Optimal Relay Research Articles

An Archimedes metaheuristic algorithm based optimum relay coordination in microgrid and combined overhead/cable distribution network

An Archimedes metaheuristic algorithm based optimum relay coordination in microgrid and combined overhead/cable distribution network

Optimal Relay Probing for UAV Millimeter Wave Communications with Beam Training Overhead

Blockage in millimeter wave (mmWave) channels can severely degrade the efficiency of air-to-ground (A2G) communications between unmanned aerial vehicles (UAVs) and base stations (BSs). Although relay-aided transmission has proved to be effective in mitigating the negative impact of blockage on mmWave communications, how to efficiently select the optimal relaying UAV for A2G communications remains an open problem. To enable optimal relay selection, the UAV under blockage should probe neighboring UAVs for the quality of their A2G links, which incurs non-negligible beam training overheads. Moreover, most existing studies ignore the impacts of UAV orientations as well as the finite sizes of beam codebooks on the performance of relay selection. In view of these challenges, we study the design of relay probing strategies for UAV mmWave A2G communications to maximize the expected achievable throughput between the UAV under blockage and the BS. Different from existing work, we jointly consider beam training overhead, the limited number of neighboring UAVs, the finite sizes of UAV codebooks, and the orientation of neighboring UAVs in our schematic design. Besides, we derive the closed-form expression for the distribution of achievable throughput when a specific neighboring UAV is adopted as the relay, in order to facilitate the development of the optimal relay probing strategy. Extensive simulation results demonstrate the accuracy of our analysis and the superiority of the obtained relay probing strategy.

Collaborative Intelligence Enabled Routing in Green IoV: A Grid and Vehicle Density Prediction-Based Protocol

Green Internet of Vehicles (IoV) is a newly-emerged research area which focuses on reducing networking overhead and improving communication efficiency in future vehicular cyber-physical systems (VCPS). A critical requirement to realize such tasks in Green IoV is the achievement of effective routing protocols for data dissemination. However, maintaining end-to-end communication and reducing the communication overhead at the same time is quite challenging due to high vehicle dynamics and complex traffic environments. In this paper, we propose a vehicle density prediction-based routing protocol called VDPGrid. Firstly, we introduce a vehicle density prediction model according to the spatio-temporal features of vehicle trajectories. Then, to reduce the communication overhead and improve communication efficiency, we divide the map into grids and present a routing path evaluation scheme that jointly considers the vehicle density, link quality, and routing length. Moreover, a grid-based routing method is proposed to select the optimal relay node according to real-time traffic information. Finally, extensive experiments using real-world vehicle trajectories are conducted to validate the effectiveness of our method.

Ensemble Learning Aided QPSO-Based Framework for Secrecy Energy Efficiency in FD CR-NOMA Systems

Cognitive radio (CR), non-orthogonal multiple access (NOMA), and full-duplex (FD) communications have been considered key technologies for providing spectrum utilization improvement and higher energy efficiency on the internet of things (IoT) networks and next-generation communication. However, security concerns are still an issue to be addressed because confidential information is exposed in wireless systems. To solve this problem, we design a novel artificial intelligence (AI)-based framework for maximizing the secrecy energy efficiency (SEE) in FD cooperative relay underlay CR-NOMA systems that are exposed to multiple eavesdroppers. First, we formulate the non-convex SEE optimization problem as bi-level optimization, subject to constraints that satisfy the quality-of-service requirements of secondary users. In particular, the outer problem is solved with ensemble learning (EL) to select the optimal relay. Regarding the inner problem, we propose a quantum particle swarm optimization (QPSO)-based technique to optimize power allocation. In addition, for comparison purposes, we describe a cooperative relay CR network with orthogonal multiple access (OMA), rate-splitting multiple access (RSMA), and half-duplex technologies. Moreover, we evaluate comparative schemes based on machine learning algorithms and swarm intelligence baseline schemes. Furthermore, the proposed EL-aided QPSO-based framework achieves performance close to the optimal solutions, with a meaningful reduction in computation complexity.

