Internet Of Things System Architecture Research Articles

Software-Defined Edge Computing (SDEC): Principle, Open IoT System Architecture, Applications, and Challenges

Edge computing is a bridge for realizing the convergence between physical space and cyber space in the Internet of Things (IoT) paradigm. Large numbers of physical objects produce a huge amount of data that needs to be efficiently processed in the edge side. This situation urgently requires novel ideas and framework in the design and management of edge computing to improve and enhance its performance. In this article, we propose an approach and principle of software-defined edge computing (SDEC) from the perspective of cyber-physical mapping, where the ultimate goal is to achieve a highly automatic and intelligent edge computing system. The SDEC can also help realize flexible management and intelligent collaboration among various edge hardware resources and services by way of software. To this end, we design an SDEC-based open IoT system architecture which decouples upper level IoT applications from the underlying physical edge resources and builds dynamically reconfigurable smart edge services. The software-definition mechanism of the SDEC platform is proposed to introduce the detailed processes that the underlying physical devices are defined in the form of software. We also describe an illustrative application case about smart factory to present the practical effectiveness of the proposed scheme. Finally, we outline several challenges which are worthy of in-depth study and research. The SDEC paradigm can share, reuse, recombine, and reconfigure edge resources and services so that the overall service capability of the edge side can be improved.

Run-time evaluation of architectures: A case study of diversification in IoT

Run-time properties of modern software system environments, such as Internet of Things (IoT), are a challenge for existing software architecture evaluation methods. Such systems are largely data-driven, characterized by their dynamism, unpredictability in operation, hyper-connectivity, and scale. Properties, such as performance, delayed delivery, and scalability, are acknowledged to pose great risk and are difficult to evaluate at design-time. Run-time evaluation could potentially be used to complement design-time evaluation, enabling significant deviations from the expected performance values to be captured. However, there are no systematic software architecture evaluation methods that intertwine and interleave design-time and run-time evaluation. This paper addresses this gap by proposing a novel run-time architecture evaluation method suited for systems that exhibit uncertainty and dynamism in their operation. Our method uses machine learning and cost-benefit analysis at run-time to continuously profile the architecture decisions made, to assess their added value. We demonstrate the applicability and effectiveness of this approach in the context of an IoT system architecture, where some architecture design decisions were diversified to meet Quality of Service (QoS) requirements. Our approach provides run-time assessment for these decisions which can inform deployment, refinement, and/or phasing-out decisions.

Introducing Hardware-Based Intelligence and Reconfigurability on Industrial IoT Edge Nodes

Editor’s note: Industrial automation is an important application area for the Internet-of-Things (IoT) technologies. In this article, Fournaris et al. discuss IoT system architectures for computing at the edge of the Internet and the use of FPGAs as edge devices.—Marilyn Wolf, Georgia Institute of Technology

On building a smarter ecosystem using the internet of intelligent things: progress and future challenges

With a growing interest in the Internet of Things (IoT), the businesses are undergoing a revolution in the way they monitor and control. In the recent past, many applications were developed using the IoT system architectures in various business verticals such as industrial, healthcare, farming, transportation, etc. However, with the development of low-complex artificial intelligence frameworks which are capable of operating on the edge devices, the IoT architectures have taken a major leap leading to the Internet of Intelligent Things (IoIT). In this paper, we discuss our focused research and applications of IoIT across the following verticals: (1) healthcare, (2) smart buildings, (3) farming, along with the recent state of the art methodologies and future challenges. Under the healthcare, we laid our main focus on the development of AI enabled computer-aided diagnosis framework, which acquires the scanning information from a wireless ultrasound transducer and automatically identifies the abnormalities present. Also, we developed a low-complex brain-controlled IoT environments framework which automatically classifies the motor imagery task performed by the user using 22 channel electroencephalography. Using IoIT, we developed a novel non-invasive technology capable of monitoring various electrical parameters without any need for cutting the wires. This developed non-invasive power monitor is capable of generating real-time alerts in the case of system malfunctions and will be a key enabler for smart buildings. Also, we developed mathematical, simulation, and experimental models for analyzing the performance of channel access mechanisms of dense traffic IoT networks.

UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network

Internet of Things (IoT) technology has been pervasively applied to environmental monitoring, due to the advantages of low cost and flexible deployment of IoT enabled systems. In many large-scale IoT systems, accurate and efficient data sampling and reconstruction is among the most critical requirements, since this can relieve the data rate of trunk link for data uploading while ensure data accuracy. To address the related challenges, we have proposed an unmanned aerial vehicle (UAV) enabled spatial data sampling scheme in this paper using denoising autoencoder (DAE) neural network. More specifically, a UAV-enabled edge-cloud collaborative IoT system architecture is first developed for data processing in large-scale IoT monitoring systems, where UAV is utilized as mobile edge computing device. Based on this system architecture, the UAV-enabled spatial data sampling scheme is further proposed, where the wireless sensor nodes of large-scale IoT systems are clustered by a newly developed bounded-size $\boldsymbol K$ -means clustering algorithm. A neural network model, i.e., DAE, is applied to each cluster for data sampling and reconstruction, by exploitation of both linear and nonlinear spatial correlation among data samples. Simulations have been conducted and the results indicate that the proposed scheme has improved data reconstruction accuracy under the sampling ratio without introducing extra complexity, as compared to the compressive sensing-based method.

An Open Internet of Things System Architecture Based on Software-Defined Device

The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

It is essential to increase the productivity of agricultural and farming processes to improve yields and cost-effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub-vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.

A system of systems architecture for the internet of things exploiting autonomous components

As the internet of things (IoT) becomes more popular, supporting systems and their components become more complex and largely heterogeneous. This paper discusses on a system of systems (SoS) architecture for IoT systems composed by autonomous components. The proposed architecture focuses on a middleware transforming sensor services to REST services, for the development of mixed-criticality applications. The middleware consisting of autonomous aggregation software running on commodity multi-core devices, such as Raspberry Pi. Self-management policies applied are discussed in the paper. The analysis of a smart building system, developed as a use case, provides solid evidence that such an architecture is realistic and can lead to highly competitive systems.

SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning.

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

Methodology for the model-driven development of service oriented IoT applications

Abstract The main problem with the current technology solutions for Internet of Things (IoT) is located at the application level, in which there is still a lack of software components, frameworks, and tools which can help end-users to easily develop IoT applications and to manage the things or objects. These requirements lead to the use of the principles of Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) for the construction of software applications, allowing the generation of models at different levels of abstraction, with the possibility of generating software implementation artifacts (code) on different platforms, as well as allow the decomposition of complex and monolithic systems into loosely coupled components. In this paper, we propose a methodology based on the MDD for solving the challenges in the IoT system developments. The methodology is composed of four phases with different levels of abstraction, viewpoint, granularity, and service-oriented. Furthermore, a set of MDD methods for the transformation of models using formal rules are proposed. We present a smart vehicle scenario in which the methodology and methods are implemented, allowing the generation of executable code as a component of a real-time system. In addition, an architecture for IoT systems composed of four layers is presented, which is based on the SOA approach. In this way, the methodology and the architecture allow the interoperability (from the perspective of models and software components) between heterogeneous devices to be guaranteed in multiple ways, establishing a bridge between the digital and physical world of the IoT domain .

Cache Freshness in Named Data Networking for the Internet of Things

The Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.

Research on Web-based Internet of Things System Architecture

Research on Web-based Internet of Things System Architecture

智慧社区火警检测与灭火系统的研究与实现

火灾自动报警系统是具有火灾预防、报警、扑救、善后处理等比较完善的监控管理体系。然而，我国的消防监控系统还不成熟，并且没有完善的体系。本文按照物联网的应用系统架构进行设计：感知层采用搭载Z-stack协议栈的CC2530主控芯片进行设计，网络层以社区内单元的嵌入式网关作为网络的基本节点，应用层主要包含火警检测和自动灭火两个模块，其中自动灭火模块由AGV小车与定位模块构成。 Fire automatic alarm system is composed of fire prevention, alarm, save and aftermath of the monitoring and management system. However, China’s fire monitoring system is not mature, and there is no perfect system. In this paper, the system is designed according to the Internet of things system architecture. The sensing layer is designed using the CC2530 master chip equipped with the Z-stack protocol stack. The network layer takes the embedded gateway of the cell in the community as the basic node of the network. The module of the application layer mainly includes fire detection and automatic fire suppression. The automatic fire extinguishing module consists of AGV car and positioning module.

