Network Load Research Articles

Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning

In the process of the collaborative work of Unmanned Aerial Vehicle (UAV) clusters, the cluster communication node test is often carried out by a single-node test, which leads to poor topology and robustness of the overall network system, an imbalanced communication network load, and high complexity of the communication test, which seriously affects the diversified needs of current users and the efficiency of large-scale task processing. To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. The system management node is used to monitor the status of the UAV testing task execution node and bandwidth resources. By taking advantage of the method of continuous interaction between the agent and the environment, the future state of the node after processing the current task to be assigned is predicted and evaluated from the perspective of interpretability, so as to achieve the effectiveness and stability of the UAV cluster testing task collaborative execution. The experimental results show that our proposed method can ensure the stable operation of the UAV cluster, accurately predict the future state, and reduce the load degree and bandwidth resource consumption of the large-scale test task network system.

Operation mode and scheduling plan optimization approach for multiple balancing zones in a distribution system

Modern power systems are developing rapidly, with distributed energy, energy storage devices, adjustable loads, and other flexible resources consolidated through microgrids, virtual power plants, and integrated source–network–load–storage systems. This consolidation under various balancing zone models facilitates synergistic operations and has become critical to enhancing distributed power consumption and ensuring the reliability of electricity supply. Therefore, in light of the challenges of inadequate economic efficiency, reduced accommodation of renewable energy, and poorer operational reliability in distribution networks, this study proposes a category selection and flexibility resource scheduling method that considers the differences in multiple balancing zone models and modes. Firstly, the approach establishes a multi-dimensional characteristic evaluation index and multiple balancing zone operation models. The characteristic evaluation indicators are then utilized to assess the unique properties of the balancing zone system and eliminate unreasonable operating modes. Finally, through analyzing the effectiveness and differences of various balancing zone operation modes, an optimal operation mode is selected, and a scheduling plan is formulated. We conclude that the scheduling plan optimization method considering the operation mode can realize a reasonable choice of operation modes and achieve benefit optimization.

Multi-rate sampled-data observer-based networked load frequency control for multi-area interconnected power systems under multiple cyber-attacks

This paper addresses the problem of adaptive event-triggered load frequency control (LFC) for multi-rate sampling multi-area interconnected power systems with integrated renewable energy. Different network channels can be vulnerable to multiple cyber-attacks, including deception attacks and DoS attacks. First, a matching mechanism is constructed to fuse the multi-rate sampled data, and a set of observers based on multi-rate sampled data is designed to independently estimate the states of the individual power systems. A networked sampled-data PI controller is then developed by introducing a new synchronous sampling scheme and an adaptive event-triggered mechanism. A co-design approach is proposed that integrates decentralized PI controllers, observers, and adaptive event-triggered mechanisms to achieve exponential mean-square stability with a given H∞ performance level for the networked LFC system in the presence of multiple cyber-attacks and multi-rate sampling. Finally, the effectiveness of the proposed method is verified through an example of an interconnected power system.

Probabilistic Time Series Forecasting Based on Similar Segment Importance in the Process Industry

Probabilistic time series forecasting is crucial in various fields, including reducing stockout risks in retail, balancing road network loads, and optimizing power distribution systems. Building forecasting models for large-scale time series is challenging due to distribution differences, amplitude fluctuations, and complex patterns across various series. To address these challenges, a probabilistic forecasting method with two different implementations that focus on historical segment importance is proposed in this paper. First, a patch squeeze and excitation (PSE) module is designed to preprocess historical data, capture segment importance, and distill information. Next, an LSTM-based network is used to generate maximum likelihood estimations of distribution parameters or different quantiles for multi-step forecasting. Experimental results demonstrate that the proposed PSE module significantly enhances the base model’s prediction performance, and direct multi-step forecasting offers more detailed information for high-frequency data than recursive forecasting.

