Dynamic Routing Scheme Research Articles

Integrated real-time signal control and routing optimization: A two-stage rolling horizon framework with decentralized solution

This paper presents an integrated framework for optimizing signal control and vehicle routing. An important feature of the proposed framework is the ability to simultaneously determine signal states and individual vehicle routes in real time. The general objective is to minimize the network travel time, which can be represented as a trade-off between the total route length of all vehicles and traffic conditions at signalized intersections. A two-stage rolling horizon framework is proposed to explicitly describe the relationship between individual vehicle routes and predicted traffic flow dynamics at signalized intersections. The first stage involves a signal optimization problem, while the second stage optimizes a joint signal control and vehicle routing problem. Both stages are formulated as mixed integer linear programming problems. The optimization procedure is decentralized, and the effects of vehicle routing on control performance is considered by incorporating the route length cost into the objective function. Simulation experiments validate the advantages of the proposed framework over advanced signal control strategies and dynamic user-optimal routing strategies in various scenarios. The effectiveness in improving network capacity, alleviating spillback, and decreasing congestion dissipation time under over-saturation conditions is discussed. The results of vehicle routing suggest that the total travel time can be reduced at a low rerouting cost. Sensitivity analyses demonstrate the network control performance under different compliance rates and model coefficients. Moreover, the computational feasibility of the framework is verified.

A dynamic routing scheme for underwater acoustic sensor networks in submarine disaster applications

IntroductionThe ocean economy serves as a critical engine for the development of human society and economy, and its stable growth is of utmost importance. However, the frequent and unexpected occurrence of natural disasters, such as submarine disasters, poses significant threats to human society, especially disasters related to wave phenomena like the Ekman current, which has particularly prominent impacts.MethodsThe study proposes a dynamic routing scheme for underwater acoustic sensor networks. It establishes a cone-shaped distributed network, utilizing autonomous underwater vehicles to collect crucial data from nodes deployed on the seabed and transmitting it through the underwater cone-shaped distributed sensor network. Additionally, it adopts source location protection (SLP) technology to ensure the privacy of source locations. To validate the practicality and stability of this scheme, simulation experiments targeting the Ekman current were conducted in MATLAB.ResultsThe experimental results demonstrate that the cone–SLP network significantly reduces the frequency of routing information exchange and energy consumption among acoustic sensors, effectively enhancing the economy and durability of the sensor network. Furthermore, it exhibits robust practicality and high stability in complex and variable submarine environments.DiscussionThis research not only provides a practical method for effectively monitoring submarine disasters but also offers valuable experience and significant reference value for other similar projects, playing a more crucial role in the sustainable development of the ocean economy.

RIde-hail vehicle routing (RIVER) as a congestion game

The RIde-hail VEhicle Routing (RIVER) problem describes how drivers in a ride-hail market form a dynamic routing strategy according to the expected reward in each zone of the market. We model this decision-making problem as a Markov decision process (MDP), and view the drivers as playing an MDP routing game, with “congestion” induced by competitive matching in a zone. The meeting probability (i.e., the probability of successfully picking up a passenger after cruising in a zone for one period) is derived from a physical model, which is specified for street-hail and e-hail service modes separately, and calibrated with simulation data. We define a Wardrop equilibrium for the MDP routing game, and then prove it exists and can be obtained by solving a fixed-point problem. We further show a system optimum – corresponding to the maximum total expected reward accumulated over time by all drivers – can be achieved if drivers make individual routing decisions according to a cooperative reward rather than the personal reward. In other words, the proposed cooperative strategy “decentralizes” the system optimal solution in the MDP routing game. The results from numerical experiments, including a case study of Chicago, indicate the service mode plays a critical role in shaping system performance. While e-hail enjoys a higher fleet utilization rate than street-hail thanks to its more advanced matching technology, it may lead to a more uneven spatial distribution of vacant vehicle supply. As expected, cooperative routing improves system performance in terms of both total reward and equal distribution of supply. Yet, its benefit is much stronger in e-hail than in street-hail, especially when the supply is overly abundant. We also find, when a local demand surge occurs, the cooperative rewards rise in sync, similar to surge pricing on the supply side. Interestingly, the impact of the price surge spreads broadly in space, propagating far beyond the epicenter of the demand surge.

