Node Load Research Articles

Adaptive Scheduling Method for Passenger Service Resources in a Terminal

To alleviate the tense situation of limited passenger service resources in the terminal and to achieve the matching of resource scheduling with the flight support process, the process–resource interdependent network is constructed according to its mapping relationship and the time-varying characteristics of the empirical network and network evolution conditions are analyzed. Then, node capacity, node load, and the cascading failure process are investigated, the impact of average service rate and service quality standard on queue length is considered, the node capacity model is constructed under the condition of resource capacity constraints, and the load-redistribution resource adaptive scheduling method based on cascading failure is proposed. Finally, the method’s effectiveness is verified by empirical analysis, the service efficiency is assessed using the total average service time and variance, and the network robustness is assessed using the proportion of maximum connected subgraph. The results indicate that the resource adaptive scheduling method is effective in improving service efficiency, and the average value of its measurement is smaller than that of the resource average allocation method by 0.069; in terms of the robustness improvement of the interdependent network, the phenomenon of re-failure after the load redistribution is significantly reduced.

Dynamic Charging Scheduling and Path Planning Scheme for Multiple MC-enabled On-demand Wireless Rechargeable Sensor Networks

With the advancement of wireless energy transfer, Wireless Rechargeable Sensor Networks (WRSNs) have become increasingly popular for efficiently charging sensor nodes. In WRSNs, determining the charging schedule for Mobile Chargers (MCs) is critical for reducing maintenance costs and improving charging efficiency. This is termed the Charging Scheduling Problem (CSP), which is proven to be NP-hard in nature. The existing schemes lack a comprehensive approach to determine the optimal number of MCs and often set a fixed charging threshold for the sensor nodes, degrading charging efficiency in dynamic networks. Additionally, relying on a single MC is unrealistic and impractical for large-scale networks, necessitating a more advanced charging strategy. Thus, this study proposes a dynamic and multi-node charging scheduling scheme named Partitioning-based Charging Schedule for Multiple Mobile Chargers (PCSMMC). The PCSMMC utilizes the traffic load of sensor nodes and energy load of MCs to estimate the optimal number of MCs and computes the progressive threshold for sensor nodes to improve the charging efficiency. Moreover, potential sojourn locations are determined and multiple network factors are integrated into a multi-attribute decision-making process to achieve an efficient charging scheduling and path planning scheme for multiple MCs. The objective of PCSMMC is to enhance the survivability rate of sensor nodes and decrease the traveling path followed by MCs to charge the sensor nodes within the network. Empirical simulation results confirm the superiority of PCSMMC in terms of charging response time, survivability rate, energy utilization efficiency, and network lifetime by a significant margin when compared to alternative approaches.

Optimal Electrification Using Renewable Energies: Microgrid Installation Model with Combined Mixture k-Means Clustering Algorithm, Mixed Integer Linear Programming, and Onsset Method

Optimal planning and design of microgrids are priorities in the electrification of off-grid areas. Indeed, in one of the Sustainable Development Goals (SDG 7), the UN recommends universal access to electricity for all at the lowest cost. Several optimization methods with different strategies have been proposed in the literature as ways to achieve this goal. This paper proposes a microgrid installation and planning model based on a combination of several techniques. The programming language Python 3.10 was used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. Four levels of study were carried out. The first level consisted of simulating the model obtained with a cluster, which is considered based on the elbow and k-means clustering method as a case study. The second level involved sizing the microgrid with a capacity of 40 kW and optimizing all the resources available on site. The example of the different resources in the Togo case was considered. At the third level, the work consisted of proposing an optimal connection model for the microgrid based on voltage stability constraints and considering, above all, the capacity limit of the source substation. Finally, the fourth level involved a planning study of electrification strategies based mainly on microgrids according to the study scenario. The results of the first level of study enabled us to obtain an optimal location for the centroid of the cluster under consideration, according to the different load positions of this cluster. Then, the results of the second level of study were used to highlight the optimal resources obtained and proposed by the optimization model formulated based on the various technology costs, such as investment, maintenance, and operating costs, which were based on the technical limits of the various technologies. In these results, solar systems account for 80% of the maximum load considered, compared to 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed based on the constraints of a voltage stability limit estimated to be 10% of the maximum voltage drop. The results obtained for the third level of study enabled us to present selective results for load nodes in relation to the source station node. Finally, the last results made it possible to plan electrification using different network technologies and systems in the short and long term. The case study of Togo was taken into account. The various results obtained from the different techniques provide the necessary leads for a feasibility study for optimal electrification of off-grid areas using microgrid systems.