Cooperative scheduling strategy for electric vehicles with Vehicle-to-Grid technology considering renewable energy generation

The randomness and intermittency of electric vehicles (EVs) charging can lead to a peak-on-peak phenomenon in the distribution network load. As the scale of EVs continues to expand, uncoordinated charging demands exacerbate the differences between peak and off-peak loads. To address this issue, this study introduces an innovative optimization method for EVs charging. First, by analyzing the charging patterns of power batteries and driver behavior, EVs charging demand is simulated and deduced using the Monte Carlo method. Subsequently, considering the constraints of EVs and the power grid, an objective function to optimize grid load is constructed based on Vehicle-to-Grid (V2G) and time-of-use pricing strategies. Finally, an Improved Backbone Particle Swarm Optimization (IBBPSO) algorithm is designed, which is further combined with a Bayesian Optimization (BO) algorithm to dynamically adjust the allocation coefficients of the objective function and the initial parameters of the IBBPSO. This approach facilitates the attainment of the optimal EVs charging and discharging scheduling strategy. Simulation results indicate that the implemented strategy effectively reduces the peak-to-valley load disparity within the power grid, flatting the adverse effects of uncoordinated EVs charging.

A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty

In this study, stochastic multi-objective allocation of wind turbines (WTs) in radial distribution networks is performed using a new multi-objective improved horse herd optimizer (MOIHHO) and an unscented transformation (UT) method for modeling the uncertainties of WTs power and network load. The objective function aims to minimize power loss, improve reliability, and reduce the costs associated with wind turbines (WTs), presenting these goals as a three-dimensional function. The Multi-Objective Improved Horse Herd Optimizer (MOIHHO) is derived from an enhanced version of the traditional horse herd optimizer. This enhancement utilizes mirror imaging based on convex lens principles to address issues of premature convergence. Additionally, the decision-making process is designed to identify the final fuzzy solution among the non-dominant solutions within the Pareto front set. The simulation results are presented with and without considering uncertainty in two scenarios of deterministic and stochastic WT allocation on 33- and 69-bus distribution networks and different objectives are compared. Also, the effect of incorporating uncertainties are evaluated on power loss and reliability using the MOIHHO. Moreover, the superiority of the MOIHHO is investigated in achieving better objective function value compared with conventional MOHHO, multi-objective particle swarm optimization (MOSPO), multi-objective gray wolf optimizer (MOGWO), and multi-objective gazelle optimization algorithm (MOGOA). The obtained results demonstrated that considering the UT-based stochastic scenario, the power losses cost is increased, and the reliability is weakened for 33- and 69-bus networks in comparison with the deterministic scenario.

Delay reduction and interference mitigation by resource management for IAB networks

To overcome the increasing demand for high-throughput data services, the 3rd Generation Partnership Project (3GPP) has proposed Integrated Access and Backhaul (IAB) in Release 16 as a scalable and cost-effective alternative to traditional fiber backhaul for 5 G systems. A key feature of IAB is the shared use of the same spectrum band for both access and backhaul communications, which introduces challenges related to interference, delays, capacity sharing, and additional complexity due to the half-duplex constraint. In this paper, we provide an overview of the 3GPP guidelines for IAB resource management and introduce a semi-centralized, heuristic-based resource management policy aimed at jointly enhancing end-to-end delay and minimizing interference in IAB networks, in compliance with 3GPP standards. We conduct a comparative study of our proposed policy against several policies, including the baseline proposed by 3GPP. The evaluation is performed through discrete-event simulations under two types of traffic and varying network loads. The simulation results underline that our semi-centralized resource management policy effectively controls interference, leading to improved network Key Performance indicators (KPIs) like system throughput and end-to-end delay. Specifically, our solution achieves up to an 11% increase in system throughput and a 30% reduction in end-to-end delay compared to the baseline for constant traffic, while for variable traffic, it results in a 16% increase in system throughput and a 26% reduction in end-to-end delay.

Optimizing IoT Networks in Smart Agriculture Using Probabilistic Models and Machine Learning Algorithms

