1. Introduction

2. Related work

Wang et al. [19] proposed a cardiovascular healthcare system based on the Internet of Medical Things (IoMT), utilizing a wearable electrocardiogram (ECG) patch to monitor patient activity. This wireless patch enhances system optimization by reducing radio noise during ECG signal transmission and improving prediction accuracy before formal diagnosis. Similarly, Qiu et al. [20] introduced a multi-sensor fusion approach using a Body Sensor Network (BSN) for healthcare monitoring. Their method analyzes medical data through gait features, employing wearable Inertial Measurement Units (IMUs) to accurately extract user-specific characteristics. Gait assessment enhances early diagnosis, contributing to reduced mortality rates. Compared to existing methods, the proposed approach significantly improves the performance of the monitoring system.

Manogaran et al. [21] introduced a cognitive data processing method for the uncertainty analysis process (CDP-UA) to enhance the performance of wearable sensors. CDP-UA consists of two stages: dissemination and aggregation. A classification algorithm is emplyed to manage mapping process between dissemination and aggregation, while a joint learning algorithm helps reduce the latency rate and improves user communication. Experimental results show that the proposed CDP-UA method reduces the time consumption rate and improves the system’s overall performance.

Liu et al. [22] proposed a transmission rate adaption-assisted energy-efficient allocation scheme for wireless body area networks (WBAN). The proposed method aims to improve the Quality of Services (QoS) by categorizing the exact features of the network. A priority-based retransmission strategy (PRS) approach is used in the retransmission process to identify key requirements to improve the QoS. Compared to existing schemes, the proposed approach proves more effective in enhancing overall service quality.

Alqahtani et al. [23] proposed a divergence-dependent transmission scheme for medical applications. Based on the recurrent feedback analysis process, free channels are identified. Sensed signals are transmitted via overlapping channels. The diagonal analysis process is used to identify the noise migration based on the assessed feedback channels. Experimental results show that the proposed method improves overall performance by providing a better communication process than the existing schemes.

Xie et al. [24] proposed a data assessment method for healthcare system based on multi-vital biosignals. Internet of Things (IoT) is widely used in healthcare applications, which helps to provide better services to patients at a needed time. IoT collects user biosignals and stores them in the database for further use. The proposed method reduces latency in accessing healthcare services and shortens patients’ waiting time for diagnosis. It also recommends nearby healthcare centers for timely service delivery. Lalouani et al. [25] introdcuced an energy-efficient data collection method for wearable sensors. This method is used to reduce the latency rate and energy consumption rate of the network. A data quantization approach is used to increase the network’s throughput and service accuracy rate. Additionally, a packet formation algorithm minimizes the energy consumption rate and improves the network bandwidth. The proposed method increases effectiveness and improves prediction accuracy.

Alsiddiky et al. [26] introduced a priority-based data transmission process using selective decision modes for wearable healthcare devices. Healthcare monitoring systems play a vital role in wearable sensor devices. The proposed method improves the data transmission process and avoids data losses via transmitting data. The proposed method uses the queuing approach to analyze the users’ requests and help provide appropriate patient services at the required time.

Rashid et al. [27] proposed a cooperative, reliable, and energy-efficient routing protocol (Co-REERP) for intrabody sensor networks (Intra-WBSN). Biosignals are collected with the help of an electrocardiogram (ECG) and directly transmitted to the body network controller (BNC) for the classification process. Sensor nodes help to improve the healthcare monitoring system. The proposed method increases energy efficiency and improves the system’s performance compared to the existing methods. Kammerdiner et al. [28] introduced a data-driven combinatorial optimization for sensor-based assessment for a 5G network. This method is used in disease detection and improves the accuracy rate in wearable sensor devices. Experimental results show that the proposed method improves overall performance, increases efficiency, and reduces latency in healthcare applications.

