Link Reversal Algorithm Research Articles

Improved route selection algorithm based on TORA over mobile adhoc network

MANETs are used in a variety of applications due to their ability to establish and release networks instantly without the need for a pre-established network or infrastructure. Here the challenging task is to search for the most efficient routing Energy is the main source of MANET and each node consumes energy during transmission. Energy-efficient protocols like AODV, DSR, TORA, etc. have already been proposed. In our proposed work we are using TORA to improve route selection. “Temporarily Ordered Routing Algorithm (TORA) is an adaptive loop-free routing protocol that works on route reversal method. The protocol is based on a link reversal algorithm and uses a direct acyclic graph (DAG) for route finding. As TORA maintains multi routes from source to destination, the energy drain is an issue so energy efficiency is of utmost importance. In this work, Mod TORA work as a load balancer with successor and feasible successor path for each connection considers the energy parameter & modifies the routing table. The purpose of selecting load balance is for the Fair battery consumption of the nodes and link failure purpose. We are changing the route selection mechanism. Mod TORA used Energy and multipath mechanism with optimized routing. The two best routes are found and if the first one fails then the second route is selected, to improve the performance of TORA and thus ultimately leading to load balancing.

Loop-Free Backpressure Routing Using Link-Reversal Algorithms

The backpressure routing policy is known to be a throughput optimal policy that supports any feasible traffic demand, but may have poor delay performance when packets traverse loops in the network. In this paper, we study loop-free backpressure routing policies that forward packets along directed acyclic graphs (DAGs) to avoid the looping problem. These policies use link reversal algorithms to improve the DAGs in order to support any achievable traffic demand. For a network with a single commodity, we show that a DAG that supports a given traffic demand can be found after a finite number of iterations of the link-reversal process. We use this to develop a joint link-reversal and backpressure routing policy, called the loop free backpressure (LFBP) algorithm. This algorithm forwards packets on the DAG, while the DAG is dynamically updated based on the growth of the queue backlogs. We show by simulations that such a DAG-based policy improves the delay over the classical backpressure routing policy. We also propose a multicommodity version of the LFBP algorithm and via simulation show that its delay performance is better than that of backpressure.

Neighbor oblivious and finite-state algorithms for circumventing local minima in geographic forwarding

We propose distributed link reversal algorithms to circumvent communication voids in geographic routing. We also solve the attendant problem of integer overflow in these algorithms. These are achieved in two steps. First, we derive partial and full link reversal algorithms that do not require one-hop neighbor information, and convert a destination-disoriented directed acyclic graph (DAG) to a destination-oriented DAG. We embed these algorithms in the framework of Gafni and Bertsekas [1] in order to establish their termination properties. We also analyze certain key properties exhibited by our neighbor oblivious link reversal algorithms, e.g., for any two neighbors, their t-states are always consecutive integers, and for any node, its t-state size is upper bounded by log(N). In the second step, we resolve the integer overflow problem by analytically deriving one-bit full link reversal and two-bit partial link reversal versions of our neighbor oblivious link reversal algorithms. We also discuss the work and time complexities of the proposed algorithms.

Time Complexity of Link Reversal Routing

Link reversal is a versatile algorithm design paradigm, originally proposed by Gafni and Bertsekas in 1981 for routing and subsequently applied to other problems including mutual exclusion, leader election, and resource allocation. Although these algorithms are well known, until now there have been only preliminary results on time complexity, even for the simplest link reversal algorithm for routing, called Full Reversal. In Full Reversal, a sink reverses all its incident links, whereas in other link reversal algorithms (e.g., Partial Reversal), a sink reverses only some of its incident links. Charron-Bost et al. introduced a generalization, called LR, that includes Full and Partial Reversal as special cases. In this article, we present an exact expression for the time complexity of LR. The expression is stated in terms of simple properties of the initial graph. The result specializes to exact formulas for the time complexity of any node in any initial acyclic directed graph for both Full and Partial Reversal. Having the exact formulas provides insight into the behavior of Full and Partial Reversal on specific graph families. Our first technical insight is to describe the behavior of Full Reversal as a dynamical system and to observe that this system is linear in min-plus algebra. Our second technical insight is to overcome the difficulty posed by the fact that LR is not linear by transforming every execution of LR from an initial graph into an execution of Full Reversal from a different initial graph while maintaining the execution's work and time complexity.

Link Reversal Routing with Binary Link Labels: Work Complexity

Full Reversal and Partial Reversal are two well-known routing algorithms that were introduced by Gafni and Bertsekas [IEEE Trans. Commun., 29 (1981), pp. 11--18]. By reversing the directions of some links of the graph, these algorithms transform a connected input DAG (directed acyclic graph) into an output DAG in which each node has at least one path to a distinguished destination node. We present a generalization of these algorithms, called the link reversal (LR) algorithm, based on a novel formalization that assigns binary labels to the links of the input DAG. We characterize the legal link labelings for which LR is guaranteed to establish routes. Moreover, we give an exact expression for the number of steps---called work complexity---taken by each node in every execution of LR from any legal input graph. Exact expressions for the per-node work complexity of Full Reversal and Partial Reversal follow from our general formula; this is the first exact expression known for Partial Reversal. Our binary link ...

Analysis of Link Reversal Routing Algorithms

Link reversal algorithms provide a simple mechanism for routing in communication networks whose topology is frequently changing, such as in mobile ad hoc networks. A link reversal algorithm routes by imposing a direction on each network link such that the resulting graph is a destination oriented DAG. Whenever a node loses routes to the destination, it reacts by reversing some (or all) of its incident links. Link reversal algorithms have been studied experimentally and have been used in practical routing algorithms, including TORA [V. D. Park and M. S. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, in Proc. INFOCOM, IEEE, Los Alamitos, CA, 1997, pp. 1405--1413]. This paper presents the first formal performance analysis of link reversal algorithms. We study these algorithms in terms of work (number of node reversals) and the time needed until the network stabilizes to a state in which all the routes are reestablished. We focus on the full reversal algorithm and the partial reversal algorithm, both due to Gafni and Bertsekas [IEEE Trans. Comm.}, 29 (1981), pp. 11--18]; the first algorithm is simpler, while the latter has been found to be more efficient for typical cases. Our results are as follows: The full reversal algorithm requires O(n2) work and time, where n is the number of nodes that have lost routes to the destination. This bound is tight in the worst case. The partial reversal algorithm requires O(n $\cdot$ a* + n2) work and time, where a* is a nonnegative integral function of the initial state of the network. Further, for every nonnegative integer $\alpha$, there exists a network and an initial state with a*=$\alpha$, and with n nodes that have lost their paths to the destination, such that the partial reversal algorithm requires $\Omega(n\cdot {a^*} + n^2)$ work and time. There is an inherent lower bound on the worst-case performance of link reversal algorithms. There exist networks such that for every deterministic link reversal algorithm, there are initial states that require $\Omega(n^2)$ work and time to stabilize. Therefore, surprisingly, the full reversal algorithm is asymptotically optimal in the worst case, while the partial reversal algorithm is not, since a* can be arbitrarily larger than n.