Optimal Coverage Research Articles

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system.

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by and , respectively. The performance of the average number of active sensor nodes decreased by and compared to existing algorithms, while the average coverage redundancy decreased by and relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

Modeling and Solving the Knapsack Problem with a Multi-Objective Equilibrium Optimizer Algorithm Based on Weighted Congestion Distance

The knapsack problem is a typical bi-objective combinatorial optimization issue, wherein maximizing the value of the packed items is achieved concurrently with minimizing the weight of the load. Due to the conflicting objectives of the knapsack problem and the typical discrete property of the items involved, swarm intelligence algorithms are commonly employed. The diversity of optimal combinations in the knapsack problem has become a focal point, which involves finding multiple packing solutions at the same value and weight. For this purpose, this paper proposes a Multi-Objective Equilibrium Optimizer Algorithm based on Weighted Congestion Distance (MOEO_WCD). The algorithm employs a non-dominated sorting method to find a set of Pareto front solutions rather than a single optimal solution, offering multiple decision-making options based on the varying needs of the decision-makers. Additionally, MOEO_WCD improves the balance pool generation mechanism and incorporates a weighted congestion incentive, emphasizing the diversity of packing combination solutions under the objectives of value and weight to explore more Pareto front solutions. Considering the discrete characteristics of the knapsack combination optimization problem, our algorithm also incorporates appropriate discrete constraint handling. This paper designs multiple sets of multi-objective knapsack combinatorial optimization problems based on the number of knapsacks, the number of items, and the weights and values of the items. This article compares five algorithms suitable for solving multi-objective problems: MODE, MO-PSO-MM, MO-Ring-PSO-SCD, NSGA-II, and DN-NSGAII. In order to evaluate the performance of the algorithm, this paper proposes a new solution set coverage index for evaluation. We also used the hypervolume indicator to evaluate the diversity of algorithms. The results show that our MOEO-WCD algorithm achieves optimal coverage of the reference composite Pareto front in the decision space of four knapsack problems. The experimental results indicate that our MOEO_WCD algorithm achieves the optimal coverage of the reference composite Pareto front in the decision space for four sets of knapsack problems. Although our MOEO_WCD algorithm covers less of the composite front in the objective space compared with the MODE algorithm for knapsack problem 1, its coverage of the integrated reference solutions in the decision space is greater than that of the MODE algorithm. The experiments demonstrate the superior performance of the MOEO_WCD algorithm on bi-objective knapsack combinatorial optimization problem instances, which provides an important solution to the search for diversity in multi-objective combinatorial optimization solutions.

Optimal vegetation coverage from the perspective of ecosystem services in the Qilian Mountains

Abstract The Qilian Mountains (QLMs) serve as a vital ecological security barrier in the northwest China, determining the optimal fractional vegetation cover (FVC) of different land-use types is crucial for ecological restoration and protection. Based on the normalized vegetation index, meteorological data, soil data and land-use data, the InVEST model, bivariate spatial autocorrelation and elastic analysis, we calculated the water conservation, soil conservation and carbon storage, analyzed the spatio-temporal distribution characteristics of FVC and ecosystem services (ES) and the correlation between them. This study aims to evaluate the optimal FVC of the different land-use types and vegetation restoration’s effect on it in the QLMs. The results showed that the FVC and ES showed an increasing trend from 2000 to 2020, with a spatial distribution pattern of high values in the southeast and low values in the northwest. There was a significant and positive correlation between FVC and ecosystem service functions (p < 0.05), FVC had the most obvious effect on soil conservation, followed by water conservation. The optimal FVC varied by land-use type, with its value between 0.4 and 0.48 for forest land, 0.24 and 0.30 for grassland and 0.19 to 0.20 for unused land. In the eastern of the QLMs, nearly 70% of the forests and 50% of the grasslands exceeded the optimal vegetation coverage, while 36% of the grasslands and 62% of the unused lands were under-covered in the western of the QLMs. The findings suggest that differentiated ecological restoration and management measures should be formulated according to the status of vegetation restoration, to enhance the comprehensive capacity of ecosystem services in the QLMs.

Proposed Multi-ST Model for Collaborating Multiple Robots in Dynamic Environments

Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement.

