Least-cost Routing Research Articles

An Unsupervised Canopy-to-Root Pathing (UCRP) Tree Segmentation Algorithm for Automatic Forest Mapping

Terrestrial laser scanners, unmanned aerial LiDAR, and unmanned aerial photogrammetry are increasingly becoming the go-to methods for forest analysis and mapping. The three-dimensionality of the point clouds generated by these technologies is ideal for capturing the structural features of trees such as trunk diameter, canopy volume, and biomass. A prerequisite for extracting these features from point clouds is tree segmentation. This paper introduces an unsupervised method for segmenting individual trees from point clouds. Our novel, canopy-to-root, least-cost routing method segments trees in a single routine, accomplishing stem location and tree segmentation simultaneously without needing prior knowledge of tree stem locations. Testing on benchmark terrestrial-laser-scanned datasets shows that we achieve state-of-the-art performances in individual tree segmentation and stem-mapping accuracy on boreal and temperate hardwood forests regardless of forest complexity. To support mapping at scale, we test on unmanned aerial photogrammetric and LiDAR point clouds and achieve similar results. The proposed algorithm’s independence from a specific data modality, along with its robust performance in simple and complex forest environments and accurate segmentation results, make it a promising step towards achieving reliable stem-mapping capabilities and, ultimately, towards building automatic forest inventory procedures.

Solving the Delay-Constrained Least-Cost routing problem using Tabu Search with Edge Betweenness

Multicast routing consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network thus becomes an important technology communication. To solve the problem, a current approach for efficiently supporting a multicast session in a network consists of establishing a multicast tree that covers the source and all terminal nodes. This problem can be reduced to a minimal Steiner tree problem (MST) which aims to look for a tree that covers a set of nodes with a minimum total cost, the problem is NP-hard. In this paper, we investigate metaheuristics approaches for the Delay-Constrained Least-Cost (DCLC) problem, we propose a novel algorithm based on Tabu Search procedure with the Edge Betweenness (EB). The EB heuristic used first to improve KMB heuristic, able to measure the edge value to being included in a given path. The obtained solution improved using the tabu search method. The performance of the proposed algorithm is evaluated by experiments on a number of benchmark instances from the Steiner library. Experimental results show that the proposed metaheuristic gives competitive results in terms of cost and delay compared to the optimal results in Steiner library and other existing algorithms in the literature.

Unicast QoS Routing Algorithms for SDN: A Comprehensive Survey and Performance Evaluation

A variety of communication networks, such as industrial communication systems, have to provide strict delay guarantees to the carried flows. Fast and close to optimal quality of service (QoS) routing algorithms, e.g., delay-constrained leastcost (DCLC) routing algorithms, are required for routing flows in such networks with strict delay requirements. The emerging software-defined networking (SDN) paradigm centralizes the network control in SDN controllers that can centrally execute QoS routing algorithms. A wide range of QoS routing algorithms have been proposed in the literature and examined in individual studies. However, a comprehensive evaluation framework and quantitative comparison of QoS routing algorithms that can serve as a basis for selecting and further advancing QoS routing in SDN networks is missing in the literature. This makes it difficult to select the most appropriate QoS routing algorithm for a particular use case, e.g., for SDN controlled industrial communications. We close this gap in the literature by conducting a comprehensive up-to-date survey of centralized QoS routing algorithms. We introduce a novel four-dimensional (4D) evaluation framework for QoS routing algorithms, whereby the 4D correspond to the type of topology, two forms of scalability of a topology, and the tightness of the delay constraint. We implemented 26 selected DCLC algorithms and compared their runtime and cost inefficiency within the 4D evaluation framework. While the main conclusion of this evaluation is that the best algorithm depends on the specific sub-space of the 4D space that is targeted, we identify two algorithms, namely Lagrange relaxation-based aggregated cost (LARAC) and search space reduction delay-cost-constrained routing (SSR+DCCR), that perform very well in most of the 4D evaluation space.

Delay-Constrained Energy Optimization in High-Latency Sensor Networks

Sensor networks deployed in high-latency environments, such as underwater acoustic and satellite channels, find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves. In order to extend the lifetime of these sensors, energy must be conserved in all layers of the protocol stack. In addition to long propagation delays, these channels are characterized by limited bandwidth and a lack of well-established closed-form analytical models. This fact makes finding cross-layer energy-optimal solutions a difficult problem to solve. Our goal is to compute near-optimal routes, schedules and transmit power levels for delay-constrained applications of high-latency sensor networks. We present a mixed-integer programming relaxation of the optimization problem. We further propose a decentralized algorithm to iteratively solve the relaxed optimization problem. Comparative simulation analysis shows that our decentralized approach is approximately 3~6 dB more energy-efficient and 2~5 dB more throughput-efficient than the heuristic, time-sensitive greedy forwarding, and least-cost routing algorithms.

