# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Internet Bandwidth Control  
IT2 controllers are not limited to controlling autonomous robots. In a paper by [PERSON] _et al._, an IT2 FLC was used for congestion control across networks [58]. In this specific case, the video streaming quality was measured. This is difficult, as the bit rate must be adjusted in order to account for network traffic volatility. In earlier research, a T1 FLC is capable of reducing packet loss and rate fluctuations; however, to cope with the uncertainty of the network traffic, an IT2 FLC was designed to demonstrate the difference. The two testing methods used were streaming a video during Internet cross-traffic and streaming a video with other videos running at the same time. The proposed IT2 FLC showed a significant performance boost over the IT1 FLC. Furthermore, when subjected to random noise, the IT2 FLC again showed a performance boost. From these results, it can be seen that an IT2 FLC is capable of optimizing congestion control during video streaming during an All-Internet Protocol network.  
A similar IT2 FLC controller was developed and implemented for Voice over Internet Protocol (VoIP) [59]. This is due to the real-time delivery requirements of VoIP and the uncertainty of network degradation. The controller infers a network state based on the average delivered perceived quality of service along with the degradation from increased network congestion. From this information, the IT2 FLC updates an adaptive multi-rate codec in real time. The codec is updated to match the network state and the voice quality. This controller was then tested, and it was shown to outperform the existing state-of-the-art scheme implemented.  
Broadband wireless Internet Protocol Television (IPTV) applications were also investigated [60]. The IT2 FLC adjusted the bit rate every frame based on the packet delay. This was compared to conventional controllers, such as TCP-friendly rate control (TFRC) and TCP emulation at receivers (TEAR) congestion controllers. This was tested by streaming more video traffic than the network could handle, which in some cases was up to 50 video streams. During an overloaded network, the IT2 FLC improved the quality by approximately 1 dB per client.  
In [61], the author used FLCs to adjust prices of bandwidth per one unit based on the demand and availability of bandwidth. It was concluded that the fuzzy logic pricing controller sets the price for the aggregated Obtainable Forwarding (OF) traffic demand at the boundary note of the DiffServ Network.  
Improving wireless sensor networks is a challenge in order to provide continuous connectivity in _ad hoc_ networks. To measure the performance of a wireless sensor network, two parameters can be used, which are latency and energy efficiency [62]. In [62], two contributions were proposed; the first was a role-based mobility-aware interference mechanism to determine the main characteristics of a wireless network's performance, such a, energy, distance, speed, _etc._; the second contribution was for the decision making process using T2 FLS. The results showed that an optimal energy balance for the network can be guaranteed for a life time.