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.
PurposeThis study aims to evaluate the usability of the augmented reality-based Evoke Education System (EES) to improve service operations in educational settings. The EES uses an animated character (Moe) to interact with children in a classroom by reproducing their teacher's movements and speech.Design/methodology/approachThis study uses a quantitative approach for the system usability evaluation. The ESS was evaluated by 71 children aged 6–8 years old, from two primary schools. After interacting with the EES, they completed a system usability questionnaire and participated in a knowledge acquisition test.FindingsThe knowledge acquisition test undertaken on the initial day showed statistically significant improvements for children taught with the EES, compared to children taught through traditional teaching approaches. However, the retest nine days later was not statistically significant (as only one school participated) due to low power. This study used confirmatory factor analysis (CFA), resulting in the identification of five essential factors (likeability, interactiveness, retention, effectiveness/attractiveness and satisfaction) that contribute to the EES's usability. The comparison with existing literature shows that these factors are consistent with the definition of system usability provided by the International Organization for Standardization and current academic literature in this field.Research limitations/implicationsThe findings presented in this study are based on the data from only two schools. The research can be extended by involving children from a greater number of schools. Mixed methods and qualitative research approaches can be used for future research in this area to generalise the results.Originality/valueThis study proposes an innovative augmented reality-based education system to help teachers deliver their key messages to the children in a fun way that can potentially increase their knowledge retention.
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