Using bio-inspired approaches to improve the quality of service in a multicast routing

This paper deals with the problem optimising the quality of service (QoS) in a multicast routing with multiple constraints (multi constrained least-cost multicast routing problem). To solve this problem, we propose two approaches. The first one named biogeography-based optimisation (BBO) is inspired by the biogeography mathematics; the second, named bat algorithm (BA) is inspired from bat behaviour. We propose to use a tree structure to represent the solution. We start optimising the graph representing the network. We propose to generate the initial population with greedy heuristic using depth first search (DFS). Then, we optimise the initial population solutions in order to minimise the execution time. We have adapted BBO and BA operators to deal with the discrete nature of the problem and the structures representing the problem solution. In experiments, we have used Waxman topology model to generate the graph. The results were encouraging in comparison with other existing algorithms.

Function Split Between Delay-Constrained Routing and Resource Allocation for Centrally Managed QoS in Industrial Networks

Industrial networks demand centrally controlled quality of service (QoS), often in the form of hard real-time guarantees. Software-defined networking (SDN) provides a convenient paradigm for central QoS control. However, existing SDN-based solutions cannot guarantee hard real-time QoS as they rely on a control loop over the forwarding (data) and control planes. We propose a novel SDN-based QoS control framework that maintains an accurate network model through network calculus to avoid a control loop over forwarding and control planes, allocates resources to and routes flows over a network of “queue links,” whereby each physical network link houses multiple queue links (with different QoS levels), and manages QoS through a function split between delay-constrained least-cost routing on the network of queue links and the resource allocation to the queue links. This function split greatly reduces the computational complexity while achieving hard real-time QoS with high bandwidth utilization. Our evaluation results indicate that our function split approach allows for online runtime admission control and can achieve bandwidth utilization above 80% while meeting deterministic real-time QoS requirements.

Routing in Dense Human Crowds Using Smartphone Movement Data and Optical Aerial Imagery

In this paper, we propose a navigation approach for smartphones that enables visitors of major events to avoid crowded areas or narrow streets and to navigate out of dense crowds quickly. Two types of sensor data are integrated. Real-time optical images acquired and transmitted by an airborne camera system are used to compute an estimation of a crowd density map. For this purpose, a patch-based approach with a Gabor filter bank for texture classification in combination with an interest point detector and a smoothing function is applied. Furthermore, the crowd density is estimated based on location and movement speed of in situ smartphone measurements. This information allows for the enhancement of the overall crowd density layer. The composed density information is input to a least-cost routing workflow. Two possible use cases are presented, namely (i) an emergency application and (ii) a basic routing application. A prototypical implementation of the system is conducted as proof of concept. Our approach is capable of increasing the security level for major events. Visitors are able to avoid dense crowds by routing around them, while security and rescue forces are able to find the fastest way into the crowd.

A Fast Hybrid ε-Approximation Algorithm for Computing Constrained Shortest Paths

Considerable efforts have been dedicated to develop both heuristic and approximation algorithms for the NP-complete delay-constrained least-cost (DCLC) routing problem, but to the best of our knowledge, no prior work has been done to mingle the two tracks of research. In this letter we introduce a novel idea to show how a heuristic method can be used to boost the average performance of an approximation algorithm. Simulations on networks of up to 8192 nodes demonstrate that our new hybrid e-approximation algorithm is faster than the best known approximation algorithm by one or two orders of magnitude (depending on the network size and e).

A scalable method for DCLC problem using hierarchical MDP model

It is well known that the delay-constrained least-cost (DCLC) routing problem is NP-complete, hence various heuristic methods have been proposed for this problem. However, these heuristic methods have poor scalability as the network scale increases. In this paper we propose a new method based on the Markov Decision Process (MDP) theory and the hierarchical routing scheme to address the scalability issue of the DCLC routing problem. We construct a new two-level hierarchy MDP model and apply an infinite-horizon discounted cost model to the upper level for the end-to-end inter-domain link selection. Since the infinite-horizon discounted cost model is independent of network scale, the scalability problem is resolved. With the proposed model, we further give the algorithm of solving the optimal policy to obtain the DCLC routing. Simulation results show that the proposed method improves the scalability significantly.

Least Cost Routing (LCR) solution for inter-domain traffic distribution

In the highly connected Internet, most of domains (Autonomous Systems, ASes) have more than one interconnection link to different providers. In this multi-homed scenario, destinations for inter-dom...

