Threshold Mechanism Research Articles

Explainable correlation-based anomaly detection for Industrial Control Systems

Anomaly detection is vital for enhancing the safety of Industrial Control Systems (ICS). However, the complicated structure of ICS creates complex temporal correlations among devices with many parameters. Current methods often ignore these correlations and poorly select parameters, missing valuable insights. Additionally, they lack interpretability, operating efficiently with limited resources, and root cause identification. This study proposes an explainable correlation-based anomaly detection method for ICS. The optimal window size of the data is determined using Long Short-Term Memory Networks—Autoencoder (LSTM-AE) and the correlation parameter set is extracted using the Pearson correlation. A Latent Correlation Matrix (LCM) is created from the correlation parameter set and a Latent Correlation Vector (LCV) is derived from LCM. Based on the LCV, the method utilizes a Multivariate Gaussian Distribution (MGD) to identify anomalies. This is achieved through an anomaly detection module that incorporates a threshold mechanism, utilizing alpha and epsilon values. The proposed method utilizes a novel set of input features extracted using the Shapley Additive explanation (SHAP) framework to train and evaluate the MGD model. The method is evaluated on the Secure Water Treatment (SWaT), Hardware-in-the-loop-based augmented ICS security (HIL-HAI), and Internet of Things Modbus dataset using precision, recall, and F-1 score metrics. Additionally, SHAP is used to gain insights into the anomalies and identify their root causes. Comparative experiments demonstrate the method's effectiveness, achieving a better 0.96% precision and 0.84% F1-score. This enhanced performance aids ICS engineers and decision-makers in identifying the root causes of anomalies. Our code is publicly available at a GitHub repository: https://github.com/Ermiyas21/Explainable-correlation-AD.

Detecting and mitigating DIS attack in RPL-based network

The Routing Protocol for Low-Power and Lossy Networks (RPL) is among the most used protocol in the Internet of Things. In RPL, the network is established by creating a tree topology called Destination Oriented Directed Acyclic Graphs (DODAG), However, RPL-based networks stability is vulnerable to different attacks such as DODAG Information Solicitation (DIS) flooding attack. In this attack, a malicious node sends large number of DIS messages in the neighborhood, which compels the receiver nodes to exchange more control messages, causing resources deplete. Unfortunately, RPL has no means to detect or mitigate DIS flooding attacks. In this paper, we present a novel Adaptive Threshold Mechanism to mitigate DIS attack called (ATM-RPL). In the proposed approach, the number of exchanged control messages is used to determine the network state which in turn used to calculate a dynamic threshold. ATM-RPL proved its effectiveness and superiority over similar proposed mechanism in terms of false positive, construction time, power consumption, and packet delivery ratio.

Event-triggered adaptive compensation control for stochastic nonlinear systems with multiple failures: An improved switching threshold strategy.

This paper considers the event-triggered adaptive fault-tolerant control (FTC) problem for a class of stochastic nonlinear systems suffering from finite number of actuator failures and abrupt system external failure. Unlike existing event-triggered mechanisms (ETMs), this paper proposes an improved switching threshold mechanism (STM) that effectively addresses the potential system security hazards caused by large signal impulses when both the magnitude size of the controller and its rate of change are too large, while also saving energy consumption. Especially, when the occurrence of both actuator failure and system external failure may lead to over-change rate of the controller, by using the multi-dimensional Taylor network (MTN) approximation technique, the adaptive fault-tolerant control scheme designed based on the improved STM not only has lower resource consumption, but also indirectly improves the control performance of the system by ensuring the system security operation. Not only does it ensure that all signals of the closed-loop system are bounded in probability and the tracking error converges through the proposed control scheme. The feasibility and superiority of the developed scheme is well shown by dynamic model simulations.

Machine Learning-Based Outlier Detection for Business Intelligence: A Scalable Time Series Analysis Framework

The exponential growth in digital business operations has resulted in an unprecedented volume of time series data generated from diverse business metrics, creating an urgent need for sophisticated anomaly detection systems. This paper presents a comprehensive framework for detecting outliers in business time series data using advanced machine learning techniques, addressing the challenges of scale, accuracy, and real-time processing. We propose a novel hybrid approach that seamlessly integrates statistical methods with deep learning architectures to identify both point anomalies and pattern deviations in multivariate business metrics. The framework incorporates adaptive thresholding mechanisms and contextual awareness, leveraging business domain knowledge to reduce false positives while maintaining high detection accuracy across varying business cycles and seasonal patterns. Our approach addresses the challenges of scalability and real-time processing through a sophisticated distributed computing architecture, making it suitable for enterprise-scale deployments. The framework demonstrates superior performance in handling concept drift, seasonal variations, and complex interdependencies between metrics, while maintaining computational efficiency and interpretability. Keywords—anomaly detection, business intelligence, machine learning, time series analysis, deep learning, statistical methods, distributed computing, real-time processing, adaptive thresholding, feature engineering