Exploring determinants of basic childhood vaccination coverage in Bangladesh: a generalized Poisson regression analysis

IntroductionChildhood vaccination is crucial for safeguarding children against potentially life-threatening diseases. This study aims to investigate the significant factors influencing vaccination coverage among children aged 12–59 months in Bangladesh.MethodsThe study utilizes cross-sectional data from the 2017–18 Bangladesh Demographic and Health Survey. A Kruskal–Wallis test is employed in bivariate analysis to identify significant predictors associated with childhood immunizations. A generalized Poisson regression model is applied to assess the impact of various risk factors on children’s incomplete vaccination status.ResultsChildren born in the Sylhet division show lower chances of receiving immunizations. Additionally, children whose mothers are younger, have no education or only primary education, and are unemployed, are less likely to be fully vaccinated. Furthermore, children born into smaller families, first birth, those whose mothers had access to media, and those who received more than four antenatal visits during pregnancy are more likely to have received all recommended vaccines.ConclusionImplementing targeted interventions to improve childhood vaccination coverage, particularly among vulnerable populations such as young and uneducated mothers with middle-income status, is crucial. Enhancing media access, promoting the utilization of antenatal care at health care facilities, and raising awareness about the benefits of vaccination can help ensure optimal vaccination coverage among children in Bangladesh.

Analysis and optimization of cesium dynamics for the HUST negative ion source

The high-density beam in the NBI negative ion source relies on surface production on the converter with low work function, which is facilitated by cesium injection. Maintaining the appropriate cesium conditions during the source operation sequence is essential for achieving optimal cesium coverage on the converter. To better understand cesium cycle in the source and to enhance beam production performance during operation sequences, analysis of cesium dynamics and optimization of cesium operation have been carried out. The analysis model is established based on the Monte-Carlo method with cesium sources, cesium transport and surface process during the operation sequence considered. Cesium distribution and converter surface coverage can be obtained from the model using input parameters from the HUST negative ion source. For the operation in the HUST negative ion source, the influence of temperature settings and time sequence is discussed, with suggested parameters for cesium injection proposed to achieve appropriate cesium coverage on converter surface through operation sequences.

Adaptive Coverage Optimization for Energy-Constrained Multicamera Surveillance Networks

Adaptive Coverage Optimization for Energy-Constrained Multicamera Surveillance Networks

Digital Twin Enhanced Multi-Agent Reinforcement Learning for Large-Scale Mobile Network Coverage Optimization

Digital Twin Enhanced Multi-Agent Reinforcement Learning for Large-Scale Mobile Network Coverage Optimization

Optimizing Colorectal Screening in Portugal with Process Mining

Abstract Background Efficient healthcare screening programs management influences patient outcomes. Process mining has emerged as a powerful tool to enhance these programs by refining complex workflows and pinpointing operational deviations. Objectives This study utilizes process mining techniques to analyze the colorectal cancer screening program in Northern Portugal, focusing on detecting deviations and performance bottlenecks in process models. Methods This screening program started in March 2018, but process mining was only used on data posterior to 2020. Over 1 million case observations from administrative data were included in the process model analysis, involving more than six key process activities and numerous resources. We used new software and statistical tools for process mining to rapidly compute observations and filter for different operational needs and monitor interventions. Results The discovery analysis identified deviations, such as unexpected performance issues and irregular activity sequences. Although over 100 traces were identified, fewer than five traces accounted for more than 90% of all case paths, indicating the most common traces that have a true impact in participation rates, referral rates, and treatment rates. Notable differences in resource performance during specific periods, such as COVID-19 or seasonal events, were also observed. These findings provided insights for adjusting resource allocation and improving coordination, with impact in minimizing seasonal phenomena or peak production periods. Lessons Process mining offers actionable insights for effective health program coordination, enabling the monitoring and timely response to process deviations. With key areas for action identified in a more timely and precise manner, we can enhance efficiency, and expand population coverage and treatment referral. Future initiatives will focus on further process optimization and national coverage expansion using these management tools. Key messages • Process mining revolutionizes healthcare by enabling the strategic management of complex oncological screening program workflows. • Comprehensive data analysis across the patient pathway enhances program efficiency and expands treatment access.