Least‐cost transportation networks predict spatial interaction of invasion vectors

Human-mediated dispersal among aquatic ecosystems often results in biotic transfer between drainage basins. Such activities may circumvent biogeographic factors, with considerable ecological, evolutionary, and economic implications. However, the efficacy of predictions concerning community changes following inter-basin movements are limited, often because the dispersal mechanism is poorly understood (e.g., quantified only partially). To date, spatial-interaction models that predict the movement of humans as vectors of biotic transfer have not incorporated patterns of human movement through transportation networks. As a necessary first step to determine the role of anglers as invasion vectors across a land-lake ecosystem, we investigate their movement potential within Ontario, Canada. To determine possible model improvements resulting from inclusion of network travel, spatial-interaction models were constructed using standard Euclidean (e.g., straight-line) distance measures and also with distances derived from least-cost routing of human transportation networks. Model comparisons determined that least-cost routing both provided the most parsimonious model and also excelled at forecasting spatial interactions, with a proportion of 0.477 total movement deviance explained. The distribution of movements was characterized by many relatively short to medium travel distances (median = 292.6 km) with fewer lengthier distances (75th percentile = 484.6 km, 95th percentile = 775.2 km); however, even the shortest movements were sufficient to overcome drainage-basin boundaries. Ranking of variables in order of their contribution within the most parsimonious model determined that distance traveled, origin outflow, lake attractiveness, and sportfish richness significantly influence movement patterns. Model improvements associated with least-cost routing of human transportation networks imply that patterns of human-mediated invasion are fundamentally linked to the spatial configuration and relative impedance of human transportation networks, placing increased importance on understanding their contribution to the invasion process.

Tabu search based algorithms for bandwidth-delay-constrained least-cost multicast routing

The advent of various real-time multimedia applications in high-speed networks creates a need for quality of service (QoS) based multicast routing. The Steiner tree problem, is a well-known NP-complete problem, provides the mathematical structure behind multicast communications. Two important QoS constraints are the bandwidth constraint and the end-to-end delay constraint. In this paper, we propose various algorithms to solve the bandwidth-delay-constrained least-cost multicast routing problem based on Tabu Search (TS), addressing issues of the selected initial solution and move type as two major building blocks in short-term memory version of Tabu Search and longer-term memory with associated intensification and diversification strategies as advanced Tabu Search techniques. We evaluate the performance and efficiency of the proposed TS-based algorithms in comparison with other existing TS-based algorithms and heuristics on a variety of random generated networks with regard to total tree cost. Finally we identify the most efficient algorithm uncovered by our testing.

Distributed network control for establishing reliability-constrained least-cost lightpaths in WDM Mesh networks

The trend in the development of intelligent optical networks has recently started moving towards a unified solution, to support voice, data, and various multimedia services. In this scenario different applications/end users may need different levels of fault-tolerance and differ in how much they are willing to pay for the service they get. A control scheme which is used to set up and tear down lightpaths, should not only be fast and efficient, but also be scalable. In addition, it should also try to minimize the number of blocked connections while satisfying the requested level of fault-tolerance. In this work we choose reliability of a connection as a quality of service (QoS) parameter to denote different levels of fault-tolerance. We prove that reliability-constrained least-cost (RCLC) routing problem is NP-complete and propose a distributed control scheme based on preferred link approach for establishing RCLC lightpaths. We prove the correctness of the proposed scheme and show that the scheme is flexible in that a variety of heuristics can be employed to order the neighboring links of any given node. Four heuristics are proposed and their performance is studied through extensive simulation experiments on wavelength selective networks for different network configurations. The simulation results show that our heuristics provide better performance in terms of average call acceptance rate, average path cost, average routing distance, and average connection setup time; when the connection requests with different reliability requirements arrive to and depart from the network randomly. Furthermore, if a network service provider feels that it can earn more revenue by admitting more number of calls with reliability requirements, it can do so by manipulating the parameters of our scheme, such as the maximum number of preferred links used at each node.

Energy conservation through resource-aware movement in heterogeneous mobile ad hoc networks

Energy conservation in mobile ad hoc networks is of paramount importance because most mobile nodes usually have very limited energy supply. Previous research on this issue focused on the design at the network or MAC or physical layer. In this paper, we study this problem from the new perspective of node mobility, i.e., analyzing the impact of node movement on energy conservation. In particular, armed with the inherent resource heterogeneity in mobile ad hoc networks, we propose a novel resource-aware movement strategy to make better use of some powerful nodes to achieve energy conservation. We also formulate the resource-aware movement as a NP-complete distance-constrained least-cost (DCLC) routing problem and propose an efficient heuristic solution. Extensive simulations have been used to demonstrate the effectiveness of the proposed schemes.

