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An Exchange-based AIoT Platform for Fast AI Application Development
... This platform enables the updating of AI models. In Ref. [83], Liang et al. developed an AIoT platform that facilitates the implementation of various AI models. Utilizing a micro-service architecture, each AI model runs concurrently. ...
... Data Types [76] Smart Manufacturing ✓ ✗ ✓ ✗ Common data types [78] Smart Environments ✓ ✗ ✗ ✗ Common data types [79] Smart Healthcare ✓ ✗ ✓ ✗ Common data types [80] Various IoT applications ✓ ✓ ✗ ✓ Common data types [81] Smart Agriculture ✓ ✓ ✗ ✓ Common data types and image [82] Various IoT Applications ✓ ✓ ✗ ✓ Common data types and image [83] Various IoT Applications ✓ ✓ ✓ ✗ Common data types, image and audio [84] Smart Homes and Environments [82], and Liang et al. in [83] have the potential to be used in various IoT applications. This is similar to our SEMAR platform, which has been implemented and integrated into various IoT application use cases. ...
... Data Types [76] Smart Manufacturing ✓ ✗ ✓ ✗ Common data types [78] Smart Environments ✓ ✗ ✗ ✗ Common data types [79] Smart Healthcare ✓ ✗ ✓ ✗ Common data types [80] Various IoT applications ✓ ✓ ✗ ✓ Common data types [81] Smart Agriculture ✓ ✓ ✗ ✓ Common data types and image [82] Various IoT Applications ✓ ✓ ✗ ✓ Common data types and image [83] Various IoT Applications ✓ ✓ ✓ ✗ Common data types, image and audio [84] Smart Homes and Environments [82], and Liang et al. in [83] have the potential to be used in various IoT applications. This is similar to our SEMAR platform, which has been implemented and integrated into various IoT application use cases. ...
In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.
The use of telehealth services is increasing rapidly to increase access and delivery of remote medical care. However, there are significant technical barriers to rapid and reliable deployment of these services and their ability to handle high patient volumes. This study looks at how DevOps approaches can benefit telehealth providers and let it to overcome this obstacle. To maximize software development and infrastructure management, DevOps focuses on collaboration between development and IT operations teams. Telehealth platforms can be deployed rapidly while leveraging key DevOps skills such as automation, infrastructure-to-code, monitoring, and continuous integration Studies show that these techniques also improve scalability to meet demand changes. This makes it possible for telehealth providers to deliver digitally enabled care to a larger patient base.
Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to 3D space. An interesting question is: Can we conduct person identification (PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/iris/face recognition have their limitations or require close contact to specific devices. Hence, these traditional technologies can not be easily deployed to drones due to dynamic change of view angle and height. In this work, we demonstrate how to retrieve IoT data from users’ wearables and correctly tag them on the human objects captured by a drone camera to identify and track ground human objects. First, we retrieve human objects from videos and conduct coordination transformation to handle the change of drone positions. Second, a fusion algorithm is applied to measure the correlation of video data and inertial data based on the extracted human motion features. Finally, we can couple human objects with their wearable IoT devices, achieving our goal of tagging wearable device data (such as personal profiles) on human objects in a drone view. Our experimental evaluation shows a recognition rate of 99.5% for varying walking paths, and 98.6% when the drone’s camera angle is within 37°. To the best of our knowledge, this is the first work integrating videos from drone cameras and IoT data from inertial sensors.
The communication protocols are an essential part for the data communication of Internet of Things (IoT) applications. However, the selection of a communication protocol is challenging because it depends on the nature of the IoT system and its data transmission system. Copious communications protocols have been developed and employed by researchers based on their requirements in the last decade. Though, none of them is able to support all criteria requirements, like energy efficiency, security, quality of service, etc. Of all types of IoT systems, communication protocols are an ongoing dilemma for the IoT industry; consequently, it is important to analyze the comportments and mechanisms of this latter to determine their best-fit scenarios. Therefore, this paper presents an evaluation of established communication protocols HTTP, MQTT, DDS, XMPP, AMQP and CoAP for IoT applications. Firstly, it presents the broad comparison among these communication protocols to introduce their characteristics comparatively. Subsequently, it performs a detailed and in-depth analysis of the related process of gaining an understanding of their strengths and limitations. Therefore, based on this detailed evaluation, the user can determine their appropriate use for various IoT applications depending on their needs, efficiency, and suitability.
Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.