2.I. Scientific session: Addressing vaccine hesitancy: from understanding determinants to implementing targeted strategies

Abstract Supported by NextGenerationEU (no. PE00000007) and EU4Health (VAXaction), the workshop will present a pan-European perspective on vaccine hesitancy and the effectiveness of counteracting interventions, especially when facing potentially pandemic infectious threats. Analysing human responses to epidemics is crucial to improve mathematical disease modelling and develop strategies to monitor behaviours during outbreaks, including assessing levels of vaccine hesitancy. This session will explore the multifaceted issue of vaccine hesitancy, which remains a significant barrier to achieving optimal vaccination coverage and is identified as one of the top threats to global health. By integrating theoretical frameworks with practical applications, the workshop aims to present a comprehensive overview of strategies employed to mitigate hesitancy across various demographics and regions. The first presentation will cover a thorough overview of the strategies employed to counteract vaccine hesitancy, leveraging existing infrastructure, health promotion policies, and educational initiatives. It will also explore the role of digital health tools and regulatory measures, such as health passes and nudging techniques, in enhancing vaccination uptake. Furthermore, the session will cover a systematic review of immunisation information systems (IISs) and the proposal of an innovative unvaccinated registry. This part of the workshop will delve into technological advancements and the integration of behavioural insights, which are essential for enhancing vaccine coverage and informing public health strategies. Another significant aspect of the discussion will be the evolution of vaccine mandates, from their historical roots to their contemporary applications. This discussion will reflect on policy shifts necessitated by emerging public health challenges and the need for adaptable strategies that can respond effectively to fluctuating vaccine coverage and acceptance rates. Panel discussions will feature notable experts such as Prof. Carlo Signorelli, President of the Italian NITAG, and Prof. Maria Ganczak, Vice President of the EUPHA Infectious Diseases Control section. These discussions will provide a comparative analysis of vaccination policies across different European countries, highlighting diverse approaches to mandates and the implementation of public health interventions aimed at combating vaccine hesitancy. By providing a unified approach to understanding and combating vaccine hesitancy, we aim to facilitate a critical exchange of ideas and strategies among experts in the field. It will serve as a dynamic platform for discussing evidence-based interventions tailored to specific population needs, enhancing public health outcomes across Europe. This engaging session promises to offer valuable insights into the complexities of vaccine hesitancy and the collaborative efforts required to address this global health challenge effectively. Key messages • Enhanced data collection and analysis are essential to combat vaccine hesitancy, allowing for tailored interventions that address specific community needs and barriers to vaccination. • Effective strategies against vaccine hesitancy require historical understanding and flexibility to adapt to new challenges, ensuring public health policies are responsive and culturally sensitive.

Multi-Objective Optimization of Urban Gas Station Site Selection Under Territorial Spatial Planning Constraints

The traditional process for selecting urban gas station sites often emphasizes economic benefits and return on investment, frequently overlooking mandatory and guiding constraints established by territorial spatial planning regulations. This neglect can compromise the effective layout and future growth of cities, potentially affecting their long-term development. To address this issue, this study develops a systematic framework for urban gas station site selection that integrates both mandatory and guiding constraints. By conducting detailed analyses of feasible construction areas and fuel demand, the framework quantifies relevant indicators and establishes a comprehensive index system for site selection. A multi-objective optimization model employing genetic algorithms was utilized to maximize fuel demand coverage, minimize inter-station redundancy, and achieve optimal site coverage. This framework was applied to the central urban area of Lishui City, China, as a case study. The site selection schemes achieved a coverage rate exceeding 90%, an inter-station redundancy rate around 30%, and a demand coverage rate surpassing 90%, optimizing the key objectives. Compared to traditional methods that often ignore territorial spatial planning constraints, this framework effectively avoids conflicts with urban planning and regulatory requirements. It enhances infrastructure coordination, supports environmental sustainability, and exhibits strong adaptability to diverse urban contexts, thus offering valuable support for practical decision-making.

A genome-based survey of invasive pneumococci in Norway over four decades reveals lineage-specific responses to vaccination