IoT-based milk-run routing for manufacturing system: an application case in an automotive company

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines’ performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains’ fleet was improved, and (iii) the daily path was minimized.

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users’ multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely Coarse-grained Interest Extracting (CIE) and Fine-grained Behavioral Correlation (FBC) , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.

Dynamic User Routing, Paid and Free Users, Web applications, Content Delivery Network (CDN)

Abstract: This research paper introduces a novel approach for efficient routing between paid and free users in web applications by harnessing the capabilities of a Content Delivery Network (CDN). The primary objective of this study is to optimize user experience and resource allocation based on user type, aiming to deliver personalized content while maximizing resource utilization. The proposed routing mechanism dynamically directs users to the appropriate pages or content based on their subscription status, leveraging the integration of a CDN into the web application architecture. The paper thoroughly explores the implementation details, emphasizing the seamless interaction between the CDN and user authentication mechanisms, and investigates the routing algorithms employed to intelligently determine the most suitable content delivery path for each user type. Through comprehensive experimentation and evaluation, the research demonstrates the efficiency and scalability of the proposed approach. Various user scenarios are considered, and factors such as response time, resource utilization, and content availability are evaluated. The experimental results showcase the significant enhancement of the overall user experience achieved through the dynamic user routing mechanism, providing tailored content delivery while effectively managing resource allocation. The findings of this research contribute to the field of web application development, particularly in managing different user types and delivering personalized content. By effectively leveraging CDNs and implementing dynamic routing strategies, web applications can optimize user satisfaction, ensure efficient resource utilization, and accommodate varying user subscription models. This research offers valuable insights and practical implications for designing and implementing usercentric web applications that deliver customized experiences and efficiently allocate resources based on user types. The paper delves into the implementation details, highlighting the key components and functionalities of the proposed solution. It explores the integration of the CDN into the web application architecture, emphasizing the seamless interaction between the CDN and user authentication mechanisms. Additionally, it investigates the routing algorithms employed to intelligently determine the appropriate content delivery path for each user type.

A survivable multipath resource allocation strategy based on fragmentation-sensitive fragmentation-aware in space division multiplexing elastic optical networks

The space division multiplexing elastic optical network (SDM-EONs) can effectively supply the large-capacity for the current optical network. If a link failure occurs, it will bring huge data losses or disruptions for the operator. We propose a survivable multipath fragmentation-sensitive fragmentation-aware routing, core and spectrum assignment (SM-FSFA-RSCA) scheme to solving the problem of survivability of the request in SDM-EONs and spectrum fragmentation that caused by the dynamic allocation and release of spectrum resources. We design a fragmentation-sensitive fragmentation metric (FSFM) to estimate the spectrum fragmentation in the established lightpaths, and design a dynamic routing and core selection strategy for the request to control spectrum fragmentation in the core according to FSFM. We consider survivable multipath routing strategy for saving spectrum resources, each path of the multipath can be used as a working path or a protection path. In addition, for spectrum allocation, a survivable multipath with shared spectrum allocation strategy is adopted to maximize the utilization of frequency resources. The simulation results show that the algorithm proposed in this paper significantly reduces the bandwidth blocking probability,fragmentation ratio and also improves the spectrum utilization.

Simultaneous improvement of multiple transportation performances on link-coupled networks by global dynamic routing

The interconnected networks are facing with critical congestion issue due to the rapid growth of the traffic and the information on the links with limited capacity. We show that the traditional routing strategies are generally confronted with a tradeoff between the network capacity and the link usage when applying to the link-coupled network. To take a step to the issue, we propose a global dynamic routing (GDR) strategy that can simultaneously achieve multiple improvement of the transport performance with acceptable computational complexity at the cost of the average arrival time. Further analysis indicates the improvement is related to the nontrivial load distribution resulting from GDR. More surprisingly, the simulation experiments suggest our strategy GDR can suppress the occurrence of Braess-like paradox, which is a long-standing problem in transportation.

