Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) gives a strong structure for connecting things to the internet to facilitate Machine to Machine (M2M) communication and data transmission through basic network protocols such as TCP/IP. IoT is growing at a fast pace, and billions of devices are now associated, with the amount expected to reach trillions in the coming years. Many fields, including the army, farming, manufacturing, healthcare, robotics, and biotechnology, are adopting IoT for advanced solutions as technology advances. This paper offers a detailed view of the current IoT paradigm, specifically proposed for robots, namely the Internet of Robotic Things (IoRT). IoRT is a collection of various developments such as Cloud Computing, Artificial Intelligence (AI), Machine Learning, and the (IoT). This paper also goes over architecture, which would be essential in the design of Multi-Role Robotic Systems for IoRT. Furthermore, includes systems underlying IoRT, as well as IoRT implementations. The paper provides the foundation for researchers to imagine the idea of IoRT and to look beyond the frame while designing and implementing IoRT-based robotic systems in real-world implementations.

Cloud computing, robotics, the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) are some of the technologies that make up the Internet of Robotic Things (IoRT). Transportation, industry, healthcare, and security all heavily rely on IoRT. IoRT has the potential to significantly accelerate human growth. Robots can send and receive data to and from other people and devices thanks to IoRT. This study reviews IoRT in terms of comparable architectures, methodologies, and capabilities. The associated research difficulties are so outlined. When creating robotic systems and objects, IoRT architectures are crucial. Human-robot cooperation, or HRC, is quickly emerging as a crucial element of smart manufacturing, healthcare, and service sectors due to the rapid integration of Internet of Things technology. However, real-world implementation of such systems poses challenging scheduling challenges due to the dynamic, diverse, and resource-constrained nature of IoT contexts. This paper presents a novel scheduling problem for real-world human–robot collaboration in Internet of Things applications, which aims to balance communication disruptions, computation demands, and safety-critical interactions. In contrast to traditional scheduling methods, which primarily deal with deterministic scenarios, the proposed perspective emphasizes flexibility in response to erratic human behavior, changing network conditions, and diverse device capabilities. The challenge is increased by the need to guarantee energy efficiency, real-time responsiveness, and secure data exchange across scattered IoT nodes. This scheduling problem needs to be fixed to improve system productivity and human safety, reduce job execution latency, and accomplish seamless coordination. This work lays the foundation for exploring new scheduling models that bridge the gap between theoretical frameworks and the practical requirements of IoT-enabled human–robot collaboration.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

Federated Learning (FL) is a paradigm in distributed machine learning, which has gained significant attention in the recent years especially in the domain of Internet of Things. Federated Learning saves communication bandwidth when compared to centralized machine learning process and is also considered to be privacy preserving as the raw data at the clients need not be transmitted to the server for the FL learning. Recent studies on FL have exposed privacy vulnerabilities particularly in the form of Gradient Leakage Attacks (GLA) in which the adversaries can generate training data of the client from shared gradients or model updates from clients. The available solutions in the literature for GLA based on perturbing the gradients from clients leads to a drop in the performance of the FL system while attempting to preserve privacy. We propose HAFedL, an improved novel hessian aware adaptive privacy preserving FL scheme in which the performance of the model is not significantly affected due to the aspects introduced in the FL architecture to improve the privacy of the model against the GLA. The HAFedL is also robust to the data heterogeneity and device heterogeneity (particularly straggler effect) which may be present in the clients participating in the FL. The performance of HAFedL is tested for two applications- IoT device identification and digit classification. The proposed HAFedL scheme can be utilized in privacy sensitive domains such as smart city applications, Industrial Internet of Things, Internet of Robotic Things, Internet of Medical Things, etc.

By the 4th Industrial Revolution, Internet of Things and automated frameworks (robotisation) are intently coordinating, reshaping their relations, and figuring out how to grow new-age gadgets. Such troublesome innovation relates to the foundation of the supposed manufacturing with the concept of 4th Industrial Revolution. The combination of automated specialists and Internet of Things prompts the idea of the Internet of Robotic Things (IoRT), in which development in computerized frameworks are attracting additional opportunities both modern and research fields, covering a few areas, for example, fabricating, horticulture, well-being, reconnaissance, furthermore, instruction, to name a few. In this original copy, the best in class of IoRT claims to be delineated, intending to stamp their effect on a few examination fields and concentrating on the principle open difficulties of the joining of mechanical innovations into savvy spaces. IoRT advances and applications are likewise underlined their impact on regular day-to-day existence, inciting the requirement for more investigation into far off and computerized uses.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