Ben-Romdhane et al. [29] proposed a data transfer optimization process using an event-driven approach for healthcare applications. Electrocardiogram (ECG) signals are used in the proposed method for the data transmission process. The proposed method uses a level-crossing analog-to-digital convertor (LC-ADC) to find out the exact signals that ECG generates, which helps to give a better diagnosis process to the users. Compared with other methods, the proposed scheme reduces the network’s cost and time consumption rate. Karmakar et al. [30] proposed a fault detection and recovery framework for remote healthcare monitoring systems using a wireless body area network (WBAN). Patients’ healthcare details are transmitted as a signal to the healthcare centers, which is used in the diagnosis process. This method is also used to identify the fault sensor nodes and helps to reduce the storage space. Experimental results show that the proposed framework increases reliability and accuracy and reduces the time consumption rate while providing user services.

Khan et al. [31] proposed a fragmentation-based media access control (MAC) scheme (FROG-MAC) for wireless sensor networks (WSN). FROG-AMC improves overall latency rate and reduces the traffic rate in the WSN system. Experimental results demonstrate that the proposed FROG-MAC method increases the overall performance and reduces the time consumption rate while processing services to users compared to existing schemes. Faisa et al. [32] introduced TVA (Time-Varying Analytics), a secure and flexible time series evaluation system that utilizes outsourced MPC. TVA safeguards data by spreading trust and extends previous efforts’ capabilities without compromising security or performance. To reduce the strain between both of these, TVA uses innovative procedures, optimizations across several layers, and vectorized basic elements that allow it to handle big datasets with limited usage of resources. Allioui, H., & Mourdi, Y. [33] examined the possibility of IoT for improved financial growth and stability, with a specific emphasis on IoT management of data, connectivity, IoT data analytics, IoT data privacy, as well as other pertinent factors that influence the IoT environment. Their study offers a comprehensive analysis of current IoT research across multiple domains and serves as a valuable guide for researchers and practitioners to identify emerging opportunities and drive future advancements in the field.

Xiaoding Wang et al. [34] suggested Hierarchical Federated Learning for Accurate Anomaly Detection in the Industrial Internet of Things. The goal of federated learning is to construct a global anomaly detection model using the deep reinforcement learning (DRL) method to train individual local models. There is less risk of privacy leakage during federated learning since local data sets are not needed. Detection design and the author’s introduction of privacy leakage degree may also substantially enhance detection accuracy. According to validation trials, the suggested approach positively impacts latency and anomaly detection accuracy. These results bode well for privacy protection across a range of IIoT applications.

Xiaoding Wang et al. [35] proposed the Blockchain-Empowered Internet of Things for Secure Data Aggregation Strategy in Edge Computing. Mobile data collectors (MDCs) and other task receivers may be limited in their ability to search for and accept tasks by adding a security label to the block header that specifies the job’s security level (SL) and the criteria for its completion. New rules for block creation have been created to enhance the system’s performance regarding transaction latency and throughput. In addition, BSDA protects against privacy exposure by breaking out sensitive jobs and task recipients into categories. While data aggregation tasks often have lower SLs, an improved self-adaptive double bootstrapped deep deterministic policy gradient (IDDPG) is a deep reinforcement learning approach that may be used to construct energy-efficient MDC routes.

Wang Xiaoding et al. [36] proposed the Heterogeneous Blockchain and AI-Driven Hierarchical Trust Evaluation (BHTE) for 5G-enabled Intelligent Transportation Systems (ITS). The hierarchical incentive systems ensure that incentives and penalties are rational and fair, and the trust between ITS users and task distributors is evaluated using federated deep learning. ITS users’ and task distributers’ trust is kept on hierarchical and diverse blockchains to verify trust. The comprehensive experimental findings demonstrate that (i) the proposed BHTE framework provides fair and accurate trust evaluations and (ii) the BHTE performs efficiently with low latency and great system throughput.

