Optimal Placement Problem Research Articles

The satellite network cache placement strategy based on content popularity and node collaboration.

Proposed is a Satellite network cache placement strategy (PNCCP) based on popularity and node cooperation to address the issue of significant delays in end-to-end connectivity due to instability among satellites. Initially, the strategy employs spectral clustering algorithm to partition the satellite network's topology, limiting the retrieval scope of content and reducing unnecessary propagation delays. Within each partition, a cache collaboration open mechanism among satellites is devised to share cache resources, utilizing the proximity of neighboring nodes to share popular content and cache space. Furthermore, the data naming network (NDN) cache model is enhanced and integrated with the open mechanism, with an update mechanism designed to address the invalidation caused by the dynamic nature of satellite networks. Finally, aiming to minimize users' average retrieval delay, the artificial bee colony algorithm is employed to solve the optimal cache placement problem. Simulation results demonstrate that compared to three contrasting cache strategies, the proposed strategy reduces user content retrieval delays, improves cache hit rates, and holds an advantage in reducing request hop counts.

Quantum‐Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures

Over the last decade, concepts such as industry 4.0 and the Internet of Things (IoT) have contributed to the increase in the availability and affordability of sensing technology. In this context, structural health monitoring (SHM) arises as an especially interesting field to integrate and develop these new sensing capabilities, given the criticality of structural application for human life and the elevated costs of manual monitoring. Due to the scale of structural systems, one of the main challenges when designing a modern monitoring system is the optimal sensor placement (OSP) problem. The OSP problem is combinatorial in nature, making its exact solution infeasible in most practical cases, usually requiring the use of metaheuristic optimization techniques. While approaches such as genetic algorithms (GAs) have been able to produce significant results in many practical case studies, their ability to scale up to more complex structures is still an area of open research. This study proposes a novel quantum‐based combinatorial optimization approach to solve the OSP problem approximately, within the context of SHM. For this purpose, a quadratic unconstrained binary optimization (QUBO) model formulation is developed, taking as a starting point of the modal strain energy (MSE) of the structure. The framework is tested using numerical simulations of Warren truss bridges of varying scales. The results obtained using the proposed framework are compared against exhaustive search approaches to verify their performance. More importantly, a detailed discussion of the current limitations of the technology and the future paths of research in the area is presented to the reader.

Optimal resilient sensor placement problem for secure state estimation

In this paper, we consider a sensor placement problem for secure state estimation in an adversarial environment. Specifically, this paper deals with a system consisting of n agents where up to l sensor measurements are potentially compromised by malicious adversaries and tackles the problem of sensor placement such that the system state can be reconstructed even if the measurements are compromised. However, since it is costly to deploy sensors for all agents, we also aim to place sensors at the lowest possible cost. We call this problem the optimal resilient sensor placement problem (ORSPP) and show that this problem is coNP-hard. Therefore, it is, in general, impossible to compute the ORSPP in polynomial time unless P=coNP. On the positive side, however, we also provide that this problem can be solved in polynomial time if the network structure of the agents satisfies certain conditions. The concrete algorithm for the ORSPP is provided, and further, in the process of providing the coNP-hardness, we also derive a new form of a necessary and sufficient condition for secure state estimation. Finally, we show the results of numerical simulations using a random graph and a diffusion process.

Joint Optimization of Caching Placement and Power Allocation in Virtualized Satellite-Terrestrial Network

With the rapid development of mobile services and applications, the transmitting of massive data makes low-cost communication a challenge. Edge-based wireless communication technology is developed to be a promising approach to satisfy the communication requirements. Edge caching technology is one of effective methods to reduce the overhead of communication system and the pressure of backhauls. In this paper, the joint optimization problem of caching placement and power allocation in virtualized low earth orbit (LEO) satellite-terrestrial networks is proposed, which is based on cooperative caching, by considering cache size limits and power constraints. The optimization problem is solved using an algorithm inspired by the courtship movements and random flights of mayflies. Simulation results show the effectiveness of the proposed scheme in improving system performance and reducing power consumption.

Dual-UAV Collaborative High-Precision Passive Localization Method Based on Optoelectronic Platform

Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.

Antenna Placement Optimization for Distributed MIMO Radar Based on a Reinforcement Learning Algorithm

This paper studies an optimization problem of antenna placement for multiple heading angles of the target in a distributed multiple-input multiple-output (MIMO) radar system. An improved method to calculate the system’s coverage area in light of the changing target heading is presented. The antenna placement optimization problem is mathematically modelled as a sequential decision problem for compatibility with reinforcement learning solutions. A reinforcement learning agent is established, which uses the long short-term memory (LSTM)-based proximal policy optimization (PPO) method as the core algorithm to solve the antenna placement problem. Finally, the experimental findings demonstrate that the method can enhance the coverage area of antenna placement and thus has reference value for providing new ideas for the antenna placement optimization of distributed MIMO radar.