Cloud–Fog Collaborative Computing Based Task Offloading Strategy in Internet of Vehicles

Vehicle terminals in the mobile internet of vehicles are faced with difficulty in the requirements for computation-intensive and delay-sensitive tasks, and vehicle mobility also causes dynamic changes in vehicle-to-vehicle (V2V) communication links, which results in a lower task offloading quality. To solve the above problems, a new task offloading strategy based on cloud–fog collaborative computing is proposed. Firstly, the V2V-assisted task forwarding mechanism is introduced under cloud–fog collaborative computing, and a forwarding vehicles predicting algorithm based on environmental information is designed; then, considering the parallel computing relationship of tasks in each computing node, a task offloading cost model is constructed with the goal of minimizing delay and energy consumption; finally, a multi-strategy improved genetic algorithm (MSI-GA) is proposed to solve the above task offloading optimization problem, which adapts the chaotic sequence to initialize the population, comprehensively considers the influence factors to optimize the adaptive operator, and introduces Gaussian perturbation to enhance the local optimization ability of the algorithm. The simulation experiments show that compared with the existing strategies, the proposed task offloading strategy has the lower task offloading cost for a different number of tasks and fog nodes; additionally, the introduced V2V auxiliary task forwarding mechanism can reduce the forwarding load of fog nodes by cooperative vehicles to forward tasks.

An efficient scheduling scheme for intelligent driving tasks in a novel vehicle-edge architecture considering mobility and load balancing

With the continuous popularization and evolution of 5G and 6G, mobile edge computing has achieved rapid development. This study explores the New Generation Mobile Edge Computing (NGMEC) architecture, which leverages numerous mobile nodes to provide users with enhanced computing services. Despite its advantages, NGMEC faces challenges such as high node mobility, load balancing difficulties, and incomplete environmental perception by agents, particularly in intelligent driving task offloading scenarios. We address these challenges by introducing novel applications of NGMEC and proposing specialized algorithms for node selection and load balancing. Furthermore, to tackle the issue of environmental perception incompleteness in NGMEC task offloading, we develop the Gated Recurrent Self-Encoding Deep Reinforcement Learning (GRSE-DRL) algorithm. Our research also includes the development of two platforms: the End-Edge-Cloud Simulation Experiment Platform and the Edge Computing Offloading Algorithm Energy Efficiency Test Platform. Experimental results demonstrate that our proposed scheme effectively maintains load balance among nodes, enhances task completion and connection success rates, and optimizes the trade-off between transmission delay and intelligent driving algorithms’ effectiveness, enabling more efficient decision-making.

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Clustering protocols for underwater acoustic sensor networks (UASNs) have gained widespread attention due to their importance in reducing network complexity. Congestion occurs when the intra-cluster load is greater than the upper limit of the intra-cluster information transmission capacity, which leads to a dramatic deterioration of network performance despite the reduction of network complexity. To avoid congestion, we propose a node load and location-based clustering protocol for UASNs (LLCP). First, a node load and location-based optimization mechanism is proposed. The number of cluster members is optimized based on node load and location to maximize the number of cluster members while avoiding congestion. Then, a node degree and location-based cluster member selection mechanism is proposed to select the optimal cluster members. Finally, a priority-based clustering mechanism is proposed. The node clustering order is adjusted based on the clustering priority to maximize the reduction of network complexity by increasing the average number of cluster members. Simulation results show that our proposed LLCP minimizes the network complexity while avoiding congestion.

