A Fuzzy QOS Routing Algorithm for Communication Networks

This paper proposes a parallel shortest path-searching algorithm and implements it on a newly structured parallel reconfigurable processor, DAPDNA-2 (IPFlex Inc). Routing determines the shortest paths from the source to the ultimate destination through intermediate nodes. In open shortest path first (OSPF), Dijkstra's shortest path algorithm, which is the conventional one, finds the shortest paths from the source on a program counter-based processor. The calculation time for Dijkstra's algorithm is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) when the number of nodes is N. When the network scale is large, calculation time required by Dijkstra's algorithm increases rapidly. It's very difficult to compute Dijkstra's algorithm in parallel because of the need for previous calculation results, so Dijkstra's algorithm is unsuitable for parallel processors. Our proposed scheme finds the shortest paths using a simultaneous multi-path search method. In contrast with Dijkstra's algorithm, several nodes can be determined at one time. Moreover, we partition the network into different groups (network groups) and find the all-node pair's shortest path in each group using a pipeline operation. Networks can be abstracted, and the shortest paths in very large networks can be found easily. The proposed scheme can decrease calculation time from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(N) using a pipeline operation on DAPDNA-2. Our simulations show that the proposed algorithm uses 99.6% less calculation time than Dijkstra's algorithm. The proposed algorithm can be applied to the very large Internet network designs of the future.

The fundamental path planning problem for a mobile robot involves generating a trajectory for point-to-point navigation while avoiding obstacles. Heuristic-based search algorithms like A* have been shown to be efficient in solving such planning problems. Recently, there has been an increased interest in specifying complex path planning problem using temporal logic. In the state-of-the-art algorithm, the temporal logic path planning problem is reduced to a graph search problem, and Dijkstra's shortest path algorithm is used to compute the optimal trajectory satisfying the specification.The A* algorithm, when used with an appropriate heuristic for the distance from the destination, can generate an optimal path in a graph more efficiently than Dijkstra's shortest path algorithm. The primary challenge for using A* algorithm in temporal logic path planning is that there is no notion of a single destination state for the robot. We present a novel path planning algorithm T* that uses the A* search procedure opportunistically to generate an optimal trajectory satisfying a temporal logic query. Our experimental results demonstrate that T* achieves an order of magnitude improvement over the state-of-the-art algorithm to solve many temporal logic path planning problems in 2-D as well as 3-D workspaces.

A theorem on the expected complexity of dijkstra's shortest path algorithm

Efficient implementations of Dijkstra's shortest path algorithm are investigated. A new data structure, called the radix heap , is proposed for use in this algorithm. On a network with n vertices, m edges, and nonnegative integer arc costs bounded by C , a one-level form of radix heap gives a time bound for Dijkstra's algorithm of O ( m + n log C ). A two-level form of radix heap gives a bound of O ( m + n log C /log log C ). A combination of a radix heap and a previously known data structure called a Fibonacci heap gives a bound of O ( m + n a @@@@log C ). The best previously known bounds are O ( m + n log n ) using Fibonacci heaps alone and O ( m log log C ) using the priority queue structure of Van Emde Boas et al. [ 17].

When designing routing protocols for space-based networks, we must take into consideration the unique characteristics of such networks. Since space-based networks are inherently sparse with constrained resources, one needs to design smart routing algorithms that use the resources efficiently to maximize network performance. In Space Exploration Missions, the trajectories and orbits of spacecraft are predetermined, thus communication opportunities are predictable. This a-priori knowledge can be used to the advantage of scheduling and routing. In this paper, we focus on analyzing Contact Graph Routing (CGR) for space-based networks. CGR makes use of the predictable nature of the contacts to make routing decisions. Mars and Lunar mission-like scenarios were used in our simulations to gather statistics on routing protocol performance in terms of delay and buffer usage. We provide improvements to the underlying cost function of CGR to avoid routing loops and suggest applying Dijkstra's shortest path algorithm for path selection. The cost function change was incorporated into the latest Internet Draft posted for CGR. Dijkstra's shortest path algorithm was successfully implemented and tested in NASA's Interplanetary Overlay Network (ION) implementation of the DTN protocols.

Numerous studies on energy-efficient routing for multihop ad hoc networks (MANET) look at extending battery life by minimizing the cost at the transmitting node. In this paper, we study the complexity of energy-efficient routing when the energy cost of receiving packets is also considered. We first prove that, surprisingly, even when all nodes transmit at the same power, finding a simple unicast path that guarantees enough remaining energy locally at each node in the network then becomes an NP-complete problem. Hence we propose several heuristics based on Dijkstra's shortest path algorithm in which we integrate the notion of remaining energy in order to satisfy flow requirements. We show with several simulations that these heuristics not only allow the computation of routes that save energy of nodes with low battery but also allow the network to handle more flows

Anypath routing has been proposed to improve the performance of unreliable wireless networks by exploiting the spatial diversity and broadcast nature of the wireless medium. In this paper, we focus on anypath routing subject to K constraints, and present a polynomial time K-approximation algorithm. When K = 1, our algorithm is the optimal polynomial time algorithm for the corresponding problem. When K ≥ 2, the corresponding problem is NP-hard. We are the first to present an O(1)-approximation algorithm. Furthermore, our algorithm is as simple as Dijkstra's shortest path algorithm, and is therefore suitable for implementation in actual wireless routing protocols.

