Large language models (LLMs) have opened new opportunities for transforming natural language user intents into executable actions. This capability enables embodied AI agents to perform complex tasks, without involvement of an expert, making human-robot interaction (HRI) more convenient. However these developments raise significant security and privacy challenges such as self-preferencing, where a single LLM service provider dominates the market and uses this power to promote their own preferences. LLM oracles have been recently proposed as a mechanism to decentralize LLMs by executing multiple LLMs from different vendors and aggregating their outputs to obtain a more reliable and trustworthy final result. However, the accuracy of these approaches highly depends on the aggregation method. The current aggregation methods mostly use semantic similarity between various LLM outputs, not suitable for robotic task planning, where the temporal order of tasks is important. To fill the gap, we propose an LLM oracle with a new aggregation method for robotic task planning. In addition, we propose a decentralized multi-robot infrastructure based on Hyperledger Fabric that can host the proposed oracle. The proposed infrastructure enables users to express their natural language intent to the system, which then can be decomposed into subtasks. These subtasks require coordinating different robots from different vendors, while enforcing fine-grained access control management on the data. To evaluate our methodology, we created the SkillChain-RTD benchmark made it publicly available. Our experimental results demonstrate the feasibility of the proposed architecture, and the proposed aggregation method outperforms other aggregation methods currently in use.

Employing a minimal array of electroencephalography (EEG) channels for neonatal sleep stage classification is essential for data acquisition in the Internet of Medical Things (IoMT), as single-channel and edge-based features can reduce data transfer and processing requirements, enhancing cost-effectiveness and practicality. In this paper, we evaluate the efficacy of a single channel and the viability of a binary classification scheme for discerning awake and sleep states and transitions to quiet sleep. For this, two datasets of EEG signals for neonate sleep analysis were recorded from Children's Hospital of Fudan University, Shanghai, comprising recordings from 64 and 19 neonates, respectively. From each epoch, a diverse ensemble of 490 features was extracted through a blend of discrete and continuous wavelet transforms (DWT, CWT), spectral statistics, and temporal features. In addition, we introduced an innovative hybrid univariate and ensemble feature selection approach with multidomain feature fusion, a stacking-based ensemble classifier that outperforms existing work. We achieved 90.37%, 91.13%, and 94.88% accuracy for sleep/awake, quiet sleep/non-quiet sleep, and quiet sleep/awake, respectively. This was corroborated by significant Kappa values of 77.5%, 80.29%, and 89.76%. Using SelectPercentile, we devised three distinct feature selection mechanisms: one using DWT, one with CWT, and another incorporating both spectral and temporal features. Subsequently, SelectKBest was used to determine the most effective features. For our stacked model, we incorporated a trifecta of the ExtraTree model with variable estimators, a Random Forest, and an Artificial Neural Network (ANN) as base classifiers, and for the final prediction phase, ANN was implemented again. The model's performance was evaluated using K-fold and leave-one-subject cross-validation.

Negative emotions are linked to the onset of neurodegenerative diseases and dementia, yet they are often difficult to detect through observation. Physiological signals from wearable devices offer a promising noninvasive method for continuous emotion monitoring. In this study, we propose a lightweight, resource-efficient machine learning approach for binary emotion classification, distinguishing between negative (sadness, disgust, anger) and positive (amusement, tenderness, gratitude) affective states using only electrocardiography (ECG) signals. The method is designed for deployment in resource-constrained systems, such as Internet of Things (IoT) devices, by reducing battery consumption and cloud data transmission through the avoidance of computationally expensive multimodal inputs. We utilized ECG data from 218 CSV files extracted from four studies in the Psychophysiology of Positive and Negative Emotions (POPANE) dataset, which comprises recordings from 1,157 healthy participants across seven studies. Each file represents a unique subject emotion, and the ECG signals, recorded at 1000 Hz, were segmented into$\mathbf{1 0}$-second epochs to reflect real-world usage. Our approach integrates multidomain feature extraction, selective feature fusion, and a voting classifier. We evaluated it using a participant-exclusive generalized model and a participantinclusive personalized model. The personalized model achieved the best performance, with an average accuracy of 95.59 %, outperforming the generalized model, which reached 69.92 % accuracy. Comparisons with other studies on the POPANE and similar datasets show that our approach consistently outperforms existing methods. This work highlights the effectiveness of personalized models in emotion recognition and their suitability for wearable applications that require accurate, low-power, and realtime emotion tracking. Code availability at GitHub.