Mehta and Patel [37] offer CMAF-IIoT, a Chaotic Map-Based Authentication Framework, to solve IIoT security concerns. The main issue is the lack of lightweight, robust, and scalable authentication solutions for resource-constrained IIoT systems, which are routinely attacked by sophisticated assaults. The authors produce random and safe authentication tokens using elliptic curve encryption and a chaotic map function. Formal security analysis and performance evaluation on compute time, energy usage, and communication overhead are included. The method dramatically reduces authentication delay and improves replay and impersonation resistance. The framework’s dependence on pre-distributed keys and lack of dynamic network topology deployment and testing are drawbacks. The report recommends more research to improve flexibility in highly mobile IIoT networks and test scalability in large-scale implementations.

Karia and Rana [38] develop the PUF and Authenticated Encryption-Based Authentication Framework to secure IoT-enabled smart healthcare systems. The research addresses illegal access and data manipulation in sensitive medical situations where typical encryption approaches are too computationally intensive. Physically Unclonable Functions (PUFs) with authenticated encryption guarantee secure and lightweight identity verification and data sharing. Protocol design, simulation-based performance analysis, and AVISPA security validation are their methods. PAAF-SHS is efficient in overhead communication and resistant to man-in-the-middle and eavesdropping. The framework’s reliance on static device settings and limited testing on dynamic healthcare scenarios like mobile health or emergency response systems are drawbacks. Future research should include adaptive security provisioning and machine learning anomaly detection in real-time healthcare monitoring.

Sharma et al. [39] offer a resource-efficient and secure data transfer technique for restricted smart wearable devices. Inefficient use of processing and network resources during safe data exchange can compromise wearable devices’ real-time health monitoring capability. The authors present a lightweight authentication and data sharing protocol using elliptic curve integrated encryption and hash-based message authentication codes. For IoT contexts, they use NS-3 and Contiki OS for mathematical modeling, protocol creation, and simulation. The mechanism improves energy efficiency, communication cost, and delay. The restricted focus on symmetric cryptography and static key distribution may not scale well for large or heterogeneous device networks. The system has not been evaluated in multi-hop or federated learning-based wearable networks, limiting its real-world usefulness in different mobility and data fusion scenarios.

3. The proposed fair dividend interrupt method

The proposed smart biomedical technology is designed to play an important role in the healthcare system medical diagnosis functions through smart biomedical instruments. In a smart biomedical scenario, medical diagnostic devices such as Fingertip Pulse Oximeter, EEG, ECG, etc., help monitor or measure various health parameters. These diagnostic monitors are a combination of software and hardware components used to process and organize information from the healthcare industry. The healthcare monitor includes a wearable sensor to collect information such as heartbeat rate, pulse level, brain waves, etc. This data is used to organize data from wearable sensors and manage the operations of the medical diagnosis devices. In the proposed work, the FDIM improves the evaluation of exact information and the accuracy of diagnoses through the aggregation of processing devices. Fig 1 presents an illustration of the proposed method in the healthcare scenario. The FDIM is designed to improve the accumulation of streaming/transmitting exact information from the organization based on wearable sensor data inputs. These inputs are collected from the human body at regular intervals based on key physiological changes. The accumulated data is analyzed using a multi-bridging processing device for precise health record augmentation (Fig 1).

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Fig 1. Proposed method.

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Table 1 describes aberrant event detection, medical risk prediction, and federated learning-based scheduling. To optimise data streaming, error estimates, and diagnosis across dynamic sensor observations, normalised input, deviation functions, and classification labels are crucial. The main goal of this technology is to reduce error factor and interrupt ratio observing from medical diagnosis. The challenging role is the maximization and connection of observing sequences with the existing instances. The observing sequence is organized using multi-bridging devices from the WS specifications. The WS inputs can be of any type based on identifying pulse levels, brain waves, etc. In a WS input instance, the input received is derived as

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Table 1. Notation and its description.

