Wireless communication in a network of mobile devices is a challenging and resource-demanding task, due to the highly dynamic network topology and the wireless channel randomness. This paper investigates information broadcast schemes in 2-D mobile ad hoc networks where nodes are initially randomly distributed and then move following a random direction mobility model. Based on an in-depth analysis of the popular susceptible-infectious-recovered epidemic broadcast scheme, this paper proposes a novel energy and bandwidth-efficient broadcast scheme, named the energy-efficient broadcast scheme, which is able to adapt to fast-changing network topology and channel randomness. Analytical results are provided to characterize the performance of the proposed scheme, including the fraction of nodes that can receive the information and the delay of the information dissemination process. The accuracy of analytical results is verified using simulations driven by both the random direction mobility model and a real-world trace.
In @cite_18 , studied the speed of information propagation in a mobile network where nodes move independently at random over a square area. They obtained an upper bound on the flooding time, which is the maximum time required for all nodes of the network to be informed. In @cite_13 , studied the information propagation speed in mobile networks where nodes are uniformly distributed in a bounded area. The nodes were assumed to move following an i.i.d. random trajectory. An upper bound on the information propagation speed, viz. ratio of the distance traveled by information over a given amount of time, was obtained.
@cite_18 @cite_13
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We consider a Mobile Ad-hoc NETworks (MANET) formed by "n" nodes that move independently at random over a finite square region of the plane. Nodes exchange data if they are at distance at most "r" within each other, where r>0 is the node transmission radius. The "flooding time" is the number of time steps required to broadcast a message from a source node to every node of the network. Flooding time is an important measure of the speed of information spreading in dynamic networks. We derive a nearly-tight upper bound on the flooding time which is a decreasing function of the maximal "velocity" of the nodes. It turns out that, when the node velocity is sufficiently high, even if the node transmission radius "r" is far below the "connectivity threshold", the flooding time does not asymptotically depend on "r". This implies that flooding can be very fast even though every "snapshot" (i.e. the static random geometric graph at any fixed time) of the MANET is fully disconnected. Data reach all nodes quickly despite these ones use very low transmission power. Our result is the first analytical evidence of the fact that high, random node mobility strongly speed-up information spreading and, at the same time, let nodes save energy. We consider Mobile Ad-hoc NETworks (MANETs) formed by n nodes that move independently at random over a finite square region of the plane. Nodes exchange data if they are at distance at most r within each other, where r > 0 is the node transmission radius . The flooding time is the number of time steps required to broadcast a message from a source node to every node of the network. Flooding time is an important measure of the speed of information spreading in dynamic networks. We derive a nearly-tight upper bound on the flooding time which is a decreasing function of the maximal velocity of the nodes. It turns out that, when the node velocity is "sufficiently" high, even if the node transmission radius r is far below the connectivity threshold , the flooding time does not asymptotically depend on r . So, flooding can be very fast even though every snapshot (i.e. the static random geometric graph at any fixed time) of the MANET is fully disconnected. Our result is the first analytical evidence of the fact that high, random node mobility strongly speed-up information spreading and, at the same time, let nodes save energy . In this paper, we study information propagation in a 2D mobile ad-hoc network, where mobile nodes are randomly and independently distributed on a torus following a homogeneous Poisson process with a given density. Nodes in the network move following a random direction mobility model. A piece of information is broadcast from a source node to all other nodes in the network, using a Susceptible-Infectious-Recovered (SIR) epidemic routing protocol. A distinguishing feature of the SIR algorithm, which leverages the mobility of mobile users, is that a relay node carries and forwards a piece of information for a specified amount of time. We first propose a metric fundamentally characterizing the information propagation in mobile ad-hoc networks. Then analytical results are derived for the probability that a non-zero fraction of nodes receive the information in the limit of large network size and for the expected fraction of nodes that receive the information. The analytical results are verified using simulations. The research provides useful insights on the design of mobile ad-hoc networks. The goal of this paper is to increase our under- standing of the fundamental performance limits of mobile and Delay Tolerant Networks (DTNs), where end-to-end multihop paths may not exist and communication routes may only be available through time and mobility. We use analytical tools to derive generic theoretical upper bounds for the information propagation speed in large scale mobile and intermittently con- nected networks. In other words, we upper-bound the optimal performance, in terms of delay, that can be achieved using any routing algorithm. We then show how our analysis can be applied to specific mobility models to obtain specific analytical estimates. In particular, in 2-D networks, when nodes move at a maximum speed and their density is small (the network is sparse and asymptotically almost surely disconnected), we prove that the information propagation speed is upper bounded by in random waypoint-like models, while it is upper bounded by for other mobility models (random walk, Brownian motion). We also present simulations that confirm the validity of the bounds in these scenarios. Finally, we generalize our results to 1-D and 3-D networks.