An O( mlog log D) algorithm for shortest paths

Most applications use distance as a weight to solve the shortest path problem. We employed a variety of parameters in this paper, including (Route Distance, Speed, and Capacity of the Road, and the time it takes each trip to the road). The major goal of this study is to determine the most effective approach for communicating this essential spatial information to end-users using GIS software. To discover the best route, most algorithm for best route (Dijkstra's shortest path algorithm, Euler's algorithm, etc.) are utilized. The proposed upgraded approach was applied to the Baghdad city road network as a case study. Baghdad has a problem in Processes for urban transportation planning are being implemented, particularly in terms of selecting appropriate transportation policies to address mobility issues. The Origin - Destination Matrix is a crucial component of the transportation planning process. One of the necessary aims of transportation studies in Baghdad city is the O-D matrix, which is the travel demand between (central Baghdad city and the commercial centers). The findings of this study can help local transportation authority's choose the best transportation policy, maximize income, and better manage resources.

A way to apply the shortest path ray tracing (SPR) method to irregularly tetrahedralized velocity structure and its efficiency are described. SPR is the ray tracing method utilizing Dijkstra's shortest path algorithm in network graph theory. The tetrahedralization by Delaunay tessellation is one of the irregular parameterization of 3D velocity structure in travel time tomography. The irregular parameterization has great advantage of flexible expression of velocity structure though the computational costs in ray-tracing are relatively high. The hybrid ray tracing between SPR (for initialization) and pseudo-bending (for optimization) might be more economic. It has been known as the well proven scheme in the sense of the simplicity and robustness. Though SPR was originally developed under regular parameterization, it can easily be expanded to irregular parameterized structure without any theoretical modification. We propose a simple design of network for SPR that conforms the shape of tetrahedron. Generally, the cost and accuracy of SPR depends on the network design. In this paper, we have carried out a synthetic examination with a complex velocity model to assess the cost and accuracy of SPR with irregular network. Result reveals that the proposed ray tracing method is practically economic and accurate (almost the same accuracy as SPR under the regular cubic cell model). Further, it has potential to obtain more accuracy by lower costs due to the economical network integrated with flexible element size.

As the network is growing exponentially, challenges and issues also increases. They affect the performance of the network. Many factors contribute to improve the performance of the network. One such factor is finding the path quickly. In this paper, we are going to find a shortest path faster compared to Dijkstra's shortest path algorithm. We have introduced new ideas in building the end-to-end path. Various steps involved in finding the shortest path are refined. The simulated algorithm shows better results than the existing algorithm. We are sure that, the outcome of this paper reduces computation overhead and processing overload.

More than travel time: New accessibility index capturing the connectivity of transit services

In this paper, we present a fast and robust algorithm for centerline extraction in blood vessels. The algorithm is suitable for catheter simulation in CT data of blood vessels. It creates an initial centerline based on two user-defined points (start- and endpoint). For curved vessel structures, this initial centerline is computed by Dijkstra's shortest path algorithm. For linear vessel structures, the algorithm directly connects the start- and the endpoint to get the initial centerline. Thereafter, this initial path will be aligned in the blood vessel, resulting in the vessels centerline (i.e. an optimal catheter simulation path). The alignment is done by an active contour model combined with polyhedra placed along it. Results of the proposed centerline algorithm are demonstrated for CTA with variations in anatomy and location of pathology.

The logic, implementation, and analysis of a network design approach, lowest measure paths, are discussed. The approach was conceived in order to design a signaling network dedicated to government use. By intelligently picking a subset of facility routes from the large pool of AT&T private line and commercial signaling networks using Dijkstra's shortest path algorithm, the approach generates designs that are nearly as survivable and robust as the full pool. >

Numerical simulation of Ground penetrating radar (GPR) signals is potentially valuable in both survey design and data interpretation. We develop a method for numerically synthesizing GPR received signals using geometrical ray theory. The GPR reflected ray tracing scheme is implemented using wave front expanding method, based on Fermat's principle, reciprocity principle, and Dijkstra's shortest path algorithm. This method is good for complex layered models with inhomogeneous material and undulate interfaces, and can trace all reflected ray paths from one non-planar interface at one time, for a pair of source and receiver. Simulation experiments show it is effective.