IoT Hub Research Articles

Replacing IOT Hub with Multiple Block chain for IOT Data Aggregation and Predicting Land Sliding Through Machine Learning Technique

The IoT devices in remote areas are very much vulnerable. The data collecting hub is not secured for recent attacks. The data transferred is attacked before reaching the server. In the existing system, the blockchain is used for data aggregation. The data is secured by this technique but the cost of performing this is very high. In the proposed system the data aggregation is done by using multiple block chains which increase performance as well as the security provided. After receiving the data, the land sliding is predicted with heuristics. The device energy will be consumed very low when compared to the existing system. Security will be high by using multiple chains. The overload of mining and cryptography will be avoided.

Enhancing Device Provisioning and Connectivity in IoT Systems Using Azure IoT Hub and DPS: A Case Study

Enhancing Device Provisioning and Connectivity in IoT Systems Using Azure IoT Hub and DPS: A Case Study

A review of energy saving routing schemes for WSN assisted IoT network

Abstract In WSN assisted IoT networks, the devices have to search the path up to the IoT hub for data transmission. Although the device’s hardware capabilities are increasing day by day, the device’s battery capacity is not increasing at the same rate. IoT devices have limited-power batteries that are not rechargeable. It is also challenging to replace their battery while the devices are in use. Moreover, we need advanced power-saving schemes to make these IoT technologies more adaptable. Due to the energy constraints and limited memory capacity of these IoT devices, we require more energy-efficient routing protocols to route data to the IoT hub. This paper has surveyed the most recent state-of-the-art routing protocol and highlighted their limits and path for future research.

A Review on Data Securing Techniques using Internet of Medical Things

At present, the traditional healthcare system is completely replaced by the revolutionary technique, the Internet of Medical Things (IoMT). Internet of Medical Things is the IoT hub that comprises of medical devices and applications which are interconnected through online computer networks. The basic principle of IoMT is machine-to-machine communication that takes place online. The major goal of IoMT is to reduce frequent or unwanted visits to the hospitals which makes it comfortable and is also highly preferred by the older people. Another advantage of this methodology is that the interpreted or collected data is stored in cloud modules unlike amazon and Mhealth, making it accessible remotely. Although there are countless advantages in IoMT, the critical factor lies in data security or encryption. A surplus number of threat related to devices, connectivity, and cloud might occur under unforeseen or threatening circumstances which makes the person in the situation helpless. Yet, with the help of data security techniques designed especially for Internet of Medical Things, it is possible to address these challenges. In this paper, a review on data securing techniques for the internet of medical things is made along with a discussion on related concepts.

UAV-Based and WebRTC-Based Open Universal Framework to Monitor Urban and Industrial Areas.

Nowadays, we are observing a rapid development of UAV-based monitoring systems, which are faced with more and more new tasks, such as high temporal resolution and high spatial resolution of measurements, or Artificial Intelligence on board. This paper presents the open universal framework intended for fast prototyping or building a short series of specialized flying monitoring systems able to work in urban and industrial areas. The proposed framework combines mobility of UAV with IoT measurements and full-stack WebRTC communications. WebRTC offers simultaneous transmission of both a real-time video stream and the flow of data coming from sensors, and ensures a kind of protection of data flow, which leads to preserving its near-real-time character and enables contextual communication. Addition of the AI accelerator hardware makes this system AI-ready, i.e., the IoT communication hub, which is the air component of our system, is able to perform tasks of AI-supported computing. The exemplary prototype of this system was evaluated in terms of the ability to work with fast-response sensors, the ability to work with high temporal and high spatial resolutions, video information in poor visibility conditions and AI-readiness. Results show that prototypes based on the proposed framework are able to meet the challenges of monitoring systems in smart cities and industrial areas.