Програмний дизайн розподіленої високонавантаженої системи електроенергетичної мережі на базі моделі акторів із застосуванням смарт-контрактів

In the article, the authors provide an example of a high-level design of a distributed high-load power system based on the actor model using smart contracts in the form of a cluster topology, which allows for a unified description of the power grid in the context of the Ukrainian electricity market. Based on typical node loads in the cluster system, an expres-sion is derived formalizing the relationships between cluster nodes, which can be utilized for managing individual cluster objects and the cluster as a whole.

A post-disaster load supply restoration model for urban integrated energy systems based on multi-energy coordination

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES.

Robustness analysis of large scientific facilities development network with different cascading failure modes

Developing a large scientific facility involves numerous sub-products and research organizations, which pose systematic risks of potential global cascading failure. The development process can be abstracted as an interdependent network, the Large Scientific Facility Development (LSFD) network, consisting of sub-networks of product and organization. This paper offers a comprehensive risk cascading model by integrating the Load-capacity and Epidemic models. It explores the risk cascading mechanism and robustness of the LSFD network under diverse assumption conditions. The results show that the robustness is controlled by network structure, coupling patterns, attack strategy, node load & capacity, and probability of infected & removed. Enlarging the network size leads to a decrease in robustness, and increasing the average degree of the product network can improve the robustness. The assortative coupled networks exhibit the worst robustness when encountering an intention attack. Moreover, adjusting capacity parameters can significantly impact cascade failures, highlighting the inevitability of risk cascade even with sufficient resistance capacity. The removed probability λ determines whether the network faces nearly collapse: if λ ≥ 0.5, over 90 % of nodes will fail. These simulation results provide practical implications for managing systematic risks and enhancing the robustness of the LSFD network.

Multi-objective optimization of SFC deployment using service aggregation and computing offload

New technologies such as virtualization and Software Defined Networking (SDN) have given traditional networks unprecedented flexibility. A Service Function Chain (SFC) formed by concatenating multiple Virtual Network Functions (VNFs) expands network functions to meet the growing personalized network requirements of applications. However, due to the decentralization of VNF instances and the tight cloud load, how to ensure Quality of Service (Qos) while deploying the SFC brings new challenges. Hence, a SFC deployment strategy based on multi-objective optimization, named MO-SACO is proposed, which regards delay optimization, reliability assurance and cost reduction as the main objectives. Specifically, we first design a VNF aggregation rule to handle raw SFC requests, which effectively reduces the SFC path latency and improves reliability. Then, VNF placement and traffic routing is modeled as a computation offloading decision based on cloud-fog-edge collaboration with the goal of target optimization according to functional, location, and resource constraints. Finally, we also take into account the performance changes of deployed SFCs and handle non-compliant requests in a timely manner. We conduct extensive simulations in networks of varying sizes and the results demonstrate that the proposed MO-SACO strategy not only achieves lower latency and cost, higher reliability compared with the state-of-the-art methods, but also has unexpected performance in terms of the deployment success rate and node load.

Reliability Evaluation of Distribution Networks Considering Natural Gas Network Integration

Abstract With the transformation of energy structure, the distribution network in the form of multi-energy interconnection is gradually emphasized. The access of gas turbines to the distribution network puts high requirements on the reliability assessment of the distribution network counting and natural gas network access. Models of distribution network components and natural gas network equipment are established. Based on the traditional sequential MC reliability assessment, the calculation of reliability indexes for the distribution network with gas turbine access is accomplished by identifying the islanded areas under fault conditions and judging the gas turbine output state, taking into account the energy flow constraints. The results of the calculations show that the reliability of the distribution network with natural gas network access is improved, and the reliability of the load nodes near the gas turbine is improved significantly.

