Routing Policies Research Articles

Improved Dynamic Routing Algorithm for Information Aggregation

Information aggregation is an essential component of text encoding, but it has been paid less attention. The pooling-based (max or average pooling) aggregation method is a bottom-up and passive aggregation method, and loses a lot of important information. Recently, attention mechanism and dynamic routing policy are separately used to aggregate information, but their aggregation capabilities can be further improved. In this paper, we proposed an novel aggregation method combining attention mechanism and dynamic routing, which can strengthen the ability of information aggregation and improve the quality of text encoding. Then, a novel Leaky Natural Logarithm (LNL) squash function is designed to alleviate the “saturation” problem of the squash function of the original dynamic routing. Layer Normalization is added to the dynamic routing policy for speeding up routing convergence as well. A series of experiments are conducted on five text classification benchmarks. Experimental results show that our method outperforms other aggregating methods.

Dynamic routing in a distributed parallel many-server service system: The effect of [formula omitted]-choice

We consider a queueing system with multiple stations serving a single class of customers. Each station has many servers, and its own dedicated queue. The system manager must decide to which station each new customer is routed. We propose and analyze a minimum-expected-delay faster-server-first with ξ-choice (MED-FSF(ξ)) routing policy, under which an integer ξ, with P{ξ≥2}>0, is randomly generated for each new customer. Then, a subset of ξ stations will be randomly collected, with state information being retrieved. Among these collected stations, the system manager assigns the new customer to: (i) the one with minimum expected delay if all these ξ stations are fully occupied; or (ii) the one with fastest available server. We prove that in the Halfin–Whitt regime this policy is asymptotically equivalent to the MED-FSF policy, which only works when full information of the system state is available. Moreover, we compare the MED-FSF(ξ) policy with the random routing policy, which uses no information and routes each incoming customer to a station in a random manner. Using simulation experiments, we validate the theoretical results, and find that our proposed policy significantly outperforms the random routing policy. This suggests that a slight increase in information at each routing epoch can substantially improve system performance.

Express package routing problem with occasional couriers

This study investigates the viability of a “courier friendly” crowd-shipping (CS) model to carry out express package deliveries in an urban area. Imposing almost no managerial control over the occasional couriers (OC) to maximize their willingness to participate, the envisioned model suggests using transshipment points to enhance operational efficiency and a company-controlled backup delivery capacity to account for the uncertainty in the crowd-provided delivery capacity. A dynamic programming (DP) approach is developed to address the package routing problem that needs to be solved for the real-time management of the CS network. The suggested methodology does not assume any specific distributions for the courier and demand arrivals and provides the optimal package routing policy when the courier and service point capacities are not binding. Several extensions of the basic DP approach are studied to consider limited courier and service point capacities and take advantage of the extra information provided by those couriers that declare their arrivals in advance. To the best of the author’s knowledge, this study presents the first example of using Monte-Carlo simulations to determine “shadow costs” of capacity utilizations and use them in making the assignment (matching) decisions. An extensive computational study demonstrates the efficacy of the solution approach. It provides valuable insights into, among others, the potential benefits of delivering packages with a coordinated effort of OCs (with transshipments) under short delivery lead time restrictions, the impact of the spatial and temporal distribution of the demand and courier arrivals on the system performance, and the importance of the notice times for OC arrivals.

A security‐driven network architecture for routing in industrial Internet of Things

AbstractIndustrial Internet of Things (IIoT), as the integration of industrial network and IoT, has attracted a lot of attention from both academia and industry. However, with such integration deepens, many issues suffered by the industrial networks are exposed and magnified. The secure routing is a key focus, since the industrial environment is extremely vulnerable and many potential threats (eg, distributed denial of service and selfish attacks) are introduced due to the connection to the Internet. In order to prevent the threats from the Internet and embrace the benefits of IIoT, a security‐driven network framework for routing is particularly designed. Specifically, we first leverage the technologies of software‐defined networking (SDN), network function virtualization (NFV), and blockchain to create the overall framework which is flexible, programmable, and secure. On one hand, the three‐level structure of SDN/NFV is applied to each domain, based on which we can flexibly control and program the underlying forwarding devices in this domain for the purpose of calculating the optimal routing policies. On the other hand, the blockchain is applied among different SDN controllers to create a trust and untamperable environment. Then, under this framework, a secure routing mechanism is proposed as well to prevent the attacks on the basis of node identity authentication and node behavior authentication. The simulation experiments are carried out on the OMNet++ platform and the experimental results indicate that the proposed mechanism outperforms the other state‐of‐the‐art methods in terms of scalability and robustness especially when encountering malicious attacks.

