Path Set Selection Research Articles

Efficient Optimization of Control Libraries

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. The library is either evaluated on a fixed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing submodular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.

Multi-Objective Function-Based Node-Disjoint Multipath Routing for Mobile Ad Hoc Networks

The main goal is to find multiple node-disjoint paths that meet the multi-objective optimization problem in terms of energy consumption minimization and network lifetime improvement. Due to the battery-dependent nodes in mobile ad hoc networks, the performance of the network will degrade. Hence, it is necessary to choose multiple optimal node-disjoint paths between source and destination for data transfer. Additionally, it improves the Quality of Service (QoS) of wireless networks. Multi-objective function is used to select a path such that it gives an optimum result based on the energy consumption, hop, and traffic load. From the simulation results, it is proved that the proposed system is achieving less energy consumption and improved network lifetime compared with existing Dynamic Source Routing (DSR), Hopfield Neural Network-based Disjoint Path set Selection (HNNDPS) and Multipath DSR (MDSR).

Transport Waste Separation and Recovery Trajectory Judged Based on Dijkstra’s Algorithm

Urban waste classification and recycling is an important research node in people’s livelihood, which is related to the corresponding relationship between efficiency and quality. The optimization of the recycling transportation path means that the cost is less. In the process of garbage transportation classification and recycling, optimizing and merging the routes of garbage transportation based on Dijkstra’s algorithm can optimize the shared transportation resources, to achieve the result of saving operating costs. The immediate transportation recycling event is planned as the path planning optimization. Multi-source multi-object and multi-path planning problems, and rehearse the optimization model of the total length of the transport path set without repeated paths. When the scale of the transportation and recovery network grows to a set value, it is difficult to achieve a theoretical solution by accurately calculating the optimal value of the model through an optimization algorithm. An optimal path set selection algorithm based on a random walk is feasible. The effectiveness and operability of the method are verified by small experiments and simulation experiments. The path is the shortest, and compared with the summation algorithm, it not only has high accuracy, but also has high execution efficiency.

PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm. Link expiration time (LET) between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in single phase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.

High reliable disjoint path set selection in mobile ad-hoc network using Hopfield neural network

Mobile ad-hoc network (MANET) is a collection of wireless mobile computers forming a temporary network without any fixed infrastructure or wired backbone. Topological changes in MANET frequently render routing paths unusable. A suitable technique for addressing this problem is to enhance the diversity of paths between the source and destination. However, multipath routing is a challenging task. In particular, the correlation between the failures of the paths in a path set should be as small as possible. Shared nodes and links between the paths are usual failure points. Disjointed path sets require the multiple paths to be link-disjoint or node-disjoint. However, selecting an optimal path set is an NP-complete problem. Artificial neural networks have been proposed as computational tools to solve constrained optimisation problems. The use of Hopfield neural network as a path set selection algorithm is explored. Since this algorithm produces a set of backup paths with much higher reliability, it is beneficial for MANETs. We use link expiration time (LET) between two nodes to estimate link reliability. In this approach, node-disjoint and link-disjoint path sets can be found simultaneously with route discovery algorithm. So, if someone wants to find both node-disjoint and link-disjoint path sets, there is no need to submit extra control messages, as overhead, to the MANET. Simulation results show that the proposed protocol can find path sets with higher reliability in comparison to the other recent proposed algorithms.

Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs. Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs. In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability of the proposed algorithm is up to 4.5 times more than the shortest path reliability. Also, the proposed algorithm has better performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to 20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths as compared to HNN-based algorithm in less number of iterations.

Modelling and Implementation of QoS in Wireless Sensor Networks: A Multiconstrained Traffic Engineering Model

This paper revisits the problem of Quality of Service (QoS) provisioning to assess the relevance of using multipath routing to improve the reliability and packet delivery in wireless sensor networks while maintaining lower power consumption levels. Building upon a previous benchmark, we propose a traffic engineering model that relies on delay, reliability, and energy-constrained paths to achieve faster, reliable, and energy-efficient transmission of the information routed by a wireless sensor network. As a step forward into the implementation of the proposed QoS model, we describe the initial steps of its packet forwarding protocol and highlight the tradeoff between the complexity of the model and the ease of implementation. Using simulation, we demonstrate the relative efficiency of our proposed model compared to single path routing, disjoint path routing, and the previously proposed benchmarks. The results reveal that by achieving a good tradeoff between delay minimization, reliability maximization, and path set selection, our model outperforms the other models in terms of energy consumption and quality of paths used to route the information.