With the rapid increase of interest in independent living along with the continual growth of the global aging population, there is a significant need for developing technological solutions that are trustworthy, functional, and scalable. Towards this vision, we developed a novel framework that provides an organic, evolving, and immersive assistive interaction between human, robot, and their environment. Our contribution is in the system design and the computing aspect of the proposed solution. At the system level, we are utilizing the concept of the Internet of Robotic Things (IoRT) to integrate smart personal devices and smart space automation technologies with social companion robot(s) and mixed/augmented reality smart glasses. At the computing level, we propose utilizing advancements in the field of Artificial Intelligence (AI) and Machine Learning (ML) towards providing task assistance in daily activities, smart space control, and improving emotion and mood through Affective Computing. We provide details of our IoRT system integration and quantitative experimental results on Facial Emotion Recognition (FER) which is one of the core components for Affective Computing. We improved FER utilizing Deep Learning on the edge and then deployed it on our Social Companion Robot (SCR) called Miro. We integrated the SCR with a Mixed Reality smart glass (Microsoft Hololens 2) and smart home automaton hub (Google). While the smart automation controller is directly in charge of controlling the environment (e.g., lighting, temperature), the robot can increase the control level with a more context-aware decision providing a socially embodied control.KeywordsHuman-robot-interactionInternet of Robotic Things (IoRT)Social companion robotsSmart home automationSocial embodimentAffective computingMixed/augmented realityIndependent living

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The method of data lossless compression with preliminary sorting in real time is considered in the paper. The compression method is based on the analysis of the frequency distribution of the incoming data stream, the selection of the constant by sorting and, on the basis of this, subsequent compression. The combination of sorting and data compression proposed in the article allows saving processing time and dynamically manage the network controller load. The hardware algorithm implementation on FPGA for sorting and compressing data during stream processing of information is considered. The solution makes it possible to implement an algorithm in the form of an IP core, with the ability to adapt it to the characteristics of solving its task of compressing data, thereby increasing system performance. This algorithm can be used to create embedded applications with limited computing resources and time-critical requirements. The device, based on the method considered, showed stable operation in the task of processing data from a multichannel system of high-speed sensors. This algorithm can be applied in solving problems of creation the telecommunications networks of distributed control systems, data processing subsystems Internet of Things and Internet of Robotic Things.

The integration of communication and radar systems could enhance the robustness of future communication systems to support advanced application demands, e.g., target sensing, data exchange, and parallel computation. In this article, we investigate the beamforming design for integrated sensing, computing, and communication (ISCC) in the Internet of Robotic Things (IoRT) scenario. Specifically, we assume that each robot uploads its preprocessed sensing information to the access point (AP). Meanwhile, leveraging the additive features of the spatial wireless channels between robots and AP, over-the-air computation (AirComp) through multirobot cooperation could bolster system performance, particularly in tasks like target localization through sensing. To get a full picture of the effects of antenna array structures and beampatterns on the ISCC system, we evaluate the performance by considering the shared and separated antenna structures, as well as the omnidirectional and directional beampatterns. Based on these setups, the nonconvex optimization problems for the performance tradeoff between sensing and AirComp are formulated to minimize the mean-squared error (MSE) of AirComp and sensing. To efficiently solve these optimization problems, we designed the gradient descent augmented Lagrangian (GDAL) algorithm, which involves dynamically adjusting the step sizes while updating the variables. Simulation results show that the separated antenna structure achieves a lower AirComp MSE than the shared antenna setup because it has greater beam steering Degrees of Freedom. Moreover, the beampattern types have almost no effect on the AirComp MSE for the given antenna structure setup. This comprehensive investigation provides useful guidelines for ISCC framework implementation in IoRT applications.

Communications among user equipment (UE) play a pivotal role in the coordination and information sharing in order to accomplish the predefined collaborative tasks of UE via the internet of robotic things (IoRT). Cloud radio access network (C-RAN) emerges as one of the most compelling architectures to ensure the UE demands. However, to optimize the power usage by fulfilling UE demand over a long operational period, the radio resource allocation (RRA) in C-RAN requires to be more visionary. To solve this challenge, we propose a deep reinforcement learning (DRL) based algorithm consisting of two different value-based networks. One network generates the target value for the second network for the purpose of better convergence. Under the same UE demands, simulation results verify that the proposed DRL algorithm outperforms the Deep Q Network (DQN) and conventional approaches in terms of power consumption.

