Problem In Networks Research Articles

Implementation of a Cuckoo Search intelligent simulation system for node placement problem in Wireless Mesh Networks: Performance evaluation for tuning hyperparameters [formula omitted] and [formula omitted]

In recent years, Internet of Things (IoT) is the hottest topics in the research and implementation of wireless networks. Most of the research of IoT-based applications is related with the data collecting and processing using sensors and actuators. As the network environment for connecting IoT devices are considered WLAN or Bluetooth. The Wireless Mesh Networks (WMNs) are the most suitable networks for the Internet of Things (IoT) due to their numerous benefits. However, they have several issues related to wireless communications. One solution is the optimization of position and location of mesh routers. But finding the best location of mesh routers is an NP-hard problem. To deal with this issue, we propose and implement an intelligent simulation system based on the Cuckoo Search (CS) algorithm called WMN-CS. In the implemented system, the hyperparameters should be tuned to find better solutions. This paper evaluates the WMN-CS performance for various combination of hyperparameters by tuning host bird discovery rate (pa) and scale parameter (γ). The simulation results show that a better performance is achieved when the scale parameter (γ) is between 0.07 and 0.09 and the host bird recognition rate (pa) is between 0.900 and 0.925.

Secured Osprey-Based Energy Efficient Routing and Congestion Control in WSN

An adaptive metaheuristic optimization-based QoS-aware, energy-balancing, secure routing protocol (AQoS-ESRP) is proposed in this article. The network is modelled as a biconcentric hexagon (BiCon-HexA), and the clusters are formed within the BiCon-HexA network. The BiCon-HexA is divided into six sectors to support effective data aggregation, and then clusters are formed within all sectors. The optimal cluster head (CH) selection mechanism is modelled by an Adaptive Hunter-Prey Optimization (AdapH-PO) algorithm considering QoS parameters. Data aggregation is then done securely with an enhanced encryption approach. Here, upgraded elliptic curve cryptography (UEllip-CC) is used to encode data in CH. This UEllip-CC approach provides security improvements in data transmission. Furthermore, in this study, CHs are combined in the multi-hop routing of data packets to reduce the power consumption problems of wireless sensor networks (WSN). To determine the optimal route for data transmission, an energy-balanced multi-path routing algorithm called improved convolutional osprey network (ICON) is presented. Nevertheless, the data transmission nodes can be overloaded in the data routing phase. Here, the congestion problem can be solved by applying an improved version of the Random Early Detection (RED) congestion control model to discard the data packets more noticeably. The simulation of AQoS-ESRP is done with Matlab, and the performance is evaluated using different metrics. When compared to existing systems, the simulation results clearly indicate a significantly higher throughput and delay. Thus, the AQoS ESRP model is employed to maximize the overall data transfer in the WSN.

Kullback-Leibler Control in Boolean Control Networks.

This article addresses the Kullback-Leibler (KL) control problem in Boolean control networks. In the considered problem, an extended stage cost function depending on the control inputs is introduced; in contrast to a stage cost of the conventional KL control problems in the Markov decision process cannot take into consideration the control inputs. An associated Bellman equation and a matrix-based iteration algorithm are presented. The theoretical analysis shows that the proposed KL control results in an approximated form of conventional dynamic programming (DP). Furthermore, the convergence analysis is presented, with the weight parameter converging to zero and diverging to infinity. In practical application examples, a comparison of the conventional DP and proposed KL control is illustrated.

Research on fault location method of active distribution network based on improved multiverse algorithm

Abstract After the distributed power supply is connected to the distribution network, the network structure becomes more complex, and the fault characteristics change greatly compared with the traditional distribution network fault, so the original traditional fault location method is not satisfactory. This paper presents a fault location method for active distribution networks based on improved multi-node optimization (IMVO). According to the fault characteristics of the distribution network, the multiverse optimization algorithm is binary coded, and the algorithm parameters are improved to optimize the iterative process and enhance the performance of the algorithm. The simulation results show that this method can effectively solve the fault location problem of active distribution networks and has good advantages.

Theoretical concept of error-rejecting entanglement purification of quantum-dots electronic spins in single-sided optical microcavity

We use the quantum-dots (QD) coupled with single-sided microcavities system to construct the error-rejecting controlled not (CNOT) gate of electronic spins system in QD; We have designed an error-rejecting entanglement purification protocol (EPP) of electronic spins system in QD by using the error-rejecting CNOT gate and unitary operation. It can extract the high entanglement from the mixed entanglement states of the electronic spins with low entangled states. This EPP can eliminate the operation errors caused by the nonideal interaction between photons and QD coupled with optical microcavities system, and improve the fidelity of the EPP through iteration. Our scheme is more practical in future long-distance quantum communications, especially providing significant benefits for solving decoherence problems in quantum networks and quantum repeaters.