Optimal sensor placement for corrosion induced thickness loss monitoring in ship structures

Current monitoring strategies revolve around proactive maintenance. Corrosion-induced thickness loss (CITL) is one of the most common types of structural deterioration in ship hulls. Recently, Structural Health Monitoring (SHM) has emerged as an appealing alternative that may drive the transition to predictive maintenance. The complexity of ship structures requires that any SHM system must be designed to fully exploit information collected from a limited number of sensors. The aim of this work is to take a step in this direction by framing the design of an SHM system for CITL monitoring in hull structures under the guise of an optimization problem. The problem of detecting the existence of a certain level of thickness loss in hull structural elements is considered and treated using detection theory. The Optimal Sensor Placement (OSP) problem aims to generate strain sensor architectures that maximize the performance of the employed detector and minimize the costs incurred by its erroneous decisions. A computational implementation is considered, and synthetic strain data are generated using a detailed FE model of a reference vessel. Uncertainty is propagated to the data through a Monte Carlo Simulation (MCS). A Genetic Algorithm (GA) is employed to treat the optimization problem and the resultant sensor designs are compared to manually selected alternatives.

Physics-Guided V-Net: An Efficient Surrogate Model for Well Placement Optimization in Petroleum Reservoirs

Summary Determination of the optimal well placement strategy in oil or gas fields is crucial for economic reservoir development. The optimization process, however, can be computationally intensive as a result of the potentially high-dimensional search space and the expensive numerical simulation. In this study, machine-learning-based surrogate models are constructed as efficient alternatives to numerical simulators to accelerate the optimization process. A V-Net neural network architecture is used with features of skip connections, 3D convolutional filters, and a residual learning structure to handle 3D parameter fields effectively. Physical guidance is incorporated into the neural network training process by adding governing equation constraints to the loss function in the discretized form, resulting in a physics-guided machine learning architecture: PgV-Net. Well placement optimization problems in a 3D oil-bearing formation with strong porosity and permeability heterogeneity are studied using the PgV-Net-based surrogate model along with genetic algorithms (GAs). Three optimization problems with increased complexity are solved, and the results are compared with regular approaches using a numerical simulator. Good agreement between the two approaches is observed, and the computational efficiency improves dramatically (up to 30 times). The proposed PgV-Net neural network training architecture reduces the requirement of expensive training data and can be used for more challenging problems such as multiphase flow modeling.

A novel relay placement method for smart energy IoT systems in offices with genetic algorithm

<p>Smart energy in large offices and organizations is an important research area of the Internet of Things (IoT). The energy efficiency of buildings is vital for the environment and global sustainability. To achieve satisfactory performance for this goal, WiFi access point (AP) indoor coverage is of high importance. As it costs a lot to add more WiFi to have good coverage in all parts of the office’s buildings, we consider relay node (RN) instead of adding more APs. So in this paper, we propose a novel RN placement in order to improve the indoor coverage of offices considering the signal attenuation using a path-loss model as the main measure for determining positions. The main problem is the placement of these RNs and the required number of them by considering existing APs. At first, we obtain the radio propagation model parameters by considering the data that are collected from the AP. Then based on these parameters, the proposed solution uses a genetic algorithm (GA) for RN placement optimization problem. The experimental results display the effectiveness of the proposed solution for the RN placement problem.</p>

A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.

Allocation of synchronized phasor measurement units for power grid observability using advanced binary accelerated particle swarm optimization approach

Large-scale power grid observability is still a challenge because of deteriorating infrastructure and the incorporation of renewable energy sources. A smart grid that makes use of cutting-edge technology, such as a phasor measurement unit (PMU), is an excellent option for monitoring and bringing networks up to speed with the latest information. Latterly, the considerable investment required for the deployment locations has slowed down the adoption of PMU. Therefore, because PMUs are expensive, it is necessary to deploy them in the best possible places on large-scale power grids. The most significant share of optimal PMU placement problems (OPPP) is defined as 0–1 knapsack problems. Considering this, the development of an effective optimization technique that can handle difficulties has emerged as an appealing topic in recent years. In this paper, a meta-heuristic algorithm based on the binary particle swarm algorithm (BPSO), a binary accelerated particle swarm optimization (BAPSO), is offered for solving OPPP. Since earlier research has shown that BPSO is likely to stick to local optima, the majority of them evaluated their suggested technique using small-scale test systems. The technique that has been suggested searches for the optimal solution by employing two topologies—one global and one local—that are analogous to BPSO. This work determines the optimal PMU position for a large network in a reasonable amount of time by fine-tuning the acceleration factor. Additionally, in order to employ fewer PMUs, an integration strategy was put into place for the radial buses. The OPPP solutions are provided by the suggested method within a reasonable period with prior solutions published in reliable publications, according to computational findings.

