Open Internet Of Things Research Articles

Developing an IoT Identity Management System Using Blockchain

Identity (ID) management systems have evolved based on traditional data modelling and authentication protocols that are facing security, privacy, and trust challenges with the growth of Internet of Things (IoT). Research surveys reveal that blockchain technology offers special features of self-sovereign identity and cryptography that can be leveraged to address the issues of security breach and privacy leaks prevalent in existing ID management systems. Although research studies are recently exploring the suitability of blockchain based support to existing infrastructure, there is a lack of focus on IoT ecosystem in the secured ID management with data provenance of digital assets in businesses. In this paper, we propose a blockchain based ID management system for computing assets in an IoT ecosystem comprising of devices, software, users, and data operations. We design and develop a proof-of-concept prototype using a federated and distributed blockchain platform with smart contracts to support highly trusted data storage and secure authentication of IoT resources and operations within a business case scenario.

An Overview of Current Solutions for Privacy in the Internet of Things

As the Internet of Things (IoT) applications have been introduced into daily life, privacy issues have become significant concerns to users, network service providers, device producers, and related roles. This study provides a high-level introduction of current privacy-preserving solutions in IoT systems within the three phases of data collection, transmission, and storage. In these three phases, the following aspects were examined: (1). security protocols at the physical and data link layers; (2). network solutions; and (3). data storage and sharing approaches. Real-world implementations often involve more than one phase, and numerous technologies are combined to ensure privacy. Thus, an understanding of all phases and their technologies can be helpful for IoT research, design, development, and operation.

Technique for IoT malware detection based on control flow graph analysis

The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats. The rapid integration of the Internet into the IoT infrastructure in various areas of human activity, including vulnerable critical infrastructure, makes the detection of malware in the Internet of Things increasingly important. Annual reports from IoT infrastructure cybersecurity companies and antivirus software vendors show an increase in malware attacks targeting IoT infrastructure. This demonstrates the failure of modern methods for detecting malware on the Internet of things. This is why there is an urgent need for new approaches to IoT malware detection and to protect IoT devices from IoT malware attacks. The subject of the research is the malware detection process on the Internet of Things. This study aims to develop a technique for malware detection based on the control flow graph analysis. Results. This paper presents a new approach for IoT malware detection based on control flow graph analysis. Control flow graphs were built for suspicious IoT applications. The control flow graph is represented as a directed graph, which contains information about the components of the suspicious program and the transitions between them. Based on the control flow graph, metrics can be extracted that describe the structure of the program. Considering that IoT applications are small due to the simplicity and limitations of the IoT operating system environment, malware detection based on control flow graph analysis seems to be possible in the IoT environment. To analyze the behavior of the IoT application for each control flow graph, the action graph is to be built. It shows an abstract graph and a description of the program. Based on the action graph for each IoT application, a sequence is formed. This allows for defining the program’s behavior. Thus, with the aim of IoT malware detection, two malware detection models based on control flow graph metrics and the action sequences are used. Since the approach allows you to analyze both the overall structure and behavior of each application, it allows you to achieve high malware detection accuracy. The proposed approach allows the detection of unknown IoT malware, which are the modified versions of known IoT malware. As the mean of conclusion-making concerning the malware presence, the set of machine learning classifiers was employed. The experimental results demonstrated the high accuracy of IoT malware detection. Conclusions. A new technique for IoT malware detection based on control flow graph analysis has been developed. It can detect IoT malware with high efficiency.

An open Internet of Things (IoT)-based framework for feedback controlof photosynthetic activities

Active control of photosynthetic activities is important in plant physiological study. Although models of plant photosynthesis have been built at different scales, they have not been fully examined for their application in plant growth control. However, we do not have an infrastructure to support such experiments since current plant growth chambers usually use fixed control protocols. In our current paper, an open IoT-based framework is proposed. This framework allows a plant scientist or agricultural engineer, through an application programming interface (API), in a desirable programming language, (1) to gather environmental data and plant physiological responses; (2) to program and execute control algorithms based on their models, and then (3) to implement real-time commands to control environmental factors. A plant growth chamber was developed to demonstrate the concept of the proposed open framework.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