Investigating the performance of non‐standard characteristics‐based overcurrent relays and their optimum coordination in distributed generators connected networks

AbstractTo handle the challenges attributed to the distributed generators, the protection system of distribution networks needs to be evolved. Recent protection schemes modify the standard characteristics of overcurrent relays, called non‐standard characteristics. These non‐standard characteristics are mostly case‐dependent and have limitations in solving the variety of protection challenges attributed to distributed generators. This paper analyses the performance of six voltage‐based non‐standard characteristics on photovoltaic distributed generators (PVDG) penetrated 3‐bus, 8‐bus, and IEEE 30‐bus distribution grid, as well as the 9‐bus Canadian radial distribution grid with synchronous distributed generator, wind turbine generator, and PVDG penetrations. Besides that, the optimum coordination of these non‐standard characteristics‐based overcurrent relays is a complex problem due to the involvement of additional controlling parameters. To find the best suitable method, a comparative analysis of the genetic algorithm, differential evolution, artificial bee colony, harmony search algorithm, firefly algorithm, cuckoo search algorithm and particle swarm optimization are performed for optimum relay coordination. The outcomes of the paper are thoroughly discussed for future research on non‐standard characteristics development.

Protective Relaying Coordination in Power Systems Comprising Renewable Sources: Challenges and Future Insights

This article provides a comprehensive review of optimal relay coordination (ORC) in distribution networks (DNs) that include distributed generators (DGs). The integration of DGs into DNs has become a real challenge for power system protection, as the power flow changes from unidirectional to bidirectional, which complicates the relay settings. The introduction of DGs in DNs requires changes and modifications in the protective schemes to maintain proper operation, reliability, stability, and security of the system. This paper focuses on the impacts of DGs penetration into DNs, including the effects on protective scheme coordination. Various expressions for characterizing the overcurrent (OC) coordination problem, as well as related solution attempts, are discussed. Several optimization strategies and techniques are suggested by scientists to deal with coordination optimization problems aiming to achieve less computation time and better accuracy. All these efforts ultimately aim to define optimal relay settings to achieve ORC by generating the optimal setting of cascading relative OC relays. This comprehensive review provides a broad overview of the contributions of scholars in recent publications in this field, with more than 210 articles reviewed and analyzed. It is a valuable resource for other researchers in the same field who aim to tackle ORC problems in their future endeavors.

FTOR-Mod PSO: A fault tolerance and an optimal relay node selection algorithm for wireless sensor networks using modified PSO

The wireless sensor network (WSN) is deployed with enormous sensors responsible for data collection. The most critical issue in WSN is higher energy utilization which degrades network lifetime and communication ability. Hence, several methods are applied in WSNs to expand network lifetime. Clustering is a proven technique for reducing energy consumption in WSNs. CH collects data from different sensor nodes (SN) in every cluster and then forwards it to the sink node. On the other hand, fault occurrence in CH collapses entire data communication. Hence, this paper proposes a fault-tolerant cluster-based routing technique to avoid such difficulties. The proposed scheme is fault tolerance and optimal relay node with modified Particle Swarm Optimization FTOR-Mod PSO. Here, fault tolerance is achieved by selecting the backup cluster head (BKCH) for every cluster through the battle royale optimization (BRO) algorithm. Moreover, an aggregator node (AG) is adopted for inter-cluster data transmission, and modified PSO (Mod PSO) identifies CH and AG’s best relay. Results demonstrate that the proposed method reduces delay to 0.15 s by reducing the routing overhead to 2.4%, thereby improving the network lifetime to 92.714%.

An integrated approach for dual resource optimization of relay‐based mobile edge computing system

SummaryThe evolution of IoT, 5G and 6G aims to provide almost zero latency. Computation tasks size is different for different users. A framework for task computation achieving almost zero latency for stochastic demand is a challenge. A relay‐based D2D mobile edge computing (MEC) system is proposed. The idle device present in the networks is used as relay resources (RS). Mobile devices (MDs) communicate task to relay resources (RS) using D2D communication link. The RS perform computation and offloaded to edge server (ES). It aims to minimize total cost, energy expenditure and overall latency. Problem is formulated as mixed‐integer nonlinear‐constrained problem (MINCP). A three‐step algorithm to optimize relay selection, power allocation and computation resource allocation is proposed. In the initial step optimal relay selection is obtained by the Kuhn‐Munkres (KM) algorithm. In the next step, power allocation is obtained using Q‐learning. In the last step, the main problem is converted into a cost optimization problem deciphered by the proposed algorithm. The substantial simulation results indicate the relay‐based MEC system to achieve an astounding outcome in terms of latency, energy consumption and cost. Compared with other baseline methods, the proposed algorithm can achieve reduced energy consumption and cost for almost zero latency.

