A group mobility model for ad hoc wireless networks
MSWiM '99 Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
ANEJOS: a java based simulator for ad hoc networks
Future Generation Computer Systems
Research Issues in Ad-Hoc Distributed Personal Networking
Wireless Personal Communications: An International Journal
Pocket switched networks and human mobility in conference environments
Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking
Personal Network (PN) Applications
Wireless Personal Communications: An International Journal
A community based mobility model for ad hoc network research
REALMAN '06 Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality
A Self-organized Personal Network Architecture
ICNS '07 Proceedings of the Third International Conference on Networking and Services
Mobility Modeling and Performance Evaluation of Heterogeneous Wireless Networks
IEEE Transactions on Mobile Computing
Clustering in Ad Hoc Personal Network Formation
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Stationary Distributions for the Random Waypoint Mobility Model
IEEE Transactions on Mobile Computing
Perceived Quality Measurement Model Supporting Full Session Mobility in Multimedia Service Delivery
Wireless Personal Communications: An International Journal
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A Personal Network (PN) is a user-centric design interconnecting numerous devices belonging to a user in different geographic locations, such as home, office, car, etc., to form a single global network to offer fully personalized services. In PNs devices of the user move in different groups, where these groups merge and split. In this paper, we design and simulate a PN Mobility Model (PNMM) to capture the characteristics of movements of devices in PNs. We propose a simple stability evaluation method for group mobility models and apply the method to PNMM. Through the stability evaluation, we find that PNMM possesses long-term steady state behavior. Moreover, for the evaluation of mobility models, some evaluation methods have been proposed, which include non-homogenous node mobility, relative node mobility in a group, and dynamics of group merging and splitting. Analysis shows that PNMM is better than other models to represent the PN mobility properties. In addition, the impact of PNMM on the performance of a PN Clustering Protocol (PNCP) has been studied. Simulation results provide insights into the performance of PNCP, and provide guidelines for future design of PN clustering. PNMM can be easily applied to any Personal Area Network or Body Area Network with slight modifications.