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(1)

Where, is the active wearable sensor input and , and are the minimum and maximum WS data observed in different time intervals. The variable and are helps to represent the detecting of abnormal events and predict medical risks. The detection of abnormal events is computed as the total number of incongruous instances accessed at any different observations. There are some conditions of error factors in based on abnormal events and medical risks of . Therefore, these problems affect the at any instance, for which the normalization of vital changes in the human body at regular intervals is estimated as

(2)

From the above equation (2), the normalization of , follows the maximum detection of abnormal events and the prediction of medical risks . Here, is a way to identify the medical risks, and is the abnormal event detection, for which the accurate computation of is observed. Based on and , the sequence of these error factors is estimated as

(3)

Equation (3) denotes the error factor in this healthcare industry as a consequence of observation until is active in pursuing the healthcare process. The pursued information maintaining of the control depends on the time interval until the medical diagnosis requires a service such as streaming/transmitting. The above instance of aggregation efficiency is analyzed using federated learning. In a smart healthcare medical diagnosis scenario, the sensed information must be transmitted into precise observations.

A smart biomedical environment consists of a centralized bridging unit responsible for streaming/transmitting precise observations of smart biomedical instruments and streaming intervals. The operation of the instruments is managed using key changes in the human body and WS inputs. The FDIM method functions between abnormal events and predicting medical risks. The working of the device is maintained by the bridging unit, where smart evaluation and intelligent prediction are made. Predicting precise observations improves the accuracy and efficiency of WS input in diagnosis. Fig 2 illustrates the data aggregation process from the WS.

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Fig 2. Data aggregation process.

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Feature alignment uses statistical matching and dynamic time warping (DTW) to synchronize diverse sensor data streams. To make sure that different sensor resolutions and sampling rates are consistent, normalization uses min-max scaling and z-score standardization. Using a federated learning architecture, adaptive weighting methods, and differential privacy-preserving aggregation, FDIM resolves feature distribution disparities across devices. To address problems with non-IID data, model changes are synchronized worldwide using federated averaging (FedAvg) and momentum-based optimization while being trained locally on device-specific distributions.

The inputs shared in different time intervals are first classified for their priority. Based on the priority, the organization is performed. In this organization process, and is validated to improve the classification ratio. This is required to leverage the aggregation ratio and mitigate deviation and errors (Refer to Fig 2).

The deployed wearable sensors sense healthcare industry information from smart biomedical diagnoses. The bridge unit is a way to transmit information from wearable sensors to processing devices. Besides, the spread control is to be instantly to meet the healthcare/medical diagnosis requirements. Therefore, using federated learning, the aggregation efficiency of WS inputs helps with estimation. We consider transmission or streaming of information issues, let the characteristic represented by until for the characteristics, the corresponding streaming of labels from the space label from the sensing organization. Let the corresponding characteristics of the efficiency of WS inputs be denoted by and , respectively.

(4)

For any and , the objective of this transmission is to build an operation such that

(5)

For our given condition of predicting medical risks and abnormal event detection, we represent the condition of dividing interrupts and streaming intervals as labels , and the condition of non-dividing interrupts and non-streaming information as . Based on this condition, the complete data can be shared for streaming of at different streaming interval instances of and are the root of the WS input for the federated learning process. For an estimation, the consequence of precise observation is computed as

(6)

Hence, in federated learning, the sequence of data can be observed and organized in sensor instances. This learning consists of two methods: priority checking and linear scheduling, followed by the result. The multi-bridging operations and their diagnosis accuracy treated by federated are defined per the equation (7).

(7)

In the above equation (7), and are the observed features that are used for satisfying the conditions of the . As per the streaming of and , the linear scheduling of and jointly gives the output of at its nearest possible conjunction. During the time interval, interrupts can occur, and the sequence of interrupts can be divided using FDIM. The FDIM is a method of dividing interrupts based on priority and WS specifications using federated learning. Each of the possible transmission information and its features can be analyzed using the federated learning process executed for observation. The learning process for different classifications is presented in Fig 3. In the first classification, the condition and the second classification relies on is administered. In the further processes, the from 1 to instances are assessed for . Contrarily, in the derived sequence post-classification, the deviations are performed as sequence and error, as illustrated in Fig 3.

