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The simplest broadcasting scheme in the mobile ad hoc networks (MANETs) uses flooding which may result in the broadcast storm problem. The MultiPoint Relays (MPR) is another broadcasting scheme for solving the broadcast storm problem. In MPR, the mobile hosts (MHs) use a greedy algorithm to find a connected dominating set (CDS) by the exchange of 2-hop neighbor information. In order to reduce the size of CDS, the greedy algorithm used by MPR usually selects the farthest nodes from the source called the border nodes as the forwarding nodes. Since the border nodes have a higher probability of moving out the transmission range, the routing paths in MPR from the source to some destinations may be unstable. In this paper, we propose a broadcasting scheme called Dynamic Power-aware and Stability-aware MultiPoint Relays (DPS-MPR) which avoids selecting the border nodes as the forwarding nodes. As a result, the transmission range of MHs can be reduced for saving energy and the negative impact of unstable forwarding nodes can also be reduced. In addition, we use a range buffer to further enhance the stability of the forwarding nodes. We evaluate the performance of the proposed DPS-MPR by using NS2 and compared it with the existing schemes. The experimental result shows that DPS-MPR saves 20%~25% of energy and increases the lifetime of forwarding nodes by several seconds.

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Various ad hoc routing algorithms were proposed to increase the performance in ad hoc network. In order to estimate the performance of routing algorithms, they always simulate in ad hoc network environment, and a mobility model is also used for network simulation. There are two types of mobility models: trace and synthetic models. Trace models are more accurate than synthetic ones, and their simulation results are more accurate, too. However, new routing network environments are not easily modeled if traces have not yet been created. That is why most of the simulations are based on synthetic models.In this paper, a tool is implemented for generating a special trace model, called ant mobility model. It mimics the ants' movements of an ant colony. The movement is similar to the workers in a store. We focus on the integration between the ant mobility model and the network environment. When users setup the parameters for generating an ant mobility model, the generated model can be used directly in the network environment. In order to illustrate the feasibility of ant mobility model, it is compared with the random waypoint mobility model based on the same routing algorithm, AODV, by using NS-2 simulator. Three major performance metrics, throughput, network latency, and control overhead message, are estimated. As we expected, these three metrics of the ant mobility model are better than that of the random waypoint mobility model. We suggest that a trace model, like ant mobility model, should be integrated quickly with the network environment in the same way.