On the Deadline Miss Probability of Various Routing Policies in Wireless Sensor Networks

Moving data across communication networks is often subject to deadline requirements. An example is early warning of disasters of natural origin, where sensor measurements at the disaster location must be communicated across a network within a predefined maximum delay in order for a consequent warning to be timely. In this work, we present a probabilistic model that allows for characterizing the delay experienced by sensor measurements in a wireless sensor network from source to sink depending upon the routing metric used for forwarding the data through the network. Using link delay probability distributions and the probabilities of following different paths to the sink, source-to-sink delay distributions are found for routing policies based on minimum hop-count, minimum mean delay and the Joint Latency (JLAT) protocol. An algorithm for calculating the end-to-end source to sink delay probability density function (PDF) is presented for the general case of networks that use routing tables whose input for routing decisions is the remaining time-to-deadline. The work provides a general tool for routing delay analysis, allowing for comparison of the deadline miss probability between different routing policies. An improved form of JLAT is proposed. Its deadline miss probability is found using the presented algorithm and compared to the ones determined for minimum hop-count, minimum mean delay and JLAT by means of an example.

Novel Online Sequential Learning-Based Adaptive Routing for Edge Software-Defined Vehicular Networks

To provide efficient networking services at the edge of Internet-of-Vehicles (IoV), Software-Defined Vehicular Network (SDVN) has been a promising technology to enable intelligent data exchange without giving additional duties to the resource constrained vehicles. Compared with conventional centralized SDVNs, hybrid SDVNs combine the centralized control of SDVNs and self-organized distributed routing of Vehicular Ad-hoc NETworks (VANETs) to mitigate the burden on the central controller caused by the frequent uplink and downlink transmissions. Although a wide variety of routing protocols have been developed, existing protocols are designed for specific scenarios without considering flexibility and adaptivity in dynamic vehicular networks. To address this problem, we propose an efficient online sequential learning-based adaptive routing scheme, namely, Penicillium reproduction-based Online Learning Adaptive Routing scheme (POLAR) for hybrid SDVNs. By utilizing the computational power of edge servers, this scheme can dynamically select a routing strategy for a specific traffic scenario by learning the pattern from network traffic. Firstly, this paper applies Geohash to divide the large geographical area into multiple grids, which facilitates the collection and processing of real-time traffic data for regional management in controller. Secondly, a new Penicillium Reproduction Algorithm (PRA) with outstanding optimization capabilities is designed to improve the learning effectiveness of Online Sequential Extreme Learning Machine (OS-ELM). Finally, POLAR is deployed in control plane to generate decision-making model (i.e., routing policy). Based on the real-time featured data, this scheme can choose the optimal routing strategy for a specific area. Extensive simulation results show that POLAR is superior to a single traditional routing protocol in terms of packet delivery ratio and latency.

Application of Traffic Weighted Multi-Map Optimization Strategies to Traffic Assignment

Traffic Assignment Problem (TAP) is a critical issue for transportation and mobility models that deals mainly with the calculus and delivery of best-cost routes for the trips in a traffic network. It is a computationally complex problem focused on finding user equilibrium (UE) and system optimum (SO). The Traffic Weighted Multi-Maps (TWM) technique offers a new perspective for TAP calculus, based on routing decisions using different traffic network views. These TWM are complementary cost maps that combine physical traffic networks, traffic occupation data, and routing policies. This paper shows how evolutionary algorithms can find optimal cost maps that solve TAP from the SO perspective, minimizing total travel time and providing the best-cost routes to vehicles. Several strategies are compared: a baseline algorithm that optimizes the whole network and two algorithms based on extended k-shortest path mappings. Algorithms are analyzed following a simulation-optimization methodology over synthetic and real traffic networks. Obtained results show that TWM algorithms generate solutions close to the static UE traffic assignment methods at a reasonable computational cost. A crucial aspect of TWM is its good performance in terms of optimal routing at the system level, avoiding the need for continuous route calculus based on traffic status data streaming.

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

The Spillover Effects of Capacity Pooling in Hospitals

Off-service placement is a common capacity pooling strategy that hospitals utilize to address mismatches in supply and demand that arise from the day-to-day variation in patient demand. This strategy involves placing patients in a bed in a unit that is designated for another specialty service. Building on prior work that documents the negative first-order effects of off-service placement on patients who are placed off service themselves, we quantify the spillover effects of this practice on patients who are actually placed on service. Using an instrumental variables approach, we find that patients placed on service experience a substantial spillover effect of off-service placement, manifesting as a longer length of stay, a higher likelihood of hospital readmission, and a higher likelihood of clinical trigger activation. Our analyses show it is not only the average level of off-service placement, but also the volatility of off-service placement, that contributes to this spillover effect. Through a series of counterfactual analyses, we propose alternate routing policies that could meaningfully improve outcomes around the efficiency and quality of care in the inpatient setting.