Backup path set selection in ad hoc wireless network using link expiration time

Topological changes in mobile ad hoc networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on quality of service. A suitable technique for eliminating this problem is to use multiple backup paths between the source and the destination in the network. Most of the proposed on-demand routing protocols however, build and rely on single route for each data session. Whenever there is a link disconnection on the active route, the routing protocol must perform a path recovery process. This paper proposes an effective and efficient protocol for backup and disjoint path set in an ad hoc wireless network. This protocol converges into a highly reliable path set very fast with no message exchange overhead. The paths selection according to this algorithm is beneficial for mobile ad hoc networks, since it produces a set of backup paths with much higher reliability. Simulations are conducted to evaluate the performance of our algorithm in terms of route numbers in the path set and its reliability. In order to acquire link reliability estimates, we use link expiration time (LET) between each two nodes. In another experiment, we save the LET of entire links in the ad hoc network during a specific time period, then use them as a data base for predicting the probability of proper operation of links. Links reliability obtains from LET. Prediction is done by using a multi-layer perceptron (MLP) network which is trained with error back-propagation error algorithm. Experimental results show that the MLP net can be a good choice to predict the reliability of the links between the mobile nodes with more accuracy.

Traffic Engineering for Provisioning Restorable Hose-Model VPNs

Virtual Private Networks (VPNs) are overlay networks established on top of a public network backbone with the goal of providing a service comparable to Private Networks (PNs). The recently proposed VPN hose-model provides customers with flexible and convenient ways to specify their bandwidth requirements. To meet the specified bandwidth requirements, the Network Service Provider (NSP) must reserve sufficient bandwidth on the data transmission paths between each pair of endpoints in a VPN. In addition, the reliability of a VPN depends on the reliability of the data transmission paths. Italiano et al. proposed an algorithm that finds a set of backup paths for a given VPN (VPN tree) under the single-link failure model [1]. When a link failure is detected on a VPN tree, a backup path corresponding to the failed link can be activated to restore the disconnected VPN tree into a new one, thereby ensuring the reliability of the VPN. However, Italiano's algorithm cannot guarantee that the specified bandwidth requirement of the given VPN under the single-link failure model will be met. To address this issue, we propose a new backup path set selection algorithm called BANGUAD in this paper. In addition, the problem of establishing multiple bandwidth-guaranteed hose-model VPNs under the single-link failure model has not been investigated previously. However in this problem, bandwidth-sharing algorithms have the potential to improve the performance of a provisioning algorithm significantly. Therefore, we also propose a bandwidth sharing algorithm and three provisioning algorithms for establishing multiple bandwidth-guaranteed hose-model VPNs under the single-link failure model. Simulations that compare the performance of the proposed algorithms are reported.

An optimal representative set selection method

The optimal representative set selection problem is defined thus: given a set of test requirements and a test suite that satisfies all test requirements, find a subset of the test suite containing a minimum number of test cases that still satisfies all test requirements. Existing methods for solving the representative set selection problem do not guarantee that obtained representative sets are optimal (i.e. minimal). The enhanced zero–one optimal path set selection method [C.G. Chung, J.G. Lee, An enhanced zero–one optimal path set selection method, Journal of Systems and Software, 39(2) (1997) 145–164] solves the so-called optimal path set selection problem, and can be adapted to solve the optimal representative set selection problem by considering paths as test cases and components to be covered (e.g. branches) as test requirements.

An enhanced zero-one optimal path set selection method

One issue in structural program testing is how to select a mimimal set of test paths to meet certain test requirements. This is referred to as the optimal path set selection problem. The previously proposed zero-one optimal path set selection method is a generalized method that can be applied to most coverage criteria. However, the major drawback of this method is that for a large program the computation may take ten or more hours because the computation is exponentially proportional to the number of candidate paths and proportional to the number of components to be covered. To alleviate the drawback, this paper enhances the method by (1) defining five reduction rules, and (2) reusing previously selected path sets to reduce both the number of candidate paths and the number of components to be covered. Since both the number of candidate paths and the number of components to be covered are reduced, the computation time can be greatly reduced. In addition, an efficient approach to handling infeasible paths is proposed. An evaluation of the enhanced zero-one method, the original zero-one method, and a greedy method is executed and the result is presented.