Features of multi-agent evaluation of of microgrid systems efficiency in parallel operation with the power system according to the reliability criterion

Ensuring reliable electricity supply to consumers in the face of destruction of critical energy infrastructure and shortage of generating capacities in Ukraine requires the development of distribution systems and their management systems. The purpose of the study was to substantiate the development of models for ensuring the optimal structure of electrical distribution networks under the conditions of connecting micro-networks according to the reliability criterion in conditions of limited investments. The paper uses a method for assessing the structural reliability of complex electrical systems with microgrid structures and forms a rational power distribution of such structures according to the criterion of optimising the reliability indicators of the studied electric power system. A mathematical optimisation model based on a computational system was proposed, designed to solve non-convex problems with minimising integral reliability indicators, considering financial constraints and the investment efficiency curve. Based on the research, the possibilities of optimisation using the BARON solver available on the NEOS server were examined. The results of the model’s performance are demonstrated using examples, considering the parameters of distribution system objects and their limitations on network components. An algorithm and programme for solving the problem of targeting power flows of microgrid structures in multi-node regional power systems are proposed. Algorithms and scenarios for the response of dispatching services are developed, provided that investments are limited, which will ensure the survivability of the power system as a whole. It is established that the development of rational electricity flows of microgrid structures will minimise the under-supply of electric energy by specific load nodes and determine their shares in covering the demand of the energy island in conditions of power shortage. The findings can be used in the operational management of power systems

Study of the probabilistic and temporal characteristics of wireless networks using the CSMA/CA access method

Objectives. The aim of this study is to develop analytical methods to evaluate the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. These methods enable the process of selecting rational operating modes to be automated and the impact of collisions in networks implementing the 802.11 protocols to be reduced.Methods. The methods employed herein include reliability theory, theory of random processes, queuing theory, and the Laplace–Stieltjes transform.Results. A problem statement is presented and developed, along with an analytical method for evaluating the probabilistic and temporal characteristics and performance of wireless networks using the CSMA/CA access method. This method considers time constraints on information transmission, thus expanding the applicability of previously proposed analytical approaches for studying Ethernet local area networks. The analysis of networks that use the CSMA/CA access method was carried out. An original mathematical model was developed that allows evaluating various characteristics of packet transmission processes in wireless networks under time constraints on the transmission. These characteristics include latency, packet transfer time, node load, and network performance. A software package was developed to simplify the analysis and evaluation of various operation modes of wireless networks using the CSMA/CA access method.Conclusions. We demonstrate the need for developing nested analytical models describing packet transmission processes in wireless networks under time constraints on link-layer transmission. This implies the development of more complex models for more exact description of packet transmission processes in such networks. The software package developed herein enables the various options for the functioning of the network to be studied and analytical calculations to be performed. Calculations were carried out, in order to assess the probabilistic and temporal characteristics of packet transmission processes and the wireless network performance. The research involved varying the number of workstations and the intensity of packet flows entering the network nodes under the time constraint on packet transmission. The application of the developed mathematical models will be useful in creating and optimizing wireless networks such as Wi-Fi networks, networks based on the IEEE 802.11 standard, and other data transmission systems using the CSMA/CA access method. Such models and the analysis based on them will be useful in optimizing network performance, adjusting parameters, as well as selecting the capacity and configuration of wireless networks.