Evolutionary cross-layer interactive model for sensor communication

Cross-layer based approach has significant benefits compared to conventional layered based routing approach in wireless sensor network (WSN); however, existing approaches in this direction have been found to offer a symptomatic solution towards any problem. The proposed system emphasises on establishing interactivity among the logical link layer, MAC layer, and network layer. Usage of CLIF model ensures message ranking mechanism that assists in tagging the priorities to the control message which facilitates to offer better signal processing services by WSN. Hence, an evolutionary novel approach is introduced in this paper which predominantly emphasise on the interaction between the cross-layer. The model is developed to 1) ensure better network lifetime, 2) incorporate better supportability to ad-hoc routing policies, 3) leverage cooperative transmission scheme, and 4) maintain sustainability between data forwarding mechanism and energy consumption. The outcome of the simulation result shows that the proposed scheme offers minimal delay, maximised data delivery, and reduced energy consumption in WSN.

Optimal Routing Policy for a Mixed Traffic Flow of Connected Vehicles and Regular Vehicles with En-route Information

Optimal Routing Policy for a Mixed Traffic Flow of Connected Vehicles and Regular Vehicles with En-route Information

Convolutional LSTM models to estimate network traffic

Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration—details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration—is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effiectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.

Reliable energy preserving cluster-based routing policy with optimal route selection for wireless sensor networks

Wireless sensor networks (WSNs) is one of the hottest research areas, owing to its wider applicability. Due to its effectiveness and inexpensiveness, most of the domains enjoy the benefits of WSN. ...

Temporal and Spectral 2D Fragmentation-Aware RMSA Algorithm for Advance Reservation Requests in EONs

In this paper, we explore the problem of routing, modulation and spectrum assignment (RMSA) for advance reservation (AR) requests in elastic optical networks (EONs), with the objective to further reduce the blocking probability and improve the spectrum utilization. On the one hand, we propose a minimum resource consumption routing policy (MRC) for leaving more free resources for future requests. On the other hand, we introduce a two-dimensional (2D) path resource model, in which each AR request can be transformed into a rectangle block whose length and width are related to request bandwidth and request duration respectively. Therefore, the spectrum assignment problem of AR requests is simplified to the two-dimensional rectangle packing problem. Furthermore, we design several factors to evaluate the two-dimensional fragmentation caused by a rectangle packing operation, where we consider the fragmentation situation in the rectangle block neighborhood for the first time. Based on these factors, we propose a two-dimensional fragmentation-aware spectrum assignment strategy (2D-FA). Through the combination of MRC and 2D-FA, we propose a novel RMSA algorithm named MRC-2D-FA for dynamically provisioning AR requests in EONs. Simulation results show that MRC-2D-FA can achieve lower bandwidth blocking probability and higher spectrum utilization compared with several well-performed benchmark algorithms.

Research on Optimization Policy Routing Technology of Optical Fiber Communication Network

With the development of global informatization, information has penetrated into every corner of human society. With the popularization of Internet, the transmission bandwidth of information becomes more and more important. Nowadays, people’s demand and dependence on information are becoming stronger and stronger. It is urgent to expand the capacity of wide area backbone communication network, and the optical network composed of wavelength division multiplexing (WDM) can just take this role. In WDM network, it can transmit multiple optical signals of different wavelengths in one fiber at the same time. WDM has the advantages of large transmission capacity, strong technical adaptability, simple implementation and easy expansion. In addition, WDM optical network can also make full use of the huge bandwidth of optical fiber itself to carry out high-speed transmission. Now, the optical network using WDM technology has become the backbone network of long-distance transmission. This paper studies the optical fiber communication system and analyzes the structure of the optical fiber network. For the optical network, how to find the appropriate routing is a very important technology. After analyzing all kinds of basic routing technologies, this paper proposes a new policy based routing technology, and carries out relevant experiments. The experimental results show that the new routing technology has some advantages compared with the old routing technology.