This study aims to enhance the performance of Internet of Things (IoT) networks in smart agriculture by integrating probabilistic models and machine learning algorithms. The research addresses the need for efficient, reliable, and scalable IoT infrastructures to support precision farming and sustainable agricultural practices in increasingly complex and data-intensive farming environments. A multi-faceted approach was employed, combining probabilistic modeling and machine learning techniques. IoT sensor networks were simulated using real-world agricultural data, supplemented by a small-scale field deployment. Bayesian networks and Hidden Markov Models were applied to capture system dynamics, while Random Forest, Support Vector Machines, and Long Short-Term Memory networks were used for pattern recognition and predictive analytics. Network performance was evaluated using metrics including latency, throughput, packet loss rate, energy efficiency, scalability, reliability, and data accuracy. The integrated approach significantly improved IoT network performance in agricultural settings. Key improvements include: 25% reduction in latency, 35% increase in throughput, 60% decrease in packet loss rate, 30% improvement in energy efficiency, and 6.5% increase in data accuracy. Adaptive routing algorithms, informed by machine learning predictions, were particularly effective in managing network load during peak agricultural seasons, reducing latency by 18% during these periods. The study relied primarily on simulations and limited field trials. Comprehensive real-world implementation across diverse agricultural environments and larger scale deployments is necessary to fully validate the findings. The research did not address potential cybersecurity challenges in optimized IoT networks, which remains an important area for future investigation.

A single and multiobjective robust optimization of a microgrid in distribution network considering uncertainty risk

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24 h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters.

Study on two‐stage optimal scheduling of DC distribution networks considering flexible load response

AbstractDue to the aim of developing sustainable energy systems, promoting the large‐scale accommodation of distributed renewable energy sources (DRESs) and flexible loads in DC distribution networks (DCDNs) is significant. The uncertainty of DRESs and the insufficient use of flexible loads pose a considerable challenge to the economical and safe operation of DCDNs. To address the challenge, this paper puts forward a two‐stage optimal scheduling model for the DCDNs considering flexible load response. The proposed model realises joint economic optimisation and reactive power optimisation, which is solved by the hybrid NSGAII‐MOPSO algorithm and the CPLEX. The performance of the proposed model in the modified Institute of Electrical and Electronics Engineers 33‐node system with the DCDNs is validated under different scenarios. The hybrid NSGAII‐MOPSO performs better in obtaining the Pareto front than the NSGA‐II and MOPSO individually. Compared to the traditional scheduling model, the proposed model can realise the power coordination of the flexible loads and energy storage systems to reduce the negative impact of uncertainty of DRESs while decreasing operating costs and carbon emissions by 3.94% and 36.4%. In addition, the proposed model can alleviate the network losses and ensure the node voltage for the DCDNs. Hence, the efficiency of the proposed model has been confirmed.

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

Lightening Backhaul Load for Aerial Multi-Hop Integrated Access and Backhaul Networks

Lightening Backhaul Load for Aerial Multi-Hop Integrated Access and Backhaul Networks

A Review of Expert Systems Integration in Signal Plan Optimisation

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

Analyzing Operational Dynamics: Centralized Load Distribution and Suspended Wagons in Self‐Propelled Trains

ABSTRACTThis paper primarily focuses on the distribution of axial loads in railway networks. The analysis assesses the advantages and disadvantages of articulated trains. In addition, the perspectives and proposed solutions of academicians are analyzed. Simpack accurately models all aspects of this articulated train model. Subsequently, the program emulates the control system of the train. This study demonstrates a control system that utilizes a hydraulic actuator. The proposed model and control system actuator's dynamic response are evaluated according to EN14363 and UIC518 standards to validate the functionality of the component. The stability of a railway train, particularly when making turns, relies on the proper allocation of weight on the wheels and axles. This research investigates the hazards associated with the uneven distribution of loads in train wagons, particularly when navigating curving tracks. A train designed for intercity travel was constructed utilizing a specific control system. The discrepancy in axle load is mitigated by implementing a straightforward control method. The train's carriage, which is suspended without bogies, resulted in fluctuating axle load. A disparity in axle load was established by an actuator positioned between the wagons. The control mechanism minimized load disparity and regulated axle load.

Severe motor impairment is associated with lower contralesional brain age in chronic stroke.

