Random Way Point modibility model: use files in Random_Waypoint. Entry point: test_Execute.m For more information: http://www​.mathworks​.com/matla​bcentral/f​ileexchang​e/30939-ra​ndom-waypo​int-mobili​ty-model --> ./mobility​_track_inp​ut/vs_node​_50_7_24.m​at

Random Way Point modibility model: use files in Random_Waypoint. Entry point: test_Execute.m
For more information: http://www.mathworks.com/matlabcentral/fileexchange/30939-random-waypoint-mobility-model
--> ./mobility_track_input/vs_node_50_7_24.mat

Use 'genMobileData.m' for other input generation and data pre-processing:
* process the 1-hop neighbors -> encMat_50_7_24.mat
* number of service requests in queue 
* number of concurrent requests 
* cost/reward
--> 20000 results will be generated and stored in allData60_20000.mat
* malicious nodes -> malNode1.mat
* distributing data to each nodes
--> accHist_50_7_24.mat

Use 'preprocess_accHist.m' for preprocessing data based on recommendation attacks
--> aggrHist_mal***_50_7_24.mat

Use 'genServiceHistory.m'
* binary service satisfaction
--> 'servBin_50_7_24.mat'
* similarity for nodes (used for Adaptive trust) 
--> sim_adaptive.mat

NOTE: you should manually input your \beta_j @gt_weight

For Beta Reputation:
Binary observations are accumulated by 'preprocess_accHist.m'
--> numInd_mal***_50_7_24.mat

[Walter Roberson]

Please explain the difficulty you are observing. How would we be able to tell whether the output was correct for your needs or not?