Recent ship traffic and developing shipping trends on the Northern Sea Route—Policy implications for future arctic shipping

Information on ship traffic on the Northern Sea Route (NSR) has been lacking. Previous studies have analyzed transit shipping via the NSR, but no data has been available on the overall traffic volume (number of voyages) and the magnitude and characteristics of domestic and destination shipping. Information on shipping trends and the role of Russian and non-Russian shipping companies in ongoing shipping operations has been incomplete. This study provides such statistical data and evaluation of traffic on the NSR during 2016–2019 and discusses policy implications for future NSR shipping. The study shows that Russian domestic shipping dominates shipping activities on the NSR in terms of number of shipping companies, vessels and voyages. There are much fewer Asian shipping companies working on the NSR than European companies, contrary to media reports that often depict a large Asian shipping influence on the NSR. Much more frequent voyages are also taking place between the NSR and European ports than ports in the Asian Pacific region. Developing logistics operations involve year-round shipments of commodities from remote locations within the NSR to ice-free transshipment and storage hubs located outside the NSR, but within Russia.

Ant Colony Optimization-based distributed multilayer routing and restoration in IP/MPLS over optical networks

The management of multilayer networks is often a complex task, since each layer is typically independently operated and maintained, and there is a lack of shared network resource knowledge between layers. In this work, we have illustrated that Ant Colony Optimization (ACO) algorithms can be a suitable solution for coordinated management of disjoint IP and optical layers with limited or no visibility between the control planes of each layer. ACO algorithms are used to achieve fully-distributed multilayer routing policies in IP-over-optical networks. Simulations have demonstrated that ACO-based policies can achieve lower levels of blocking probability and higher levels of restorability for IP connections compared to fixed-alternate routing policies in almost all cases. This better performance is achieved with only small increases in the connection setup and restoration times, and without requiring a significant amount of communication overhead.

A Joint Comparative Analysis of Routing Heuristics and Paperless Picking Technologies Using Simulation and Data Envelopment Analysis

Recent literature demonstrates that warehouse order picking performance is reflected in the logistics performance of downstream retailers. Warehouse solutions and policies significantly contribute to the improvement of distribution and delivery to retailers. This paper therefore reports an analysis of the joint performance of routing policies and picking technologies, and provides insights into the best ways to combine routing strategies and paperless solutions in order to optimize cost efficiency. We follow a multistage approach that combines mixed integer linear programming algorithms, data envelopment analysis (DEA), and ranking and selection. The results show that traversal-voice picking and midpoint-voice picking combinations are equally distributed over the most efficient subsets and that superior technology can enhance picking efficiency only to a certain level. The study provides guidelines for logistics managers on ways to combine warehouse solutions and policies in order to better streamline the operations. It offers an original framework to analyze the joint performance of picking routing and picking solutions by considering the effect of picking errors.

Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks

As a promising technology in the Internet of Underwater Things, underwater sensor networks (UWSNs) have drawn a widespread attention from both academia and industry. However, designing a routing protocol for UWSNs is a great challenge due to high energy consumption and large latency in the underwater environment. This article proposes a Q-learning -based localization-free anypath routing (QLFR) protocol to prolong the lifetime as well as reduce the end-to-end delay for UWSNs. Aiming at optimal routing policies, the Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two reward functions (i.e., depth-related and energy-related rewards) for Q-learning with the objective of reducing latency and extending network lifetime. In addition, a new holding time mechanism for packet forwarding is designed according to the priority of forwarding candidate nodes. Furthermore, mathematical analyses are presented to analyze the performance and computational complexity of the proposed routing protocol. Extensive simulation results demonstrate the superiority performance of the proposed routing protocol in terms of the end-to-end delay and the network lifetime.

DRL-ER: An Intelligent Energy-Aware Routing Protocol With Guaranteed Delay Bounds in Satellite Mega-Constellations

Major space companies are developing satellite mega-constellations to provide global Internet coverage and services. Limited battery capacity is one of the biggest obstacles on mega-constellations due to the restricted weight and volume of satellites. Massive Internet packet routing tasks pose a big challenge to the energy system in such mega constellations. Incorrect use of satellite batteries during routing phases may significantly increase the energy consumption and cause node failure quickly. Existing state-of-the-art works on energy-saving routing for satellite networks paid much attention on traffic distribution and end-to-end delay issues. However, these methodologies were using many real-time network information for optimization which is not practical in mega-constellations. Note also that these works did not consider energy efficiency and guaranteed end-to-end delay simultaneously. In this paper, we propose a novel deep reinforcement learning based energy-efficient routing protocol called DRL-ER, which avoids the battery energy imbalance of constellations and can also guarantee a required bounded end-to-end delay. In DRL-ER, satellites can learn a routing policy that will balance energy usage among satellites. Extensive simulation results show that our proposed DRL-ER protocol reduces the energy consumption of satellites in average by more than 55% compared to the current state-of-the-art work, and prolongs the lifetime of constellations significantly.