QoS-Aware Multipath Routing in Software-Defined Networks

The emergence of new applications, such as online gaming and virtual reality, necessitates the underlying network capable of fulfilling high bandwidth and low latency requirements. The software-defined multipath routing is a viable approach to fulfill such quality-of-service (QoS) requirements by improving the data delivery performance through multipath. In this paper, we propose a QoS-aware dynamic multipath routing scheme for enhancing QoS of high-bandwidth applications in an SDN-enabled network. The proposed scheme consists of three phases – flow splitting, multipath routing, and flow reordering. In the first phase, we propose a flow splitting scheme to decide how to split the incoming flows to enable multipath routing in the network. In the second phase, we design a cost function for routing the splittable subflows and formulate a min-cost routing problem as an integer linear program (ILP). To solve the problem in polynomial time, we propose a greedy heuristic approach. Finally, in the third phase, we propose a flow reordering scheme for the received subflows through multiple paths to maintain the desired flow sequence at the destination. The experimental results show that the proposed scheme achieves higher network throughput by 22% compared to the benchmark schemes. Further, the proposed scheme achieves a reduction in QoS violated flows by 24% compared to the benchmark schemes.

Dynamic Multihop Routing in Terahertz Flow-Guided Nanosensor Networks: A Reinforcement Learning Approach

The Internet of Nano-Things (IoNT) is an emerging paradigm in which devices sized to the nanoscale (nanonodes) and transmitting in the terahertz (THz) band can become decisive actors in future medical applications. Flow-guided nanonetworks are well-known THz networks aimed at deploying the IoNT inside the human body, among other issues. In these networks, nanonodes flowing through the bloodstream monitor-sensitive biological/physical parameters and dispatch these data via electromagnetic (EM) waves to a nanorouter implanted in human tissue, which operates as a gateway to external Internet connectivity devices. Under these premises, two shortcomings arise. First, the use of the THz band greatly limits the nanonode’s communication range. Second, the nanonodes lack resources for processing, memory, and batteries. To minimize the impact of these concerns in EM nanocommunications, a novel dynamic multihop routing scheme is proposed to model in-body, flow-guided nanonetwork architecture. To this end, a reinforcement learning-based framework is conceived, combining the features of EM nanocommunications and hemodynamics or fluid dynamics applied to the bloodstream. A generic Markov decision process (MDP) approach is derived to maximize the throughput metric, analytically modeling: 1) the movement of the nanonodes in the bloodstream as laminar flow; 2) energy consumption (including energy-harvesting issues); and 3) prioritized events. A thoroughly THz flow-guided nanonetwork case of study is also defined. Under the umbrella of this case, diverse testbeds are planned to create a procedure of evaluation, validation, and discussion. Results reveal that multihop scenarios obtain better performance than direct nanonode-nanorouter communication, specifically, the two-hop scenario, which, for instance, quadrupled the throughput in a hand vein without sharply penalizing other aspects such as energy consumption.

Lightweight Stepless Super-Resolution of Remote Sensing Images via Saliency-Aware Dynamic Routing Strategy

Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of subimage patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good tradeoff between performance and complexity. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hanlinwu/SalDRN</uri> .

AQROM: A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

In Software-Defined Networks (SDNs), determining how to efficiently achieve Quality of Service (QoS)-aware routing is challenging but critical for significantly improving the performance of a network, where the metrics of QoS can be defined as, for example, average latency, packet loss ratio, and throughput. The SDN controller can use network statistics and a Deep Reinforcement Learning (DRL) method to resolve this challenge. In this paper, we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism (AQROM) to determine routing strategies that balance the traffic loads in the network. AQROM can improve the QoS of the network and reduce the training time via dynamic routing strategy updates; that is, the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic pattern. AQROM can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states, and actions with respect to the operations in the SDN. Extensive simulations were conducted using OMNeT++ and the results demonstrated that AQROM 1) achieved much faster and stable convergence than the Deep Deterministic Policy Gradient (DDPG) and Advantage Actor-Critic (A2C), 2) incurred a lower packet loss ratio and latency than Open Shortest Path First (OSPF), DDPG, and A2C, and 3) resulted in higher and more stable throughput than OSPF, DDPG, and A2C.