Blockchain-Enabled Access Management System for Edge Computing

In the post-cloud era, edge computing is a new computing paradigm with data processed at the edge of the network, which can process the data close to the end-user in real time and offload the cloud task intelligently. Meanwhile, the decentralization, tamper-proof and anonymity of blockchain technology can provide a new trusted computing environment for edge computing. However, it does raise considerable concerns of security, privacy, fault-tolerance and so on. For example, identity authentication and access control rely on third parties, heterogeneous devices and different vendors in IoT, leading to security and privacy risks, etc. How to combine the advantages of the two has become the highlight of academic research, especially the issue of secure resource management. Comprehensive security and privacy involve all aspects of platform, data, application and access control. In. this paper, the architecture and behavior of an Access Management System (AMS) in a proof of concept (PoC) prototype are proposed with a Color Petri Net (CPN) model. The two domains of blockchain and edge computing are organically connected by interfaces and interactions. The simulation of operation, activity and role association proves the feasibility and effectiveness of the AMS. The instances of platform business access control, data access control, database services, IOT hub service are run on Advantech WISE-PaaS through User Account and Authentication (UAA). Finally, fine-grained and distributed access control can be realized with the help of a blockchain attribute. Namely, smart contracts are used to register, broadcast, and revoke access authorization, as well as to create specific transactions to define access control policies.

Monitorowanie ruchu ulicznego z wykorzystaniem chmury obliczeniowej i techniki RFID

The article presents the architecture and implementation of a street traffic monitoring system. It uses RFID identifiers to recognize vehicles, including special meaning, such as ambulances, city buses, vehicles with reduced exhaust gas emissions. Traffic data is sent to the IoT Hub service in the Azure cloud. On their basis, road situations are analyzed and decisions are made regarding traffic control. Control information is fed back to traffic control devices by means of street lights, barriers, information boards. The article describes the method of communication with the computing cloud and the possibilities of implementing traffic monitoring and control algorithms using IoT Hub.

WITHDRAWN: Applying machine learning algorithm to sensor coupled IoT devices in prediction of cardiac stress – An integrated approach

An unpleasant event or thought that makes us feel angry, disappointed, frustrated, or anxious is stress. Sometimes stress can be positive, when it expressed as short bursts, like helping to cope in difficult situations or to complete a deadline. In contrast, most of the stress which forces out for a longer period may harm the health badly and causes life-threatening issues such as high blood pressure, diabetes, obesity, depression thereby ultimately it leads to cardiac arrest or stroke. Analyzing the changes in the body concerning stress and combining the output signals with the Internet of Things can be a superior option for combating both physical and mental health issues. The proposed work emphasizes on constructing an IoT system to detect the degree of stress level of an individual and the information obtained through the sensor coupled IoT can be helpful to the individual to cope up with the stresses. The sensing system senses and monitors for temperature, blood pressure, pulse, and other proposed data set of cardiac patients involved in stress. IoT Edge intelligent devices, which perceives the signals from the sensors, thereby managing and monitoring the output using MQTT protocol. The signals are then processed and analyzed using streaming analytics and real-time data processing will be generated by IoT Hub by connecting with a large set of devices. The prediction model for the stress analysis is modeled using a machine learning algorithm such as Decision Tree, K Neighbours Classifier, and Support Vector Classifier. The patient's data will be stored and secured in a data bank known as the Blob storage system and works by the SQL server. Through the machine learning model system, the new patient's data can be interacting with the existing patients in data storage by processing through Kubernetes. The comparative results determine the level of stress-induced and thereby predicting the severity of the cardiac disease. The sensor-based IoT device is a secured system for protecting, managing, and analyzing the patient's information in specialty hospitals and thereby fulfill the medical emergency needs.

Bonseyes AI Pipeline—Bringing AI to You

Next generation of embedded Information and Communication Technology (ICT) systems are interconnected and collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy and overcome functional and non-functional requirements. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge , which, ultimately, slows down the adoption of AI on applications in our daily life. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of particular tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: (i) data ingestion, (ii) model training, (iii) deployment optimization, and (iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, Low-Power Deep Neural Network (LPDNN), into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification, and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.

IoT Edge Data Retrieval System for Big Data Analytics in Smart Retail S tores

An IoT Edge system is to collect the sensor, set point and other device data with the help of micro controller and send those to IoT Hub via Edge Hub. The Building Management System (BMS) playing major role to control, communicate between various devices such as sensors, actuators with user interfaces, consumers and other technical devices. Commercial buildings like retail stores have two important systems called HVAC (Heating, Ventilating, and Air Conditioning) and refrigeration control. The proposed IoT Edge system collects the various device data via BAS and send it to IoT Hub. IoT Hub is a secure and two-way communication service system between the IoT Edge and the cloud platform where we can process telemetry data generated from sensor devices. IoT Edge helps to a computing near datasource. IoT Edge agent monitor and control all the modules running on it. With high speed internet connection users can access information and computational resources from anywhere in the world. Cloud platform can supply a range of Virtual machines with shared resources with big power and storage using inexpensive disks, which are much necessary for enterprise applications with Big Data. Disks.