Detection of HIF Using Machine Learning Techniques: A Review

Abstract The major problem in Power Transmission and Distribution networks is to detect the High Impedance Fault (HIF) which is caused by a conducting medium when it comes in contact with a surface having high resistance value. HIF faults are hazardous to the public and a major task to deal with for operating personnel. The objective of this paper is to present review on the literature pertaining to the past research on such type of faults and the importance of Machine Learning Techniques for classifying and detecting them.

Trust Evaluation Techniques for 6G Networks: A Comprehensive Survey with Fuzzy Algorithm Approach

Sixth-generation (6G) networks are poised to support an array of advanced technologies and promising high-quality and secure services. However, ensuring robust security, privacy protection, operational efficiency, and superior service delivery poses significant challenges. In this context, trust emerges as a foundational element that is critical for addressing the multifaceted challenges inherent in 6G networks. This review article comprehensively examines trust concepts, methodologies, and techniques that are vital for establishing and maintaining a secure and reliable 6G ecosystem. Beginning with an overview of the trust problem in 6G networks, this study underscores their pivotal role in navigating the network’s complexities. It proceeds to explore the conceptual frameworks underpinning trust and discuss various trust models tailored to the unique demands of 6G networks. Moreover, this article surveys a range of scholarly works presenting diverse techniques for evaluating trust by using the fuzzy logic algorithm, which is essential for ensuring the integrity and resilience of 6G networks. Through a meticulous analysis of these techniques, this study elucidates their technical nuances, advantages, and limitations. By offering a comprehensive assessment of trust evaluation methodologies, this review facilitates informed decision making in the design and implementation of secure and trustworthy 6G networks.

Stable recovery of piecewise constant conductance on spider networks

ABSTRACT We address the discrete inverse conductance problem for well-connected spider networks, that is, to recover the conductance function on a well-connected spider network from the Dirichlet-to-Neumann map. It is well-known that this inverse problem is exponentially ill-posed, requiring the implementation of a regularization strategy for numerical solutions. Our focus lies in exploring whether prior knowledge of the conductance being piecewise constant within a partition of the edge set comprising a few subsets enables stable conductance recovery. To achieve this, we propose formulating the problem as a polynomial optimization problem, incorporating a regularization term that accounts for the piecewise constant hypothesis. We show several experimental examples in which the stable conductance recovery under the aforementioned hypothesis is feasible.

Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

Relation coarsest partition method to observability of probabilistic Boolean networks

Using the relational coarsest partition (RCP) method, this article aims to study the observability problem of probabilistic Boolean networks (PBNs). The key step to solving this problem is to find the parallel cycle and the coarsest refinement partition of the given initial partition. Firstly, several definitions of observability for PBNs are proposed, and the connections between finite-time observability with positive probability (FTOPP), asymptotical observability (AO), and finite-time observability with probability one (FTOPO) are revealed. Secondly, by defining the appropriate transition relation, the RCP method is introduced into PBNs. In addition, based on the output signal mapping set, the initial partition is constructed. Thirdly, the concept of a parallel cycle for PBNs is proposed, and two algorithms are presented for computing the cycle and parallel cycle of PBNs. Moreover, with the help of the RCP method, a series of criteria are derived to solve the observability problem of PBNs, which works more efficiently. Fourth, when PBN is unobservable, an algorithm is proposed to design the state-flipping set with the smallest number of states, which can make the unobservable PBN observable. To show the validity and effectiveness of the obtained results, examples are finally given.

CIBORG: CIrcuit-Based and ORiented Graph theory permutation routing protocol for single-hop IoT networks

The Internet of Things (IoT) has emerged as a promising paradigm which facilitates the seamless integration of physical devices and digital systems, thereby transforming multiple sectors such as healthcare, transportation, and urban planning. This paradigm is also known as ad-hoc networks. IoT is characterized by several pieces of equipment called objects. These objects have different and limited capacities such as battery, memory, and computing power. These limited capabilities make it difficult to design routing protocols for IoT networks because of the high number of objects in a network. In IoT, objects often have data which does not belong to them and which should be sent to other objects, then leading to a problem known as permutation routing problems. The solution to that problem is found when each object receives its items. In this paper, we propose a new approach to addressing the permutation routing problem in single-hop IoT networks. To this end, we start by representing an IoT network as an oriented graph, and then, based on a reservation channel protocol, we first define a permutation routing protocol for an IoT in a single channel. Secondly, we generalize the previous protocol to make it work in multiple channels. Routing is done using graph theory approaches. The obtained results show that the wake-up times and activities of IoT objects are greatly reduced, thus optimizing network lifetime. This is an effective solution for the permutation routing problem in IoT networks. The proposed approach considerably reduces energy consumption and computation time. It saves 5.2 to 32.04% residual energy depending on the number of items and channels used. Low energy and low computational cost demonstrate that the performance of circuit-based and oriented graph theory is better than the state-of-the-art protocol and therefore is a better candidate for the resolution of the permutation routing problem in single-hop environment.