Effect of grain size gradient on the mechanical behavior of gradient nanograined pure iron: an atomic study

Abstract Using molecular dynamics simulation, the deformation mechanisms of gradient nanograined (GNG) pure iron (Fe) were investigated. Simulations of uniaxial tensile experiments were conducted on samples exhibiting different grain size gradients. The simulation results reveal the presence of a critical GNG parameter (g), at which point the GNG-Fe attains its highest strength. The deformation mechanisms of three representative samples, namely GNG-2 with the g value at the threshold, GNG-1 with a g value smaller than the critical threshold and GNG-4 with a g value exceeding it, were thoroughly investigated. Within the coarse-grained (CG) region of GNG-1, the primary deformation mechanism is predominantly characterized by planar defects, rather than being dominated by dislocations. Furthermore, the mechanisms of both ‘strain hardening’ and ‘softening’ were observed and discussed in this region. The deformation of the coarse grains occurs in a coordinated manner, and the magnitude of the back-stress is insufficient to trigger grain boundary (GB) motion in the fine-grained (FG) region. In contrast, the deformation of the CG region in the GNG-4 primarily depends on dislocation. The ‘hardening’ and ‘softening’ effects of the dislocations were described and discussed. In the FG region of GNG-4, the grains undergo deformation primarily through GB motion, a phenomenon attributed to the significant back-stress generated by the uncoordinated deformation exhibited by the coarse grains. In the CG area of sample 2 with the g value at threshold, both dislocation- and planar defects-controlled mechanisms are observed. In the FG of this sample, neither GB migration and grain rotation are found. Only the GB width becomes larger, indicating that the back-stress transferred from the CG area makes the GB more active, but not large enough to induce the GB migration or grain rotation. The results of this work may provide some theoretical supports for the deformation mechanism of the GNG materials.

Noise Detection and Suppression From ECG Through Improved CEEEMD and Adaptive Wavelet Soft Thresholding

Electrocardiogram (ECG) is a noninvasive, effective and economical biomedical signal that is vital in diagnosing cardiovascular diseases. However, the acquiring process contaminates the ECG signal with several types of noises like Motion Artifacts, Power Line Interference and Baseline Wander. Hence, this paper proposes a new approach to detect and suppress the noises from ECG signals. The complete methodology comprises two stages: noise detection and noise suppression. The former stage applies Improved Complete Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noisy ECG into Intrinsic Mode Functions (IMFs). Next, Maximum Absolute Amplitude (MAA) and Auto-Correlation Maximum Amplitude (AMA) are extracted and used to classify the type of noises from ECG. Then, the noisy ECG segments are processed through the second stage and decomposed into sub-bands through Discrete Wavelet Transform (DWT). Then, the sub-bands are categorized into noise-dominant and signal-dominant frequency bins, and only noise-dominant frequency bins are subjected to noise suppression through a newly proposed adaptive soft thresholding mechanism. The effectiveness of the proposed method is assessed by contaminating the ECG signals acquired from the MIT-BIH arrhythmia database with different noises at different Signal-Noise Ratios (SNRs). Three performance metrics, namely Output SNR, Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC), are employed to explore the superiority of the proposed method over state-of-the-art methods, which considered EMD and CEEMD as decomposition filters. The proposed method improved by an average of 3.5 dB in output SNR and 0.0290 in RMSE.

Combined response of polar magnetotaxis to oxygen and pH: Insights from hanging drop assays and microcosm experiments

Magnetotactic bacteria (MTB) combine passive alignment with the Earth magnetic field with a chemotactic response (magneto-chemotaxis) to reach their optimal living depth in chemically stratified environments. Current magneto-aerotaxis models fail to explain the occurrence of MTB far below the oxic-anoxic interface and the coexistence of MTB cells with opposite magnetotactic polarity at depths that are unrelated with the redox gradient. Here we propose a modified model of polar magnetotaxis which explains these observations, as well as the distinct concentration profiles and magnetotactic advantages of two types of MTB inhabiting a freshwater sediment: a group of unidentified cocci (MC), and a giant rod-shaped bacterium (MB) apparently identical to M. bavaricum (MB). This model assumed that magnetotactic polarity is set by a threshold mechanism in counter gradients of oxygen and a second group of repellents, with, in case of MB, includes H+ ions. MTB possessing this type of polar magnetotaxis can shuttle between two limit depths across the redox gradient (redox taxis), as previously postulated for M. bavaricum and other members of the Nitrospirota group. The magnetotaxis of MB and MC is predominantly dipolar whenever the presence of a magnetic field ensures a magnetotactic advantage. In addition, MB can overcome unfavorable magnetic field configurations through a temporal sensing mechanism. The availability of threshold and temporal sensing mechanisms of different substances can generate a rich variety of responses by different types of MTB, enabling them to exploit multiple ecological niches.

