Convergence Time Evaluation of Algorithms in MANETs

Annapurna P Patil; Narmada Sambaturu; Krittaya Chunhaviriyakul

Home/Archive/arXiv

arXiv · 0910.1475

Convergence Time Evaluation of Algorithms in MANETs

Annapurna P Patil·Narmada Sambaturu·Krittaya Chunhaviriyakul

View original source ↗Read source document ↗Export metadata ↓

Abstract

Since the advent of wireless communication, the need for mobile ad hoc networks has been growing exponentially. This has opened up a Pandoras Box of algorithms for dealing with mobile ad hoc networks, or MANETs, as they are generally referred to. Most attempts made at evaluating these algorithms so far have focused on parameters such as throughput, packet delivery ratio, overhead etc. An analysis of the convergence times of these algorithms is still an open issue. The work carried out fills this gap by evaluating the algorithms on the basis of convergence time. Algorithms for MANETs can be classified into three categories: reactive, proactive, and hybrid protocols. In this project, we compare the convergence times of representative algorithms in each category, namely Ad hoc On Demand Distance Vector (AODV) reactive, Destination Sequence Distance Vector protocol (DSDV) proactive, and Temporally Ordered Routing Algorithm (TORA) hybrid. The algorithm performances are compared by simulating them in ns2. Tcl is used to conduct the simulations, while perl is used to extract data from the simulation output and calculate convergence time. The design of the experiments carried on is documented using Unified modeling Language. Also, a user interface is created using perl, which enables the user to either run a desired simulation and measure convergence time, or measure the convergence time of a simulation that has been run earlier.

Explore related subjects

cs.DS cs.NI

Keep this discovery

BibTeX RIS

Annapurna P Patil, Narmada Sambaturu, Krittaya Chunhaviriyakul. 2009-10-08. Convergence Time Evaluation of Algorithms in MANETs. https://arxiv.org/abs/0910.1475

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS↗

arXiv2026

A deterministic $(1+\varepsilon)^n$ approximation for the permanent of a nonnegative matrix

For every fixed $0<\varepsilon\le1$, we give a deterministic strongly polynomial algorithm that, given a nonnegative matrix $A\in\mathbb{R}_{\ge0}^{n\times n}$, returns $Q$ satisfying $\operatorname{per} A\le Q\le(1+\varepsilon)^n\operatorname{per} A$.

cs.DS↗

arXiv2026

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS↗