You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Opportunistic mobile ad hoc networks (MANETs) are a special class of sparse and disconnected MANETs where data communication exploits sporadic contact opportunities among nodes. We consider opportunistic MANETs where nodes move independently at random over a square of the plane. Nodes exchange data if they are at a 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 how fast information can spread in dynamic networks. We derive the first upper bound on the flooding time, which is a decreasing function of the maximal speed of the nodes. The bound holds with high probability, and it is nearly tight. Our bound shows that, thanks to node mobility, even when the network is sparse and disconnected, information spreading can be fast.

[2]: 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.
Different from the aforementioned two studies on the information propagation speed (i.e. [1] [2] ), this paper focuses on analysing the fraction of nodes that can receive the information. We choose to focus on the fraction of recipients nodes because it is a key performance metric for information broadcast in a network using the SIR scheme. Compared with the Susceptible-Infectious (SI) forwarding scheme used in [1] [2] , using the SIR scheme a relay node does forward the received packets indefinitely, which reflects the real world scenarios where mobile devices usually have limited energy supply and or buffer size. On the other hand, unlike the SI-like schemes, the SIR scheme does not guarantee that the information can be received by in a network, and consequently the fraction of recipients becomes a key performance metric, which is studied in this paper.