Optimum relay coordination for reliability evaluation of distribution system with allocation of wind turbine generators

ABSTRACT In this work, a method of optimal relay coordination for improving system reliability has been presented in association with the Wind Energy Conversion System (WECS). The graphical method as well as changes in Plug Setting Multiplier (PSM) and Time Dial Setting (TDS) have been used to test the system’s reliability with the help of ETAP. Load flow analysis, short circuit testing under different fault conditions, reliability assessment, and relay coordination have been performed on the proposed WECS. In this research work, a test system with relay coordination is proposed that is used for transformer protection using over-current relays (OCR) and ground-fault relays (GFR) devices with a wind energy induction generator. It is shown that coordination is off when WECS is added to the system. Therefore, maximum values of the circuit breaker’s trip device setting, the relay’s pickup, and the time dial settings are desirable to increase the coordination. Further, it has been revealed that the comprehensive analysis of load flow analysis, short circuit analysis, and reliability assessment play vital roles in achieving suitable relay coordination. Also, the use of ETAP software in this analysis improves the accuracy and speed of the circuit breaker operation at a reduced cost.

An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks

In a wireless sensor network (WSN), geocasting is a location-based routing protocol used for data collection or information delivery. In geocasting, a target region usually contains many sensor nodes with limited battery capacity, and sensor nodes in multiple target regions need to transmit data to the sink. Therefore, how to use location information to construct an energy efficient geocasting path is a very important issue. FERMA is a geocasting scheme for WSNs based on Fermat points. In this paper, an efficient grid-based geocasting scheme for WSNs, which is called GB-FERMA, is proposed. The scheme uses the Fermat point theorem to search for the specific nodes as Fermat points in a grid-based WSN, and it selects the optimal relay nodes (gateways) in the grid structure to realize energy-aware forwarding. In the simulations, when the initial power 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA can effectively reduce the energy consumption and thus prolong the lifetime of the WSN.

Comparative study of optimization methods for optimal coordination of directional overcurrent relays with distributed generators

<span lang="EN-US">Due to the growing penetration of distributed generators (DGs), that are based on renewable energy, into the distribution network, it is necessary to address the coordination of directional overcurrent relays (DOCR) in the presence of these generators. This problem has been solved by many metaheuristic optimization techniques to obtain the optimal relay parameters and to have an optimal coordination of the protection relays by considering the coordination constraints. In this article, a comparative study of the optimization techniques proposed in the literature addresses the optimal coordination problem using digital DOCRs with standard properties according to IEC60-255. For this purpose, the three most efficient and robust optimization techniques, which are particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are considered. Simulations were performed using MATLAB R2021a by applying the optimization methods to an interconnected 9-bus and 15-bus power distribution systems. The obtained simulation results show that, in case of distributed generation, the best optimization method to solve the relay protection coordination problem is the differential evolution DE. </span>

Cooperative non-orthogonal multiple access with D2D communication and opportunistic relaying in large-scale cellular networks

In order to improve the spectrum efficiency and communication robustness, we propose a cooperative non-orthogonal multiple access (NOMA) scheme in large-scale cellular networks with Device-to-Device (D2D) communication and opportunistic relaying. The distribution of base stations (BSs) is assumed to follow homogeneous Poisson Point Process (PPP). Each BS communicates to both a near-user (CU1) and a far-user (CU2) in its coverage area. A collaborative region is defined between each BS and CU2, wherein there are multiple D2D transmitters (DTs) uniformly distributed and intending to communicate with a common D2D receiver (DR). Among all the DTs that has correctly decoded the data of CU2, one is selected to perform the single-hop decode-and-forward relaying. The selected DT superimposes its own data onto CU2’s data, and broadcasts the composite signal. If there is no DT available to forward the cellular data, the DT with the best channel quality towards DR will be selected to transmit the pure D2D data. Through properly modeling the aggregate interference, we analyze the throughput of each terminal. Numerical results show that with specific parameter settings, the NOMA scheme with optimal relay selection can greatly outperform random relay selection in terms of the far-user’s throughput and the system throughput. With optimal relay selection, the proposed NOMA scheme can achieve much higher throughput than the orthogonal multiple access scheme.

