Simulating mission critical mobile ad hoc networks

In this paper we present a mobility model for ad hoc networks operating in mission critical situations, like for example natural or man-made disasters, military activities or emergency healthcare services. The proposed model captures the properties of mobility in situations like the above by incorporating hierarchical node organisation, typical for such scenarios modes of node activity, event-based destination selection and presence of physical obstacles that affect both the node movement and the signal propagation. The nodes are divided into groups with each group leader responsible for choosing the destination points. These choices resemble the events that occur in the network deployment area and the corresponding missions that are assigned to the node groups. The proposed model includes two modes of node activity that represent the two types of nodes primarily comprising such networks: the emergency workers and the medical staff. Each event belongs to a certain class, according to which reinforcements are called to provide further assistance. The conducted simulation study highlights the differences between the proposed model and other mobility models, by investigating their properties in terms of the resulting network topology and their impact on the performance of an ad hoc network operating under a well known routing protocol.

[1]  Ahmed Helmy,et al.  A SURVEY OF MOBILITY MODELS in Wireless Adhoc Networks , 2004 .

[2]  Ioannis Chatzigiannakis,et al.  TRAILS, a Toolkit for Efficient, Realistic and Evolving Models of Mobility, Faults and Obstacles in Wireless Networks , 2008, 41st Annual Simulation Symposium (anss-41 2008).

[3]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[4]  Xiaoyan Hong,et al.  A group mobility model for ad hoc wireless networks , 1999, MSWiM '99.

[5]  Kevin C. Almeroth,et al.  Real-world environment models for mobile network evaluation , 2005, IEEE Journal on Selected Areas in Communications.

[6]  Charles E. Perkins,et al.  Ad hoc On-Demand Distance Vector (AODV) Routing , 2001, RFC.

[7]  Louise E. Moser,et al.  An analysis of the optimum node density for ad hoc mobile networks , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).

[8]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[9]  Christian Wagner,et al.  The Spatial Node Distribution of the Random Waypoint Mobility Model , 2002, WMAN.

[10]  Vanessa Ann Davies,et al.  EVALUATING MOBILITY MODELS WITHIN AN AD HOC NETWORK , 2000 .

[11]  Tasos Dagiuklas,et al.  An obstacle-aware human mobility model for ad hoc networks , 2009, 2009 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems.

[12]  Öznur Özkasap,et al.  A Classification and Performance Comparison of Mobility Models for Ad Hoc Networks , 2006, ADHOC-NOW.

[13]  Hamid R. Rabiee,et al.  MobiSim: A Framework for Simulation of Mobility Models in Mobile Ad-Hoc Networks , 2007 .

[14]  J. Broch,et al.  Dynamic source routing in ad hoc wireless networks , 1998 .

[15]  Jörg Hähner,et al.  Graph-based mobility model for mobile ad hoc network simulation , 2002, Proceedings 35th Annual Simulation Symposium. SS 2002.

[16]  Gang Lu,et al.  A Novel Environment-Aware Mobility Model for Mobile Ad Hoc Networks , 2005, MSN.

[17]  Christian Bettstetter,et al.  Smooth is better than sharp: a random mobility model for simulation of wireless networks , 2001, MSWIM '01.

[18]  Paolo Santi,et al.  The Node Distribution of the Random Waypoint Mobility Model for Wireless Ad Hoc Networks , 2003, IEEE Trans. Mob. Comput..

[19]  Pietro Manzoni,et al.  ANEJOS: a Java based simulator for ad hoc networks , 2001, Future Gener. Comput. Syst..

[20]  Mingyan Liu,et al.  Random waypoint considered harmful , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[21]  Ahmed Helmy,et al.  A survey of mobility modeling and analysis in wireless adhoc networks , 2004 .

[22]  Hamid Sarbazi-Azad,et al.  Analysis of Time-Based Random Waypoint Mobility Model for Wireless Mobile Networks , 2007, Fourth International Conference on Information Technology (ITNG'07).

[23]  Gianluca Rossi,et al.  MOMOSE: a mobility model simulation environment for mobile wireless ad-hoc networks , 2008, SimuTools.

[24]  Jamal N. Al-Karaki,et al.  A New Realistic Mobility Model for Mobile Ad Hoc Networks , 2007, 2007 IEEE International Conference on Communications.

[25]  Rabindranath Nandi,et al.  An Obstacle Based Realistic Ad-Hoc Mobility Model for Social Networks , 2006, J. Networks.

[26]  Mahmood Fathy,et al.  Obstacle Mobility Model Based on Activity Area in Ad Hoc Networks , 2007, ICCSA.

[27]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for PCS networks , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[28]  Emmanouel A. Varvarigos,et al.  Implementing distributed multicost routing in mobile ad hoc networks using dsr , 2008, MobiWac '08.

[29]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[30]  Lixia Zhang,et al.  Shared tree wireless network multicast , 1997, Proceedings of Sixth International Conference on Computer Communications and Networks.

[31]  Zygmunt J. Haas,et al.  A new routing protocol for the reconfigurable wireless networks , 1997, Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications.

[32]  Ahmed Helmy,et al.  The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks , 2003, Ad Hoc Networks.