Early detection and accurate diagnosis are essential to improving patient outcomes. The use of convolutional neural networks (CNNs) for tumor detection has shown promise, but existing models often suffer from overparameterization, which limits their performance gains. In this study, fuzzy sigmoid convolution (FSC) is introduced along with two additional modules: top-of-the-funnel and middle-of-the-funnel. The proposed methodology significantly reduces the number of trainable parameters without compromising classification accuracy. A novel convolutional operator is central to this approach, effectively dilating the receptive field while preserving input data integrity. This enables efficient feature map reduction and enhances the model’s tumor detection capability. In the FSC-based model, fuzzy sigmoid activation functions are incorporated within convolutional layers to improve feature extraction and classification. The inclusion of fuzzy logic into the architecture improves its adaptability and robustness. Extensive experiments on three benchmark datasets demonstrate the superior performance and efficiency of the proposed model. The FSC-based architecture achieved classification accuracies of 99.17 %, 99.75 %, and 99.89 % on three different datasets. The model employs 100 times fewer parameters than large-scale transfer learning architectures, highlighting its computational efficiency and suitability for detecting brain tumors early. This research offers lightweight, high-performance deep-learning models for medical imaging applications. Code: https://github.com/irfan334590/Fuzzy-Sigmoid-Conv.git

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness. They offer notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this survey presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly investigating fusion strategies that incorporate frame-based cameras, inertial measurement units, and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.

A timely diagnosis and treatment of sleep disorders in neonates during their first week of life is crucial. Current methods for staging neonatal sleep rely heavily on multiple electroencephalography (EEG) channels. These channels increase computational complexity, require a large amount of data to be transferred to the cloud, and may cause skin irritation. We propose an innovative automated classification approach that integrates multi-view feature fusion, AdaptiSelect-based feature optimization, the smart cloud data transfer and reconstruction (STREAM) module, and a rotational ensemble stacking model. The data reduction module significantly enhances edge-cloud systems’ performance in IoT-based healthcare environments by reducing data transmission by a factor of 153.6 through efficient feature selection and compact data packet formation. This module ensures minimal bandwidth usage, reduces the computational load on resource-constrained edge devices, and lowers cloud storage requirements while maintaining full data reconstruction. The dataset used in this research combines two large datasets collected over four years from the Children’s Hospital Fudan University, Shanghai. A unique set of 315 features are extracted from each epoch of a single channel using flexible analytical wavelet transform (FAWT), dual-tree complex wavelet transform (DTCWT), enhanced covariance (ECOV), and spectral features based on α, β, θ, and δ brain waves. These features are refined using AdaptiSelect, achieving an accuracy of 81.16% and a Kappa of 72.17% with one channel. Accuracy improves to 82.79% with a Kappa of 74.70% when using two channels, validated through 10-fold cross-validation. Additionally, Leave-One-Subject-Out crossvalidation (LOSO-CV) further demonstrates the effectiveness of the proposed approach as a generalized solution. Using both single and multichannel setups, the proposed approach outperforms the most significant state-of-the-art methods in neonatal sleep analysis.

As multi-robot systems continue to advance and become integral to various applications, managing conflicts and ensuring secure access control are critical challenges that need to be addressed. Access control is essential in multi-robot systems to ensure secure and authorized interactions among robots, protect sensitive data, and prevent unauthorized access to resources. This paper presents a novel framework for customizable conflict resolution and attribute-based access control in multi-robot systems for ROS 2 leveraging the Hyperledger Fabric blockchain. We introduce an attribute-based access control (ABAC) Fabric-ROS 2 bridge to enable secure communication and control between users and robots. By defining conflict resolution policies based on task priorities, robot capabilities, and user-defined constraints, our framework offers a flexible way to resolve conflicts. Additionally, it incorporates attribute-based access control, granting access rights based on user and robot attributes. ABAC offers a modular approach to control access compared to existing access control approaches in ROS 2, such as SROS2. Through this framework, multi-robot systems can be managed efficiently, securely, and adaptably, ensuring controlled access to resources and managing conflicts. Our experimental evaluation shows that our framework marginally improves latency and throughput over exiting Fabric and ROS 2 integration solutions. At higher network load, it is the only solution to operate reliably without a diverging transaction commitment latency. We also demonstrate how conflicts arising from simultaneous control or a robot by two users are resolved in real-time and motion distortion is effectively eliminated.