Optimal actuator placement for control of vibrations induced by pedestrian-bridge interactions

In this paper, an optimal actuator placement problem, with a linear wave equation as the constraint, is considered. In particular, this work presents the framework for finding the best location of actuators depending upon the given initial conditions, and where the dependence on the initial conditions is averaged out. The problem is motivated by the need to control vibrations induced by pedestrian-bridge interactions. An approach based on the shape optimization techniques is used to solve the problem. Specifically, the shape sensitivities involving a cost functional are determined using the averaged adjoint approach. A numerical algorithm based on these sensitivities is used as a solution strategy. Numerical tests illustrate the theoretical results.

Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing

With the convergence of the Internet of Things, 5G, and artificial intelligence, limited network bandwidth and bursts of incoming service requests seem to be the most important factors affecting user experience. Therefore, caching technology was introduced. In this paper, a caching placement optimization strategy based on comprehensive utility (CPOSCU) in edge computing is proposed. Firstly, the strategy involves quantifying the placement factors of data blocks, which include the popularity of data blocks, the remaining validity ratio of data blocks, and the substitution rate of servers. By analyzing the characteristics of cache objects and servers, these placement factors are modeled to determine the cache value of data blocks. Then, the optimization problem for cache placement is quantified comprehensively based on the cache value of data blocks, data block retrieval costs, data block placement costs, and replacement costs. Finally, to break out of the partial optimal solution for cache placement, a penalty strategy is introduced, and an improved tabu search algorithm is used to find the best edge server placement for cached objects. Experimental results demonstrate that the proposed caching strategy enhances the cache service rate, reduces user request latency and system overhead, and enhances the user experience.

Proportionally fair load balancing with statistical quality of service provisioning for aerial base stations

AbstractAerial base stations (ABSs) seem promising to enhance the coverage and capacity of fifth‐generation and upcoming networks. With the flexible mobility of ABSs, they can be positioned in air to maximize the number of users served with a guaranteed quality of service (QoS). However, ABSs may be overloaded or underutilized given inefficient placement, and user association has not been well addressed. Hence, we propose a three‐dimensional ABS placement scheme with a delay‐QoS‐driven user association to balance loading among ABSs. First, a load balancing utility function is designed based on proportional fairness. Then, an optimization problem for joint ABS placement and user association is formulated to maximize the utility function subject to statistical delay QoS requirements and ABS collision avoidance constraints. To solve this problem, we introduce an efficient modified gray wolf optimizer for ABS placement with a greedy user association strategy. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of load balancing and delay QoS provisioning.

A Review of Various Optimization Strategies for Handling Optimal PMU Placement Problems

Abstract: The Phasor Measurement Unit (PMU) is the most fundamental power system technology that is utilized to protect, monitor, and control power networks. It not only allows real-time, synchronized voltage measurements on the buses but also the current phasor of lines connected to the buses where these PMUs are placed. The positioning of PMUs at each bus node is unnecessary and is restricted by the installation's high cost and complexity. As a result, the fundamental goal of Optimal PMU placement (OPP) is reducing the total no. of PMUs along with maximum observability, while simultaneously considering measurement redundancy. There are several methods for fixing this problem, and they fall into two categories: Metaheuristic methods and mathematical approaches. This study examines a variety of optimization strategies for handling OPP problems

Golden Jackal Algorithm for Optimal Size and Location of Distributed Generation in Unbalanced Distribution Networks

The Golden Jackal Optimization algorithm (GJO) is used in this study to address the problem of optimal placement and sizing of single and multiple distributed generators (DGs) on the IEEE123 test system. The proposed approach attempts to minimize the total power loss of the system while respecting the voltage and power limits. The GJO algorithm is a new meta-heuristic algorithm inspired by the behavior of the golden jackal in the wild. The GJO algorithm is used to find the ideal location and sizing of DGs, and the results are compared with those obtained by other meta-heuristic techniques. According to the simulation results, the GJO method outperforms other metaheuristic algorithms in terms of problem-solving, while satisfying all constraints of the system. The proposed approach also demonstrates the effectiveness of the GJO algorithm in the solution of complex optimization problems in power systems