An IoT-Based Online Monitoring System for Continuous Steel Casting

Monitoring solutions using the Internet of Things (IoT) techniques, can continuously gather sensory data, such as temperature and pressure, and provide abundant information for a monitoring center. Nevertheless, the heterogeneous and massive data bring significant challenges to real-time monitoring and decision making, particularly in time-sensitive industrial environments. This paper presents an online monitoring system based on an IoT system architecture which is composed of four layers: 1) sensing; 2) network; 3) service resource; and 4) application layers. It integrates various data processing techniques including protocol conversion, data filtering, and data conversion. The proposed system has been implemented and demonstrated through a real continuous steel casting production line, and integrated with the TeamCenter platform. Results indicate that the proposed solution well addresses the challenge of heterogeneous data and multiple communication protocols in real-world industrial environments.

Architecture and the Key Technologies of Medical Equipment Management System Based on the Internet of Things

With the development of the Internet of Things (IOT), its application in the field of health care has become more and more widely. Based on the introduction of the IOT system architecture, key technology and existing problems and the three layer general framework, combined with the actual characteristics of the hospital, an IOT-based medical equipment management system architecture, and application of Internet technology, sensing technology, communication technology and software information technology, analyzes the related key technologies of building in, including intelligent gateway, middleware, information management platform etc. in order to achieve intensive, digital and informatized medical equipment management mode.

Architecture and the Key Technologies of Medical Equipment Management System Based on the Internet of Things

With the development of the Internet of Things (IOT), its application in the field of health care has become more and more widely. Based on the introduction of the IOT system architecture, key technology and existing problems and the three layer general framework, combined with the actual characteristics of the hospital, an IOT-based medical equipment management system architecture, and application of Internet technology, sensing technology, communication technology and software information technology, analyzes the related key technologies of building in, including intelligent gateway, middleware, information management platform etc. in order to achieve intensive, digital and informatized medical equipment management mode.

New Security Architecture for IoT Network

We explain the notion of security architecture for Internet of Things (IoT) based on software-defined networking (SDN). In this context, the SDN-based architecture works with or without infrastructure, that we call SDN-Domain. This work describes the operation of the proposed architecture and summarizes the opportunity to achieve network security in a more efficient and flexible with SDN. An overview of existing SDN security applications were discussed and tackles its issues, presenting a new IoT system's architecture. In this paper we considered the network access control and global traffic monitoring for ad-hoc networks. Finally, we point out architectural design choices for SDN using OpenFlow and discuss their performance implications.

Cyberentity Security in the Internet of Things

A proposed Internet of Things system architecture offers a solution to the broad array of challenges researchers face in terms of general system security, network security, and application security.

Technology classification, industry, and education for Future Internet of Things

SUMMARYThe Internet of Things (IoT) is developing rapidly and becoming a hot topic around the world. On the basis of the reorganized Unit IoT and Ubiquitous IoT, two models for Future IoT are proposed in this paper. A dimension model is established to classify the complicated IoT technologies and a layer model is built for Future IoT system architecture. Then, the IoT vision and its development phases prediction are presented. Furthermore, the thought of regarding IoT as an emerging industry is explained to be inappropriate because IoT is a new stage of intelligentization and informatization development. Meanwhile, the necessity of training qualified personnel in colleges is introduced. Then, the relation between IoT and the science and technology system and IoT relevant subjects is analyzed. At the end, this paper raises the problem of setting IoT as a major in college and proposes some suggestions. Copyright © 2012 John Wiley & Sons, Ltd.