Optimal Deployment Research Articles

Solving Optimal Electric Vehicle Charger Deployment Problem

Electric vehicles (EVs) have already been acknowledged to be the most viable solution to the climate change that the entire globe has long been combating. Along the same line, it is a salient subject to expand the availability of EV charging infrastructure, which quintessentially necessitates the optimization of the charger’s locations. This paper proposes to formulate the optimal EV charger location problem into a facility location problem (FLP). As an effort to find an efficient method to solve the well-known nonpolynomial deterministic (NP) hard problem, we present a comparative quantification among several representative solving techniques. This paper features two comprehensive case studies representing regions with an average and a high density of EVs. As such, this paper shows that the proposed framework can lead to successful location optimization with adequate refinement of solving techniques.

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

AbstractThis paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

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

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

3D Printing of Subclavian Artery: Utility for Preprocedural Planning and Correlation With Subclavian Artery Percutaneous Vascular Interventions.

Three-dimensional (3D) printing for subclavian artery (SA) percutaneous vascular interventions (PVI) may allow superior understanding of patient specific complex anatomy and aid with preprocedural planning. Five patients with computed tomography angiography (CTA) of the neck who underwent SA PVI were queried retrospectively. 3D printing of aortic arch and great vessels was accomplished with 3D slicer software and painted with acrylic paint to highlight anatomic features. The aortic arch type and implications for preprocedural planning for SA interventions including complex chronic total occlusion (CTO) lesions were determined. Comparisons were made with SA angiograms and 3D-CTA. Of the 5 patients, type I (n = 2), type II (n = 1), and type III (n = 2) aortic arches were identified. Proximal and distal reference vessel size and total lesion length were determined using a digital millimeter caliper and correlated with intraprocedural balloons and stents. In 3D-printed models (3D-PMs) of patients with SA-CTO (n = 2), cap morphology (tapered vs blunt) and distal vessel filling were visualized, permitting optimal arterial access site selection for successful cap crossing. The vertebral arteries (VAs) were also 3D printed which further allowed the ability to delineate optimal stent deployment site (proximal or distal to VA), a common dilemma that is faced intraprocedurally. The 3D-PMs also allowed preprocedural precision in stent and balloon size and length, potentially leading to procedural efficiency and cost-effectiveness. 3D printing of aortic arch and great vessel anatomy for SA-PVI allows multiple procedure-related factors to be predicted in advance, translating to decrease in contrast volume, radiation time, procedure and fluoroscopic time, thereby improving procedure and cost efficiency.

Learning-based data-driven optimal deployment control of tethered space robot

To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, a data-driven optimal control framework with an improve deep learning based Koopman operator is proposed in this work. In consideration of nonlinearity of tethered space robot dynamics, its finite dimensional global linear representation called lifted linear system is derived with the Koopman operator theory. A deep learning scheme is adopted to find the embedding functions associate with Koopman operator. And an auxiliary neural network is developed to encode the nonlinear control term of finite dimensional lifted system. Then a controllability constraint is considered for learning a controllable lifted linear system. Besides two loss functions that relate to reconstruction and prediction ability of lifted linear system are designed for training the deep neural network. With the learned lifted linear dynamics, Linear Quadratic Regulator (LQR) is applied to derive the optimal control policy for the tethered space robot deployment. Finally, simulation results verify the effectiveness of proposed framework and show that it could deploy tethered space robot more quickly with less swing of in-plane angle.

Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

Multi-Objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage

Optimal Deployment of Wireless Sensor Nodes using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO)

Abstract: Wireless Sensor Networks (WSNs) are emerging as a significant area of research due to their potential to autonomously monitor physical and environmental conditions. These networks comprise spatially distributed, low-cost sensor nodes with limited transmission range, processing capabilities, storage, and energy resources. The primary function of these networks is to collect data from various nodes and transmit it to a base station for subsequent processing. WSNs present several challenges, including optimal sensor deployment, node localization, base station placement, target node location, energy-aware clustering, and data aggregation. Recently, global researchers have been employing a bio-inspired optimization algorithm, Particle Swarm Optimization (PSO), to enhance the efficiency of WSNs. This report explores the application of the PSO algorithm for optimal sensor deployment in WSNs, contributing to the ongoing efforts to maximize the potential of these networks.