Granular behaviour under bi-directional shear with constant vertical stress and constant volume

This paper aims to investigate the role of bi-directional shear in the mechanical behaviour of granular materials and macro-micro relations by conducting experiments and discrete element method (DEM) modelling. The bi-directional shear consists of a static shear consolidation and subsequent shear under constant vertical stress and constant volume conditions. A side wall node loading method is used to exert bi-directional shear of various angles. The results show that bi-directional shear can significantly influence the mechanical behaviour of granular materials. However, the relationship between bi-directional shear and mechanical responses relies on loading conditions, i.e. constant vertical stress or constant volume conditions. The stress states induced by static shear consolidation are affected by loading angles, which are enlarged by subsequent shear, consistent with the relationship between bi-directional shear and principal stresses. It provides evidence for the dissipation of stresses accompanying static liquefaction of granular materials. The presence of bi-directional principal stress rotation (PSR) is demonstrated, which evidences why the bi-directional shear of loading angles with components in two directions results in faster dissipations of stresses with static liquefaction. Contant volume shearing leads to cross-anisotropic stress and fabric at micro-contacts, but constant vertical stress shearing leads to complete anisotropic stress and fabric at micro-contacts. It explains the differentiating relationship between stress-strain responses and fabric anisotropy under these two conditions. Micromechanical signatures such as the slip state of micro-contacts and coordination number are also examined, providing further insights into understanding granular behaviour under bi-directional shear.

ENSURING THE THREAD CAPACITY OF ELECTRICAL TRANSMISSION LINES UNDER THE CONDITIONS OF THEIR RESERVATION

A method of increasing the power transmission capacity in the post-emergency mode is considered, when a redundant line is connected to one of the working power transmission lines (LEP) with a voltage of 10 kV using the point of automatic switching on of the reserve (AVR). The effectiveness of such redundancy in most cases turns out to be low due to the limited capacity of trunk sections of mutually reserved lines with smaller cross-sections of wires at the end sections of these lines, which were built as radial lines. This leads to increased losses of active power and voltage, and, as a result, unacceptable deviation of voltage in remote load nodes of the redundant power transmission line. In order to improve the parameters of the operation mode of power transmission, it is proposed to use devices of longitudinal capacitive compensation and reactive power compensation in the scheme of the AVR point. An assessment of the effectiveness of the proposed measure was made.

GUARANTEED ORIGIN OF ELECTRICITY IN THE LOCAL POWER SYSTEM WITH RENEWABLE ENERGY SOURCES

The article is focused on the development of a method for determining the part of electricity generated by renewable energy sources (RES) in the electricity flows in the branches of the electric grid. The method is based on a mathematical model of the electric grid, in which the coefficients of current distribution in the branches of the circuit from RES nodes and nodal voltages are used to determine the electricity flows in the branches of the electric grid. As a result, a matrix of power distribution coefficients of RES generation units to load nodes of the power grid is formed. This way, the component of the load power that depends on the power in the RES nodes is determined. The voltages used to form the RES power distribution matrix are determined based on the results of calculating the steady-state modes of the power grid or on the measurement data of the ACEMS. Since the forming of local electric power systems (LES) based on RES is actual, the article considers LES as a balancing group within the electric power system (EPS), which can operate as an autonomous system according to certain conditions. It is shown that for this purpose, a RES reservation system should be created in the power system due to their dependence on natural conditions and, accordingly, the instability of their electricity generation. When a transmission system operates as part of an EPS, a general system reserve is usually used (mainly maneuverable capacities of thermal and hydroelectric power plants). In order for the LES can operate as an autonomous one, it is proposed to use other means of power reserve available today: electrochemical energy storage, hydrogen and biogas technologies. An effective way is to coordinate the schedules of RES generation in the LES with the load schedules of active consumers. The example shows that in the case of a power deficit in the LES, RES located in the power grid can be used. The values of the necessary power at the LES connection node, power flows in the branches connecting the LES and RES grid, and power losses in them are determined by the offered method.

A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs.