Post-transplant G-CSF impedes engraftment of gene-edited human hematopoietic stem cells by exacerbating p53-mediated DNA damage response.

Granulocyte-colony-stimulating factor (G-CSF) is commonly used to accelerate recovery from neutropenia following chemotherapy and autologous transplantation of hematopoietic stem and progenitor cells (HSPCs) for malignant disorders. However, its utility after exvivo gene therapy in human HSPCs remains unexplored. We show that administering G-CSF from day 1 to 14 post-transplant impedes engraftment of CRISPR-Cas9 gene-edited human HSPCs in murine xenograft models. G-CSF affects gene-edited HSPCs through a cell-intrinsic mechanism, causing proliferative stress and amplifying the early p53-mediated DNA damage response triggered by Cas9-mediated DNA double-strand breaks. This underscores a threshold mechanism where p53 activation must reach a critical level to impair cellular function. Transiently inhibiting p53 or delaying the initiation of G-CSF treatment to day 5 post-transplant attenuates its negative impact on gene-edited HSPCs. The potential for increased HSPC toxicity associated with post-transplant G-CSF administration in CRISPR-Cas9 autologous HSPC gene therapy warrants consideration in clinical trials.

Enhancing Efficacy in Breast Cancer Screening with Nesterov Momentum Optimization Techniques

In the contemporary landscape of healthcare, machine learning models are pivotal in facilitating precise predictions, particularly in the nuanced diagnosis of complex ailments such as breast cancer. Traditional diagnostic methodologies grapple with inherent challenges, including excessive complexity, elevated costs, and reliance on subjective interpretation, which frequently culminate in inaccuracies. The urgency of early detection cannot be overstated, as it markedly broadens treatment modalities and significantly enhances survival rates. This paper delineates an innovative optimization framework designed to augment diagnostic accuracy by amalgamating momentum-based optimization techniques within a neural network paradigm. Conventional machine learning approaches are often encumbered by issues of overfitting, data imbalance, and the inadequacy of capturing intricate patterns in high-dimensional datasets. To counter these limitations, we propose a sophisticated framework that integrates an adaptive threshold mechanism across an array of gradient-based optimizers, including SGD, RMSprop, adam, adagrad, adamax, adadelta, nadam and Nesterov momentum. This novel approach effectively mitigates oscillatory behavior, refines parameter updates, and accelerates convergence. A salient feature of our methodology is the incorporation of a momentum threshold for early stopping, which ceases training upon the stabilization of momentum below a pre-defined threshold, thereby pre-emptively preventing overfitting. Leveraging the Wisconsin Breast Cancer Dataset, our model achieved a remarkable 99.72% accuracy and 100% sensitivity, significantly curtailing misclassification rates compared to traditional methodologies. This framework stands as a robust solution for early breast cancer diagnosis, thereby enhancing clinical decision making and improving patient outcomes.

Deep video steganography using temporal-attention-based frame selection and spatial sparse adversarial attack

With the development of deep learning-based steganalysis, video steganography is facing with great challenges. To address the insufficient security against steganalysis of existing deep video steganography, given that the video has both spatial and temporal dimensions, this paper proposes a deep video steganography method using temporal frame selection and spatial sparse adversarial attack. In temporal dimension, a stego frame selection module based on temporal attention is designed to calculate the weight of each frame and selects frames with high weights for message and sparse perturbation embedding. In spatial dimension, sparse adversarial perturbations are performed in the selected frames to improve the ability of resisting steganalysis. Moreover, to control the adversarial perturbations’ sparsity flexibly, an intra-frame dynamic sparsity threshold mechanism is designed by using percentile. Experimental results demonstrate that the proposed method effectively enhances the visual quality and security against steganalysis of video steganography and has controllable sparsity of adversarial perturbations.

Thresholding Computing with Heterogeneous Integration of Memristive Kernel with Metal-Oxide-Semiconductor Capacitor for Temporal Data Analysis.

