TY  - JOUR
T1  - Formative Impact of Gauss Markov Mobility Model on Data Availability in MANET
AU - Kumar, R. Sathish AU - Pariselvam, S. 
JO  - Asian Journal of Information Technology
VL  - 11
IS  - 3
SP  - 108
EP  - 116
PY  - 2012
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2012.108.116
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2012.108.116
KW  - Mobile Ad hoc Networks (MANETs)
KW  -Ad hoc on-demand Distance Vector (AODV)
KW  -Dynamic Source Routing (DSR)
KW  -Quality of Service (QOS)
KW  -Authenticated Routing for Ad hoc Networks (ARAN)
KW  -Secure Efficient Ad hoc Distance vector (SEAD)
AB  - Recent advancements in wireless communication and the miniaturization 
  of computers support communications among mobile nodes connected to each other 
  by one-hop or multihop links. In Mobile Ad hoc Network (MANET), mobile nodes 
  need to share and access data held by other mobile nodes in applications such 
  as collaborative rescue operations at a disaster site, military operations and 
  sensor networks. Since, disconnections often occur, data in two separated networks 
  become inaccessible to each other. Preventing the deterioration of data availability 
  at the point of network partitioning is a very significant issue. Data replication 
  and data diffusion (dissemination) address the issue of data unavailability. 
  The results of these conventional researches have revealed that mobility heavily 
  affects data availability. The influence of mobility on data availability is 
  determined from various perspectives without using specific applications or 
  specific data replication/diffusion protocols. But several general metrics like 
  the average size of the partitions, distribution of partition sizes, the size 
  of partitions to which the node belongs, the change in size of partition to 
  which the node belongs, the total number of connected nodes to a node, the no 
  of nodes to which a node can disseminate its data and the distribution of the 
  connected nodes are analyzed to determine the impact of mobility on the data 
  availability. The values of proposed metrics are computed and compared for both 
  Gauss Markov and RPGM mobility models. From the simulation results, it is known 
  that the Gauss Markov Mobility Model works well in terms of the data storage 
  capacity and partition stability. For designing the data replication protocol 
  it is effective to share the data among the nodes in the same partition and 
  for designing the data dissemination protocol, aggressive data dissemination 
  is effective in terms of distribution rapidness.
ER  -