on interactive games over mobile networks," Wireless Communications and Mobile Computing, vol. 13, no. 3, pp. 212-229, 2013.
[4] B. Rong, X. Qiu, M. Kadoch, S. Sun and W. Li, 5G heterogeneous networks, 1st ed. Springer.
[5] J. Saldana and M. Suznjevic, "Online Games:Traffic Characterization and Network Support," in IEEE consumer communications & networking conference, Las Vegas, 2014.

Performance Analysis on VoIP over WiFi

Abstract:
During these years, since the wireless communication based technology become more and more popular, improving communication speed, quality and stabilization will be very important when building this type of technology. VoIP is an essential for the delivery of voice communication and sees wide applications in office and home use. VoIP over Wifi although not as high in quality as VoIP over Ethernet is connectionless and is popularized by mobile phone applications such as NetTalk and Viber. We will be examining how delay, jitter, packet loss contribute to congestion and affect the voice quality.

References:
[1] Understanding Jitter in Packet Voice Networks (Cisco IOS Platforms) Available:http://www.cisco.com/c/en/us/support/docs/voice/voice-quality/18902-jitter-packet-voice.html.
[2] John Cox Living Legend: Vic Hayes 802.11 wireless is stimulating innovation worldwide MAY 9, 2011.Available:http://www.networkworld.com/article/2202036/wireless/living-legend--vic-hayes.html.
[3] L. Chu, "Implementation and Application of VoIP Networks, IEEE AIMSEC Conference, pp2139-2141, 2011. Available:http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6011122.
[4] Reasons for Choosing Voice over IP - VoIP Advantages https://www.lifewire.com/reasons-for-choosing-voice-over-3426719.
[5] Understanding Delay in Packet Voice Networks [Online].Available: http://www.cisco.com/c/en/us/support/docs/voice/voice-quality/5125-delay-details.html.

DDoS Attack Analysis and Prevention Measures

Abstract:
Distributed denial-of-service (DDoS) attacks are becoming a global issue to businesses nowadays. They are a constant threat to organizations and institutions by threatening service performance and shutting down websites and mail servers. DDoS attack is a type of DoS attack where multiple compromised systems are being used to target a single system. The compromised systems traffic is flooded which leads to service denial to legitimate users. There are different types of DDoS attacks such as Traffic attacks, Bandwidth attacks and Application attacks. In this project we will be simulating DDoS attack(s), using ns-2, to analyze the affect of such an assault and the damage it can cause to the services provided by a system. This will be followed by possible solutions or preventive measures, such as early detection. If time permits then simulation with the proposed fixes will be presented as well.

References:
[1] "What is DDoS - Distributed Denial of Service? Webopedia", Webopedia.com, 2017. [Online]. Available: http://www.webopedia.com/TERM/D/DDoS_attack.html. [Accessed: 13- Feb- 2017].
[2] "Distributed Denial of Service Attacks - The Internet Protocol Journal - Volume 7, Number 4", Cisco, 2017. [Online]. Available: http://www.cisco.com/c/en/us/about/press/internet-protocol-journal/back-issues/table-contents-30/dos-attacks.html. [Accessed: 13- Feb- 2017].
[3] P. Services, D. Literature and W. Papers, "Defeating DDOS Attacks", Cisco, 2017. [Online]. Available: http://www.cisco.com/c/en/us/products/collateral/security/traffic-anomaly-detector-xt-5600a/prod_white_paper0900aecd8011e927.html. [Accessed: 13- Feb- 2017].
[4] J. Zhang, P. Liu, J. He, and Y. Zhang, "A Hadoop Based Analysis and Detection Model for IP Spoofing Typed DDoS Attack," 2016 IEEE Trustcom/BigDataSE/ISPA, 2016.
[5] H. Zhang, A. Taha, R. Trapero, J. Luna, and N. Suri, "SENTRY: A Novel Approach for Mitigating Application Layer DDoS Threats," 2016 IEEE Trustcom/BigDataSE/ISPA, 2016.

Local Network Peer to Peer Simulation and Analysis

Abstract:
The idea of peer to peer was originally developed in 1999. After that the peer to peer technology was widely applied in campus, company and laboratory network system. In this project we are going to design a local area wireless peer to peer network and test its popular features which attract people to use them. To achieve the goal of this project our team will use WANET technology to build a new wireless peer to peer network environment in campus and use riverbed to simulate it to see its performance.

References:
[1] B.Leuf, Peer to Peer: Collaboration and Sharing over the Internet, Addison-Wesley Professional, June 14, 2002,pg 1-400 [Accessed: March 7, 2017]
[2] unknown , “Peer to Peer”, Wikipedia , [online], Available: https://en.wikipedia.org/wiki/Peer-to-peer [Accessed: March 7, 2017]
[3] D. Satyajeet, A. R. Deshmukh, “Heterogeneous Approaches for Cluster based Routing Protocol in Vehicular Ad Hoc Network (VANET)” [online], Available: http://www.ijcaonline.org/research/volume134/number12/satyajeet-2016-ijca-908080.pdf
[4] Morteza M. Zanjireh, Ali Shahrabi, and Hadi Larija, “nANCH: A New Clustering Algorithm for Wireless Sensor Networks” [online], Available: https://www.researchgate.net/publication/257373717_ANCH_A_New_Clustering_Algorithm_for_Wireless_Sensor_Networks
[5] D. Johnson, “The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4” [online], Available: https://tools.ietf.org/html/rfc4728

