Dynamic Shortest Path Research Articles

Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion.
 This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC).
 We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements.
 We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT).
 Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future.

Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects

The Shortest Path (SP) problem resembles a variety of real-world situations where one needs to find paths between origins and destinations. A generalization of the SP is the Dynamic Shortest Path (DSP) problem, which also models changes in the graph at any time. When a graph changes, DSP algorithms partially recompute the paths while taking advantage of the previous computations. Although the DSP problem represents many real situations, it leaves out some fundamental aspects of decision-making. One of these aspects is the existence of multiple, potentially conflicting objectives that must be optimized simultaneously. Recently, we performed a first incursion on the so-called Multi-Objective Dynamic Shortest Path (MODSP), presenting the first algorithm able to take the MODM perspective into account when solving a DSP problem. In this paper, we go beyond and formally define the MODSP problem, thus establishing and clarifying it with respect to its simpler counterparts. In particular, we start with a brief overview of the related literature and then present a complete formalization of the MODSP problem class, highlighting its distinguishing features as compared to similar problems and representing their relationship through a novel taxonomy. This work also motivates the relevance of the MODSP problem by enumerating real-world scenarios that involve all its ingredients, such as multiple objectives and dynamically updated graph topologies. Finally, we discuss the challenges and open questions for this new class of shortest path problems, aiming at future work directions. We hope this work sheds light on the theme and contributes to leveraging relevant research on the topic.

Backpressure-Based Distributed Dynamic Route Control for Connected and Automated Vehicles

This study focuses on the dynamic route control (DRC) problem for connected and automated vehicles (CAVs). The problem is usually solved using two popular methods, i.e., dynamic shortest path (DSP) and dynamic system optimal assignment (DSO). However, the DSP algorithm is unproductive under congested conditions, while the DSO, although can achieve system optimal, possesses considerable algorithm complexity. Furthermore, the two methods are usually solved in a centralized system due to the need for global information of the entire network, which is both communicationally and computationally ineffective. With the emergence of mobile edge computing (MEC), a new distributed DRC method only relying on local information is more achievable and practical. In this context, this research developed a novel DRC algorithm based on the distributed Backpressure (BP) principle for the MEC-enabled CAVs. The BP herein is modified as a function of the real-time density, which avoids the unrealistic point queue assumption in the original BP algorithm. In addition, the real-time traffic state is pre-identified to determine whether re-routing is essential, thereby reducing the possibility of CAVs guided to unnecessarily long routes. Results from the case study simulated by the traffic simulation software Simulation of Urban MObility (SUMO) indicate that the proposed method outperforms the BP method without congestion identification in low-demand cases, and the performance is significantly better than the DSP while approximating the DSO in congested cases. More importantly, both communicational cost and algorithm execution time was greatly reduced when compared with the DSO.

Maintaining Constrained Path Problem for Directed Acyclic Graphs

In order to restore the faulty path in network more effectively, we propose the maintaining constrained path problem. Give a directed acyclic graph (DAG) [Formula: see text] with some faulty edges, where [Formula: see text], [Formula: see text]. For any positive number [Formula: see text], we give effective maintain algorithm for finding and maintaining the path between source vertex [Formula: see text] and destination [Formula: see text] with length at most [Formula: see text]. In this paper, we consider the parameters [Formula: see text] and [Formula: see text] which are used to measure the numbers of edges and vertices which are influenced by faulty edges, respectively. The main technique of this paper is to define and solve a subproblem called the one to set constrained path problem (OSCPP) which has not been addressed before. On the DAG, compared with the dynamic shortest path algorithm with time complexity [Formula: see text] [16] and the shortest path algorithm with time complexity [Formula: see text] [18], based on the algorithm for OSCPP, we develop a maintaining constrained path algorithm and improve the time complexity to [Formula: see text] in the case that all shortest paths from each vertex [Formula: see text] to [Formula: see text] have been given.

Protecting Source Location Privacy in a Clustered Wireless Sensor Networks against Local Eavesdroppers

Recently, Wireless Sensor Networks (WSNs) have been extensively employed in several tactical applications out of which target detection, habit monitoring, and earthquake monitoring. The open characteristic nature of WSNs makes it vulnerable to be attacked by an eavesdropper who aims at disrupting the network operation or access confidential information. Consequently, ensuring the security and privacy in WSNs is considered an essential issue and definitely cannot be left without proper investigations. There are two types of threats that may occur for the WSNs privacy, that are, contextual and content privacy. Location privacy is considered an example of contextual privacy. In this paper, three source privacy protection schemes, which are based on clustering methodology, are proposed to protect contextual privacy. These schemes are dynamic shortest path (DSP) scheme, dynamic tree (DT) scheme, and hybrid scheme. Interestingly, a grid-based clustering technique is adopted to divide the network into several square clusters. Matlab simulations are used to evaluate the performance of the three proposed schemes in terms of latency, energy consumption, and safety period. The results undoubtedly confirm the effectiveness of the proposed schemes. Very high safety period is achieved and an adequate amount of energy is consumed. Furthermore, acceptable latency is achieved when compared to the shortest path scheme.

