Placement Algorithm Research Articles

Enhancing network lifetime in wireless sensor networks through coverage-aware optimized sensor activation

Wireless sensor networks (WSNs) are a pivotal technology in the modern era, enabling the monitoring and sensing of environmental conditions across vast areas with unprecedented precision and flexibility. At the heart of WSNs lie crucial challenges such as optimizing coverage, extending network lifetime, and strategizing node deployment to ensure efficient operation while conserving energy. This paper introduces the coverage-aware optimized sensor activation and deployment (CAOSAD) Strategy, a novel methodology designed to address these challenges. By integrating advanced node placement algorithms and scheduling techniques, the EcoNet lifespan maximization (ELM) strategy significantly enhances area and target coverage, minimizes energy consumption, and thereby prolongs the network’s operational lifespan. We present a comprehensive framework that dynamically adjusts node activity based on a predictive model, ensuring robust coverage and connectivity with minimal energy expenditure. Through a series of simulations, the ELM strategy demonstrates a substantial improvement in network sustainability compared to existing methodologies, offering a promising approach for the development of future WSNs. By focusing on the synergy between coverage optimization, energy-efficient node deployment, and innovative scheduling algorithms, this paper contributes a ground-breaking perspective to the research and application of WSNs, setting a new benchmark for the design of eco-friendly and durable sensing infrastructures.

Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments

Discrete Black Widow Optimization Algorithm for Multi-Objective IoT Application Placement in Fog Computing Environments

LPulse: An efficient algorithm for service function chain placement and routing with delay guarantee

Modern network services increasingly depend on the effective orchestration of the Service Function Chain (SFC) with stringent end-to-end delay guarantees. To achieve this, the Delay-Constrained Service Function Chain Placement and Routing (DC-SFCPR) problem must be addressed. This problem involves the optimal selection of nodes for placing network functions and routes that adhere to a specific sequence of service functions, to minimize network bandwidth and CPU costs while strictly adhering to stringent end-to-end delay constraints. The DC-SFCPR problem is NP-hard and existing algorithms either fail to guarantee strict delay constraints or are computationally expensive, making them unsuitable for expanding network topologies. We propose the LPulse algorithm, designed to efficiently solve the DC-SFCPR problem. This algorithm utilizes a layered graph to embed the requirements of service functions, transforming the DC-SFCPR problem into a Delay-Constrained Shortest Path (DCSP) problem. The LPulse algorithm then applies Pulse, a depth-first search framework enhanced with efficient pruning strategies, and incorporates two novel acceleration strategies to solve the DCSP problem. We prove that LPulse ensures the optimality of solutions. Evaluations conducted across various topologies, with node scales ranging from 22 to 10,000, show that LPulse surpasses existing algorithms in both solution quality and speed. For instance, the number of cases meeting strict delay constraints with LPulse is 1.9× that of those solved by deep reinforcement learning algorithms; furthermore, its solving efficiency is 4.9× that of the highest-performing existing optimal algorithm, the LagrangianKsp algorithm.

The League Championship Algorithm (LCA) for optimal placement and sizing of shunt capacitors

The League Championship Algorithm (LCA) for optimal placement and sizing of shunt capacitors

Metaheuristic algorithms for capacitated controller placement in software defined networks considering failure resilience

SummarySoftware‐defined networking (SDN) has revolutionized network architectures by decoupling the control plane from the data plane. An intriguing challenge within this paradigm is the strategic placement of controllers and the allocation of switches to optimize network performance and resilience. In the event of a controller failure, the switches are disconnected from the controller until they are reassigned to other active controllers possessing sufficient spare capacity. The reassignment could lead to a significant rise in propagation latency. This correspondence presents a mathematical model for capacitated controller placement, strategically designed to anticipate failures and prevent a substantial increase in worst‐case latency and disconnections. The aim is to minimize the worst‐case latency between switches and their backup controllers and among the controllers. Four metaheuristic algorithms are proposed including, an enhanced genetic algorithm (CCPCFR‐EGA), particle swarm optimization (CCPCFR‐PSO), a hybrid particle swarm optimization and simulated annealing algorithm (CCPCFR‐HPSOSA), and a grey wolf optimization algorithm (CCPCFR‐GWO). These algorithms are compared with a simulated annealing method and an optimal method. Evaluation conducted on four network datasets demonstrates that the proposed metaheuristic methods are faster than the optimal method. The experimental outcome indicates that CCPCFR‐HPSOSA and CCPCFR‐GWO outperform the other methods, consistently providing near‐optimal solutions. However, CCPCFR‐GWO is preferred over CCPCFR‐HPSOSA due to its faster execution time. Specifically, CCPCFR‐GWO achieves an average speed‐up of 3.9 over the optimal for smaller networks and an average speed‐up of 31.78 for larger networks, while still producing near‐optimal solutions.

MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing

MOHHO: multi-objective Harris hawks optimization algorithm for service placement in fog computing

A hybrid chaotic bat algorithm for optimal placement and sizing of dg units in radial distribution networks

Appropriate location and sizing of distributed generation (DG) units in a radial distribution system (RDS) play a significant role in the improvement of its overall performance. Indeed, proper DG placement and sizing help in maintaining the balance between power supply and demand, decreasing energy losses and enhancing voltage profile. Within this context, this paper presents an effective approach for the determination of the optimal location and the appropriate capacity of a photovoltaic distributed generation (PVDG) unit in RDSs. In the suggested approach, the best position of the PVDG is selected using the loss sensitivity factor (LSF). Meanwhile, a new optimization technique incorporating chaos, bats’ self-adaptive compensation, and Doppler effect into the original bat algorithm (BA) is developed to find the optimal size of the PVDG unit. The optimal PVDG size is optimally determined in such a way that total active power losses of the RDS is reduced and voltage profile is enhanced. The performance of the proposed optimization technique, symbolized by (CSA-DC)BA, is evaluated using various benchmark functions. Moreover, the applicability and effectiveness of the suggested approach are verified on the IEEE 33-bus and the IEEE 69-bus RDSs. Obtained results revealed that the proposed (CSA-DC)BA outperforms the comparison optimization techniques.

Sparse Flow Sensor Placement Optimization for Flight-by-Feel Control of 2D Airfoils

This research introduces the Sparse Sensor Placement Optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement Optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10° and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

An improved particle swarm optimisation algorithm for optimum placement and sizing of biogas-fuelled distributed generators for seasonal loads in a radial distribution network

Optimal allocation is critical for effective planning and operation of grid connected distributed generator(s) (DGs) subject to efficient optimisation approach(es). In this paper, an improved particle swarm optimisation (IPSO) algorithm based on weighted randomised acceleration coefficients and adaptive inertia weight for dynamic movement of the particles towards an optimal solution is proposed. The technique is expected to determine optimal placement, sizing and power factor of biogas-fuelled distributed generators (B-DGs such as internal combustion engine (ICE) and fuel cells (FC)) in a radial distribution network. The proposed IPSO is applied to simultaneously optimise three technical objectives namely total active power loss (TAPL), voltage deviation (VD) and voltage stability index (VSI) considering load growth and seasonal voltage dependence. The proposed IPSO is validated using an IEEE 33 bus radial distribution network to evaluate its effectiveness through various combinations of B-DGs for different scenarios and cases with a maximum of 3 B-DGs. Some of the key findings indicate that operating 3 ICEs at optimal power factor reduces TAPL by 93.52 %, reduces VD by 99.62 % and improves VSI by 23.32 % compared to 61.80 % TAPL reduction, 94.77 % VD reduction and 17.89 % VSI improvement for 3 FCs operating at unity power factor. The proposed IPSO gives better results than the standard PSO in terms of solution quality, convergence speed and statistical results. It is also perceived to be better compared to most of the recent state-of-the-art optimization algorithms found in literature.

Reinforcement learning-based charging cluster determination algorithm for optimal charger placement in wireless rechargeable sensor networks

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms.