Joint user association and caching placement for cache-enabling UAV networks

Cache-enabling unmanned aerial vehicles (UAVs) are considered for storing popular contents and providing downlink data offloading in cellular networks. In this context, we formulate a joint optimization problem of user association, caching placement, and backhaul bandwidth allocation for minimizing content acquisition delay with consideration of UAVs' energy constraint. We decompose the formulated problem into two subproblems: i) user association and caching placement and ii) backhaul bandwidth allocation. We first obtain the optimal bandwidth allocation with given user association and caching placement by the Lagrangian multiplier approach. After that, embedding the backhaul bandwidth allocation algorithm, we solve the user association and caching placement problem by a three-dimensional (3D) matching method. Then we decompose it into two two-dimensional (2D) matching problems and develop low-complexity algorithms. The proposed scheme converges and exhibits a low computational complexity. Simulation results demonstrate that the proposed cache-enabling UAV framework outperforms the conventional UAV-assisted cellular networks in terms of content acquisition delay and the proposed scheme achieves significantly lower content acquisition delay compared with other two benchmark schemes.

On Attack-Resilient Service Placement and Availability in Edge-Enabled IoV Networks

Achieving network resilience in terms of attack tolerance and service availability is critically important for Internet of Vehicles (IoV) networks where vehicles require assistance in sensitive and safety-critical applications like driving. It is particularly challenging in time-varying conditions of IoV traffic. In this paper, we study an attack-resilient optimal service placement problem to ensure disruption-free service availability to the users in edge-enabled IoV network. Our work aims to improve the user experience while minimizing the delay and simultaneously considering efficient utilization of limited edge resources. First, an optimal service placement is performed while considering traffic dynamicity and meeting the service requirements with the use of a deep reinforcement learning (DRL) framework. Next, an optimal secondary mapping and service recovery placements are performed to account for the attacks/failures at the edge. The use of DRL framework helps to adapt to dynamically varying IoV traffic and service demands. In this work, we develop three ILP models and use them in the DRL-based framework to provide attack-resilient service placement and ensure service availability with efficient network performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed approach.

An Autonomic Workload Prediction and Resource Allocation Framework for Fog-Enabled Industrial IoT

The Internet of Things (IoT) has revolutionized the industrial field with numerous facilities and advancements. The industrial IoT system demands delay-aware workload execution with the aid of a fog computing platform, and precise resource allocation is required in fog nodes (FNs) to execute the fluctuating industrial IoT workloads with minimal cost and delay. In view of the issue mentioned above, we introduce an autonomic workload prediction and resource allocation framework that efficiently allocates resources among fog nodes. In the proposed framework, the workloads are predicted in the analysis phase with the guidance of the Deep Auto Encoder (DAE) model, and the fog nodes are scaled based on the demand of Industrial IoT workloads. The crow search algorithm (CSA) is integrated with the framework for optimal fog node selection to improve cost and delay objectives. The proposed scheme is evaluated and compared with the existing optimization models in terms of execution cost, request rejection ratio, throughput, and response time. The simulation results establish that the proposed scheme outperformed other optimization models. The method provided a suitable solution for the optimal fog node placement problems in efficiently executing dynamic industrial IoT workloads.

Aerodynamic optimization with large shape and topology changes using a differentiable embedded boundary method

Embedded (or immersed) boundary methods (EBMs) for CFD and fluid-structure interaction (FSI) are attractive for aerodynamic optimization problems characterized by large shape deformations and surface topology changes. At each iteration, they eliminate the need for explicitly remeshing a computational fluid domain and avoid the pitfalls of transferring information from one CFD mesh to another. However, they are vulnerable to discrete events that compromise the smoothness of the aerodynamic objective and/or constraint functions they may compute. For this reason, the realization of EBMs for gradient-based aerodynamic shape optimization requires first their endowment with sufficient smoothness guarantees. Drawing on a recently developed EBM for the solution of compressible viscous fluid and FSI problems with such guarantees, this paper demonstrates the potential of shape-differentiable EBMs for discovering optimal aerodynamic designs featuring significant shape deformations and surface topology changes. Specifically, it highlights their advantages in terms of reliability with respect to large shape adjustments and surface topology changes, efficiency, simplicity, and reduced need for user intervention. To this end, the paper showcases the application of an advanced EBM with smoothness guarantees to wing shape, wing section, and nacelle-pylon placement optimization problems formulated for a NASA Common Research Model (CRM).