Collaboration-Aware Relay Selection for AUV in Internet of Underwater Network: Evolving Contextual Bandit Learning Approach

In Internet of Underwater Things, data collection is assisted by autonomous underwater vehicle (AUV) to enhance the reliable transmission. AUV acts as a mobile collector and transmits the collected data to the station via relay nodes. However, the highly mobile nature of AUV needs an adaptive and efficient relay selection scheme for achieving good capacity performance. In this article, we propose a new contextual multiarmed bandit with evolving relay set (CMAB-ERS) learning framework, which successfully addresses crucial issues, including dynamic environment conditions and evolving relay set. To deal with the evolving relay set, CMAB-ERS incorporates collaborative effects into inference as well as learning processes, the new relays will acquire prior knowledge by having experienced nodes sharing observations, reducing the learning time significantly. To overcome the uncertainty of environmental information, we exploit the contextual environment factors to assist relay reward estimation and execute time-sensitive parameter update after every transmit–receive cycle, aiming for minimizing potential loss due to the time-varying channel. Correspondingly, the collaboration-aware online contextual bandit learning (COCBL) algorithm is designed that enables AUV to switch optimal relay adaptively and promises high-capacity transmission. Further, we rigorously prove the convergence of the COCBL algorithm by considering the evolving relay set and give its upper bound on the cumulative regret. Finally, extensive simulation results elucidate the effectiveness of the proposed COCBL.

Overcurrent relay optimization in a radial distribution network considering different fault locations

Overcurrent relay optimization in a radial distribution network considering different fault locations

Optimized adaptive protection coordination of microgrids by dual-setting directional overcurrent relays considering different topologies based on limited independent relays’ setting groups

• Optimal adaptive protection, considering the limits on the number of setting groups. • Using the DS-DOCRs incorporating independent changes in the setting groups. • Optimal relay curve types and breakpoints for the DS-DOCRs. • Fast protection without selectivity constraint violations in various configurations. • Around 41.66% improvement compared to available studies. Several studies, such as adaptive protection schemes, have been done in the area of smart grids’ protection. However, there is a research gap in developing an adaptive scheme with dual-setting (DS) directional overcurrent relays (DOCRs), considering the limitations of the number of setting groups of protective relays. This research aims to fill such a research gap. Optimizing the relay curve types of the first and second parts of the DS relay models and breakpoints (BPs) improves the speed of the protection scheme based on independent changes in the setting groups of DOCRs. Also, there is no coordination constraint violation in various configurations by applying the proposed method, while it requires only a limited number of setting groups. Therefore, the proposed method can be practical in actual systems because the suggested solutions and protective settings can be easily implemented using commercial relays. The proposed adaptive protection scheme is applied to two test systems (the distribution networks of the IEEE 14-bus and IEEE 30-bus test systems). The comparative test results infer that significant improvement in the speed of the protection system of smart grids can be achieved compared to available studies by the proposed method.

Secured Reliable Communication Through Authentication and Optimal Relay Selection in Blockchain Enabled Cellular IoT Networks

Secured Reliable Communication Through Authentication and Optimal Relay Selection in Blockchain Enabled Cellular IoT Networks