The Multi-Point Relay (MPR) is one of the core technologies for Optimizing Link State Routing (OLSR) protocols, offering significant advantages in reducing network overhead, enhancing throughput, maintaining network scalability, and adaptability. However, due to the restriction that only MPR nodes can forward control messages in the network, the current evaluation criteria for selecting MPR nodes are relatively limited, making it challenging to flexibly choose MPR nodes based on current link states in dynamic networks. Therefore, the selection of MPR nodes is crucial in dynamic networks. To address issues such as unstable links, poor transmission accuracy, and lack of real-time performance caused by mobility in dynamic networks, we propose a comprehensive evaluation algorithm of MPR based on link-state awareness. This algorithm defines five state evaluation parameters from the perspectives of node mobility and load. Subsequently, we use the entropy weight method to determine weight coefficients and employing the method of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for comprehensive evaluation to select MPR nodes. Finally, the Comprehensive Evaluation based on Link-state awareness of OLSR (CEL-OLSR) protocol is proposed, and simulated experiments are conducted using NS-3. The results indicate that, compared to PM-OLSR, ML-OLSR, LD-OLSR, and OLSR, CEL-OLSR significantly improves network performance in terms of packet delivery rate, average end-to-end delay, network throughput, and control overhead.

Construction and Optimization of Power Transfer Route in Array Wireless Power Transfer Network With Multiple Load Nodes

Construction and Optimization of Power Transfer Route in Array Wireless Power Transfer Network With Multiple Load Nodes

Analytical method of determining conditions for full compensation of reactive power in the power supply system

Goal. The purpose of the article is the development of an analytical method for determining the conditions for achieving full compensation in the generalized power supply system based on the use of substitute circuits, which are obtained using equivalent transformations of the topology of the original circuit. Methodology. The article proposes a methodology for replacing series reactive power compensation in high-voltage paths of the power supply system with parallel reactive power compensation in a low-voltage load node. Results. An algorithm for successive transformations of the power supply circuit has been developed, which makes it possible to estimate the values of the capacitances of compensating capacitors, at which full compensation of reactive power in the system is achieved. Originality. The proposed analytical method for calculating the parameters of the compensation unit makes it possible to dispense with complex optimization computer methods and makes it possible to estimate the compensation capacities that fall on the share of the load and the network. Practical value. The proposed technique allows, using a simple algorithm, to determine with high accuracy the necessary parameters of the compensating device, which provide the optimal mode in the power supply system. The proposed algorithm can easily be implemented in a microcontroller system for automatic control of the modes of the power supply system.

An efficient failure-resilient mutual exclusion algorithm for distributed systems leveraging a novel zero-message overlay structure

In this paper, we provide a novel failure-resilient token-based mutual exclusion (ME) algorithm for distributed systems. Like a few other solutions in the literature, the proposed solution leverages a logical tree as its underlying topology. However, unlike any other solution, the tree is built using a probabilistic, fully distributed approach, without exchanging messages. The current tree-based ME algorithms often overlook considerations for node/link failures or offer costly methods for failure recovery. The proposed algorithm overcomes these limitations by providing an effective solution to maintain a logarithmic cost in case of node failures. The overlay structure – a unique logical tree – used to control access to the critical section maintains its consistency even when nodes fail. An extensive simulation study demonstrates the viability and efficiency of the proposed algorithm under various node failure models, and relevant metrics (e.g., node queue dimension, number of exchanged messages, and number of disconnected nodes) indicate a graceful degradation in performance with decreasing number of functioning nodes. For instance, for 4,096 nodes organized in a logical tree of arity 4 and with 4 physical nodes for logical node, a negligible number of nodes is disconnected from the tree after 250 epochs when the per-node per-epoch failure probability is pf≤0.0008. With a pf=0.0016, less than 10% of the nodes are disconnected. The proposed algorithm avoids node disconnection while minimally impacting the load of the logical tree nodes. In addition, for the same architecture, when both tree arity and cardinality are 4, after 250 epochs, the node load has demonstrated minimal variation and remains in close proximity to the original load. Moreover, experimental results also reveal a graceful degradation of algorithm performance. The fully distributed solution, rich parametrization for different trade-offs, and viability and resilience of the proposed algorithm also pave the way for future research.