The industrial edge generates an abundance of spatially distributed and dynamic data that needs to remain on-site for privacy and security reasons. Collaborative training at the edge can leverage this data to refine pre-trained models locally for specific industrial tasks and environments and have them adapt to local changes for enhanced performance, agility, and resilience. However, communication between the devices during training is a key bottleneck and is not modelled by existing frameworks such as MxNet, PyTorch and TensorFlow. This paper introduces DAI-NET, a co-simulation framework for examining communication and its associated costs, and provides results from an implementation using Python, OMNET++ and INET. To validate it and showcase its utility, the developed platform is applied in the analysis of (i) the performance and cost of collaboratively training a Multilayer Perceptron model, and (ii) the influence of computational heterogeneity. Communication costs generated during the training are captured at the device and system levels. In computationally heterogeneous clusters , the root cause of stragglers is exposed. In addition, the key performance contributors are identified to be a cluster’s computation capability and the variation in the relative computation capabilities of its devices. This study is particularly useful for Artificial Intelligence of Things (AIoT) systems, whose bandwidth and energy resources are limited. It lends the way for more practical research on communication-efficient algorithms, network protocols and architectures for the AIoT edge.

In smart healthcare facilities designed especially for the elderly, noncontact electrocardiogram (ECG) measurements could provide essential information about an elderly person’s health by enabling long-term health analytics. In this research work, we propose an Internet of Things (IoT)-based noncontact ECG measurement system. The noncontact measurement is done using flexible electrodes that are made of fabric. These fabric-based flexible electrodes are designed to measure ECG signals from the sole of the feet (SOF) or the palms of the hands (POHs) without touching human skin. To mitigate the impact of nearby electromagnetic radiation on the electrodes, a double layer of isopotential shielding is placed underneath the two active electrodes. The gathered biosignals are stored in the IoT device and transmitted to the cloud. To reduce the amount of stored and transmitted data, we improved our adaptive coding algorithm. The adaptive coding results in an average data reduction of 72%. The data can be fully recovered in the cloud for further analyses using advanced cloud-based tools in ThingSpeak. The study tested the proposed system on 35 participants, including elderly persons, adults, and children. Based on the experiments, the proposed system accurately measures the ECG signal. We validated the results with the ground truth data [polysomnography (PSG)] showing an average heart rate (HR) error of$\mp 1$beat per minute (BPM). Moreover, we compared QRS complexes detected on wrists with those detected from SOF (with or without socks), POH (with or without gloves), and one hand and one foot (with or without a sock and glove), and found no significant differences.

With the increasing ubiquity of autonomous robotic solutions, the interest in their connectivity and in the cooperation within multi-robot systems is rising. Two aspects that are a matter of current research are robot security and secure multirobot collaboration robust to byzantine agents. Blockchain and other distributed ledger technologies (DLTs) have been proposed to address the challenges in both domains. Nonetheless, some key challenges include scalability and deployment within realworld networks. This paper presents an approach to integrating IOTA and ROS 2 for more scalable DLT-based robotic systems while allowing for network partition tolerance after deployment. This is, to the best of our knowledge, the first implementation of IOTA smart contracts for robotic systems, and the first integrated design with ROS 2. This is in comparison to the vast majority of the literature which relies on Ethereum. We present a general IOTA+ROS 2 architecture leading to partitiontolerant decision-making processes that also inherit byzantine tolerance properties from the embedded blockchain structures. We demonstrate the effectiveness of the proposed framework for a cooperative mapping application in a system with intermittent network connectivity. We show both superior performance with respect to Ethereum in the presence of network partitions, and a low impact in terms of computational resource utilization. These results open the path for wider integration of blockchain solutions in distributed robotic systems with less stringent connectivity and computational requirements.