Precise event detection within time-series data is increasingly critical, particularly in noisy environments. Reservoir computing, a robust computing method widely utilized with memristive devices, is efficient in processing temporal signals. However, it typically lacks intrinsic thresholding mechanisms essential for precise event detection. This study introduces a new approach by integrating two Pt/HfO2/TiN (PHT) memristors and one Ni/HfO2/n-Si (NHS)metal-oxide-semiconductor capacitor (2M1MOS) to implement a tunable thresholding function. The current-voltage nonlinearity of memristors combined with the capacitance-voltage nonlinearity of the capacitor forms the basis of the 2M1MOS kernel system. The proposed kernel hardware effectively records feature-specified information of the input signal onto the memristors through capacitive thresholding. In electrocardiogram analysis, the memristive response exhibited a more than ten-fold difference between arrhythmia and normal beats. In isolated spoken digit classification, the kernel achieved an error rate of only 0.7% by tuning thresholds for various time-specific conditions. The kernel is also applied to biometric authentication by extracting personal features using various threshold times, presenting more complex and multifaceted uses of heartbeats and voice data as bio-indicators. These demonstrations highlight the potential of thresholding computing in a memristive framework with heterogeneous integration.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

Temporal convolutional network with soft threshold and contractile self-attention mechanism for remaining useful life prediction of rolling bearings

Abstract RUL prediction is an effective approach to prevent system failures and cut maintenance expenditures. Due to the wide receptive field and the avoidance of future information leakage, temporal convolutional neural network (TCN) is widely applied for RUL estimation of bearings. However, the predictive performance of TCN is limited by the loss of degradation features and the breakdown of continuity of timing information. To overcome the above defects, hybrid temporal convolutional neural network with soft threshold and contractile self-attention mechanism (HTCN-SC) is presented. Firstly, the adaptive threshold is determined by the contraction self-attention mechanism with higher interpretability, which captures the contribution of different features to the estimation of RUL. Then, the soft threshold is employed to activate the degraded features. On the one hand, the degeneracy features endowed by the dilated causal convolution with obvious negative values are fully preserved. On the other hand, the noise components that are given low weights are completely suppressed compared to the original TCN. Finally, parallel branch composed of one-dimensional convolutional networks are used to supplement the continuity of time series. Degradation signals from different working conditions and bearings are employed to verify the performance of the HTCN-SC. The results indicate that HTCN-SC with accurate RUL estimation and generalization ability is an effective tool for rolling bearing health monitoring.

Shannonian Maximum Entropy Balking Threshold Mechanism (BTM) for a Stable M/G/1 Queue with Significant Applications of M/G/1 Queue Theory to Augmented Reality (AR)

An exposition is undertaken to analytically derive validate the Shannonian Maximum Entropy BTM for the underlying stable queue. Most importantly, the analytic derivation of the upper and lower bounds 0f the absolute difference between Shannonian Cumulative service time distribution functions (CDFs) with and without balking. Typical numerical experiments are provided. Additionally, some applications of M/G/1 queue theory to AR are given. Some challenging open problems are addressed combined with closing remarks and future research directions.

Semi-supervised lung adenocarcinoma histopathology image classification based on multi-teacher knowledge distillation.

In this study, we propose a semi-supervised learning scheme using a patch-based deep learning framework to tackle the challenge of high-precision classification of seven lung tumor growth patterns, despite having a small amount of labeled data in whole slide images (WSIs). This scheme aims to enhance generalization ability with limited data and reduce dependence on large amounts of labeled data. It effectively addresses the common challenge of high demand for labeled data in medical image analysis.

Approach. To address these challenges, the study employs a semi-supervised learning approach enhanced by a dynamic confidence threshold mechanism. This mechanism adjusts based on the quantity and quality of pseudo labels generated. This dynamic thresholding mechanism helps avoid the imbalance of pseudo-label categories and the low number of pseudo-labels that may result from a higher fixed threshold. Furthermore, the research introduces a multi-teacher knowledge distillation technique. This technique adaptively weights predictions from multiple teacher models to transfer reliable knowledge and safeguard student models from low-quality teacher predictions.

Main results. The framework underwent rigorous training and evaluation using a dataset of 150 WSIs, each representing one of the seven growth patterns. The experimental results demonstrate that the framework is highly accurate in classifying lung tumor growth patterns in histopathology images. Notably, the performance of the framework is comparable to that of fully supervised models and human pathologists. In addition, the framework's evaluation metrics on a publicly available dataset are higher than those of previous studies, indicating good generalizability.

