On the Delay of Reactive-Greedy-Reactive Routing in Unmanned Aeronautical Ad-hoc Networks

AbstractReactive-Greedy-Reactive (RGR) has been proposed as a promising routing protocol in highly mobile density-variable Unmanned Aeronautical Ad-hoc Networks (UAANETs). In RGR, location information of Unmanned Aerial Vehicles (UAVs) as well as reactive end-to-end paths are employed in the routing process. It had already been shown that RGR outperforms existing routing protocols in terms of packet delivery ratio. In this paper, the delay performance of RGR is evaluated and compared against Ad-hoc On-demand Distance Vector (AODV) and Greedy Geographic Forwarding (GGF).We considerextensive simulation scenariostocover both searchingand tracking applicationsofUAANETs. The results illustrate that when the number of UAVs is high enough in a searching mission to form a connected UAANET, RGR performs well. In sparsely connected searching scenarios or dense tracking scenarios, RGR may also slightly decrease delay compared to traditional reactive routing protocols for similar PDR

Practical Distance Functions for Path-Planning in Planar Domains

Path planning is an important problem in robotics. One way to plan a path between two points $x,y$ within a (not necessarily simply-connected) planar domain $\Omega$, is to define a non-negative distance function $d(x,y)$ on $\Omega\times\Omega$ such that following the (descending) gradient of this distance function traces such a path. This presents two equally important challenges: A mathematical challenge -- to define $d$ such that $d(x,y)$ has a single minimum for any fixed $y$ (and this is when $x=y$), since a local minimum is in effect a "dead end", A computational challenge -- to define $d$ such that it may be computed efficiently. In this paper, given a description of $\Omega$, we show how to assign coordinates to each point of $\Omega$ and define a family of distance functions between points using these coordinates, such that both the mathematical and the computational challenges are met. This is done using the concepts of \emph{harmonic measure} and \emph{$f$-divergences}. In practice, path planning is done on a discrete network defined on a finite set of \emph{sites} sampled from $\Omega$, so any method that works well on the continuous domain must be adapted so that it still works well on the discrete domain. Given a set of sites sampled from $\Omega$, we show how to define a network connecting these sites such that a \emph{greedy routing} algorithm (which is the discrete equivalent of continuous gradient descent) based on the distance function mentioned above is guaranteed to generate a path in the network between any two such sites. In many cases, this network is close to a (desirable) planar graph, especially if the set of sites is dense

Remarks on Category-Based Routing in Social Networks

It is well known that individuals can route messages on short paths through social networks, given only simple information about the target and using only local knowledge about the topology. Sociologists conjecture that people find routes greedily by passing the message to an acquaintance that has more in common with the target than themselves, e.g. if a dentist in Saarbr\"ucken wants to send a message to a specific lawyer in Munich, he may forward it to someone who is a lawyer and/or lives in Munich. Modelling this setting, Eppstein et al. introduced the notion of category-based routing. The goal is to assign a set of categories to each node of a graph such that greedy routing is possible. By proving bounds on the number of categories a node has to be in we can argue about the plausibility of the underlying sociological model. In this paper we substantially improve the upper bounds introduced by Eppstein et al. and prove new lower bounds.Comment: 21 page