Artificial intelligence garbage sorting vehicle based on deepstream

In this paper, an intelligent garbage sorting vehicle based on the upper computer jetson nano and STM32 is designed. The upper computer jetson nano collects images of garbage objects through a usb camera, and the collected data is applied to the deep learning algorithm YOLO model to complete the garbage identification and classification function, and the radar is used to locate and avoid obstacles. Through the communication between the upper computer and the lower computer STM32, the trolley is driven to the specified position, and the garbage is sorted to the corresponding trash can by the mechanical arm. STM32 with the Internet of things function, can display the quantity and type of each garbage on the mobile app, so that the administrator can timely operation and maintenance. In this paper, the hardware and software design and joint debugging of the above system are completed from the reality.

Research on Cloud-edge Interaction Technology of Power Internet of Things Based on MQTT Protocol

Under the development trend of power industry with energy Internet as the core, with the update of information technologies such as intelligent devices, mobile communication technology and cloud computing, the application of Internet of things (IOT) in power industry has developed from the concept of specific link service to the application of large-scale market. The core of the next power Internet of things construction is to build an open IOT management platform to realize the ubiquitous access of massive devices, the deep sharing of cross-professional data, business integration, and the provision of precise user services. Based on this, an industrial alliance will be formed and a power industry ecosystem with power grid enterprises as the core will be established. At present, the application of power Internet of things in China is still in the primary stage of research and application. The research on what the IOT management platform should manage and the interaction technology between the IOT management platform and edge agents is still in the stage of exploration and pilot. This paper mainly studies the cloud-edge interaction technology, including the interaction object, process and message format. Then it formulates a set of cloud-edge format specifications to meet the requirements of the platform’s own management and business system message transmission, so as to provide reference for the cloud-edge interaction of power Internet of things.

The role of block chain technology and Internet of Things (IoT) to protect financial transactions in crypto currency market

The most well-known cryptocurrency, Bitcoin, is the one for which blockchain technology was created. A cryptocurrency is a digital means of exchange that uses encryption techniques to manage the creation of monetary units and verify the transfer of funds. A blockchain is a decentralised ledger that records all peer-to-peer transactions. Blockchain is also recognised as the protocol of trust. This security concept uses decentralisation to establish a global index of all financial transactions that occur in a distinct network and makes them absolute.Blockchain technology establishes trust and harmony in direct communication between two parties without the involvement of a third party. Participants can confirm transactions without the requirement for a central clearing authority using this technology. Third-party trusted intermediaries have a character for triggering transaction delays. There would be speedier reconciliation between transacting parties and participants if such intermediaries were not present.In addition, the Internet of Things is a new technology that allows virtual and physical items to link and communicate with one another, resulting in the creation of new digitised services. When the number of devices connected through an IoT network grows. However, this data is not encrypted; it does not provide privacy. In this regard, blockchain technology might give massive benefits because it can quickly secure the anonymity of the obtained data.Furthermore, Blockchain can assist in solving most of the security vulnerabilities and traceability concerns in financial transactions by keeping track of how devices interact. In this state, they are, and how they transact with other IoT devices. A blockchain is hack-resistant and has a low danger of being hacked to improve data security.This article aims to explain the structure and operation of Blockchain and IoT in the context of securing financial transactions in the cryptocurrency market.

The influence of risks on the outturn cost of ICT infrastructure network projects

PurposeThe internet of things (IoT) is becoming an increasingly inescapable part of society. IoT paradigm cannot function without the networking infrastructure. High-speed data networks are essential to enable the IoT future. Thus, the purpose of this study is on the identification of risks that influence the development, installation and operation of information and communication technology (ICT) infrastructure network project cost outcomes. So far, there has been little attention has been paid to risks problems in these types of IoT enabling projects.Design/methodology/approachThis research follows a quantitative analysis approach. Data for this study were collected by a survey from 209 professionals. Multiple regression analysis was used to model the relationship between risks and outturn cost of infrastructure needed to enable the operation of IoT technologies.FindingsThe main risk factors that were identified were planning and development, people and management, operations, technology and hardware.Research limitations/implicationsThis research has expanded the existing literature by documenting and clustering ICT infrastructure network project risks into themes, and has developed a scale (risk statements) for measuring such risks. Further, the research has advanced the understanding by identifying the most likely risks that will contribute to the overrun of these projects.Originality/valueThis research establishes a reliable regression method for the assessment of the risks that influence the development, installation and operation of ICT infrastructure network projects outturn cost. No other research has measured or studied the risks in this type of project.