A Retroactive Approach for Dynamic Shortest Path Problem

Dynamic shortest path algorithms modify the existing shortest path tree or graph, taking into account changes in the underlying graph configuration. In the premise of this paper, the dynamic Dijkstra algorithm is specifically considered which is used for the solution of single source shortest path problem in dynamic graphs. Dynamic Single Source Shortest Path (DSSSP) problem is considered from a completely different perspective. For incorporating the dynamic changes into the solution, the retroactive data structure has been used. Dynamics of retroactive data structures provide a natural order for propagating the desired changes in the underlying graph configuration. DSSSP has been solved in efficient way with worst case complexity O(m log n) and also proved the correctness of our proposed algorithm.

Critical links detection in stochastic networks: application to the transport networks

PurposeThe purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.Design/methodology/approachA Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.FindingsThe numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.Research limitations/implicationsThe research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.Practical implicationsTaking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.Originality/valueThe paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

Dispatch approaches for scheduling radio telescope observations

Radio telescopes are a scarce resource designed to provide experimental data for astrophysical research, and various studies have focused on the design of observing schedules that make optimal use of available telescope time. We consider strategies for minimising the time required to observe a fixed set of pulsars, which provides an excellent example of scheduling in an unpredictable environment. First, owing to scintillation, the intensity of a pulsar signal is variable and random; therefore, the decision to abort or prolong an observation can be made only after some fraction of the scheduled observation has been completed. Second, observations may be interrupted by radio frequency interference or when the source sets below the horizon. Some sources are visible for more or less time depending on their declination and the latitude of the observing telescope. Formulating the problem in these terms leads to a highly dynamic shortest path problem with uncertainty. Unlike other documented telescope scheduling approaches, we demonstrate how a simple earliest setting policy achieves sets of pulsar observations in a rather short timespan. The policy is fast to apply due to a novel algorithm that pre-calculates the subset of next candidates before the end of the current integration. Our simulation also clarifies that the uncertainty arising from the scintillation (signal strength) encountered on arrival adds significantly to the variation in overall durations and that different hourly start times can favour or hamper the progress of a set of observations.

ANN Based Framework for Energy Efficient Routing In Multi-Hop WSNs

This paper proposes a soft computing based framework for optimal path routing in the large scale WSNs. The proposed approach works in two phases; initialization phase and the operational phase. In order to evaluate route costs, the paper first proposes an ANN based integrated link cost (ILC) measure. ILC is a function of residual sensor node energy, average end to end delay (EED) and network throughput. In the initialization phase the framework sets up and self-organizes the WSN and creates routing tables and initial set of s-t paths through cluster heads. In the operational phase optimal routes under given timing constraints are evolved using BB-BC optimization approach. Timing constraints are imposed due to dynamic conditions imposed by energy expenditure of nodes. Once the near shortest path/optimal routes are available, data transmission for a predefined interval takes place in the WSN. The ILC based dynamic shortest path routing approach improves throughput, reduces average end to end delay and improves the life time of the WSN. We implemented the proposed framework in MATLAB and its performance on optimal path enumeration was simulated. The framework was observed to be working extremely efficiently by evaluating near least cost path, thus keeping track on throughput, end to end delay and energy efficiency of the given WSN.

EFS: Entropy First Search for Dynamic Shortest Path Problem in Large Sparse Graphs

Searching the dynamic shortest path is a hot topic recently. In this paper we proposed a new method to solve the dynamic single source shortest paths (SSSP) problem in sparse graphs. The main contributions are three: firstly, in the preprocessing stage, we use the unreachable and unstartable characteristics to avoid most of the non-solution path search. Secondly, Entropy first search (EFS) is introduced to speed up the search process in finding the possible shortest path and then the algorithm can converges as soon as possible. In addition, the update algorithm for dynamic shortest path search is proposed for practical use. The experiments in large random sparse graphs show the efficiency and benefits of the proposed method.

Proactive Vehicular Traffic Rerouting for Lower Travel Time

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time. The proposed strategies proactively compute individually tailored rerouting guidance to be pushed to vehicles when signs of congestion are observed on their route. The five proposed strategies are the dynamic shortest path (DSP), the A* shortest path with repulsion (AR*), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP). Extensive simulation results show that the proposed strategies are capable of reducing the travel time as much as a state-of-the-art dynamic traffic assignment (DTA) algorithm while avoiding the issues that make DTA impractical, such as the lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows tuning the system to different levels of tradeoffs between rerouting effectiveness and computational efficiency. In addition, the proposed traffic guidance system can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.

