A comparison of Mamdani and Sugeno fuzzy based packet scheduler for MANET with a realistic wireless propagation model

Abstract

The mobile nature of the nodes in a wireless mobile ad-hoc network (MANET) and the error prone link connectivity between nodes pose many challenges. These include frequent route changes, high packet loss, etc. Such problems increase the end-toend delay and decrease the throughput. This paper proposes two adaptive priority packet scheduling algorithms for MANET based on Mamdani and Sugeno fuzzy inference system. The fuzzy systems consist of three input variables: data rate, signal-to-noise ratio (SNR) and queue size. The fuzzy decision system has been optimised to improve its efficiency. Both fuzzy systems were verified using the Matlab fuzzy toolbox and the performance of both algorithms were evaluated using the riverbed modeler (formally known as OPNET modeler). The results were compared to an existing fuzzy scheduler under various network loads, for constant-bit-rate (CBR) and variable-bit-rate (VBR) traffic. The measuring metrics which form the basis for performance evaluation are end-to-end delay, throughput and packet delivery ratio. The proposed Mamdani and Sugeno scheduler perform better than the existing scheduler for CBR traffic. The end-to-end delay for Mamdani and Sugeno scheduler was reduced by an average of 52 % and 54 %, respectively. The performance of the throughput and packet delivery ratio for CBR traffic are very similar to the existing scheduler because of the characteristic of the traffic. The network was also at full capacity. The proposed schedulers also showed a better performance for VBR traffic. The end-to-end delay was reduced by an average of 38 % and 52 %, respectively. Both the throughput and packet delivery ratio (PDR) increased by an average of 53 % and 47 %, respectively. The Mamdani scheduler is more computationally complex than the Sugeno scheduler, even though they both showed similar network performance. Thus, the Sugeno scheduler is more suitable for real-time applications.

Fuzzy Rule-Based and Particle Swarm Optimisation MPPT Techniques for a Fuel Cell Stack

“…Fuzzy Inference Systems are based on two methods: the Mamdani Fuzzy Inference technique [21,[35][36][37][38][39][40] and the Takagi-Sugeno-Kang Inference method [35,36,38,39,[41][42][43][44][45][46][47][48]. The major difference between them lies in the consequent fuzzy rules and defuzzification procedures; the Mamdani Inference method uses fuzzy sets as rule consequent, while Sugeno Inference considers linear functions of input variables.…”

Optimization of quality of service parameters for efficient channel allocation

“…The FLC has calculated the crisp value of priority index as output which was used for final packet allocation. They also proposed two adaptive priority packet scheduling algorithms based on Mamdani (APPS-M) and Sugeno adaptive fuzzy logic (APPS-S) [18]. C.Gomathy and S.Shanmugavel [19] proposed a fuzzy based scheduler for scheduling the packets based on its priority index.…”

“…This shows that the proposed algorithm with parameter set (b) has the shortest delay. In Figure 10, the throughput (%) comparison of proposed algorithm with parameter sets (a), (b) and (c) is done with the algorithms CCF, PCCP, PBRC-SD [17], NH-CCS, APPS-M and APPS-S [18] and No priority, Priority, Weighted hops, Round robin and Greedy [20]. The throughput (percentage) is the best for the proposed algorithm amongst the compared algorithms.…”

“…The loss probability of proposed algorithm is higher than the average loss probabilities of the algorithms considered. The comparison of the Packet Delivery Ratio of the proposed algorithm with parameter sets (a), (b) and (c) with the algorithms NH-CCS, APPS-M and APPS-S [18] and NH-CCS [15] and PBS [19] is shown in Figure 12. It shows that the packet delivery ratio is second best after PBS algorithm.…”

Adaptive Sampling Technique for Computer Network Traffic Parameters Using a Combination of Fuzzy System and Regression Model

“…The performance of a network based on crisp logic can either be good or bad. However, in fuzzy logic, a network can simultaneously be a member of good and bad performance sets with different degrees of memberships [6]. Fuzzy Inference System (FIS) is an implementation of fuzzy logic where numeric inputs are first fuzzified into linguistic terms then through inferencing decisions are made by comparing the fuzzified inputs with the data that are coded (for example using a number of IF-THEN rules) in the knowledge base and finally the outputs are determined by defizzifying the linguistic results into numeric form [7] [8].…”