Real-time Internet Of Things System Research Articles

Advancing Applied Nonlinear Real-Time IoT System for Disaster-Resilient Dam Management through Water Level Monitoring Systems

The nonlinear dynamics of water management in dams are critical to mitigating floods and guaranteeing a long-term water supply for neighboring towns. With the integration of sensor networks, this project seeks to create an advanced monitoring and warning system that can dynamically regulate water levels. By utilizing the Internet of Things (IoT), the system aims to provide flood protection in the neighborhood a nonlinear component. Water levels are closely monitored by a number of sensors, which send out alerts via GSM to allow for prompt communication. Information is effectively sent via the system to the cloud, where application software effortlessly delivers it to users. By using a nonlinear strategy, this project not only satisfies requirements but also serves as a strong safety net, reducing possible losses in the event of major water-related problems.

Efficient hybrid centralized and blockchain-based authentication architecture for heterogeneous IoT systems

With the rapid increase in the number of Internet of Things (IoT) devices in recent years, massive amounts of sensitive IoT data are being generated and transmitted over the Internet. Despite its growing adoption in various fields, IoT security remains a major challenge requiring further research. IoT authentication is an essential security mechanism for building trust in IoT systems. However, conventional authentication approaches use expensive cryptographic primitives that do not align with the resource-constrained nature of IoT devices. Furthermore, centralized authentication schemes have proven to be inapplicable for cross-domain authentication and do not limit the scalability of IoT networks. Recently, blockchain technology has been applied to building decentralized authentication between IoT devices. Nevertheless, most existing blockchain-based authentication approaches incur high overhead in IoT computation, storage, and energy consumption. Authentication time is another critical issue in real-time IoT systems. When numerous IoT authentication requests are transferred to the blockchain, an additional time delay is imposed, in addition to the high computational cost of the blockchain caused by the consensus mechanism. This study proposes a hybrid centralized and blockchain-based authentication architecture for IoT systems. Edge servers are deployed to provide centralized authentication for associated IoT devices. A blockchain network of centralized edge servers is then established to ensure decentralized authentication and verification of IoT devices that belong to different and heterogeneous IoT systems. Lightweight cryptographic methods are implemented to achieve efficient authentication, in which limiting the consumption of IoT resources is required. The architecture is demonstrated using a local Ethereum blockchain network. The results indicate that the proposed method achieves significant improvements in terms of computation cost, execution time, and power consumption for IoT compared with centralized and blockchain-based authentication schemes. A security analysis proves the ability of our architecture to mitigate attacks and satisfy the IoT security requirements.

Approach for Designing Real-Time IoT Systems

Along with the rapid development of Internet of Things (IoT) technology over the past few years, opportunities for its implementation in service areas that require real-time requirements have begun to be recognized. In this regard, one of the most important criteria is to maintain Quality of Service (QoS) parameters at an appropriate and sufficiently high level. The QoS level should ensure the delivery of data packets in the shortest time possible while preventing critical parameters relevant to real-time transmission from being exceeded. This article proposes a new methodology for designing real-time IoT systems. The premise of the proposed approach is to adapt selected solutions used in other types of systems working with real-time requirements. Some analogy to embedded systems with a distributed architecture has been noted and used in this regard. The main differences from the concept of built-in systems can primarily be seen in the communication layer. The methodology proposed in this article is based on the authors’ proposed model of real-time system functional specification and its mapping to the IoT architecture. In addition, the developed methodology makes extensive use of selected IoT architecture elements described in this article, as well as selected task scheduling methods and communication protocols. The proposed methodology for designing RTIoT systems is based on dedicated transmission serialization methods and dedicated routing protocols. These methods ensure that the time constraints for the assumed bandwidth of IoT links are met by appropriately prioritizing transmissions and determining communication routes. The presented approach can be used to design a broad class of RTIoT systems.

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system—entitled EGYPADS—based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.

Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have significantly evolved and been employed in diverse applications, such as computer vision, natural language processing, automated speech recognition, etc. Real-time safety-critical embedded and Internet of Things (IoT) systems, such as autonomous driving systems, UAVs, drones, security robots, etc., heavily rely on ML/DL-based technologies, accelerated with the improvement of hardware technologies. The cost of a deadline (required time constraint) missed by ML/DL algorithms would be catastrophic in these safety-critical systems. However, ML/DL algorithm-based applications have more concerns about accuracy than strict time requirements. Accordingly, researchers from the real-time systems (RTSs) community address the strict timing requirements of ML/DL technologies to include in RTSs. This article will rigorously explore the state-of-the-art results emphasizing the strengths and weaknesses in ML/DL-based scheduling techniques, accuracy versus execution time tradeoff policies of ML algorithms, and security and privacy of learning-based algorithms in real-time IoT systems.

An Adaptive Modeling and Performance Evaluation Framework for Edge-Enabled Green IoT Systems

The enormous growth in Internet of Things (IoT) has caused large-scale transformation in data acquisition and communication mechanism for conventional IoT systems. The continuously increasing requirements for delay-tolerant delivery of services in IoT applications has led to the emergence of more scalable and energy-efficient computing platforms like edge computing. However, the massive growth in volume of data being offloaded from low-powered IoT devices to the edge has imposed challenges on edge servers in terms of traffic bottlenecks, latency, and wastage of energy. In this view, a Local Data Reduction (LDR) framework is proposed which addresses the latency issues and cost constraints to facilitate energy-efficient processing of IoT data. We exploit the Markovian birth-death process to model edge-based IoT systems and derive performance metrics for the proposed LDR model. We also provide explicit analytical solution for the total expected cost function incurred pertaining to the LDR and without LDR (WLDR) models. Through extensive numerical illustrations we validate our findings and observe that the proposed LDR model outperforms the WLDR model. Hence, the LDR model operates well to meet the Quality of Service (QoS) requirements for real-time IoT systems by favouring green computing paradigms.

Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In the considered model, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamics of the physical process vary over time, each device should find an optimal sampling frequency to sample the real-time dynamics of the physical system and send sampled information to a base station (BS). Due to limited wireless resources, the BS can only select a subset of devices to transmit their sampled information. Thus, edge devices can cooperatively sample their monitored dynamics based on the local observations and the BS will collect the sampled information from the devices immediately, hence avoiding the additional time and energy used for sampling and information transmission. To this end, it is necessary to jointly optimize the sampling policy of each device and the device selection scheme of the BS so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI cost and energy consumption. To solve this problem, we propose a novel distributed reinforcement learning (RL) approach for the sampling policy optimization. The proposed algorithm enables edge devices to cooperatively find the global optimal sampling policy using their own local observations. Given the sampling policy, the device selection scheme can be optimized thus minimizing the weighted sum of AoI and energy consumption of all devices. Simulations with real PM 2.5 pollution data show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9%, respectively, and the total energy consumption by up to 13.2% and 35.1%, respectively, compared to a conventional deep Q network method and a uniform sampling policy.

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

The explosive growth of Internet of Things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical-layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN), and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

A Hybrid Optimized LSTM Models for Human Activity Recognition with IOT Devices

With the advent of Internet of things(IoT),HA (HA) recognition has contributed the more application in health care in terms of diagnosis and Clinical process. These devices must be aware of human movements to provide better aid in the clinical applications as well as user’s daily activity.Also , In addition to machine and deep learning algorithms, HA recognition systems has significantly improved in terms of high accurate recognition. However, the most of the existing models designed needs improvisation in terms of accuracy and computational overhead. In this research paper, we proposed a BAT optimized Long Short term Memory (BAT-LSTM) for an effective recognition of human activities using real time IoT systems. The data are collected by implanting the Internet of things) devices invasively. Then, proposed BAT-LSTM is deployed to extract the temporal features which are then used for classification to HA. Nearly 10,0000 dataset were collected and used for evaluating the proposed model. For the validation of proposed framework, accuracy, precision, recall, specificity and F1-score parameters are chosen and comparison is done with the other state-of-art deep learning models. The finding shows the proposed model outperforms the other learning models and finds its suitability for the HA recognition.