Stroke leads to complex chronic structural and functional brain changes that specifically affect motor outcomes. The brain-predicted age difference (brain-PAD) has emerged as a sensitive biomarker. Our previous study showed higher global brain-PAD associated with poorer motor function post-stroke. However, the relationship between local stroke lesion load, regional brain age, and motor impairment remains unclear. We studied 501 individuals with chronic unilateral stroke (>180 days post-stroke) from the ENIGMA Stroke Recovery Working Group dataset (34 cohorts). Structural T1-weighted MRI scans were used to estimate regional brain-PAD in 18 predefined functional subregions via a graph convolutional network algorithm. Lesion load for each region was calculated based on lesion overlap. Linear mixed-effects models assessed associations between lesion size, local lesion load, and regional brain-PAD. Machine learning classifiers predicted motor outcomes using lesion loads and regional brain-PADs. Structural equation modeling examined directional relationships among corticospinal tract lesion load (CST-LL), ipsilesional brain-PAD, motor outcomes, and contralesional brain-PAD. Larger total lesion size was positively associated with higher ipsilesional regional brain-PADs (older brain age) across most regions (p□<□0.05), and with lower contralesional brain-PAD, notably in the ventral attention-language network (p□<□0.05). Higher local lesion loads showed similar patterns. Specifically, lesion load in the salience network significantly influenced regional brain-PADs across both hemispheres. Machine learning models identified CST-LL, salience network lesion load, and regional brain-PAD in the contralesional frontoparietal network as the top three predictors of motor outcomes. Structural equation modeling revealed that larger stroke damage was associated with poorer motor outcomes (β□=□-0.355, p□<□0.001), which were further linked to younger contralesional brain age (β□=□0.204, p□<□0.001), suggesting that severe motor impairment is linked to compensatory decreases in contralesional brain age. Our findings reveal that larger stroke lesions are associated with accelerated aging in the ipsilesional hemisphere and paradoxically decelerated brain aging in the contralesional hemisphere, suggesting compensatory neural mechanisms. Assessing regional brain age may serve as a biomarker for neuroplasticity and inform targeted interventions to enhance motor recovery after stroke. Micheal J Fox Foundation, National Institutes of Health, Canadian Institutes of Health Research, National Health and Medical Research Council, Australian Brain Foundation, Wicking Trust, Collie Trust, and Sidney and Fiona Myer Family Foundation, National Heart Foundation, Hospital Israelita Albert Einstein, Australian Research Council Future Fellowship, Wellcome Trust, National Institute for Health Research Imperial Biomedical Research Centre, European Research Council, Deutsche Forschungsgemeinschaft, REACT Pilot, National Resource Center, Research Council of Norway, South-Eastern Norway Regional Health Authority, Norwegian Extra Foundation for Health and Rehabilitation, Sunnaas Rehabilitation Hospital HT, University of Oslo, and VA Rehabilitation Research and Development.

Enhancing Network Security in Distributed Systems Using Middle Roman Dominating Functions

A Middle Roman dominating function (MRDN) on a graph G = (V,E) is a function f:v→{0,1,2,3} satisfying the condition that every vertex u with f(u)=0 is adjacent to at most one vertex v with f(v)=2 or 3. Further if a vertex is assigned 2, then at most two of its vertices can be assigned 0 and if a vertex is assigned 3, then all its neighbours can be assigned 0. The weight of a MRDF is the value f(V(G))=∑_uϵV▒〖f(u〗). The Middle Roman domination number γ_MR (G) is the minimum weight of a MRDF on G. In this paper, we introduce Middle Roman Domination number denoted as γ_MR (G), study the properties of the function, present some characterization and determine γ_MR (G)-value for some graphs. Middle Roman Dominating Functions (MRDFs) present a unique approach within graph theory, with significant implications for various fields in computer science. By assigning values to vertices under specific constraints, MRDFs enable the optimization of resources, enhancing network security, load balancing, and energy efficiency in distributed systems. This paper explores the application of MRDFs in scenarios such as intrusion detection, resilient network design, task scheduling, and sensor activation. By minimizing the overall weight while maintaining functional requirements, MRDFs provide an effective strategy for addressing challenges in network topology, resource allocation, and fault tolerance. The versatility of MRDFs makes them a valuable tool in the development of robust and efficient computing systems.

6G Self‐Evolution Network for IoT Using Rainbow Deep Q‐Network Based on Decision‐Making

ABSTRACTIn recent trends, the development of 6G technology has provided a critical focus on its core network architecture which facilitates the robust foundation for entire network. A solution of self‐evolving networks for 6G is under consideration, in which networks develop their autonomy as a key perspective in the decision‐making process. Till now, various decision‐making mechanisms are identified, such as their key features, individual theoretical analyzes, and customized for 6G networks. To reduce the network load and improve system utilization, a decision‐making based on Rainbow Deep Q‐Network (RDQN) is proposed in this research. This methodology considers distributed decision‐making scenarios in which IoT devices enhance the quality of experience (QoE) with low training. Furthermore, it aims at a community of self‐evolving networks to show the improvements in 6G technologies and accelerates the learning rate. From the result analysis, it evidently displays that proposed RDQN has better training rate than other algorithms and requires fewer rounds of approximately 20 for a high number of episodes (500). Similarly, while evaluating QoE utility value, the proposed RDQN demonstrates a higher value of 100 when the number of target customers is large which outperforms the other existing models. The above results prove that the proposed RDQN is more efficient in IoT traffic monitoring and more suitable for the 6G environment.