Combining Blockchains, Smart Contracts, and Complex Sensors Management Platform for Hyper-Connected SmartCities: An IoT Data Marketplace Use Case

In this paper, we demonstrate the multiple points of innovation when combining blockchain technology with Internet of Things (IoT) and security frameworks. The deployment and use of IoT device networks in smart city environments has produced an enormous amount of data. The fact that those data are possessed by multiple sources that use independent systems for data collection, storage, and use impedes the exploitation of their value. Blockchains, as distributed ledgers, can be used for addressing the development of a universal system for data collection and distribution. Smart contracts can be used to automate all the processes of such a network, while at the same time, blockchain and the InterPlanetary File System (IPFS) protect sensitive data through anonymity and distributed storage. An innovative and open IoT blockchain market of applications, data, and services is proposed that: (i) provides the framework upon which objects and people can exchange value in form of virtual currencies, for assets (data and services) received; (ii) defines the motivation incentives according to social and business context for humans and smart objects to interact. The specific marketplace is piloted through a cross-border trial between Santander and Fujisawa, in the context of the M-Sec project, validating thus the interoperability, efficiency, and data protection principles.

Task Scheduling for Energy-Harvesting-Based IoT: A Survey and Critical Analysis

The Internet of Things (IoT) has important applications in our daily lives, including health and fitness tracking, environmental monitoring, and transportation. However, sensor nodes in IoT suffer from the limited lifetime of batteries resulting from their finite energy availability. A promising solution is to harvest energy from environmental sources, such as solar, kinetic, thermal, and radio-frequency (RF) waves, for perpetual and continuous operation of IoT sensor nodes. In addition to energy generation, recently energy harvesters have been used for context detection, eliminating the need for conventional activity sensors (e.g., accelerometers), saving space, cost, and energy consumption. Using energy harvesters for simultaneous sensing and energy harvesting enables energy positive sensing-an important and emerging class of sensors, which harvest more energy than required for context detection and the additional energy can be used to power other components of the system. Although simultaneous sensing and energy harvesting is an important step forward toward autonomous self-powered sensor nodes, the energy and information availability can be still intermittent, unpredictable, and temporally misaligned with various computational tasks on the sensor node. This article provides a comprehensive survey on task scheduling algorithms for the emerging class of energy harvesting-based sensors (i.e., energy positive sensors) to achieve the sustainable operation of IoT. We discuss inherent differences between conventional sensing and energy positive sensing and provide an extensive critical analysis for devising revised task scheduling algorithms incorporating this new class of sensors. Finally, we outline future research directions toward the implementation of autonomous and self-powered IoT.

Performance Evaluation of IoT-based SpO2 Monitoring Systems for COVID-19 Patients

Internet of Things (IoT) applications can be used in healthcare services to monitor patients remotely. One implementation is that it is used to monitor COVID-19 patients. During the COVID-19 pandemic, people who are infected without symptoms must self-isolate so that the virus does not spread. Measurement of blood oxygen levels or SpO2 is one of the measurements that must be carried out in routine examination procedures for self-isolating patients for early detection of silent hypoxemia in COVID-19 patients. Previous research has developed an IoT-based health monitoring system with a Wireless Body Sensor Network (WBSN) and a gateway that can be used for data acquisition and transmission. The system uses a home pulse oximeter to measure SpO2 and heart rate and an Android application that functions as an IoT gateway to collect data from sensors and add location information before sending data to the server. The WBSN has been successfully integrated with two types of open source IoT platforms, namely ThingsBoard and Elasticsearch Logstash Kibana (ELK). However, it is necessary to carry out further studies on analytical and experimental performance tests of the two systems. Therefore, the purpose of this study is to develop a performance evaluation of the IoT-based SpO2 monitoring systems using the Thingsboard and ELK as IoT platforms. To evaluate the performace we ran the monitoring system on both platforms using pulse oximeter and Android device as IoT gateway with HTTP and MQTT as transport protocol for sending the data to the server. From this study we found that average time of message delivery in ELK compared to ThingsBoard using the same protocols was higher but stable.