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Fig 3. Classification process.

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Adaptive client weighting and customized model aggregation approaches are used to mitigate the effect of non-IID data on federated learning convergence. Grouping clients with comparable data distributions improves local model generalization in FDIM’s clustered federated learning technique before global aggregation. Stable updates across heterogeneous wearable sensor data are ensured using FedProx-based regularization to prevent local model drift. Dynamic batch normalization is also used to match feature distributions across devices further and reduce statistical inconsistencies. The resilience of FDIM for real-world deployment is confirmed by an empirical evaluation of convergence stability, which measures gradient divergence and model correctness over different degrees of non-IID situations. Therefore, the deviations in different time instances are identified based on changes in the above classifications. Therefore, the error from is isolated to prevent further deviations. In contrast, the succeeding sequences are prevented from entering the above condition, and hence, the validations are performed without . Now, as represented in the above equations, the first instance and then further consecutive instance, the multi-bridging properties are as defined in equation (6), and hence, is the result of the next properties and, therefore, the wearable sensor value of is retained without precise information.

Using equation (8), the sigmoid function and its associated estimations of , merging to is given as

(8)

Where the variables and denotes the observations of and to the possible solutions. It is to be pointed out that not complete data can be observed with both and . Now, the priority checking output for the and conditions are computed as in equation (9)

Condition 1:

Priority Checking:

(9)

Condition 2:

Priority Checking:

(10)

The derived priority checking result follows for instances where the transmission and are the incrementing factors for deriving the output of the linear scheduling process. The data can be streamed priority-wise and processed using the linear scheduling function over the WS specifications. The data can be checked priority-wise while others are held and then checked for priority. This looping process is continuously performed at different time intervals. This requires linear scheduling of and estimating the further sequence of data.

(11)

The above equation (11) denotes the linearity scheduling in observed as is an output for following abnormal events. Now, the following next instances for is designed for the priority and WS specifications output for the condition as estimated as

(12)

Streaming precise observation instances helps improve data accumulation and reduces error and interruption. Fig 4 presents the real-time application scenario for the above method. To improve adaptability in various medical contexts, FDIM incorporates adaptive priority algorithms that modify data transmission and processing in real-time according to clinical urgency. We use a hierarchical priority scheduling system to sort the data from wearable sensors into critical, moderate, and low levels. Optimized transmission policies are learned by reinforcement learning-based priority adaptation using trends in past patient data and real-time sensor readings, further refining scheduling. While maintaining overall system efficiency, latency-aware task reallocation guarantees that high-priority data, such as aberrant vitals, gets timely processing.

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Fig 4. Application scenario.

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In a real-time scenario, the mobile device is an aggregator from which different inputs are sensed and sent to the healthcare center. The WS, such as heartbeat sensors, headbands, wristwatches, etc., periodically senses psychological signals from the human body and shares them with the healthcare center. The healthcare center is reachable through cloud and infrastructure units. In this center, input consistency and analysis of erroneous observations are validated. The analysis is performed based on previously stored medical record data to detect any event. Such events are identified for recommendation through direct or clinical recommendations and transmitted to the mobile device.

The healthcare applications in the mobile device translate the response/ recommendation as useful information through text or multimedia messages. The aggregating device incorporates the proposed method to make it available in different instances. These instances must ensure two requisites for the clinical analysis, namely errorless data aggregation and sufficient availability. The proposed method satisfies the requirements for maximizing the correlation and precise diagnosis results. In Table 2, the normalization required for different input instances is tabulated. To adjust to changes in data distribution over time, the federated learning model in FDIM is updated periodically using an adaptive federated optimization technique. A dynamic update interval is used to avoid unnecessary communication overhead and guarantee rapid model adaption, where the aggregation frequency is changed in response to real-time data drift detection. This study combines concept drift detection methods like incremental learning and distributional similarity tests to find patterns in data collected by wearable sensors that have changed significantly. Weighted client updates and transfer learning are two examples of tailored federated learning strategies that improve responsiveness to individual patient differences.

