Asymptotically Optimal Bounds for (t,2) Broadcast Domination on Finite Grids

Let $G=(V,E)$ be a graph and $t,r$ be positive integers. The \emph{signal} that a tower vertex $T$ of signal strength $t$ supplies to a vertex $v$ is defined as $sig(T,v)=max(t-dist(T,v),0),$ where $dist(T,v)$ denotes the distance between the vertices $v$ and $T$. In 2015 Blessing, Insko, Johnson, and Mauretour defined a \emph{$(t,r)$ broadcast dominating set}, or simply a \emph{$(t,r)$ broadcast}, on $G$ as a set $\mathbb{T}\subseteq V$ such that the sum of all signals received at each vertex $v \in V$ from the set of towers $\mathbb{T}$ is at least $r$. The $(t,r)$ broadcast domination number of a finite graph $G$, denoted $\gamma_{t,r}(G)$, is the minimum cardinality over all $(t,r)$ broadcasts for $G$. Recent research has focused on bounding the $(t,r)$ broadcast domination number for the $m \times n$ grid graph $G_{m,n}$. In 2014, Grez and Farina bounded the $k$-distance domination number for grid graphs, equivalent to bounding $\gamma_{t,1}(G_{m,n})$. In 2015, Blessing et al. established bounds on $\gamma_{2,2}(G_{m,n})$, $\gamma_{3,2}(G_{m,n})$, and $\gamma_{3,3}(G_{m,n})$. In this paper, we take the next step and provide a tight upper bound on $\gamma_{t,2}(G_{m,n})$ for all $t>2$. We also prove the conjecture of Blessing et al. that their bound on $\gamma_{3,2}(G_{m,n})$ is tight for large values of $m$ and $n$.Comment: 8 pages, 4 figure

Routing versus energy optimization in a linear network

In wireless networks, devices (or nodes) often have a limited battery supply to use for the sending and reception of transmissions. By allowing nodes to relay messages for other nodes, the distance that needs to be bridged can be reduced, thus limiting the energy needed for a transmission. However, the number of transmissions a node needs to perform increases, costing more energy. Defining the lifetime of the network as the time until the first node depletes its battery, we investigate the impact of routing choices on the lifetime. In particular we focus on a linear network with nodes sending messages directly to all other nodes, or using full routing where transmissions are only sent to neighbouring nodes. We distinguish between networks with nodes on a grid or uniformly distributed and with full or random battery supply. Using simulation we validate our analytical results and discuss intermediate options for relaying of transmissions

A Robust Information Source Estimator with Sparse Observations

In this paper, we consider the problem of locating the information source with sparse observations. We assume that a piece of information spreads in a network following a heterogeneous susceptible-infected-recovered (SIR) model and that a small subset of infected nodes are reported, from which we need to find the source of the information. We adopt the sample path based estimator developed in [1], and prove that on infinite trees, the sample path based estimator is a Jordan infection center with respect to the set of observed infected nodes. In other words, the sample path based estimator minimizes the maximum distance to observed infected nodes. We further prove that the distance between the estimator and the actual source is upper bounded by a constant independent of the number of infected nodes with a high probability on infinite trees. Our simulations on tree networks and real world networks show that the sample path based estimator is closer to the actual source than several other algorithms

Infinite-message Interactive Function Computation in Collocated Networks

An interactive function computation problem in a collocated network is studied in a distributed block source coding framework. With the goal of computing a desired function at the sink, the source nodes exchange messages through a sequence of error-free broadcasts. The infinite-message minimum sum-rate is viewed as a functional of the joint source pmf and is characterized as the least element in a partially ordered family of functionals having certain convex-geometric properties. This characterization leads to a family of lower bounds for the infinite-message minimum sum-rate and a simple optimality test for any achievable infinite-message sum-rate. An iterative algorithm for evaluating the infinite-message minimum sum-rate functional is proposed and is demonstrated through an example of computing the minimum function of three sources.Comment: 5 pages. 2 figures. This draft has been submitted to IEEE International Symposium on Information Theory (ISIT) 201

Asymptotically Optimal Bounds for (t,2) Broadcast Domination on Finite Grids

Let G = (V,E) be a graph and t,r be positive integers. The signal that a tower vertex T of signal strength t supplies to a vertex v is defined as sig(T, v) = max(t − dist(T,v),0), where dist(T,v) denotes the distance between the vertices v and T. In 2015 Blessing, Insko, Johnson, and Mauretour defined a (t, r) broadcast dominating set, or simply a (t, r) broadcast, on G as a set T ⊆ V such that the sum of all signal received at each vertex v ∈ V from the set of towers T is at least r. The (t, r) broadcast domination number of a finite graph G, denoted γt,r(G), is the minimum cardinality over all (t,r) broadcasts for G. Recent research has focused on bounding the (t, r) broadcast domination number for the m×n grid graph Gm,n. In 2014, Grez and Farina bounded the k-distance domination number for grid graphs, equivalent to bounding γt,1(Gm,n). In 2015, Blessing et al. established bounds on γ2,2(Gm,n), γ3,2(Gm,n), and γ3,3(Gm,n). In this paper, we take the next step and provide a tight upper bound on γt,2(Gm,n) for all t \u3e 2. We also prove the conjecture of Blessing et al. that their bound on γ3,2(Gm,n) is tight for large values of m and n