A cooperative approach for generation and lines expansion planning in microgrid‐based active distribution networks

AbstractWith the growth of the load in the electricity networks, sufficient investment in the generation and lines expansion should be made in order to provide the energy needed by consumers with the lowest possible investment and operation costs. This issue is especially important in distribution networks, which are faced with the uncertainties of renewable energy generation and the development of microgrids and related issues.In this article, the planning of generation and lines expansion has been modeled with the aim of minimizing the total costs of microgrids, based on the cooperative approach. For this purpose, a bi‐level model has been developed; on the upper level, microgrids make investment decisions with a cooperative approach, and a constrained stochastic formulation has been developed with considering operational uncertainties on the lower level. Also, in this article, in order to ensure the supply of critical loads in island conditions, the self‐sufficiency index is defined. Three case studies have been considered to ensure the effectiveness of the developed model. In case 1, each microgrid will be able to supply its load only by generating of its units and purchasing from the retail market. In case 2, the possibility of trading with other microgrids in a non‐cooperative approach will also be available to the microgrids operators, and in case 3, microgrids can exchange energy with other microgrids in a cooperative manner. The simulation results showed that due to the possibility of using nearby microgrid resources, the cost of microgrid load supply in case 2 was reduced by 4.84% compared to case 1. Also, this cost in case 3 was reduced by 5.23% and 0.38%, respectively, compared to cases 1 and 2, due to the use of a cooperative manner in microgrid load providing.

Continuous Integration and Continuous Deployment (CI/CD) Optimization

The advent of Continuous Integration and Continuous Deployment (CI/CD) has fundamentally altered the landscape of software development, enabling teams to deliver updates with unprecedented speed and reliability. By automating the integration of code changes from multiple developers into a central repository, CI/CD practices ensure that software is continuously tested and deployed. This ongoing cycle not only facilitates quicker release cycles but also enhances collaboration among team members and fosters a culture of shared responsibility for code quality. Despite these advancements, organizations face significant challenges in optimizing their CI/CD pipelines. As software systems grow in complexity, the demand for swift and dependable deployments intensifies. This paper explores various techniques and strategies for optimizing CI/CD processes to minimize deployment times while maintaining system reliability. Key optimization methods discussed include: Parallelization of Build Processes: This technique involves breaking down the build process into smaller, independent tasks that can be executed concurrently. By leveraging distributed computing resources, organizations can significantly reduce build times, allowing for faster iterations and deployments. Dependency Caching: Caching dependencies can drastically decrease build times by reusing previously downloaded components. This approach not only speeds up the build process but also minimizes network load and enhances the overall efficiency of the CI/CD pipeline. Incremental Builds: Unlike full builds that compile the entire codebase, incremental builds focus on compiling only the changes made since the last build. This strategy reduces the amount of work needed for each build, accelerating the overall development process. The paper also delves into advanced rollback mechanisms such as blue-green deployments and canary releases. Blue-green deployments allow teams to maintain two identical production environments, enabling smooth transitions and quick rollbacks in case of issues. Canary deployments, on the other hand, introduce new features to a small subset of users before a full rollout, allowing teams to monitor the impact and catch potential failures early. Additionally, automated rollback mechanisms play a vital role in maintaining system reliability, ensuring that any failed deployments can be reverted swiftly to avoid downtime and user disruption. The impact of automation tools on deployment speed and error reduction is another critical aspect examined in this research. Automation frameworks can streamline various stages of the CI/CD process, from code integration to testing and deployment, minimizing human error and ensuring consistent, repeatable processes. By implementing robust automation strategies, organizations can not only accelerate their deployment cycles but also improve overall software quality.

Smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs

RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.