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Table 2. Normalization for different instances.

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Table 2 presents the normalization required for different under observed characteristics and sequences. The proposed method identifies and based on for which different is performed. Therefore, as the characteristics vary, the observing sequences vary, for which and are modified for maximizing satisfaction. In Table 2, the changes for different sequences are tabulated.

In Table 3, the requisites in different is identified based on the conditions 1 and 2. Therefore, as the is high, then is high due to high and less observed through . This influences the and for aggregating different without high . Thus, the requirements for are not without maximum validations (condition 2). An analysis for scheduling % and error for different is presented in Fig 5.

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Table 3. changes for different sequences.

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Fig 5. Scheduling % and error for different .

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Fig 5 presents the scheduling % and error for different ∆ conditions. The conditional analysis is performed to verify priority regardless of and . These two factors vary and for reducing . In the classifier process, based variations are defaced in reducing from equation (11) process. Therefore, the is achieved for in maximizing the scheduling %. As the scheduling % increases, the error decreases until and is classified using and . Fig 6 presents the normalization under different sequences.

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Fig 6. Normalization for different Sequences.

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An analysis for normalization in different sequences is presented in Fig 6. The normalization is required for experiencing instances such that requirements are satisfied. The prolonging features depend on the and are required for based diagnosis. Therefore, for requires positive normalization maximization . In the contrary case of , the analysis is performed under for reducing its impact.

4. Results and discussion

This section briefs the proposed method’s analysis using dataset analysis for different WS inputs. The dataset [40] provides accelerometer, gyroscope, heartbeat sensor, and EEG sensor data observed from 12 human subjects at different time intervals. The priority is set as 1 for EEG and 2 for heartbeat inputs. The data input varies from 1 MB to 16 MB in size. This input analyzes the performance using access time, complexity, data utilization, processing time, aggregation ratio, and error. In the comparative analysis, the existing TRAP [22], CDF-UA [21], and PDT-FL [26] are considered. The adaptive aggregation technique optimizes the frequency of federated learning transmission by reducing bandwidth overhead by dynamically modifying synchronization intervals depending on model divergence criteria. To ensure real-time processing, on-device execution profiling measures forward pass delays under varied compute loads to quantify local model inference latency. Utilizing low-rank decomposition and parameter quantization to decrease footprint, we assess memory usage by looking at the per-device storage allocation for model weights, gradients, and intermediate activations. To prove that FDIM can integrate EHRs in a scalable and energy-efficient manner, we do end-to-end efficiency analyses across several sensor platforms as part of our experimental validation.

4.1. Access time

Fig 7 presents the comparative analysis of access time for input instances and priority probabilities. The proposed method achieves less access time for different and priority estimations. In this method, the is split based on and that validates using . Therefore, the is reduced under different validation classes as performed using the classifier learning; contrarily, the instances are segregated for less processing in maximizing the aggregation ratio. This enhances the available data and is ready for utilization. The available data is readily available for healthcare application support. The indistinct and classified through to in the classification, instances are reduced for the aggregated data. Therefore, unavailability or erroneous processing is not required for this method. This enhances the data utilization in less access time for different .

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Fig 7. Access time analysis.

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4.2. Complexity

The proposed method achieves less complexity than the other methods presented in Fig 8. The proposed method reduces based on and . In both validations, the is identified using execution. This is required for distinguishing under under different estimations. In multiple classification instances, the above conditions are validated for and . The proposed method extracts the and in different to based on the previous and . In multiple classification instances, and are isolated for providing less computation-based validations. Therefore, if is achieved, then is alone validated, preventing additional computation and reducing complexity under different input instances and priority predictions.

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Fig 8. Complexity analysis.