5G Wireless Technology: Spectrum Sharing and Heterogeneous Networks

Abstract: This paper addresses the critical challenge of 5G network cell planning, emphasizing the essential role of estimating the number of cells or base stations required for a given area and user bandwidth. The proposed model explores four scenarios with varying network parameters to determine the optimal base station deployment. The increasing demand for 5G networks necessitates a focus on small cells or femtocells, offering advantages such as consistent coverage, power adjustment, energy efficiency, and higher data rates. However, deploying excessive femtocells may lead to unnecessary handovers, requiring optimization strategies. The study introduces an analytical model for heterogeneous cellular networks, integrating fourth and fifth-generation systems. The model, represented by a two dimensional Markov Chain, employs a novel decomposition approach for analyzing system performance measures. Validation is conducted through simulation and simultaneous equation systems. The proliferation of mobile devices and data traffic necessitates dense 5G network deployments, with small cells gaining traction due to their versatile coverage, power adaptability, energy efficiency, and higher data rates. However, an excessive deployment of small cells can lead to frequent handovers, prompting the need for enhanced handover strategies and coexistence with other technologies in the 5G landscape. The paper concludes by highlighting the impending need for practical implementation testing of 5G systems, emphasizing the significant role of small cell deployments. It discusses an initiative focusing on testing small cellenabled operator business models in real-world scenarios, anticipating the pivotal role of small cells in future 5G systems.

Optimized provisioning technique of future services with different QoS requirements in multi-access edge computing

With the present trend towards the use of microservices instead of monolithic software, future applications with diverse computational and delay-sensitive requirements, such as virtual reality (VR) or holographic communications (HC), will be redesigned based on loosely coupled microservices due to their more efficient deployment according to available resources. However, it remains a big challenge to provide such services under stringent quality of service (QoS) requirements at minimum time and cost and with an efficient utilization of the network. Presently, multi-access edge computing (MEC) has been proposed as a promising alternative to deploy future microservice-based applications to achieve faster response times while reducing the overall network load. However, network conditions, edge nodes state and service requests are dynamic, increasing the difficulty for the provisioning of services by MECs. In this context, we propose an optimization deployment heuristic (ODA) to obtain sub-optimal deployments in reasonable time, meeting the requirements of some representative future applications under study. In particular, according to latency requirements, there will be delay-tolerant and delay-sensitive applications, playing a major role regarding the alternatives for the deployment onto single or multiple MEC systems. The evaluation analysis shows that our proposal performs significantly better than other benchmark solutions in most of the metrics used for the experiments, especially in minimizing deployment costs in minimal time.

Bicuspid valve CT registry of balloon-expandable TAVR: BETTER TAVR registry.

The anatomic substrate of bicuspid valves may lead to suboptimal TAVR stent expansion and geometry. We evaluated determinants of stent geometry in bicuspid valves treated with Sapien transcatheter aortic valve replacement (TAVR) valves. A multicenter retrospective registry of patients (February 2019 to August 2022) who underwent post-TAVR computed tomography to determine stent area (vs. nominal valve area) and stent ellipticity (maximum diameter/minimum diameter). Predictors of relative stent expansion (minimum area/average of inflow + outflow area) and stent ellipticity were evaluated in a multivariable regression model, including valve calcium volume (indexed by annular area), presence of raphe calcium, sinus diameters indexed by area-derived annular diameter, and performance of pre-dilation and post-dilation. The registry enrolled 101 patients from four centers. The minimum stent area (vs. nominal area) was 88.1%, and the maximum ellipticity was 1.10, with both observed near the midframe of the valve in all cases. Relative stent expansion ≥90% was observed in 64/101 patients. The only significant predictor of relative stent expansion ≥90% was the performance of post-dilation (OR: 4.79, p = 0.018). Relative stent expansion ≥90% was seen in 86% of patients with post-dilation compared to 57% without (p < 0.001). The stent ellipticity ≥1.1 was observed in 47/101 patients. The significant predictors of stent ellipticity ≥1.1 were the indexed maximum sinus diameter (OR: 0.582, p = 0.021) and indexed intercommisural diameter at 4 mm (OR: 2.42, p = 0.001). Stent expansion has a weak negative correlation with post-TAVR mean gradient (r = -0.324, p < 0.001). Relative stent expansion ≥90% was associated with the performance of post-dilation, and stent ellipticity ≥1.1 was associated with indexed intercommisural diameter and indexed maximum sinus diameter. Further studies to determine optimal deployment strategies in bicuspid valves are needed.