Lidar technology has evolved significantly over the last decade, with higher resolution, better accuracy, and lower cost devices available today. In addition, new scanning modalities and novel sensor technologies have emerged in recent years. Public datasets have enabled benchmarking of algorithms and have set standards for the cutting edge technology. However, existing datasets are not representative of the technological landscape, with only a reduced number of lidars available. This inherently limits the development and comparison of general-purpose algorithms in the evolving landscape. This paper presents a novel multi-modal lidar dataset with sensors showcasing different scanning modalities (spinning and solid-state), sensing technologies, and lidar cameras. The focus of the dataset is on low-drift odometry, with ground truth data available in both indoors and outdoors environment with sub-millimeter accuracy from a motion capture (MOCAP) system. For comparison in longer distances, we also include data recorded in larger spaces indoors and outdoors. The dataset contains point cloud data from spinning lidars and solid-state lidars. Also, it provides range images from high resolution spinning lidars, RGB and depth images from a lidar camera, and inertial data from built-in IMUs. This is, to the best of our knowledge, the lidar dataset with the most variety of sensors and environments where ground truth data is available. This dataset can be widely used in multiple research areas, such as 3D LiDAR simultaneous localization and mapping (SLAM), performance comparison between multi-modal lidars, appearance recognition and loop closure detection. The datasets are available at: https://github.com/TIERS/tiers-lidars-dataset.

In remote monitoring edge-based IoT applications, high latency caused by the mobility of a sensor device can cause serious consequences such as inaccurate analysis and low quality of services. Therefore, it is required to have mobility support approaches that help reduce latency while maintaining a connection, high quality of service, and energy efficiency. However, the number of mobility support approaches for high data rate IoT applications using Bluetooth Low Energy (BLE) is limited and they have some disadvantages. For example. they have not been designed for edge-based applications where local computation occurs frequently. Many of them have not been implemented and tested in daily working environments with actual mobility cases. They have not comprehensively analyzed the mobility latency and energy consumption of sensor devices. Hence, this paper presents three possible mobility support approaches including passive and active handover mechanisms for edge-based IoT applications using high data rate BLE5. These approaches based on passive and active handover mechanisms are implemented and tested in an office environment. The results of latency and power consumption of a sensor device via many experiments are measured and analyzed. The results show that the presented mobility support approaches maintain the connection during mobility with a latency of around 900ms for many cases. The results also show that using BLE5’s LE 2M physical layer consumes less power than using LE 1M physical layer. Specifically, it can reduce energy consumption when sending or receiving larger data sizes at faster rates.

As autonomous robots are becoming more widespread, more attention is being paid to the security of robotic operations. Autonomous robots can be seen as cyber–physical systems: they can operate in virtual, physical, and human realms. Therefore, securing the operations of autonomous robots requires not only securing their data (e.g., sensor inputs and mission instructions) but securing their interactions with their environment. There is currently a deficiency of methods that would allow robots to securely ensure their sensors and actuators are operating correctly without external feedback. This article introduces an encoding method and end-to-end validation framework for the missions of autonomous robots. In particular, we present a proof of concept of a map encoding method, which allows robots to navigate realistic environments and validate operational instructions with almost zeroa prioriknowledge. We demonstrate our framework using two different encoded maps in experiments with simulated and real robots. Our encoded maps have the same advantages as typical landmark-based navigation, but with the added benefit of cryptographic hashes that enable end-to-end information validation. Our method is applicable to any aspect of the robotic operation in which there is a predefined set of actions or instructions given to the robot.

More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of situational awareness are required. The options in this direction are limited in existing robotic swarms, mostly homogeneous robots with limited operational and reconfiguration flexibility. We address this by bringing elastic computing techniques and dynamic resource management from the edge-cloud computing domain to the swarm robotics domain. This enables the dynamic provisioning of collective capabilities in the swarm for different applications. Therefore, we transform a swarm into a distributed sensing and computing platform capable of complex data processing tasks, which can then be offered as a service. In particular, we discuss how this can be applied to adaptive resource management in a heterogeneous swarm of drones, and how we are implementing the dynamic deployment of distributed data processing algorithms. With an elastic drone swarm built on reconfigurable hardware and containerized services, it will be possible to raise the self-awareness, degree of intelligence, and level of autonomy of heterogeneous swarms of robots. We describe novel directions for collaborative perception, and new ways of interacting with a robotic swarm.