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4.3. Data utilization

The proposed method improves data utilization by reducing different classification instances. The is disintegrated using and for different and in reducing the erroneous date. As the varies, as do the assignment and aggregation for data availability. For different data input instances, the classification process, based on availability and reduces its impact on the accumulated data. This intern requires an organized allocation of the sequences as defined in equation (6). The subsequent from the classification process organizes based on such that is reduced in . This enhances the utilization based on availability; hence, the conditional analysis reduces access complexity. The process achieves high data utilization for the identified α and β conditions (Fig 9).

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Fig 9. Data utilization analysis.

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4.4. Processing time

The proposed method reduces processing time for different data inputs and priority probability (Fig 10). The proposed method classifies prioritized and non-prioritized for reducing the processing time. The and organization reduces the occurrence regardless of multiple instances provided . In the alternating validation, verification of is performed. This validation reduces the validation, preventing the additional data processing steps. In the complemented processing instances, if or true, then either of the conditions is alone validated. Therefore, the processing is performed for to or . Thus, the processing is allocated depending on the ∃ classification used to distinguish prioritized requirements. Therefore, the classification processing for both instances is distinguished under controlled time.

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Fig 10. Processing time analysis.

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4.5. Aggregation ratio

The proposed method achieves a high aggregation ratio compared to the other methods. The input data instances are organized based on proceeding such that stagnancy is less. In the different processing instances, the based modifications are performed without reducing the incoming data. In contrast to the aggregated sequences of , the validation increases the validation based on it, and thus, the unsalted incoming is retained. Therefore, the classifier is trained to validate and in different prediction instances. The consecutive instances are validated for indistinct and for preventing input stagnancy. The contrast formulation deviated based on and such that is achieved. In the proposed method, the available assessments are performed without preventing and leaving and validation. Therefore, the aggregation for filling up the difference is utilized to increase the aggregation ratio. The proposed method regains the aggregation for different data inputs and priorities (Fig 11).

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Fig 11. Aggregation ratio analysis.

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4.6. Error

The proposed method is confined ∃ to different data instances and prediction probabilities. The proposed method performs estimation for the detection of abnormal events. In the , the available classifications are validated without reducing the occurrence. The further deviations in are distinguished as and for preventing summed errors. The distinguishing at regular intervals of the above process reduces computations. In the contrary process, and validations are performed and such that is validated in alone. Therefore, the consecutive process is identified from to is normalized. This reduces the in the first classification instance, preventing priority interruption. The contrast validations are performed for and hence the is alone assessed for reducing further errors. In the second classification using random forests, is identified to prevent further errors. This is pervasive for different and as presented in Fig 12. Table 4 and Table 5 summarize the above comparative analysis for input instances and priority probability.

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Table 4. Comparative analysis for input instances.

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Table 5. Comparative analysis for priority probability.

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Fig 12. Error analysis.

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5. Conclusion

Wearable sensor data organization and aggregation is challenging due to asynchronous device operations and priorities. This paper discussed a fair dividend interrupt method for improving wearable sensor observations’ aggregation and data utilization probability. The distinguished input instances are sequentially validated to extract the computing characteristics for multi-bridging input diagnosis. In this process, prioritized conditional analysis augments the scheduling process without reducing the data utilization. The federated learning process decides efficiency verification based on error inputs and aggregation outputs. The scheduled data is verified for its priority in distinct intervals, augmenting high aggregation. The proposed method is reliable in assisting healthcare applications through precise data aggregation and medical risk assessment. From the experimental results, the proposed method is found to achieve 10.18% less access time, 5.19% less complexity, 10.57% high data utilization, 8.48% less processing time, 9.97% high aggregation ratio, and 10.42% less error for different priority probabilities. Future work include applying this method to various WS sites and organizations outside the healthcare industry. In addition, the federated learning process strives to become even more skilled and accurate in handling various inputs. This requires ongoing improvement and a concentration on enhancing decision-making methods.