Dynamic Genetic Algorithms with Hyper-Mutation Schemes for Dynamic Shortest Path Routing Problem in Mobile Ad Hoc Networks

In recent years, the static shortest path (SP) routing problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network (WMN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. This paper proposes to use two types of hyper-mutation GAs to solve the dynamic SP routing problem in MANETs. The authors consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the two hyper-mutation GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

Snapshot Centrality Indices in Dynamic FIFO Networks

The article introduces the concept of snapshot dynamic indices as centrality measures to analyse how the importance of nodes changes over time in dynamic networks. In particular, the dynamic stress-snapshot and dynamic betweenness snapshot are investigated. We present theoretical results on dynamic shortest paths in first-in first-out dynamic networks, and then introduce some algorithms for computing these indices in the discrete-time case. Finally, we present some experimental results exploring the algorithms' efficiency and illustrating the variation of the dynamic betweenness snapshot index for some sample dynamic networks.

Improvement A* Algorithm Based on Dynamic Consistency Assumption

Efficient dynamic shortest path algorithm in static networks plays an important role in ITS. To solve this problem, this paper brings forward the dynamic form of Consistency Assumption and Dynamic A* algorithm (A* algorithm based on dynamic lower bound, DA* algorithm) based on dynamic lower bound. DA* algorithm and the dynamic form of Consistency Assumption are described in detail. It is proved that DA* algorithm can solve one origin node to one destination node shortest paths problem in dynamic networks, if DA* algorithm’s dynamic lower bound satisfies the dynamic form of Consistency Assumption.

Experimental Study of Dynamic Single-Source Shortest Path Algorithm

In this paper, software Inet 3.0 is applied to generate topology, which randomly generates dynamic topology nodes. Based on dynamic shortest path algorithms put forward by P.Narvaez, Xiaobin et al, we analyzed the time efficiency of dynamic and static shortest path algorithms, the different time efficiency inner dynamic shortest path algorithms, and the relationship of time efficiency between topology and dynamic shortest path algorithms. The result shows that Xiaobin algorithm is statistically better than Narvaez algorithm about 20-30 percent. Dynamic algorithms are not always better than static algorithms considering the amount of changed topology. Dynamic and static algorithms are roughly same when the amount of changed topology holds 10 percent. Dynamic algorithms perform better when less than 10 percent, otherwise static algorithms will be better. The time efficiency of dynamic algorithms is related to special topology.

Dynamic shortest path problems with time-varying costs

This paper concerns the problem of finding shortest paths from one node to all other nodes in networks for which arc costs can vary with time, each arc has a transit time, and parking with a corresponding time-varying cost is allowed at the nodes. The transit times can also take negative values. A general labeling method, as well as several implementations, are presented for finding shortest paths and detecting negative cycles under the assumption that arc traversal costs are piecewise linear and node parking costs are piecewise constant.

Ynamic traffic model under emergency incident and bidirectional dynamic shortest path algorithm

ynamic traffic model under emergency incident and bidirectional dynamic shortest path algorithm

Shortest Path Tree Computation in Dynamic Graphs

Let G = (V, E, omega) be a simple digraph, in which all edge weights are nonnegative real numbers. Let G' be obtained from G by an application of a set of edge weight updates to G. Let sisinV and let Ts and Ts' be Shortest Path Trees (SPTs) rooted at s in G and G', respectively. The Dynamic Shortest Path (DSP) problem is to compute Ts' from Ts. Existing work on this problem focuses on either a single edge weight change or multiple edge weight changes in which some of them are incorrect or are not optimized. We correct and extend a few state-of-the-art dynamic SPT algorithms to handle multiple edge weight updates. We prove that these algorithms are correct. Dynamic algorithms may not outperform static algorithms all the time. To evaluate the proposed dynamic algorithms, we compare them with the well-known static Dijkstra algorithm. Extensive experiments are conducted with both real-life and artificial data sets. The experimental results suggest the most appropriate algorithms to be used under different circumstances.

Dynamic shortest paths and transitive closure: Algorithmic techniques and data structures

In this paper, we survey fully dynamic algorithms for path problems on general directed graphs. In particular, we consider two fundamental problems: dynamic transitive closure and dynamic shortest paths. Although research on these problems spans over more than three decades, in the last couple of years many novel algorithmic techniques have been proposed. In this survey, we will make a special effort to abstract some combinatorial and algebraic properties, and some common data-structural tools that are at the base of those techniques. This will help us try to present some of the newest results in a unifying framework so that they can be better understood and deployed also by non-specialists.

A dynamic shortest path algorithm using multi-step ahead link travel time prediction

In this paper, a multi-step ahead prediction algorithm of link travel speeds has been developed using a Kalman filtering technique in order to calculate a dynamic shortest path. The one-step and the multi-step ahead link travel time prediction models for the calculation of the dynamic shortest path have been applied to the directed test network that is composed of 16 nodes: 3 entrance nodes, 2 exit nodes and 11 internal nodes. Time-varying traffic conditions such as flows and travel time data for the test network have been generated using the CORSIM model. The results show that the multi-step ahead algorithm is compared more favorably for searching the dynamic shortest time path than the other algorithm.