Time series: theory and methods
Time series: theory and methods
Time Series Models for Internet Data Traffic
LCN '99 Proceedings of the 24th Annual IEEE Conference on Local Computer Networks
Topology properties of Ad hoc networks with random waypoint mobility
ACM SIGMOBILE Mobile Computing and Communications Review
Recent advances in mobility modeling for mobile ad hoc network research
ACM-SE 42 Proceedings of the 42nd annual Southeast regional conference
Application of a network dynamics analysis tool to mobile ad hoc networks
Proceedings of the 9th ACM international symposium on Modeling analysis and simulation of wireless and mobile systems
Ad-hoc Networks: Fundamental Properties and Network Topologies
Ad-hoc Networks: Fundamental Properties and Network Topologies
IEEE/ACM Transactions on Networking (TON)
Time series models for internet traffic
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
Temporal characteristics of clustering in mobile ad hoc network
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence it is very suitable for temporary communication links in an emergency situation. This flexibility however is achieved at a price of communication hazard induced due to frequent topology changes. In this article we have tried to identify the system dynamics using the proven concepts of time series modeling. Here we have analyzed variation of the number of neighbor nodes of a particular node over a fixed area and for a fixed number of nodes (i) for different values of speed of nodes, (ii) the transmission power, (iii) for different sampling period (iv) for different mobility patterns. We have considered three different mobility models - (i) Gaussian mobility model, (ii) Random walk mobility model and (iii) Random Way Point mobility model. The number of neighbor nodes of a particular node behaves as a random variable for any mobility pattern. Through our analysis we found that this variation can be well modeled by an autoregressive AR(p) model. The values of p are evaluated for different scenario and we found that the value is in the range of 1 to 5. Moreover we also investigated the relationship between the speed and the time of measurement, and transmission range of a specific node under various mobility patterns.