With the constant improvement of electronics and development by research community, professionals and enthusiasts around the world, Internet of Things (IoT) based devices have seen a massive increase. These devices are now connected to our daily life in multiple ways and facilitate smooth operation of large, autonomous and semi-autonomous systems in different sectors. The communication among these systems needs to be done in a secure manner. However, as most of the IoT devices have very limited processing capability and energy source, all cryptography algorithms are not able to run on all devices. In addition, depending on the required data performance, it can be desirable to use one specific type of algorithm over others. In this paper, we analyze popularly used lightweight algorithms in terms of operational latency by running them on multiple widely used embedded modules. In addition, we measure power consumption while running an algorithm to realize its impact on battery life as an example. Finally, we discuss design-time considerations to help designers to select an appropriate cryptography algorithm for different applications.

Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion capture systems are typically utilized indoors when accurate localization is needed. However, these systems are expensive and most require a fixed setup. In this paper, we study and characterize an ultra-wideband (UWB) system for navigation and localization of aerial robots indoors based on Decawave's DWM1001 UWB node. The system is portable, inexpensive and can be battery powered in its totality. We show the viability of this system for autonomous flight of UAVs, and provide open-source methods and data that enable its widespread application even with movable anchor systems. We characterize the accuracy based on the position of the UAV with respect to the anchors, its altitude and speed, and the distribution of the anchors in space. Finally, we analyze the accuracy of the self-calibration of the anchors' positions.

Internet of Things (IoT) is a fast developing vision in which physical quantities are digitized, processed and analyzed. Internet of Mobile Things (IoMT) as one of new domains of IoT, due to mobility, requires a more demanding and rigorous solution in many aspects, especially in terms of energy efficiency. We propose a solution consisting of energy efficient and fast hardware platform for building IoMT Fog layer facilities. Experimental results are presented to prove superiority of the proposed hardware in several aspects to popular general purpose platforms.

This paper presents the design of an autonomous smart patient/home health monitoring system. Both patient physiological parameters as well as room conditions are being monitored continuously to insure patient safety. The sensors are connected on an IoT regime, where the collected data is wirelessly transferred to a nearby gateway which performs preliminary data analysis, commonly referred to as fog computing, to make sure emergency personnel and healthcare providers are notified in case patient being monitored is at risk. To achieve power autonomy three energy harvesting sources are proposed, namely, solar, RF and thermal. The design of RF energy harvesting system is demonstrated, where novel multiband antenna is fabricated as well as an efficient RF- DC rectifier achieving maximum efficiency of 84%. Finally, the sensor node is tested with different type of sensors and settings while being solely powered by a Photovoltaic (PV) solar cell.

A better lifestyle starts with a healthy heart. Unfortunately, millions of people around the world are either directly affected by heart diseases such as coronary artery disease and heart muscle disease (Cardiomyopathy), or are indirectly having heart-related problems like heart attack and/or heart rate irregularity. Monitoring and analyzing these heart conditions in some cases could save a life if proper actions are taken accordingly. A widely used method to monitor these heart conditions is to use ECG or electrocardiography. However, devices used for ECG are costly, energy inefficient, bulky, and mostly limited to the ambulatory environment. With the advancement and higher affordability of Internet of Things (IoT), it is possible to establish better health-care by providing real-time monitoring and analysis of ECG. In this paper, we present a low-cost health monitoring system that provides continuous remote monitoring of ECG together with automatic analysis and notification. The system consists of energy-efficient sensor nodes and a fog layer altogether taking advantage of IoT. The sensor nodes collect and wirelessly transmit ECG, respiration rate, and body temperature to a smart gateway which can be accessed by appropriate care-givers. In addition, the system can represent the collected data in useful ways, perform automatic decision making and provide many advanced services such as real-time notifications for immediate attention.

This study shows the derivation of a local segmented bus arbiter from an original single segment bus arbiter. The operations are performed in the formal framework of action systems and illustrated in a graphical manner using the corresponding action systems - UML profile notations. The derivation is useful both to demonstrate the capability of preserving correctness when considering an important hardware design decision and also to identify means through which this kind of decisions can be performed in a graphical environment.