As the number of Internet of Things (IoT) devices proliferates, the magnitude and velocity of data continues to increase rapidly. IoT systems rely primarily on using messaging protocols for exchanging IoT data and there exists several protocols or frameworks that support distinct types of messaging patterns. Given that IoT devices typically have limited computational resources and processing power, choosing a lightweight, reliable, scalable, interoperable, extensible and secure messaging protocol becomes a very challenging task. As a result, it is not uncommon that IoT systems may employ multiple messaging protocols for supporting device heterogeneity and different message exchange patterns. In addition, basic similarities among existing several messaging protocols or frameworks that exist today for exchanging IoT data within IoT systems suggest the potential of interoperability. Given that IoT systems help facilitate the interconnectivity among distributed, heterogeneous entities, interoperability among existing messaging protocols will play an increasingly important role in simplifying the development and deployment of IoT systems. In this paper, we present a comprehensive review of the existing messaging protocols that can be used in deploying IoT systems. Throughout this paper, we highlight the protocols’ distinctive approaches and applicability of using them across various IoT environments. In addition, we highlight challenges, strengths and weaknesses of these messaging protocols in the context of IoT.
Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.
Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.
Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and difficulties of generalization. In this work, we propose a novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN}, which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. This formulation not only leads to greater expressive power but also stronger generalization capability. On two large datasets, Kinetics and NTU-RGBD, it achieves substantial improvements over mainstream methods.
This article makes a literature review of applications developed in the health industry which are focused on patient care from home and implement a service-oriented (SOA) design in architecture. Throughout this work, the applicability of the concept of Internet of Things (IoT) in the field of telemedicine and health care in general is evaluated. It also performs an introduction to the concept of SOA and its main features, making a small emphasis on safety aspects. As a central theme, the description of different solutions that can be found in the health industry is developed, especially those whose goal is health care at home; the main component of these solutions are body sensor networks. Finally, an analysis of the literature from the perspectives of functionalities, security implementation and semantic interoperability is made to have a better understanding of what has been done and which are probable research paths to be studied in the future.
The Internet of Things (IoT) is being adopted in different application domains and is recognized as one of the key enablers of the Smart City vision. Despite the standardization efforts and wide adoption of Web standards and cloud computing technologies, however, building large-scale Smart City IoT platforms in practice remains challenging. The dynamically changing IoT environment requires these systems to be able to scale and evolve over time adopting new technologies and requirements. In response to the similar challenges in building large-scale distributed applications and platforms on the Web, microservice architecture style has emerged and gained a lot of popularity in the industry in recent years. In this work, we share our early experience of applying the microservice architecture style to design a Smart City IoT platform. Our experience suggests significant benefits provided by this architectural style compared to the more generic Service-Oriented Architecture (SOA) approaches, as well as highlights some of the challenges it introduces.
Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.
Since falls are a major public health problem in an ageing society, there is considerable demand for low-cost fall detection systems. One of the main reasons for non-acceptance of the currently available solutions by seniors is that the fall detectors using only inertial sensors generate too much false alarms. This means that some daily activities are erroneously signaled as fall, which in turn leads to frustration of the users. In this paper we present how to design and implement a low-cost system for reliable fall detection with very low false alarm ratio. The detection of the fall is done on the basis of accelerometric data and depth maps. A tri-axial accelerometer is used to indicate the potential fall as well as to indicate whether the person is in motion. If the measured acceleration is higher than an assumed threshold value, the algorithm extracts the person, calculates the features and then executes the SVM-based classifier to authenticate the fall alarm. It is a 365/7/24 embedded system permitting unobtrusive fall detection as well as preserving privacy of the user.
Although positioning in both indoor and outdoor environments has been investigated in-depth individually by various means, little attention has been paid to the intersected areas of both environments and the positioning accuracy in these boundary areas remains concern. For indoor positioning, several recent studies have applied machine learning algorithms based on WiFi signals. However, these methods may not be applicable to solve the boundary localization problem which is faced for positioning in boundary areas around a building. In this paper, we propose a deep learning model which concatenates auto-encoders with LSTM networks to perform multi-sensor multi-task fingerprint positioning. The adopted sensors include WiFi, GPS, cellular (fused with GPS), and magnetometer. In our model, we first employ a dense block-based auto-encoder to extract representative latent codes of fingerprints. Such latent codes are proven to be more distinguishable since they are revertible to their original inputs. Then, a sequence of latent codes are injected into an LSTM network performing three output tasks, responsible for location estimation, most suitable sensor selection, and indoor/outdoor classification, respectively. Extensive real-life experiments are performed on two campuses and the results demonstrate that our model achieves high positioning accuracy with an average Euclidean distance estimation error of about 0.43 meter.