A Novel Insider Attack and Machine Learning Based Detection for the Internet of Things

Due to the widespread functional benefits, such as supporting internet connectivity, having high visibility and enabling easy connectivity between sensors, the Internet of Things (IoT) has become popular and used in many applications, such as for smart city, smart health, smart home, and smart vehicle realizations. These IoT-based systems contribute to both daily life and business, including sensitive and emergency situations. In general, the devices or sensors used in the IoT have very limited computational power, storage capacity, and communication capabilities, but they help to collect a large amount of data as well as maintain communication with the other devices in the network. Since most of the IoT devices have no physical security, and often are open to everyone via radio communication and via the internet, they are highly vulnerable to existing and emerging novel security attacks. Further, the IoT devices are usually integrated with the corporate networks; in this case, the impact of attacks will be much more significant than operating in isolation. Due to the constraints of the IoT devices, and the nature of their operation, existing security mechanisms are less effective for countering the attacks that are specific to the IoT-based systems. This article presents a new insider attack, named loophole attack , that exploits the vulnerabilities present in a widely used IPv6 routing protocol in IoT-based systems, called RPL (Routing over Low Power and Lossy Networks). To protect the IoT system from this insider attack, a machine learning based security mechanism is presented. The proposed attack has been implemented using a Contiki IoT operating system that runs on the Cooja simulator, and the impacts of the attack are analyzed. Evaluation on the collected network traffic data demonstrates that the machine learning based approaches, along with the proposed features, help to accurately detect the insider attack from the network traffic data.

A Guideline on Pseudorandom Number Generation (PRNG) in the IoT

Random numbers are an essential input to many functions on the Internet of Things (IoT). Common use cases of randomness range from low-level packet transmission to advanced algorithms of artificial intelligence as well as security and trust, which heavily rely on unpredictable random sources. In the constrained IoT, though, unpredictable random sources are a challenging desire due to limited resources, deterministic real-time operations, and frequent lack of a user interface. In this article, we revisit the generation of randomness from the perspective of an IoT operating system (OS) that needs to support general purpose or crypto-secure random numbers. We analyze the potential attack surface, derive common requirements, and discuss the potentials and shortcomings of current IoT OSs. A systematic evaluation of current IoT hardware components and popular software generators based on well-established test suits and on experiments for measuring performance give rise to a set of clear recommendations on how to build such a random subsystem and which generators to use.

An edge-cloud collaborative computing platform for building AIoT applications efficiently

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT), or AIoT, has breathed a new life into IoT operations and human-machine interactions. Currently, resource-constrained IoT devices usually cannot provide sufficient capability for data storage and processing so as to support building modern AI models. An intuitive solution is to integrate cloud computing technology into AIoT and exploit the powerful and elastic computing as well as the storage capacity of the servers on the cloud end. Nevertheless, the network bandwidth and communication latency increasingly become serious bottlenecks. The emerging edge computing can complement the cloud-based AIoT in terms of communication latency, and hence attracts more and more attention from the AIoT area. In this paper, we present an industrial edge-cloud collaborative computing platform, namely Sophon Edge, that helps to build and deploy AIoT applications efficiently. As an enterprise-level solution for the AIoT computing paradigm, Sophon Edge adopts a pipeline-based computing model for streaming data from IoT devices. Besides, this platform supports an iterative way for model evolution and updating so as to enable the AIoT applications agile and data-driven. Through a real-world example, we demonstrate the effectiveness and efficiency of building an AIoT application based on the Sophon Edge platform.

