The Speed of Information Propagation in Large Wireless Networks

2013 Proceedings IEEE INFOCOM

2013

Vehicular ad hoc network (VANET) is one of the most promising large-scale applications of mobile ad hoc networks. VANET applications are rooted in advanced understanding of communication networks because both control messages and data information need to be disseminated in geographic regions (i.e., Geocast). The challenges come from highly dynamic environments in VANET. Destination nodes in geocast are dynamic over time due to vehicle mobility, which undermines existing results on dissemination latency and information propagation speed with pre-determined destinations. Moreover, the affected area by the dissemination, which is referred to as horizon of message (HOM), is critical in geocast as it determines the latency for the message reaching nodes inside the area of interest (AOI), in which the message is relevant to drivers. Therefore, we characterize the HOM in geocast by how far the message can reach within time t (referred as dissemination distance) and how long the message takes to inform nodes at certain locations (referred as hitting time). Analytic bounds of dissemination distance and hitting time are derived under four types of dissemination mechanisms, which provide insights into the spatial and temporal limits of HOM as well as how the numbers of disseminators and geographic information exchanges affect them. Applying analytic and simulation results to two real applications, we observe that geocast with AOI near the source or high reliability requirement should recruit multiple disseminators while geocast with AOI far from the source need to utilize geographic information for fast message propagation.

“…In other words, geocast is upper bounded by a constant propagation speed, which is consistent with existing results in [5,6,17].…”

Section: Theorem 2 E(|d(t)|)supporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Horizon on the move: Geocast in intermittently connected vehicular ad hoc networks

2013 Proceedings IEEE INFOCOM

2013

“…The former interference can be characterized by the wellknown protocol model [24], which has been widely adopted in homogeneous networks [9], [14], [17], [24]. Particularly, without interference with primary users, a successful transmission from a mobile secondary user v i to v j is achievable at time t if v i (t)−v j (t) ≤ r and for any other simultaneously transmitting node on the same channel…”

Section: A Network Modelsmentioning

confidence: 99%

“…The pioneering work in [13], [14] studied the packet latency for the fully connected wireless ad-hoc networks and showed that there exist bounds on the latency which are tight when the number of nodes is large enough. Instead of full connectivity, some studies [15], [16] further showed that the latency scales asymptotically at least linearly with the transmission distance in wireless sensor networks when these networks are percolated.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Network Size and Mobility on Information Delivery in Cognitive Radio Networks

Sun

IEEE Trans. on Mobile Comput.

2016

There have been extensive works on the design of opportunistic spectrum access and routing schemes to improve spectrum efficiency in Cognitive Radio Networks (CRNs), which becomes an integral component in the future communication regime. Nonetheless, the potentials of CRNs in boosting network performance yet remain to be explored to reach the full benefits of such a phenomenal technique. In this paper, we study the end-to-end latency in CRNs in order to find the sufficient and necessary conditions for real-time applications in finite networks and large-scale deployments. We first provide a general mobility framework which captures most characteristics of the existing mobility models and takes spatial heterogeneity into account. Under this general mobility framework, secondary users are mobile with an mobility radius α, which indicates how far a mobile node can reach in spatial domain. We find that there exists a cutoff point on α, below which the latency has a heavy tail and above which the tail of the latency is bounded by some Gamma distributions. As the network grows large, the latency is asymptotically scalable (linear) with respect to the dissemination distance (e.g., the number of hops or Euclidean distance). An interesting observation is that although the density of primary users adversely impacts the expected latency, it makes no influence on the dichotomy of the latency tail in finite networks and the linearity of latency in large networks. Our results encourage CRN deployment for real-time and large applications, when the mobility radius of secondary users is large enough.

“…The work in [7], [8] studied the dissemination latency in fully connected multi-hop wireless networks and showed that there exist bounds on the latency and these bounds are tight when the number of nodes are large enough. The work in [9], [10] further showed that the asymptotic latency scales at least linearly with the transmission distance in the percolated wireless networks.…”

Section: Introductionmentioning

confidence: 99%

On the Dissemination Latency of Cognitive Radio Networks under General Node Mobility

Sun

2011 IEEE International Conference on Communications (ICC)

2011

Dissemination latency is critical to the applications of cognitive radio networks, which have become an important component of current communication infrastructure. This paper investigates the distribution of dissemination latency in a cognitive radio network where licensed users (primary users) are static and cognitive radio users (secondary users) are moving under general mobility, which provides fundamental understanding of the fastest information delivery that a mobile cognitive radio network is able to accommodate. We show that the dissemination latency depends on the stationary spatial distribution and mobility capability α (characterizing the region that a mobile secondary user can reach) of secondary users. Given any stationary spatial distribution, we find that there exists a critical value on α, below which the latency is heavy-tailed and above which the right tail of the distribution is bounded by some Gamma random variable. Moreover, we find that although the traffic and spatial density of primary users inversely impact the expected latency, they make no influence on the tail distribution of the latency. The results in this paper are validated through numerical studies and can advance our understanding of CR network performance.