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks, which regards recent hydraulic states as initial conditions, and future runoff and control policy as boundary conditions. To incorporate hydraulic constraints and physical relationships into drainage modelling, physics-guided mechanisms are designed on top of the surrogate model to restrict the prediction variables with flow balance and flooding occurrence constraints. According to case results in a stormwater network, the GNN-based model is more cost-effective with better hydraulic prediction accuracy than the NN-based model after equal training epochs, and the designed mechanisms further limit prediction errors with interpretable domain knowledge. As the model structure adheres to the flow routing mechanisms and hydraulic constraints in urban drainage networks, it provides an interpretable and effective solution for data-driven surrogate modelling. Simultaneously, the surrogate model accelerates the predictive modelling of urban drainage networks for real-time use compared with the physics-based model.

Finite-time prescribed performance tracking control for nonlinear time-delay systems with state constraints and actuator hysteresis

In this paper, the problem of adaptive neural network prescribed performance tracking control for a class of non-strict feedback time-delay systems constrained by full-state is studied. Radial basis function (RBF) neural networks (NNs) are integrated into the backstepping medium to deal with the uncertain functions and the barrier Lyapunov function (BLF) technique ensures that the state of the system does not exceed its limits. Subsequently, integrated with the Lyapunov–Krasovskii functional, the proposed control scheme makes the tracking errors converge to the preset region while the state constraint is not violated. Finally, the effectiveness of the scheme is supported by two simulation experiments.

Adverse Childhood Experiences: Health Care Utilization And Expenditures In Adulthood.

Adverse childhood experiences (ACEs) have been shown to be strong predictors of socioeconomic status, risky health behaviors, chronic health conditions, and adverse outcomes. However, less is known about their association with adult health care utilization and expenditures. We used new data from the 2021 Medical Expenditure Panel Survey-Household Component (MEPS-HC) to provide the first nationally representative estimates of ACEs-related health care utilization and expenditure differences based on direct observation, rather than model-based extrapolation. Compared to demographically similar adults without ACEs, those with ACEs had substantially higher utilization and 26.3percent higher expenditures. The aggregate spending difference across the 157.6million US adults with ACEs was $292billion in 2021. Moreover, we observed large, graded relationships between ACEs and health status, health behaviors, and some dimensions of socioeconomic status. We also found associations between ACEs and a range of adverse adult circumstances, also newly measured in the 2021 MEPS, including financial and housing problems, social network problems, little or no life satisfaction, stress, food insecurity, verbal abuse, physical harm, and discrimination.

Data Farming the Parameters of Simulation-Optimization Solvers

The performance of a simulation-optimization algorithm, a.k.a. a solver, depends on its parameter settings. Much of the research to date has focused on how a solver’s parameters affect its convergence and other asymptotic behavior. While these results are important for providing a theoretical understanding of a solver, they can be of limited utility to a user who must set up and run the solver on a particular problem. When running a solver in practice, good finite-time performance is paramount. In this paper, we explore the relationship between a solver’s parameter settings and its finite-time performance by adopting a data farming approach. The approach involves conducting and analyzing the outputs of a designed experiment wherein the factors are the solver’s parameters and the responses are assorted performance metrics measuring the solver’s speed and solution quality over time. We demonstrate this approach with a study of the ASTRO-DF solver when solving a stochastic activity network problem and an inventory control problem. Through these examples, we show that how some of the solver’s parameters are set greatly affects its ability to achieve rapid, reliable progress and gain insights into the solver’s inner workings. We discuss the implications of using this framework for tuning solver parameters, as well as for addressing related questions of interest to solver specialists and generalists.