IoT Open-Source Architecture for the Maintenance of Building Facilities

The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

Consumer, Commercial, and Industrial IoT (In)Security: Attack Taxonomy and Case Studies

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications spanning from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consumer</i> domain to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">commercial</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">industrial</i> systems. The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life cycles. IoT-related vulnerabilities, if successfully exploited can affect, not only the device itself but also the application field in which the IoT device operates. Evidently, identifying and addressing every single vulnerability are an arduous, if not impossible, task. Attack taxonomies can assist in classifying attacks and their corresponding vulnerabilities. Security countermeasures and best practices can then be leveraged to mitigate threats and vulnerabilities before they emerge into catastrophic attacks and ensure overall secure IoT operation. Therefore, in this article, we provide an attack taxonomy, which takes into consideration the different layers of the IoT stack, i.e., device, infrastructure, communication, and service, and each layer’s designated characteristics, which can be exploited by adversaries. Furthermore, using nine real-world cybersecurity incidents that had targeted IoT devices deployed in the consumer, commercial, and industrial sectors, we describe the IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and potential mitigation mechanisms and protection strategies. These (and many other) incidents highlight the underlying security concerns of IoT systems and demonstrate the potential attack impacts of such connected ecosystems, while the proposed taxonomy provides a systematic procedure to categorize attacks based on the affected layer and corresponding impact.

Secure data transmission in IoTs based on CLoG edge detection

Cyber–physical Systems (CPS) have revolutionized the industry by utilizing the Internet of Things (IoT) for interconnecting various system components. IoT plays a significant role in the development of the smart system paradigm by building a link between physical components with the virtual world for improving smart services and quality of life. It significantly improves people’s activities, be it their personalized health care, living, or the way they monitor control, and organize the businesses. However, data streaming/communication over an open IoT environment creates several security issues that need to be taken care of, for transferring data securely. This paper presents an efficient information embedding solution for ensuring data security in a cyber–physicalnetwork. In this work, a new efficient edge detector called CLoG, (based on Canny and Laplacian of Gaussian detectors) has been developed and is used for the detection of edge areas in digital images. The secret information has been embedded in detected edges. The proposed detector finds finer edge details compared to state-of-art, making it possible to hide more information in a cover image. This, in turn, reduces the number of cover images required to transmit secret data and as such fulfills the requirements of resource-constrained platforms like IoT. Experimental results show that the proposed scheme is capable of providing high-quality stego-images with an average peak signal-to-noise ratio (PSNR) of 48.12 dB for a payload of 2 bits per pixel (bpp) payload. Further, the scheme has been analyzed for its efficacy in terms of histogram analysis, normalized cross-correlation (NCC), and other objective parameters. Besides, we show that the proposed scheme supports blind extraction, has less computational complexity, and is suitable for IoT based systems.

Big Data and Personalisation for Non-Intrusive Smart Home Automation

With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks.

The design of medical IoT operation information platform using reactive algorithm and e-commerce O2O mode

To apply the internet of things (IoT) to the medical operation system of the new era under the e-commerce online to offline (O2O) mode to realize the intelligent management of medical operation, first, the theoretical basis of the medical e-commerce O2O mode is put forward. First, the theoretical basis of O2O mode of medical e-commerce is proposed, and the reactive algorithm in a cloud computing environment is proposed. Based on the reactive algorithm, the medical IoT operation information platform is designed, including technical framework, development platform and operating framework; then, the database is designed, including user information management database, online registration database, parallel diagnosis service database, and electronic medical record database; the local area network (LAN) is used to realize the basic medical services under the management of the IoT by means of online registration simulation; the performance of the platform is tested, and the test index is the concurrent connection number. The proposed platform is applied to three different hospitals (H, R and K). June 2019 and August 2019 are taken as two-time nodes before and after the use of the proposed platform in three hospitals, and the relevant income of each hospital is counted. The results show that under the premise that the amount of resource increase is 60, and the concurrent connection number is 60, it is necessary to wait for 8 s before the system starts to increase the number of connections (resources), indicating that the threshold exerts a great effect on the algorithm. Meanwhile, the reactive algorithm cannot maintain the same rate. It will cause a slight stagnation according to the load state of the system. The total income of H and K hospitals decreases after using the proposed medical IoT operation information platform. However, the medical income and drug income increase, the ratio of drug income to total income increases significantly, and the proportion of K hospital increases about 0.3. The conclusion is that under the medical e-commerce O2O mode, the reactive algorithm proposed can be applied to the design of the medical IoT operation information platform, and the actual performance of the platform meets the expected results.