In the Internet of Things (IoT) era, billions of sensors and devices collect and process data from the environment, transmit them to cloud centers, and receive feedback via the internet for connectivity and perception. However, transmitting massive amounts of heterogeneous data, perceiving complex environments from these data, and then making smart decisions in a timely manner are difficult. Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing. AI introduced into the IoT heralds the era of artificial intelligence of things (AIoT). This paper presents a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, we briefly present the AIoT architecture in the context of cloud computing, fog computing, and edge computing. Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Next, we summarize some promising applications of AIoT that are likely to profoundly reshape our world. Finally, we highlight the challenges facing AIoT and some potential research opportunities.
The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.
With the advent of Industry 4.0, localization of materials and factory items will play important roles in factory automation. Since GPS signals are not available in indoor environments, a lot of indoor localization technologies have been proposed based on inertial sensors, audio signals, visible light, wireless signals, etc. In this research, we consider using magnetic fields, which usually exhibit high uniqueness at different locations especially in factories where a lot of stacks, machineries, materials, and metal partitions may coexist. These factors allow us to incorporate deep learning neural networks to learn locationrelated features. Existing works try to collect magnetic field data by human and leverage interpolation to augment dataset. However, our experiments show that the data generated by interpolation is usually different from the ground truth because magnetic fields may not be linear. Therefore, to collect a large enough dataset without human intervention, we dispatch a robot carrying a smartphone to collect dataset at a fine resolution. We use these collected data to train two localization models: deep neural network (DNN) and recurrent neural network (RNN). Besides, we augment our RNN training dataset by combining multiple single-point magnetic values to synthesize fake magnetic trajectories. We conduct field trials, which validate that our approach outperforms previous work.
With the rapid development of Internet of Things (IoT) devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In the paper, in order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model, in order to find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network (CNN) model into the fog computing environment, which significantly improves its computing efficiency. The other one is we work out an inspection model which can simultaneously indicate the defect type and its degree. The experimental results demonstrate the robustness and efficiency of the proposed method.
We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to serving. At the same time, the core code paths around model lookup and inference have been carefully optimized to avoid performance pitfalls observed in naive implementations. Google uses it in many production deployments, including a multi-tenant model hosting service called TFS.
Machine-to-machine communications are gaining tremendous interest from mobile network operators, equipment vendors, device manufacturers, as well as research and standardization bodies. Indeed, M2M is a promising technology for the development of Internet of Things communications platforms, with high potential to enable a wide range of applications in different domains. However, providing suitable answers to the issues stemming from IoT platform design requires middleware-level solutions to enable seamless interoperability between M2M-based applications and existing Internetbased services. To the best of our knowledge, available proposals in the field are still immature and tend to be proof of concept prototypes that address specific issues stemming from IoT domains. This article starts from a different perspective and aims at investigating the possibility of implementing M2M solutions on top of currently available, mature, production-level solutions. In this vein, we here present and discuss the design and implementation of an M2M application in the field of road traffic management that integrates, for the sake of efficiency, with a broad IMS-based service infrastructure.
The advanced message queuing protocol (AMQP) working group's goal is to create an open standard for an interoperable enterprise-scale asynchronous messaging protocol. AMQP is finally addressing the lack of enterprise messaging interoperability standards. This relatively simple yet compellingly powerful enterprise messaging protocol is thus poised to open up a bright new era for enterprise messaging
Michele Ciavotta, Marino Alge, Silvia Menato, Diego Rovere, and Paolo Pedrazzoli. 2017. A Microservice-Based Middleware for the Digital Factory
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- Marino Alge
- Silvia Menato
- Diego Rovere
- Paolo Pedrazzoli
- Ciavotta Michele
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
- Jiefeng Hao-Shu Fang
- Hongyang Li
- Chao Tang
- Haoyi Xu
- Yuliang Zhu
- Yong-Lu Xiu
- Cewu Li
- Lu
- Fang Hao-Shu
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, and Matteo Interlandi
- Yunseong Lee
- Alberto Scolari
- Byung-Gon Chun
- Marco Domenico Santambrogio
- Markus Weimer
- Matteo Interlandi
- Lee Yunseong
Automatic lighting control system and method
- Yen-Ann Meng-Shiuan Pan
- Ting-Chou Chen
- Yueh-Feng Chien
- Yu- Chee Lee
- Tseng
- Pan Meng-Shiuan
OpenTwins: An Open- Source Framework for the Design, Development and Integration of Effective 3D-IoT-AI-powered Digital Twins
- Julia Robles
- Cristian Martín
- Manuel Díaz
- Robles Julia