In this paper we develop a performance modeling technique for analyzing the time varying performance of mobile ad hoc networks. Our approach is a novel hybrid of discrete event simulation and numerical method based queueing analysis. Network queues are modeled using fluid-flow based differential equation models which are solved using numerical methods, while node mobility is modeled using an adjacency matrix topology representation whose values are determined via discrete event simulation techniques. Numerical results are given illustrating the approach.

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Recently, we have proposed an energy saving Gossip-based Sleep Protocol (GSP) [1] for energy conservation in mobile ad hoc networks. A gossiping node employs a random variable (p) to decide whether or not to enter a sleep mode. Our observation is that in a well connected ad hoc network there are usually many paths existing between a source and a destination, so a percentage (p) of the nodes may be in an energy conserving sleep mode without losing network connectivity. However, putting nodes into a sleep mode may interrupt the ongoing traffic. This can result in a long packet delay and a large packet loss rate. In this paper, we extend GSP to consider the ongoing traffic before changing the radio mode of a node. We term it Traffic-aware GSP (T-GSP) and show its advantages through simulation studies.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>

In this paper, we propose a novel energy saving scheme, termed the Gossip-based Sleep Protocol (GSP). With GSP, each node randomly goes to sleep for some time with gossip sleep probability p. When the value of p is small enough, the network stays connected. GSP does not require a wireless node to maintain the states of other nodes. It requires few operations and scales to large networks. We propose two versions of GSP, one for synchronous networks and one for asynchronous networks, and show the advantages of the GSP approach through both simulations and analysis.

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