Charge volume minimization in liberalized telecommunication markets

The liberalization of telecommunication markets allows the choice of call provider for each single connection. This leads to the question of which provider has to be chosen to minimize the charge volume for all the calls over an accounting period. In contrast to the simple derivation of routing policies found in existing least cost routing (LCR) tables, the incremental and global quantity discounts granted by providers are taken into account in the optimization approach developed in this paper. The dynamic, non-convex, non-linear optimization problem arising from the minimization of the charge volume is solved using the evolutionary optimization method of genetic algorithms. The mathematical formulation of the problem, the genetic algorithm used, the limitation of the search space, and the quantity discount regulation developed are presented. The optimization results obtained by applying the concept to the call data of the University of Hanover are analysed and discussed. The paper concludes with an outlook for the implementation of LCR policies.

GA-based heuristic algorithms for bandwidth-delay-constrained least-cost multicast routing

The bandwidth-delay-constrained least-cost multicast routing is a challenging problem in high-speed multimedia networks. Computing such a constrained Steiner tree is an NP-complete problem. In this paper, we propose several novel solutions to this problem based on genetic algorithms (GA). The proposed solutions consist of six different schemes for genotype representation, and also several new heuristic algorithms for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed algorithms in comparison with other existing heuristic and GA-based algorithms using simulation results. The most efficient combination of various proposed alternative algorithms is selected as our final solution by the simulation results. This proposed GA-based algorithm has overcome the existing algorithms considering average tree cost and running time.

A Delay-Constrained Least-Cost Multicast Routing Heuristic for Dynamic Multicast Groups

Many new multimedia applications involve dynamic multiple participants, have stringent end-to-end delay requirement and consume large amount of network resources. In this paper, we propose a new dynamic delay-constrained least-cost multicast routing algorithm (DDCLCMR) to support these applications. When group membership changes, the existing multicast tree is perturbed as little as possible. Simulation results show that DDCLCMR performs very well in terms of cost for both, static and dynamic multicast groups, when compared to the best multicast algorithms known. Our evaluation of the cost performance of the algorithms showed that DDCLCMR is always within 10% from BSMA which has the best cost performance among all the proposed delay-constrained static multicast heuristics, while NAIVE, the well-known dynamic multicast routing algorithm, is up to 70% worse than BSMA in some cases.

A DISTRIBUTED DYNAMIC HEURISTIC FOR DELAY-CONSTRAINED LEAST-COST MULTICAST ROUTING

Many new distributed multimedia applications involve dynamic multiple participants, have stringent end-to-end delay requirement and consume large amount of network resources. In this paper, we propose a new distributed dynamic delay-constrained least-cost multicast routing algorithm (DDDCLCMR) to support these applications. DDDCLCMR scales well because the source of the multicast tree needs only limited computation or may even not be involved in the route computation. When group membership changes, the existing multicast tree is perturbed as little as possible. Simulation results show that DDDCLCMR performs very good in terms of delay and cost for both, static and dynamic multicast groups, when compared to the best multicast algorithms known.

Preferred link based delay-constrained least-cost routing in wide area networks

Multimedia applications involving digital audio and/or digital video transmissions require strict QoS constraints (end-to-end delay bound, bandwidth availability, packet loss rate, etc.) to be met by the network. To guarantee the real-time delivery of packets satisfying these constraints, a real-time channel (D. Ferrari and D.C. Verma, A scheme for real-time channel establishment in wide-area networks. IEEE JSAC, 8(3), 368–379, 1990) needs to be established before the transmission of packets of a connection can begin. The establishment of such channels requires the development of efficient route selection algorithms that are designed to take into account the QoS constraints. The general problem of determining a least-cost delay-constrained route in a given communication network has been proved to be NP-hard (M.R. Garey and D.S. Johnson, Computers and Intractability: a guide to the theory of NP-completeness, W.H. Freeman, 1979). In this paper, we describe a preferred link approach to distributed delay-constrained least-cost routing in order to establish real-time channels. The approach attempts to combine the benefits of probing and backtracking based algorithms (better adaptiveness and wider search) with the advantages of distance-vector type algorithms (lower setup time). The scheme is flexible in that a variety of heuristics can be employed to order the neighbouring links of any given node. Three heuristics are proposed and their performance is studied through simulation experiments. The simulation results indicate that the proposed heuristics provide better performance than other preferred neighbour methods, in terms of increased call acceptance rate and lower average route cost. The heuristics are also shown to adapt much better to dynamic variations in network and link characteristics.

Combined channel allocation and routing algorithms in packet switched networks

Joint optimization of capacity and flow assignment (CFA) is considered for a least-cost routing and bandwidth allocation in packet switched networks, in which multiple links are modeled by parallel M 1 queues. A generic cost-objective function is introduced to reflect both switching and channel costs. Queueing, transmission, nodal processing and propagation delays are all incorporated into the optimization problem. The proposed CFA problem is shown to be a convex optimization problem, thus ensuring a global solution. Furthermore, a more general optimization approach has been developed to circumvent the computational limitation associated with the existing optimization formulation and to capture the discrete nature of channel allocation.