Internet of Measurement Things Architecture: Proof of Concept with Scope of Accreditation.

Many industries, such as manufacturing, aviation, and power generation, employ sensitive measurement devices to be calibrated by certified experts. The diversity and sophistication of measurement devices and their calibration needs require networked and automated solutions. Internet of Measurement Things (IoMT) is an architectural framework that is based on the Industrial Internet of Things for the calibration industry. This architecture involves a layered model with a cloud-centric middle layer. In this article, the realization of this conceptual architecture is described. The applicability of the IoMT architecture in the calibration industry is shown through an editor application for Scope of Accreditation. The cloud side of the implementation is deployed to Microsoft Azure. The editor itself is created as a cloud service, and IoT Hub is used to collect data from calibration laboratories. By adapting the IoMT architecture to a commonly used cloud platform, considerable progress is achieved to encompass Metrology data and serve the majority of the stakeholders.

Secure IoT communications for smart healthcare monitoring system

Healthcare monitoring systems in hospitals and other health centers have witnessed throughout the last decade a tremendous growth and wireless healthcare monitoring devices with various technologies have become of great interest in many nations around the world. The proposed paper aims at integrating artificial intelligence technology, such as the neural networks and fuzzy system in a secure healthcare monitoring system in order to enable the system to work as a smart healthcare model that decides the priority by itself depending on the collected health parameters from the senor nodes. The proposed model consists of a trust environment that is responsible for collecting authenticated physiological data from patient's body which is then sent through GSM module to Azure IoT Hub where raw data is converted into linguistic representation, with the help of logic-based algorithm, which is trained in fuzzy-based inference system (FBIS) to get the status of the patient. The proposed system, then, provides reliable, accurate, secure and real-time patient monitoring. The following sections describe how the FBIS can be integrated with a secure healthcare monitoring system to get the states of the patient and send it to the medical advisory for the preliminary precautions.

A Study on the Self-Strengthening Smart IoT Hub Based on Strengthening Learning

A Study on the Self-Strengthening Smart IoT Hub Based on Strengthening Learning

Zero cross algorithm performance on raspberry pi machine for ECG QRS detection

The electrocardiogram (ECG) is one of the most popular instruments in the medical field. Have been developed a smart IoT system for ECG Arrhythmia classification using Android device as the IoT Hub. In order to minimize the computing load on the Android device, some parts of the signal processing are moved to the data acquisition module by using Raspberry Pi 3 as the processor. In the Raspberry machine, a QRS detection system with Zero Cross Algorithm was implemented and its performance was measured. To measure the performance of the algorithm, a timer was placed before and after the execution of the process. The result shows that the algorithm can be run in real-time use in Raspberry Pi 3, although it is a bit slow and produced a high computational load of a single core processor in the machine.

Virtual Replication of IoT Hubs in the Cloud: A Flexible Approach to Smart Object Management

In future years, the Internet of Things is expected to interconnect billions of highly heterogeneous devices, denoted as “smart objects”, enabling the development of innovative distributed applications. Smart objects are constrained sensor/actuator-equipped devices, in terms of computational power and available memory. In order to cope with the diverse physical connectivity technologies of smart objects, the Internet Protocol is foreseen as the common “language” for full interoperability and as a unifying factor for integration with the Internet. Large-scale platforms for interconnected devices are required to effectively manage resources provided by smart objects. In this work, we present a novel architecture for the management of large numbers of resources in a scalable, seamless, and secure way. The proposed architecture is based on a network element, denoted as IoT Hub, placed at the border of the constrained network, which implements the following functions: service discovery; border router; HTTP/Constrained Application Protocol (CoAP) and CoAP/CoAP proxy; cache; and resource directory. In order to protect smart objects (which cannot, because of their constrained nature, serve a large number of concurrent requests) and the IoT Hub (which serves as a gateway to the constrained network), we introduce the concept of virtual IoT Hub replica: a Cloud-based “entity” replicating all the functions of a physical IoT Hub, which external clients will query to access resources. IoT Hub replicas are constantly synchronized with the physical IoT Hub through a low-overhead protocol based on Message Queue Telemetry Transport (MQTT). An experimental evaluation, proving the feasibility and advantages of the proposed architecture, is presented.