Capacity and delay scaling for broadcast transmission in highly mobile wireless networks

Abstract

We study broadcast capacity and minimum delay scaling laws for highly mobile wireless networks, in which each node has to disseminate or broadcast packets to all other nodes in the network. In particular, we consider a cell partitioned network under the simplified independent and identically distributed (IID) mobility model, in which each node chooses a new cell at random every time slot. We derive scaling laws for broadcast capacity and minimum delay as a function of the cell size. We propose a simple first-come-first-serve (FCFS) flooding scheme that nearly achieves both capacity and minimum delay scaling. Our results show that high mobility does not improve broadcast capacity, and that both capacity and delay improve with increasing cell sizes. In contrast to what has been speculated in the literature we show that there is (nearly) no tradeoff between capacity and delay. Our analysis makes use of the theory of Markov Evolving Graphs (MEGs) and develops two new bounds on flooding time in MEGs by relaxing the previously required expander property assumption.

Low-Latency Networking: Where Latency Lurks and How to Tame It

“…The delay of the 2-hop relay algorithm with redundancy is less than that without redundancy, but will cause decrease to the capacity. More recently, in [103] the scaling of capacity and delay for broadcast transmission in a highly mobile cell partitioned network was studied. Using an independent mobility model as in [104] it was shown that, in a dense wireless network (number of nodes per cell increases with n), the broadcast capacity scales as 1/n, while the delay scales as log log n. Surprisingly, it is also shown that both throughput and delay have worse scaling when the network is sparse.…”

Capacity and Delay Scaling for Broadcast Transmission in Highly Mobile Wireless Networks

“…with probability at least 1 − c 5 N for some c 5 > 0. Detailed arguments are given in the technical report [24].…”

“…with probability at least 1 − c 2 N for some c 2 > 0. Detailed arguments are given in the technical report [24]. Figure 3 compares the high probability upper bound with the average lower-bound on ooding time T N from eorem 2.2.…”

“…Proof: Due to space constraints we do not list the proof here. See the technical report [24] for a detailed proof. L B.2.…”

“…Proof: See [24] for a detailed proof. From Lemma B.2, we note that E [N (h)] → ∞ as N → ∞ for all βN α −1 log N ≤ h ≤ N /2.…”