Significance. This research demonstrates that a semi-supervised learning approach can achieve results comparable to fully supervised models and expert pathologists, thus opening new possibilities for efficient and cost-effective medical images analysis. The implementation of dynamic confidence thresholding and multi-teacher knowledge distillation techniques represents a significant advancement in applying deep learning to complex medical image analysis tasks. This advancement could lead to faster and more accurate diagnoses, ultimately improving patient outcomes and fostering the overall progress of healthcare technology.&#xD.

An Enhancement for Wireless Body Area Network Using Adaptive Algorithms

Wireless Body Area Networks (WBANs) are one of the most critical technologies for maintaining constant monitoring of patient’s health and diagnosing diseases. They consist of small, wearable wireless sensors transmitting signals. Within this vision, WBANs are not without unique difficulties, for instance, high energy consumption, heat from the sensor, and impaired data accuracy. This paper introduces adaptive algorithms combining Convolutional Neural Networks (CNNs) and dynamic threshold mechanisms to enhance the performance and energy efficiency of Wireless Body Area Networks. The study utilizes the MIB-BIH Arrhythmias dataset to improve the detection of arrhythmias. The results show a 10.53% improvement in battery life and a 5.62-fold enhancement in temperature management when sleep mode technology is applied. As a result, the model reached the average accuracy of ECG classification of 98% and a high level of selectivity and sensitivity to a normal type of heartbeat and quite satisfactory results in the classification of arrhythmia type of heartbeat.

Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction

Predicting tool wear significantly improves cutting efficiency and quality in intelligent manufacturing. Traditional recurrent neural networks suffer from vanishing or exploding gradients and need to be improved prediction accuracy. Therefore, this paper proposes a gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism (TCN-BiGRU-SA). Monotonicity, robustness, and trendability indicators are used to assess time and frequency domain features. Then, evaluation results of these three indicators are weighted to identify the sensitive features with the degradation trend. The attention mechanism is proposed to adaptively adjust the soft thresholding, and the SA is added to TCN-BiGRU to remain helpful features. Finally, the proposed model is validated using an experimental dataset. Compared to the TCN-BiGRU model without the SA mechanism, the predicted results show that the average mean absolute error and root mean square error are reduced by 48.29 % and 36.39 %, respectively.

G-EEGCS: Graph-based optimum electroencephalogram channel selection

Electroencephalography (EEG) is commonly used to measure brain activity in clinical research. However, the abundance of channels in EEG data poses several challenges, including increased computational complexity, noise interference, and decreased efficiency in data analysis. A new graph-based method called G-EEGCS has been developed to address these issues for EEG channel selection. The graph-based optimum EEG channel selection (G-EEGCS) method constructs a directed network by establishing channel connections based on their pairwise correlations. Statistical features are used to determine similarity, and an adjacency matrix is created to represent the connectivity between the EEG channels. The influence of each channel on information flow within the network is assessed using the centrality measures. By calculating the shortest paths between all channel pairs, the algorithm quantifies the probability of each channel serving as a bridge between different parts of the graph. Channels with high centrality scores, indicating their significance in the information flow, are given priority during the selection process. An adaptive threshold is applied to optimize channel selection, ensuring that only channels that exceed the threshold, exhibit specific characteristics, and align with the overall network structure are retained. This adaptive thresholding mechanism enhances the robustness and flexibility of the G-EEGCS method, enabling personalized channel selection tailored to individual EEG datasets. The G-EEGCS approach offers a promising solution for EEG channel selection, improving the interpretability and efficiency of EEG-based studies and applications for researchers. It achieved an average accuracy of 0.9125, outperforming state-of-the-art methods. This method provides a valuable means of addressing the challenges posed by the multitude of channels in EEG data, ultimately contributing to more effective and insightful analysis in clinical research.

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies. The algorithm introduces an optimistic response assumption, employs a message aggregation tree to collect and validate votes, and uses a dynamically adjusted threshold mechanism to reduce communication delay and improve message delivery reliability. Additionally, a dynamic channel mechanism and an asynchronous leader multi-round mechanism are introduced to address multiple points of failure in the message aggregation tree structure, minimizing dependence on a single leader. This adaptation can be flexibly applied to real-world system conditions to improve performance and responsiveness. We conduct experimental evaluations to verify the algorithm's effectiveness and superiority. Compared to the traditional HotStuff algorithm, the improved algorithm demonstrates higher efficiency and faster response times in handling faulty copies and transaction ordering.

On a dynamic and decentralized energy-aware technique for multi-robot task allocation

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots, an open-source robot simulator, and Tartarus, a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time, total task allocation time, total job allocation time, and total execution time of an experiment.