A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application.

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices’ clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.

Extracting Urban Sound Information for Residential Areas in Smart Cities Using an End-to-End IoT System

With rapid urbanization comes the increase of community, construction, and transportation noise in residential areas. The conventional approach of solely relying on sound pressure-level information to decide on the noise environment and to plan out noise control and mitigation strategies is inadequate. This article presents an end-to-end Internet-of-Things (IoT) system that extracts real-time urban sound metadata using edge devices, providing information on the sound type, location and duration, rate of occurrence, loudness, and azimuth of a dominant noise in nine residential areas. The collected metadata on environmental sound is transmitted to and aggregated in a cloud-based platform to produce detailed descriptive analytics and visualization. Our approach in integrating different building blocks, namely, hardware, software, cloud technologies, and signal processing algorithms to form our real-time IoT system is outlined. We demonstrate how some of the sound metadata extracted by our system are used to provide insights into the noise in residential areas. A scalable workflow to collect and prepare audio recordings from nine residential areas to construct our urban sound data set for training and evaluating a location-agnostic model is discussed. Some practical challenges of managing and maintaining a sensor network deployed at numerous locations are also addressed.

A new approach for the detection of pneumonia in children using CXR images based on an real-time IoT system.

Pneumonia is responsible for high infant morbidity and mortality. This disease affects the small air sacs (alveoli) in the lung and requires prompt diagnosis and appropriate treatment. Chest X-rays are one of the most common tests used to detect pneumonia. In this work, we propose a real-time Internet of Things (IoT) system to detect pneumonia in chest X-ray images. The dataset used has 6000 chest X-ray images of children, and three medical specialists performed the validations. In this work, twelve different architectures of Convolutional Neural Networks (CNNs) trained on ImageNet were adapted to operate as the resource extractors. Subsequently, the CNNs were combined with consolidated learning methods, such as k-Nearest Neighbor (kNN), Naive Bayes, Random Forest, Multilayer Perceptron (MLP), and Support Vector Machine (SVM). The results showed that the VGG19 architecture with the SVM classifier using the RBF kernel was the best model to detect pneumonia in these chest radiographs. This combination reached 96.47%, 96.46%, and 96.46% for Accuracy, F1 score, and Precision values, respectively. Compared to other works in the literature, the proposed approach had better results for the metrics used. These results show that this approach for the detection of pneumonia in children using a real-time IoT system is efficient and is, therefore, a potential tool to aid in medical diagnoses. This approach will allow specialists to obtain faster and more accurate results and thus provide the appropriate treatment.

Missing Data Imputation With Bayesian Maximum Entropy for Internet of Things Applications

Internet of Things (IoT) enables the seamless integration of sensors, actuators, and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference, and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian maximum entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT data sets demonstrate the superiority of the BME framework as regards accuracy, running time, and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.

Conceptual model of real-time IoT systems

We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & controlling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.

Designing real-time IoT system course: Prototyping with cloud platforms, laboratory experiments and term project

The Internet of things (IoT) is the next technological revolution that includes many industries and applications. IoT can offer solutions for many problems. However, it requires many skills from different disciplines that current electrical and computer engineers lack. In order to encourage the seamless internalization of the IoT and the technologies it supports, a new method should be considered in training electrical and computer engineers. The new methods should focus on applications and projects that involve the use of components such as hardware, software, and cloud platforms that are available in the market rather than class oriented theories. In this study, we introduce a methodology for teaching an IoT systems course that will encourage students to study how to develop and accomplish IoT applications in real time, at a reasonable cost, and for general use rather than meeting the detailed needs of one merchant. We exploit universally available open source software and cloud platforms to enhance teaching IoT course. The proposed course is taught at the undergraduate level in the computer engineering departments of Kocaeli and Sakarya Universities in Turkey. The results show that the designed method can have a constructive influence on the progress of computer engineering education by timely and positive assistance in educating the new IoT engineers.