Parameter Optimization of a Genetic Algorithm for the Optimal Deployment of Search and Rescue Units

Parameter Optimization of a Genetic Algorithm for the Optimal Deployment of Search and Rescue Units

Remote Controlled Agricultural Robot

Abstract: The Agri Bot project presents a pioneering endeavor in the realm of autonomous agriculture by seamlessly integrating robotics, IoT, and mobile application development. Utilizing an Arduino UNO microcontroller, ESP32 camera module, servo motors, and various sensors, the system excels in executing precise agricultural tasks such as digging, filling, seed sowing, and fertilizer dispensing. The accompanying mobile application, developed through MIT App Inventor and utilizing Bluetooth communication, provides an intuitive interface for users to remotely oversee and manage the Agri Bot's operations. Beyond its successful hardware and software integration, the project holds transformative potential for traditional farming practices, evident in the incorporation of advanced technologies such as obstacle detection sensors and machine learning for crop recognition. The modular design and collaborative potential of swarm robotics hint at scalability and adaptability for evolving agricultural needs. As the project lays the groundwork for autonomous agriculture, future prospects include autonomous navigation, sustainable power solutions, data analytics for decision support, and enhanced remote monitoring. Collaboration with agricultural experts and ongoing refinements based on real-world feedback are crucial for optimal deployment in diverse farming environments. The Agri Bot project represents a significant milestone in the convergence of technology and agriculture, heralding a future where autonomous systems play a central role in shaping the agricultural landscape.

A novel evaluation method of measurement sensitivities on common faults in VAV HVAC systems

Today, a high volume of operation interval data can be efficiently captured by a diverse range of measurements including sensors, control signals and meters, which are deployed in building automation systems (BAS). Hence, advanced data analytics tools such as fault detection and diagnostic (FDD) can be developed to analyze the operational performance of heating, ventilation and air conditioning (HVAC) systems. In the past, enormous efforts have been made to develop various FDD approaches, assuming interval data contains essential information for identifying fault signatures. However, a “data rich, but information poor” phenomenon exists due to the fact that not all measurements are sensitive to faults in HVAC systems. This highlights a significant research gap, the lack of systematic analysis of measurement sensitivity to different HVAC system faults, which is vital for FDD development, measurement deployment optimization, and control system design. To address this gap, this study introduces a novel approach to assess the sensitivity of BAS measurements in relation to various HVAC fault types. We propose two sensitivity indices (SI), the SI of fault (SI_fault) and the global measurement SI (SI_measurement_global) to quantify measurement sensitivities. The SI_fault quantifies the measurement's sensitivity to a particular fault, while the SI_measurement_global assesses its sensitivity across all fault types. These indices integrate probability distributions, enhancing the interpretability and scalability. Utilizing the HVACSIM+ fault simulation dataset, which includes 15 common faults at varying severity levels and 89 different measurements within an HVAC system, we conducted an extensive analysis of measurement sensitivities by looking at the proposed SIs.

Optimal deployment of private 5G multi-access edge computing systems at smart factories: Using hybrid crow search algorithm

In smart factories, an increasing number of mobile intelligent devices are deployed to meet the growing demands for flexible manufacturing. These devices, equipped with various sensors, synchronize a substantial amount of data with cloud servers for real-time monitoring and control. Fifth generation mobile networks (5G) combined with multi-access edge computing (MEC) provide the capabilities for multiple connections, large-scale data transmission, and efficient response times, becoming the mainstream network architectures for the needs. Additionally, with the emerging trend of energy-saving, recent works have addressed energy consumption as a significant cost factor. However, most previous studies tended to focus solely on cloud servers, neglecting energy consumption at edge computing and underestimating the impact of heterogeneous mobile device. As the scope of smart factory networks continues to expand, the challenges become increasingly critical. This study presents a private 5G MEC system architecture tailored for smart panel factories. The concerned problem is formulated as an integer programming model, which determines deployment locations and quantities for MEC servers and 5G small cells, including picocells and femtocells, so as to minimize the overall deployment costs while meeting the constraints of connectivity, capacity, latency, coverage, serviceability, and energy consumption. Since the edge server deployment problem is NP-hard, this study further proposes a hybrid metaheuristic algorithm, CSAVNS, which combines the strengths of crow search algorithm (CSA) and variable neighborhood search (VNS). In the global search phase of CSA, the VNS local search is introduced to enhance the algorithm's capabilities. Experimental analysis demonstrates that the proposed CSAVNS outperforms other algorithms in terms of solution solving capabilities.