Reinforcement learning based dynamic distributed routing scheme for mega LEO satellite networks

Recently, mega Low Earth Orbit (LEO) Satellite Network (LSN) systems have gained more and more attention due to low latency, broadband communications and global coverage for ground users. One of the primary challenges for LSN systems with inter-satellite links is the routing strategy calculation and maintenance, due to LSN constellation scale and dynamic network topology feature. In order to seek an efficient routing strategy, a Q-learning-based dynamic distributed Routing scheme for LSNs (QRLSN) is proposed in this paper. To achieve low end-to-end delay and low network traffic overhead load in LSNs, QRLSN adopts a multi-objective optimization method to find the optimal next hop for forwarding data packets. Experimental results demonstrate that the proposed scheme can effectively discover the initial routing strategy and provide long-term Quality of Service (QoS) optimization during the routing maintenance process. In addition, comparison results demonstrate that QRLSN is superior to the virtual-topology-based shortest path routing algorithm.

Dynamic Routing Strategy for Directed Transmissions of High-Valued Contents in NGSO Satellite-Based Internet

Nongeostationary orbit (NGSO) satellite-based Internet is an important part of the future network. One single NGSO satellite has the characteristics of high-speed movement, small coverage area, and short connection time; therefore, the method of multisatellite collaboration is adopted in NGSO satellite-based Internet. Caching is an effective technique to reduce the transmission load and delay, especially for high-value content. However, the traditional caching cannot be applied to the NGSO satellite network directly due to the frequent switching of service satellites. As a result, some high-valued cached data need to be transmitted among satellites, such that an effective routing between the source and destination node needs to be established orderly in the satellite network. This paper proposes a dynamic routing strategy based on multilayer satellite architecture for unicast and multicast scenarios, respectively. For the unicast, a routing strategy based on Q -learning is proposed to optimize bandwidth and delay simultaneously, and the effectiveness is verified by the comparison against the traditional routing strategy. For the multicast, an advanced Q -learning multicast routing algorithm based on spatial directionality is proposed to optimize the overall transmission success rate and resource cost, and the superiority of the proposed routing strategy is verified by simulation results.

SDN-based dynamic multi-path routing strategy for satellite networks

Software Defined Network (SDN) is a new network architecture which is efficient, dynamic, controllable and adaptable. It is suitable for networks with high bandwidth requirements and high dynamic characteristics. Although the satellite network has a large communication coverage area, factors such as dynamic and time-varying topology, unstable links, unbalanced users of distribution and limited resources can easily cause serious network congestion and high transmission delay. The use of SDN to build a satellite network is conducive to solving the above-mentioned problems. This paper proposes a multi-path routing strategy based on SDN, which uses a combination of delay, bandwidth and node load to construct a link transmission cost calculation model, and adjusts the route for end-to-end transmission in time by real-time sensing of the network status and the load of the switching nodes in the network, thereby improving transmission efficiency. In particular, considering that the transmission delay of the satellite network is mainly determined by the propagation delay, this paper uses the Systems Tool Kit (STK) to establish the propagation delay model of the inter-satellite link to improve the accuracy of the transmission cost calculation. This paper compares the existing routing methods with the method in this paper on the Mininet simulation platform. The experimental results show that the method in this paper can effectively balance the bandwidth consumption of different links and improve the throughput of the network under the circumstance that the satellite network changes dynamically.

A Milk-run routing and Scheduling model for a Smart Manufacturing System

The fourth industrial transformation can create value for companies. It can bring significant improvements: those who want to be competitive in the market must implement new methods and tools to make plants efficient and productive. Industry 4.0 introduces new paradigms promoting the adoption of innovative technologies. In this context, the material handling system plays a crucial role in increasing the effectiveness and efficiency of the manufacturing systems. This paper proposes a model based on the Vehicle Routing Problem with Time Windows. The model aims to plan a dynamic routing strategy for milk-run to deliver the materials to different working stations, ensuring a materials’ distribution consistent with the time slot and minimizing the tugger trains path. A real industrial full case of a worldwide automotive company validates the model. The results of the study show benefits under economic (cost saving of 18%), environmental (allowed to reduce the distance travelled by tugger trains by around 23% for each work shift) and social (organizational development) perspectives.