A Robust Sparse Sensor Placement Strategy Based on Indicators of Noise for Ocean Monitoring

A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, which heightens the complexity of sensor deployment. Therefore, this study optimizes the sparse sensor placement model by establishing noise indicators, including small noise weight and large noise weight, which are measured by entropy to minimize the prediction bias. Building on this, a robust sparse sensor placement algorithm is proposed, which utilizes the block coordinate update (BCU) iteration method to solve the function. During the iterative updating process, the proposed algorithm simultaneously updates the selection matrix, reconstruction matrix, and noise matrix. This allows for effective identification and mitigation of noise in the data through evaluation. Consequently, the deployed sensors achieve superior reconstruction performance compared to other deployment methods that do not incorporate noise evaluation. Experiments are also conducted on datasets of sea surface temperature (SST) and global ocean salinity, which demonstrate that our strategy significantly outperforms several other considered methods in terms of reconstruction accuracy while enabling autonomous sensor deployment under noisy conditions.

NXRouting: A GPU-Enhanced CAD Tool for European Radiation-Hardened FPGAs

Field Programmable Gate Arrays (FPGAs) have witnessed an increase in space applications in the last years, mainly due to their cost-effective high-performances and flexibility. However, the susceptibility of these devices to radiation-induced effects when working in such an environment is well known. When common mitigation techniques are not sufficient to ensure the correct completion of a task, radiation-hardened FPGAs represent one of the most effective solutions. NanoXplore, in this context, is the first European developer of rad-hard FPGAs, which embed intrinsic high complexity in their architectures preventing the user from using or developing custom placement and routing algorithms. In this paper, we overcame these issues by proposing the first tool tailored to NanoXplore devices which allows the exploration of NanoXplore device architectures and routing of points through a Python interface. We developed a model that reflects the one used by the vendor, allowing the user to extract info about routes, nets and additional logic, otherwise unavailable. The tool also performs routing of points in the programmable logic, computing the optimal path. An implementation of the router on Graphic Processing Unit (GPU) is proposed to exploit the highly parallelizable nature of the problem. Finally, routing timing analyses on different benchmarks have been performed, improving the routing routine time.

Multiresolution dynamic mode decomposition approach for wind pressure analysis and reconstruction around buildings

AbstractAccurate wind pressure analysis on high‐rise buildings is critical for wind load prediction. However, traditional methods struggle with the inherent complexity and multiscale nature of these data. Furthermore, the high cost and practical limitations of deploying extensive sensor networks restrict the data collection capabilities. This study addresses these limitations by introducing a novel framework for optimal sensor placement on high‐rise buildings. The framework leverages the strengths of multiresolution dynamic mode decomposition (mrDMD) for feature extraction and incorporates a novel regularization term within an existing sensor placement algorithm under constraints. This innovative term enables the algorithm to consider real‐world system constraints during sensor selection, leading to a more practical and efficient solution for wind pressure analysis. mrDMD effectively analyzes the multiscale features of wind pressure data. The extracted mrDMD modes, combined with the enhanced constrained QR decomposition technique, guide the selection of informative sensor locations. This approach minimizes the required number of sensors while ensuring accurate pressure field reconstruction and adhering to real‐world placement constraints. The effectiveness of this method is validated using data from a scaled building model tested in a wind tunnel. This approach has the potential to revolutionize wind pressure analysis for high‐rise buildings, paving the way for advancements in digital twins, real‐time monitoring, and risk assessment of wind loads.

An assessment of nature-inspired metaheuristic algorithms for resilient controller placement in software-defined networks

Software-defined Networking (SDN) offers flexibility and programmability, making it a desirable option for modern network architecture. SDN provides numerous benefits to network administrators due to its centralized control architecture. This allows network administrators to manage and configure the network from a single location, making it easier to manage and automate network tasks. In a network of multiple controllers, the control plane’s resiliency may impact the overall system’s performance. In a controller failure scenario, switches must be reassigned to other active controllers with adequate capacity. Thus, we define a resilient controller placement (RCP) as an optimization problem. The aim is to design physically distributed and redundant controllers to manage switches with varying resilience levels. The propagation latency may increase due to the reassignment, increasing the network cost. The objective is to determine the number of controllers required, their positions, and the allocation of the network nodes to a particular controller while reducing the average propagation latency and cost in meeting the capacity constraint of the controller. Due to the wide area network (WAN) structure, four nature-inspired metaheuristic algorithms are proposed namely, simulated annealing (RCP-SA), moth-flame optimization algorithm (RCP-MFO), particle swarm optimization (RCP-PSO), and grey wolf optimization algorithm (RCP-GWO). These algorithms are evaluated on three network datasets to determine the optimum controller number and their positions. The experimental results show that RCP-GWO performs better than RCP-SA, RCP-MFO, RCP-PSO, and the Random methods.