STUDY OF DYNAMIC STATES OF RELAY-MODAL ELECTRIC DRIVE CONTROL SYSTEM WITH ELASTIC LINK

Modal control systems have given transient characteristics, but are not optimal by any criterion. The use of modal controllers in systems with elastic dynamic objects is due to their relatively high order and tendency to oscillate. In the synthesis of such controllers, the actual distribution of roots is implemented on the basis of the N-i switching method and provides them with characteristics that are close to optimal in terms of speed. This original synthesis method has a simple and transparent mathematical apparatus.
 The use of relay systems in combination with the principles of subordinate control and modal control provides a comprehensive solution to the problem of forming the desired flow of transient processes in electric drives with elastic links. The analytical expressions for the feedback coefficients of optimal control systems found using the N-i switching method are close to the expressions for the feedback coefficients of modal control systems. The incomplete correspondence between the parameters of optimal and modal controllers can be successfully compensated based on empirical data. This makes it possible to bring the transient characteristics of modal and optimal relay systems as close as possible. The purpose of the study is to estimate the performance indexes obtained in this way and correction of them based on empirical data. To achieve it, the problem of a comparative study of typical operating modes under conditions of varying key parameters was solved, which made it possible to determine the conditions for the implementation of the highest possible dynamic characteristics in fourth-order relay-modal control systems.
 The results of the study led to the conclusion that the modification of the calculated time constants of the system by applying a correction factor can be characterized as an effective means of influencing the dynamic characteristics of relay-modal slave control systems in order to bring them closer to the optimal ones in terms of speed. The prospect of practical application of the results of the work lies in their integration into adaptive algorithms for parametric synthesis of digital controllers.

Generalized Relays Subsets Selection Algorithm in Cloud-Based 6G Large-Scale Relays Network

In this article, in order to substantially improve outage probability (OP), we propose a generalized optimal relays subsets selection algorithm in a cloud-based 6G large-scale relays network. We derive OP based on relays subsets selection for first hop and second hop, respectively. In addition, with the aid of the proposed power allocation method, we put forward optimal relays subsets paring algorithm. The generalized model can be converted into various relays selection algorithms, such as best relay selection, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> th best relay selection, partial relays selection, various relays subsets selection, and so on. Simulation results show that the proposed multiple relays selection algorithm has 2–6 orders of magnitude advantage than existing selection algorithms on OP.

Optimal Relay Selection Strategy for Efficient and Reliable Cluster-Based Cooperative Multi-Hop Transmission in Vehicular Communication

Routing is the key issue for Vehicular Networks since each node in the network has mobility. The dynamic nature of vehicular networks increases with mobility and the consequence is reflected in controlling traffic overhead. As a result, the routing technique and the type of node mobility information are completely dependent on building dependable end-to-end communication links. To address these challenges, a Reliable Cluster-based Multi-Hop Cooperative Routing (RCCR) strategy was proposed by using velocity, distance, and link quality parameters. This algorithm obtains the tradeoff between Quality of Service (QoS) and mobility constraints over link parameters. It improves routing scalability by electing cluster heads and selecting Multi-Point Relays (MPRs) while considering mobility constraints and QoS needs. The proposed technique determines the link quality for every pair of nodes based on values of signal strength and distance parameters. The relay vehicles are chosen based on the highest possible QoS value, which is calculated to assure route stability, reliability, and durability. The heuristic limitations of the multi-point relay selection strategy are handled by considering the link quality, distance from the source vehicle, and cluster-head coverage area to enhance the performance of the multi-hop network with tolerable End-to-End transmission delay. Further, we obtained an optimal number of cooperative vehicles in every hop. Finally, simulation results demonstrate the effectiveness of the proposed algorithm compared to other state of art algorithms.

Optimal Relay Selection Strategy for Energy-Efficient Cooperative Multi-Hop Image Transmission in Wireless Multimedia Sensor Network

For high-quality picture transmission through smart devices in Mobile Multimedia Sensor Networks (MWMSN), high transmission rates, throughput, and a low Bit Error Rate are necessary. At the same time, energy efficiency is always a big problem for battery-powered smart devices like smartphones and tablets. MWMSN makes extensive use of cooperative communication to realize the benefits of the Multiple-Input-Multiple-Output technology. Energy consumption and routing are major challenges for Cooperative Mobile Multimedia Sensor Networks since each node in the network have mobility. The dynamic nature of MWSNs increases with mobility and the consequence is reflected in the control traffic overhead. To address these challenges, a Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS) strategy was proposed by using velocity, distance, and link quality parameters. The proposed technique determines the link quality for every pair of nodes based on values of signal strength and distance parameters. The relay nodes are chosen based on the highest possible Quality of Service (QoS) value, which is calculated to assure route stability, reliability, and durability. Further, we obtained an optimal number of cooperative nodes in every hop with the objective to minimize end-to-end energy consumption. Finally, simulation results demonstrate the effectiveness of the proposed algorithm compared to other state of art algorithms.