Modelling and optimization of an existing onshore gas gathering network using PIPESIM

Pakistan has limited natural gas reserves, and most are found onshore. This article reports on the problems of an onshore gas gathering network (GGN) analysed through steady-state simulation modelling using PIPESIM software. The research methodology incorporates a comprehensive steady-state hydraulic analysis considering fluid flowing velocity limitations, liquid holdup and slugging along with other issues faced by gas gathering networks. The steady-state hydraulic analysis has led us to pinpoint specific GGN pipelines facing critically low gas velocities and consequent liquid holdup. Addressing these issues involved application of PIPESIM software for modelling, considering various operating schemes of gas-producing wells and their associated pipelines. To select an optimal operating scheme, the study utilized the Analytic Hierarchy Process (AHP) for operational optimization, to identify the most effective solution for reduced liquid holdup, improving production, and ensuring the safe operation among available alternatives. Findings from our hydraulic analysis highlight the importance of reducing GGN outlet pressure to mitigate challenges associated with liquid holdup which causes slugging and back pressure effect at source leading to low production and poor performance of the GGN. Study of three alternative cases reveals that decreasing outlet pressure lowers the liquid holdup, improve gas flowing velocities, and enhanced overall production. These findings validate our hypothesis that reducing GGN outlet pressure is a viable strategy to lower the liquid holdup in pipelines. This research offers significant value by providing a comprehensive solution to GGN liquid holdup, low flowing velocities, back pressure and low production challenges. The integration of steady-state hydraulic analysis, simulation modelling with PIPESIM, and the application of AHP for optimization contributes novel insights into the optimization of operation of gas gathering networks. Emphasizing the reduction of liquid holdup and enhancing production through outlet pressure adjustments offers a practical framework for optimizing the functionality of gas gathering networks.

Big data algorithm for computer network information security in the 5G era

Computers play a very important role in today’s life. A large amount of personal information exists in computers, so it is very important to protect the information security on computer networks. The development of information technology has brought a huge impact on human society, and the information security of its computer network has attracted more and more attention. Aiming at the needs of computer network information management in the age of big data, this paper has developed a special computer network information protection system to deal with various threats and constantly improve the protection system. Based on this, the concept and advantages of the 5G network are first expounded, and the management technology of the 5G network is briefly analyzed. Then the information security problems of computer networks in the 5G era are considered, and effective solutions to the information security problems are proposed. Through research and calculation, the new computer network security system can improve network information security by 13.4%.

A novel optimization framework integrating multiple initialization, automatic topologization and MINLP reduction to accelerate large-scale heat exchanger network synthesis

Ensuring the solution feasibility and achieving an acceptable solution time for the non-deterministic polynomial-time hard (NP-hard) problem of heat exchanger network (HEN) synthesis is still challenging. In this study, we developed a novel optimization framework for efficiently solving large-scale HEN synthesis problems to obtain near global optimal solutions, simultaneously addressing the solution's infeasibility and time. The optimization framework consists of a multiple initial HEN design model (M-INI model), a new distance-based auto structure transformer (A-STR transformer), and a reduced non-linear programming model (R-NLP model). The M-INI model generates various HEN designs based on pinch analysis by varying the minimum approach temperature and the modes of stream splitting and mixing. The A-STR transformer is first proposed to automatically convert various HEN designs into associated superstructure-based topologies as multiple initial starting points for accelerating the solution of the R-NLP model. A strategy for approximating the temperature difference calculation is further applied to reduce the computational loads. Four case studies were conducted using the proposed method to minimize the total annual cost (TAC). The solutions of general cases 1 and 2 are almost equal to the best results in the literature, while the solution time is reduced by 98.7 % and 99.8 %, respectively, which demonstrates the performance of the proposed method. Especially, the method was applied to two practical industrial HEN cases: a large-scale low-grade heat recovery system and a crude distillation unit. The results of large-scale case 3 show that the optimal solution near the global optimal solution only requires 251.7 s. Case 4 includes pre-splitting in scenario A and free splitting in scenario B. Compared to scenario A, the TAC and solution time in scenario B were reduced by 33.9 % and 93.1 %, indicating that the pre-splitting strategy excludes the optimal from the search space. The proposed novel optimization framework demonstrates the efficiency and applicability of handling large-scale HEN synthesis problems. It can also guide engineers in the design of a large-scale HEN with lower costs and effort.

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

A new model of electrical cyber–physical systems considering stochastic communication link failures

This article establishes a model for electrical cyber–physical systems (ECPS), integrating the power grid, power information network and control network. The power grid is modelled based on DC power flow. The power information network enables the exchange of information between the power grid and control network. Stochastic communication link failure is a common problem in real control networks. To address this issue, this article models the control network as a Bernoulli network. The incorporation of an asynchronous gossip algorithm with price penalty factors enables the model to analyse multi-target combined economic emission dispatch problems (CEED) involving line losses in an imperfect control network. The effectiveness and validity of the model are illustrated through simulations.