BackgroundStreptococcus pneumoniae is a major cause of mortality globally. The introduction of pneumococcal conjugate vaccines (PCVs) has reduced the incidence of the targeted serotypes significantly, but expansion of non-targeted serotypes, serotype replacement, and incomplete vaccine-targeting contribute to pneumococcal disease in the vaccine era. Here, we characterize the changing population genetic landscape of S. pneumoniae in Norway over a 41-year period (1982–2022).MethodsSince 2018, all cases of invasive pneumococcal disease have undergone whole-genome sequencing (WGS) at the Norwegian Institute of Public Health. In order to characterize the changing population over time, historical isolates were re-cultured and sequenced, resulting in a historical WGS dataset. Isolates were assigned to global pneumococcal sequence clusters (GPSCs) using PathogenWatch and assigned to serotypes using in silico (SeroBA) and in vitro methods (Quellung reaction). Temporal phylogenetic analyses were performed on GPSCs of particular interest.ResultsThe availability of WGS data allowed us to study capsular variation at the level of individual lineages. We detect highly divergent fates for different GPSCs following the introduction of PCVs. For two out of eight major GPSCs, we identified multiple instances of serotype switching from vaccine types to non-vaccine types. Dating analyses suggest that most instances of serotype switching predated the introduction of PCVs, but expansion occurred after their introduction. Furthermore, selection for penicillin non-susceptibility was not a driving force for the changing serotype distribution within the GPSCs over time.ConclusionsPCVs have been major shapers of the Norwegian disease-causing pneumococcal population, both at the level of serotype distributions and the underlying lineage dynamics. Overall, the introduction of PCVs has reduced the incidence of invasive disease. However, some GPSCs initially dominated by vaccine types escaped the effect of vaccination through expansion of non-vaccine serotypes. Close monitoring of circulating lineages and serotypes will be key for ensuring optimal vaccination coverage going forward.

Automated planning of curved needle channels in 3D printed patient-tailored applicators for cervical cancer brachytherapy.

Purpose&#xD;Patient-tailored intracavitary/interstitial (IC/IS) brachytherapy (BT) applicators may increase dose conformity in cervical cancer patients. Current configuration planning methods in these custom applicators rely on manual specification or a small set of (straight) needles. This work introduces and validates a two-stage approach for establishing channel configurations in the 3D printed patient-tailored ARCHITECT applicator. &#xD;&#xD;Methods&#xD;For each patient, the patient-tailored applicator shape was based on the first BT application with a commercial applicator and integrated connectors to a commercial (Geneva) intrauterine tube and two lunar ring channels. First, a large candidate set was generated of channels that steer the needle to desired poses in the target region and are contained in the applicator. The channels' centrelines were represented by Bézier curves. Channels running between straight target segments and entry points were optimised and refined to ensure (dynamic) feasibility. Second, channel configurations were selected using geometric coverage optimisation. This workflow was applied to establish patient-tailored geometries for twenty-two patients previously treated using the Venezia applicator. Treatment plans were automatically generated using the in-house developed algorithm BiCycle. Plans for the clinically used configuration, TPclin, and patient-tailored configuration, TParch, were compared.&#xD;&#xD;Results&#xD;Channel configurations could be generated in clinically feasible time (median: 2651s, range 1826-3812s). All TParchand TPclinplans were acceptable, but planning aims were more frequently attained with patient-tailored configurations (115/132 versus 100/132 instances). Median CTVIRD98and bladderD2cm3doses significantly improved (p< 0.001 andp< 0.01 respectively) in TParchplans in comparison with TPclinplans, and in approximately half of the patients dosimetric indices improved. &#xD;&#xD;Conclusion&#xD;Automated patient-tailored BT channel configuration planning for 3D printed applicators is clinically feasible. A treatment planning study showed that all plans met planning limits for the patient-tailored configurations, and in selected cases improved the plan quality in comparison with commercial applicator configurations.&#xD.

Multi-Strategy Bald Eagle Search Algorithm Embedded Orthogonal Learning for Wireless Sensor Network (WSN) Coverage Optimization.

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm's robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Lexical coverage in L1 and L2 viewing comprehension

Abstract This study aimed to investigate the relationship between lexical coverage and TV viewing comprehension. Previous studies have indicated that 95% to 98% of lexical coverage may be needed for reading comprehension (Hu &amp; Nation, 2000). To understand informal listening passages, lower coverage figures (95%-90%) may suffice. However, no study has researched the lexical coverage needed to understand audiovisual texts. We adopted a counter-balanced within-participants design, in which 5%, 10%, or 20% of the words in four 2-min documentaries were replaced with nonwords. Native and non-native speakers of English participated in this study. Results showed that comprehension scores decreased as lexical coverage decreased; comprehension at 100% coverage was significantly higher than 90% and 80% in the two groups; and optimal adequate comprehension is achieved with an optimal lexical coverage of 95%, whereas minimal adequate comprehension is reached with a minimal lexical coverage of 80%.