Software-Defined Virtual Private Network for SD-WAN

Software-Defined Wide Area Networks (SD-WANs) are an emerging Software-Defined Network (SDN) technology to reinvent Wide Area Networks (WANs) for ubiquitous network interconnections in cloud computing, edge computing, and the Internet of Everything. The state-of-the-art overlay-based SD-WANs are simply conjunctions of Virtual Private Network (VPN) and SDN architecture to leverage the controllability and programmability of SDN, which are only applicable for specific platforms and do not comply with the extensibility of SDN. This paper motivates us to refactor traditional VPNs with SDN architecture by proposing an overlay-based SD-WAN solution named Software-Defined Virtual Private Network (SD-VPN). An SDN-based auto-constructed VPN model and its evaluating metrics are put forward to automatically construct overlay WANs by node placement and service orchestration of SD-VPN. Therefore, a joint placement algorithm of VPN nodes and algorithms for overlay WAN service loading and offloading are proposed for SD-VPN controllers. Finally, a three-layer SD-VPN system is implemented and deployed in actual network environments. Simulation experiments and system tests are conducted to prove the high-efficiency controllability, real-time programmability, and auto-constructed deployability of the proposed SD-VPN. Performance trade-off between SD-VPN control channels and data channels is evaluated, and SD-VPN controllers are proven to be extensible for other VPN protocols and advanced services.

Integrating Operations Research into Very Large-Scale Integrated Circuits Placement Design: A Review

The placement stage of the physical design of very large-scale integrated circuits (VLSI) specifies the arrangement and order of standard cells and devices within an area, and the effectiveness of the placement result directly affects the chip’s performance. The advancement of process standards and reduction in the size of features size have dramatically increased the complexity of placement design. The realization of placement algorithms to deal with millions of cells has thus become an important issue in the automation of integrated circuit electronic design. In this paper, we first segment the digital integrated circuits into VLSI design styles and review them from the perspectives of practical placement problems, placement design ideas, and placement optimization methods. The discussion around traditional circuits focuses on the essence of the placement problem, placement steps, and classification of placement algorithms. In modern circuit placement, the discussion focuses on the impact of technical constraints on design. After identifying the essence of the placement problem, solutions to the packing problem are reviewed, and we then summarize our representative placement algorithms. Finally, based on the development of VLSI placement design, the optimization bottlenecks of existing placement design are summarized, and suggestions for future research are made based on the latest research topics and methodologies.

Wall-bounded flow simulation on vortex dynamics

Vortical flow animation has attracted considerable attention within the realm of computer graphics. Given that boundaries are the source of vorticity, we introduce a novel approach for the wall-bounded flow simulation in the vortex dynamics framework. We enhance the traditional Lagrangian vortex particle method in terms of boundary treatment and viscosity computation for boundary layer to furnish support for the simulation of the vortex shedding phenomenon behind the moving solids. We extend the boundary treatment strategy based on the idea of vortex generation with a reasonable placement algorithm for the generated vortex element to better satisfy the impermeability of solids. Furthermore, we modify the particle strength exchange method at solid boundaries to capture momentum transfer of moving solids. We demonstrate the efficacy of our approach by simulating a series of wall-bounded flows, such as the wake behind a delta wing and the vortex shedding behind the rotating sphere.

Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

Multi-Access Edge Computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

Development and evaluation of an algorithm for peripheral venous catheter placement (ALCOV): protocol for a quasi-experimental study

IntroductionMultiple punctures during peripheral venous catheter (PVC) placement increase the risk of complications. Scoring for adult difficult intravenous access (A-DIVA Scale) exists but has never been assessed in the framework...