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study

Context:Cloud computing’s rise as the primary platform for software development and delivery is largely driven by the potential cost savings. However, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective:This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure-as-Code artifacts that automate software (re)deployment on the cloud. Methods:A systematic examination of 152735 repositories yielded 2010 relevant hits. We then analyzed 538 relevant commits and 208 relevant issues using inductive and deductive coding and corroborated findings with discussions from Stack Overflow. Results:The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Conclusion:Although we focus on a particular Infrastructure-as-Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.

ML Assisted Foot Step Power Generation using Piezoelectric Sensors

In our rapidly evolving world, the escalating demand for energy coupled with the finite nature of traditional resources underscores the imperative for sustainable, pollution-free, and inexhaustible energy solutions. This paper introduces a pioneering approach to harnessing the kinetic energy expended during human locomotion through the innovative use of piezoelectric sensors. Leveraging the piezoelectric effect, these sensors efficiently convert mechanical energy generated by footstep pressure into electrical energy, thereby mitigating wastage and addressing the increasing energy needs. Our model advocates for the deployment of an extensive sensor network along footpaths, complemented by an RFID-based mobile charging system for enhanced convenience and functionality. Moreover, we introduce an innovative method integrating Machine Learning (ML) techniques to enhance power generation efficiency through intelligent modulation of piezoelectric element resistance. Additionally, we leverage ML algorithms to enhance the requisite daily footstep count necessary to fulfill the energy demands of specific areas. This proactive approach ensures optimal deployment of footstep power generation devices based on actual foot traffic patterns. Our research underscores the significance of this technology in the context of urban energy sustainability, particularly in densely populated regions like China and India, where foot traffic is abundant. By harnessing mechanical energy and leveraging advanced ML algorithms, our approach promises to revolutionize energy harvesting paradigms, paving the way for greener and more efficient power generation systems

Optimal deployment of dynamic wireless charging infrastructure for battery electric buses – a case study in Auckland, New Zealand

ABSTRACT Inductive power transfer (IPT) systems offer a promising solution to multiple major hurdles associated with electric vehicles, such as the lack of widespread charging infrastructure, driving range anxiety, and significant charging time. With the aid of IPT systems, dynamic wireless charging enables electric vehicles to be charged when running over the IPT power pads. The objective of this study is to minimise the overall cost of charging infrastructure by optimising the deployment of IPT transmitters and the battery size required to support charging for electric buses. A mixed-integer linear programming model is proposed and solved using the commercial solver Gurobi. Bus route information and the characteristics of electric buses, such as weight, drag coefficient and frontal area, are used as input to run the traffic simulation in SUMO to get time-varying velocity and acceleration data during bus operations. A bus route in Auckland, New Zealand, is used as a case study in the presented numerical experiments. A strong correlation between the velocity of the bus and the state of charge is identified. The largest cost component is the charging pad, followed by the battery.

Relationship between the Significant Solutions of Static Traffic Assignment Problems for Mixed Traffic Flow of Connected and Automated Vehicles and Human-Driven Vehicles

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology’s efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads.

Multibeam Line Design Based on Geometric Analysis

Multibeam bathymetry is a bathymetry technique of wide sector propagation and strip coverage by simultaneously transmitting and simultaneously receiving multiple beams. This paper mainly focuses on the problem of multibeam transmitting, combines theoretical proofs and data laws, respectively conducts qualitative analysis and quantitative analysis of the survey line layout scheme, and concludes that in the case of the shortest total length of the survey line, all the survey lines are straight lines parallel to each other, and the angle of the direction of the survey line is 90°, and the coverage rate between any neighboring strips is 10%. The total length of survey lines is modeled by geometric analysis, and the optimal survey line deployment scheme contains 34 survey lines, with a total length of 125936 m. The optimal survey line deployment scheme is based on the model of the total length of survey lines.