摘要
Mobile Ad Hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without the aid of any established infrastructure. To conduct meaningful performance analysis of MANETs, it is essential that the simulation of mobility model should reflect the realistic mobility pattern of mobile nodes i.e. placement of mobile nodes at different intervals of time. The formation of spontaneous network depends heavily on the movement of different nodes in a particular practical scenario. This research focuses on the modeling and simulation of a temporal Adaptive Mobility Model which can be adapted to any dynamic practical scenario. The mobility in the realistic environment is simulated based on a Probability Transition Matrix named as Personal Behavior Model (PBM) and validated for a practical Health Care Environment. The formation of MANET is assumed to be based on the movement of the patient i.e. mobile nodes in the health care environment. Patients waiting in front of each service point for different time intervals are taken as results and compared with the actual data.
Mobile Ad Hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without the aid of any established infrastructure. To conduct meaningful performance analysis of MANETs, it is essential that the simulation of mobility model should reflect the realistic mobility pattern of mobile nodes i.e. placement of mobile nodes at different intervals of time. The formation of spontaneous network depends heavily on the movement of different nodes in a particular practical scenario. This research focuses on the modeling and simulation of a temporal Adaptive Mobility Model which can be adapted to any dynamic practical scenario. The mobility in the realistic environment is simulated based on a Probability Transition Matrix named as Personal Behavior Model (PBM) and validated for a practical Health Care Environment. The formation of MANET is assumed to be based on the movement of the patient i.e. mobile nodes in the health care environment. Patients waiting in front of each service point for different time intervals are taken as results and compared with the actual data.