PurposeThe Internet of Things (IoT) provides exciting opportunities for the construction industry to solve its time and resource constraints and frequent defaults. This study seeks to identify and rank the perceived importance level of principal research areas associated with the IoT and the construction industry by utilising a scientific mapping tool (i.e. VOSviewer). Such knowledge would enable key drivers for successful adoption of the IoT and digitisation technologies to be outlined. An analysis of key drivers and research trends that facilitates the development of a roadmap for applying the IoT and digital technologies in the construction sector is therefore much needed.Design/methodology/approachAn interpretivist philosophical lens was adopted to analyse published work as secondary data, where each publication represented a unit of analysis. A total of 417 peer-reviewed journal review articles covering the IoT within the construction domain were systematically reviewed using a mixed-methods approach, utilising qualitative-scientometric analyses techniques.FindingsThe results revealed a field of study in a fledgling stage, with a limited number of experts operating somewhat in isolation and offering single-point solutions instead of taking an integrated “holistic” approach. Key publication outlets were identified and the main focus of research undertaken being in the technical areas of smart buildings, smart construction objects and environmental sustainability. The major effects of adopting the IoT within the construction industry were identified as high-speed reporting, complete process control, data explosion leading to deep data analytics, strict ethical and legal expectations. Key drivers of the IoT adoption were outlined: interoperability; data privacy and security; flexible governance structures; proper business planning and models.Practical implicationsThe study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of the IoT in the industry. For government agencies and policymakers, this study offers a point of reference in directing the adoption of the IoT smoothly in the construction sector and provides guidelines and standards for maximising the potential benefits.Originality/valueThe study is the first scientometric review of the existing body of knowledge in the context of application of the IoT in the construction industry. Findings expose knowledge gaps in contemporary research, specifically, a broader consideration of organisational adjustments needed to accommodate the IoT usage, economic analyses and impediments to wider acceptance.

Cloud robotics and advanced communications can foster a step-change in cooperative robots and hybrid wireless sensor networks (H-WSN) for demanding environments (e.g., disaster response, mining, demolition, and nuclear sites) by enabling the timely sharing of data and computational resources between robot and human teams. However, the operational complexity of such multi-agent systems requires defining effective architectures, coping with implementation details, and testing in realistic deployments. This article proposes X-IoCA, an Internet of robotic things (IoRT) and communication architecture consisting of a hybrid and heterogeneous network of wireless transceivers (H2WTN), based on LoRa and BLE technologies, and a robot operating system (ROS) network. The IoRT is connected to a feedback information system (FIS) distributed among multi-access edge computing (MEC) centers. Furthermore, we present SAR-IoCA, an implementation of the architecture for search and rescue (SAR) integrated into a 5G network. The FIS for this application consists of an SAR-FIS (including a path planner for UGVs considering risks detected by a LoRa H-WSN) and an ROS-FIS (for real-time monitoring and processing of information published throughout the ROS network). Moreover, we discuss lessons learned from using SAR-IoCA in a realistic exercise where three UGVs, a UAV, and responders collaborated to rescue victims from a tunnel accessible through rough terrain.

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.

Robotic systems increasingly operate in complex and distributed environments, where software deployment and orchestration pose major challenges. This paper presents a model-driven approach that automates the containerized deployment of robotic systems in Internet of Robotic Things (IoRT) environments. Our solution integrates Model-Driven Engineering (MDE) with containerization technologies to improve scalability, reproducibility, and maintainability. A dedicated metamodel introduces high-level abstractions for describing deployment architectures, repositories, and container configurations. A web-based tool enables collaborative model editing, while an external deployment automator generates validated Docker and Compose artifacts to support seamless multi-host orchestration. We validated the approach through real-world experiments, which show that the method effectively automates deployment workflows, ensures consistency across development and production environments, and significantly reduces configuration effort. These results demonstrate that model-driven automation can bridge the gap between Software Engineering (SE) and robotics, enabling Software-Defined Robotics (SDR) and supporting scalable IoRT applications.