Simulation of SFU WiFi

Abstract:
The idea of simulating Wi-Fi on campus serves the purpose of better understanding the correlation between the number of users on a Wi-Fi network, and its performance based on factors such as speed and number of packets dropped. There are many students that go to SFU and certainly many of them use the Wi-Fi network known as SFUNET-SECURE. Through personal experience, we have found this network to be laggy, or hard to connect consistently to for no apparent reason. Additionally, the connection seems to be less reliable depending on the location of the user. We would like to research the reasons behind this and verify the quality of the network. Our hypothesis is that the number of students connected to the Wi-Fi network has an impact on the data transfer rate as seen by those on the Wi-Fi network. Our approach to investigating this problem will have to be manually counting and estimation of number of students in a certain area at a certain time. Aside from this, we will also be able to model the behaviour of the Wi-Fi performance as a function of time, and find out at which times the Wi-Fi at chosen areas in the institution will be fastest, and at which times the Wi-Fi will be slowest.

References:
[1] I.S.A. Dhanapala, R. Marfievici, P. Agrawal, D. Pesch, “Towards Detecting WiFi Aggregated Interference for Wireless Sensors based on Traffic Modelling,” in 2016 International Conference on Distributed Computing in Sensor Systems, Cork, Ireland, 2016, pp. 108-109
[2] S. Sagari, K. Balachandran, J.H. Kang, K. Karakayali, K.M. Rege, “Modeling and Throughput Analysis of Distributed WiFi Networks,” in 2016 IEEE International Symposium on Personal Indoor and Mobile Radio Communications - (PIMRC): Fundamentals and PHY, Murray Hill, NJ, 2016
[3] Y. Chen, G. Qian, Y. Chen, “Modeling and Simulation of Broadband Coaxial Cable Network for WIFI Signal,” IEEE Computer Group Repository
[4] Z. Peng, Y. Xie, D. Wang, Z. Dong, “One-to-All Regularized Logistic Regression-based Classification for WiFi Indoor Localization,” New York Institute of Technology, New York, NY, 2016
[5] J. Guo, X. Liu, Z. Wang, “Optimized Indoor Positioning Based on WIFI in Mobile Classroom Project,” in 2015 11th International International Conference on Natural Computation (ICNC), Shanghai, China, 2015

Performance Analysis of LTE when Streaming Video

Abstract:
Since the release of Netflix in 2007, there has been an ever increasing demand for video streaming. Since then, many other services such as Crackle, and Amazon Instant Video have popped up to supply content for the continuously growing demand for such services. On the user side of things, the overall popularity of wireless devices has created a rich topology that has enabled users to consume these services seamlessly in a mobile manner. All of this coupled together gave us the incentive to perform simulation and analysis of video streaming performance over mobile networks such as LTE. The simulation would then be performed using Riverbed Modeler due to its ability to create a variety of necessary scenarios and collect the data we need to perform a valid analysis.

References:
[1] A.-K Al-Tamimi, “Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams”, 2010.
[2] G. Van der Auwera, P. T. David, and M. Reisslein, "Traffic and quality characterization of single-layer video streams encoded with the H.264/MPEG-4 advanced video coding standard and Scalable video coding extension," IEEE Transactions on Broadcasting, vol. 54, no. 3, pp. 698–718, Sep. 2008.
[3] P. Seeling, M. Reisslein, and B. Kulapala, "Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial," IEEE Communications Surveys & Tutorials, vol. 6, no. 3, pp. 58–78, 2004.
[4] W. Hrudey and L. Trajkovic, "Streaming Video Content Over IEEE 802.16/WiMAX Broadband Access," OPNETWORK, Washington, 2008. Available: http://www2.ensc.sfu.ca/~ljilja/papers/hrudey_trajkovic_opnetwork2008_final_revised_again.pdf
[5] Riverbed Modeler software [Online]. Available: https://www.riverbed.com/products/steelcentral/opnet.html (Links to an external site.).

Voice over LTE

Abstract:
"Long Term Evolution (LTE) is the latest high speed mobile broadband technology that is gaining widespread attention due to its high data rates and improved Quality of Service (QoS)" [1]. LTE is able to support high speed data services which uses packet switched network, but to support voice services these devices switch from LTE connection to 2G/3G mobile network which uses Circuit Switched Fallback Network (CSFB).The purpose of this project is to analyse performance of Voice over LTE networks and compare it with the traditional CSFB Network. The goal is to implement LTE using OPNET, simulating the scenario and compare the advantages and disadvantages of both these technologies.

References:
[1] P. Gururaj and Raghavendrarao, "Voice over LTE," 2012.
[2] M. A. E.-S. M. M. Ayman Elnashar, "Practical Performance Analyses of Circuit Switched," IEEE Transactions on Vehicular Technology, 2016.
[3] A. A. E. Arby and O. Thiare, "Handling Voice In LTE," IEEE 2015 International Conference on Computer, Communicatoin, and Control Technology, 2015.
[4] B. Kenneth C., C. Thomas, L. D. Tewfik and B. Wim, "Public Safety Mission Critical Voice Services," Bell Labs Technical Journal, vol. 16, no. 3, pp. 133-150, 2011.
[5] P. Vaishali, "Seamless Voice over LTE,"IEEE Xplore, 2011.