Coverage optimization for IoT-based network using Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator

In the dynamic development of technology, the Internet of Things (IoT) stands as a driving force of innovation and opening new frontiers. Data collection and processing in IoT systems provide services to decision making in various fields including home automation, disaster management, healthcare, and so on. In such services, the IoT has certain limitations to perform the required tasks and it can cause extreme consequences in the network. One of the main challenge in IoT is to ensure maximum coverage by optimally placing the sensors. To address the coverage problem, a meta-heuristic algorithm Monarch Butterfly Optimization with greedy strategy and self-adaptive crossover operator (GCMBO) is proposed for Optimal Sensor Placement (OSP) in 3D monitoring region. The performance of proposed algorithm is evaluated by a series of simulation and it provide efficient results when compared to other similar existing optimization algorithms. Additionally, a statistical analysis (ANOVA and LSD post-hoc) is exhibited from the comparative results.

New Strides in Prevention of Malaria during Pregnancy Present Multitudinous Opportunities.

Pregnant women are at a higher risk of developing complications from malaria, a mosquito-borne disease caused by Plasmodium parasites, resulting in considerable maternal and infant morbidity and mortality. Malaria in pregnancy causes unfavorable and life-threatening outcomes for both the mother and fetus not limited to maternal anemia, hypoglycaemia, cerebral malaria, pulmonary edema, and puerperal sepsis. WHO recommends wide-ranging strategies for this detrimental but preventable disease; however, numerous challenges persist in ensuring high uptake of preventive therapies, effective usage of insecticide-treated bed nets, and early initiation and optimal antenatal care coverage for pregnant women. This work distils recent global advances in preventive strategies for malaria in pregnancy. We discuss three mainstay interventions by WHO, viz. intermittent preventive treatment of malaria in pregnancy (IPTp), utilization and outcomes of insecticide-treated bed nets (ITNs), and headways in malaria case management using therapeutic drugs. We cover multitudinous facets of antenatal care, WHO-advised community-based delivery of IPTp (c-IPTp), intermittent screening and treatment for malaria in pregnancy (ISTp), a malaria vaccine for pregnant women, and auxiliary factors that are crucial for improving prevention outcomes. Despite the reduction in malaria globally, malaria in pregnancy remains a prevalent issue in endemic areas, which warrants strengthening of preventative strategies. This work attempts to consolidate pivotal observations of the prevention of malaria during pregnancy by highlighting key advances, priority areas, new opportunities, research gaps, and challenges that need to be addressed to ensure improved outcomes in pregnant women infected with malaria.

UART IP core Design and Verification using WISHBONE Interface

The organization of communication between the devices is greatly aided by the communication protocol. These protocols have a clear set of guidelines that have been agreed upon by the tools needed for effective communication. A popular protocol for serial communication is UART. The hardware description language for the SV/Verilog UART functional module is designed in this paper. The serial connection capabilities offered by this UART IP CORE enable interaction with modems and other external devices. The Universal Verification Technique is used to undertake functional verification of the UART. By comparing the collected response to the intended response via the scoreboard mechanism, the reusable UVM test bench architecture is meant to push the randomized stimuli to the unit under test to assess the functional correctness. This core is made to be as compatible as possible with commercially accepted designs. The WISHBONE INTERFACE WITH 8-BIT OR 32-BIT SELECTABLE DATA BUS MODES is one of this design's primary characteristics. Interface debugging in 32-bit data bus mode. Functionality and register-level compatibility. FIFO procedure. The UVM methodology is used to validate the design. For optimal functional coverage, the test bench is written with regression test cases. The overall execution time will be shortened if the stimulus may be reused.

Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation

Monitoring the temperature of perishable goods during transport and storage is essential to prevent waste and maintain product quality. Exploiting the unique property of phase-change materials (PCM), altering their physical state at specific temperatures, we optimize a capacitive sensor design based on a copper on polyimide interdigitated spiral (IDE) structure coated with a PCM to irreversibly detect temperature thresholds. The effect of the sensor dimensioning on its response is analyzed using a finite element model simulation. The model predicted up to 51% capacitance variation for optimal coverage of the PCM after spreading over the IDE, which was validated experimentally within a 5% error. Two melting concepts utilizing the spreading or the removal of the melted PCM over the IDE are investigated based on a capillary retention mechanism to maintain sensor sensitivity under inclination. Finally, an eco-friendly implementation of the capacitive structure and its wireless operation at 460 MHz is demonstrated on paper with a printed zinc transducer passivated with beeswax and covered with jojoba oil. Melting of the oil at a threshold temperature of 12.3 °C resulted in an irreversible shift in resonance frequency of 14 MHz. This study provides guidelines for the design and implementation of irreversible temperature monitoring capacitive sensors.