CapsPhase: Capsule Neural Network for Seismic Phase Classification and Picking

We develop a capsule neural network (CapsPhase) for seismic data classification and picking. CapsPhase consists of several layers, e.g., convolutional, primary capsule, and digit capsule layer. The convolutional layer extracts the significant features from the seismic data, while the primary capsule combines the extracted features into several vector representations named capsules. Afterward, the primary capsule is connected to the digit capsule layer using a dynamic routing strategy to obtain the vector representation of each output class, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -wave, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -wave, and noise class. CapsPhase is trained using 90% of the Southern California seismic dataset, which contains 4.5 million 4 s-three-component seismograms, and is validated and tested using the remaining 10%. Accordingly, the training accuracy reaches 98.70%, while the validation accuracy is 98.67% and the testing accuracy is 98.66%. Furthermore, the CapsPhase is tested using 300 000 earthquake waveforms recorded worldwide from the STanford EArthquake Dataset (STEAD). Accordingly, the precision, recall, and F1-score of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -picks corresponding to the CapsPhase reach 94.50%, 99.86%, and 97.10%, respectively, whereas the precision, recall, and F1-score of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -picks corresponding to the CapsPhase are 88.05%, 99.87%, and 93.60%, respectively. In addition, CapsPhase is evaluated using the Japanese seismic data and is compared to benchmark methods, e.g., short-time average/long-time average (STA/LTA), generalized phase detection (GPD), and CapsNet methods. As a result, CapsPhase reaches F1-scores of 99.10% and 98.64% for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -wave and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -wave arrival times, respectively, and outperforms the benchmark methods. The results show that the CapsPhase has the ability to pick the arrival times accurately despite the existence of strong background noise, e.g., the signal-to-noise-ratio (SNR) can be as low as −4.97 dB. Besides, the CapsPhase detects the arrival time when the earthquake has a small local magnitude, e.g., as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.1~M_{L}$ </tex-math></inline-formula> . In addition, we find that the proposed algorithm has the ability to train using a small dataset, which is valuable for regions that have limited training data.

HydroBlocks v0.2: enabling a field-scale two-way coupling between the land surface and river networks in Earth system models

Abstract. Over the past decade, there has been appreciable progress towards modeling the water, energy, and carbon cycles at field scales (10–100 m) over continental to global extents in Earth system models (ESMs). One such approach, named HydroBlocks, accomplishes this task while maintaining computational efficiency via Hydrologic Response Units (HRUs), more commonly known as “tiles” in ESMs. In HydroBlocks, these HRUs are learned via a hierarchical clustering approach from available global high-resolution environmental data. However, until now there has yet to be a river routing approach that is able to leverage HydroBlocks' approach to modeling field-scale heterogeneity; bridging this gap will make it possible to more formally include riparian zone dynamics, irrigation from surface water, and interactive floodplains in the model. This paper introduces a novel dynamic river routing scheme in HydroBlocks that is intertwined with the modeled field-scale land surface heterogeneity. Each macroscale polygon (a generalization of the concept of macroscale grid cell) is assigned its own fine-scale river network that is derived from very high resolution (∼ 30 m) digital elevation models (DEMs); the inlet–outlet reaches of a domain's macroscale polygons are then linked to assemble a full domain's river network. The river dynamics are solved at the reach-level via the kinematic wave assumption of the Saint-Venant equations. Finally, a two-way coupling between each HRU and its corresponding fine-scale river reaches is established. To implement and test the novel approach, a 1.0∘ bounding box surrounding the Atmospheric Radiation and Measurement (ARM) Southern Great Plains (SGP) site in northern Oklahoma (United States) is used. The results show (1) the implementation of the two-way coupling between the land surface and the river network leads to appreciable differences in the simulated spatial heterogeneity of the surface energy balance, (2) a limited number of HRUs (∼ 300 per 0.25∘ cell) are required to approximate the fully distributed simulation adequately, and (3) the surface energy balance partitioning is sensitive to the river routing model parameters. The resulting routing scheme provides an effective and efficient path forward to enable a two-way coupling between the high-resolution river networks and state-of-the-art tiling schemes in ESMs.

Research on dynamic route selection method of D2D communication under multi hop transmission

In order to find the best path in wireless sensor networks, a dynamic energy-saving routing strategy based on multi hop transmission is proposed. The dynamic state transition optimization rules are designed to increase the search probability of new nodes reasonably, so as to find the global optimal solution quickly and effectively, so as to save the search time, increase the optimal path search probability and prolong the network survival time. Simulation results and analysis show that the dynamic route selection method of D2D communication under multi hop transmission greatly increases the search probability of the global optimal solution, achieves the global optimal solution quickly and effectively, saves the energy consumption of nodes, and is conducive to extending the network survival time.

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

AbstractGround‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.