Cloud Of Things Research Articles

Raising data availability and quality for improved disruption and carbon footprint management through a novel approach to primary data sharing: Virtual Watch Tower / VWT

Abstract The global supply chain and logistics industry is a self-organizing ecosystem consisting of numerous actors that work together to move goods from end to end. The different stakeholders involved are usually interdependent organizations, like freight forwarders, carriers, terminals, and homeland security agencies and information exchange between them is required to coordinate the activities along the individual transport chains. However, the exchange of information has often been analog, flawed, late, and incomplete. New circumstances, like unprecedented supply chain disruptions, new regulatory requirements around greenhouse gas (GHG) emissions, and generally growing shipper expectations create an urgent need for improved data sharing between actors. New technologies, like digital platforms, networks, and architectures as well as social media, mobile, analytics, cloud, and internet of things (SMACT) have brought some improvements, but not the required holistic digital perspective required or expected. Averages and approximations are usually insufficient to close data gaps, e.g., only primary data allows for accurate GHG emission calculations. Primary data sharing is widely seen as the missing piece of the puzzle. Primary data is data from the source providing an accurate state and picture of a situation. Primary data sharing at scale requires a new form of digital collaboration. We propose a rethink of digital collaboration as a means for broader primary private data sharing for complete end-to-end datasets and data quality, particularly focusing on the sharing of data associated with both transport plans and progress made in the respective movements of goods for more better disruption and carbon footprint management through more accurate calculations of estimated times of arrival (ETA) and GHG emissions. We introduce an example for digital collaboration in end-to-end supply chains that is focusing specifically on primary data sharing. The new thinking around digital collaboration manifests itself in the Virtual Watch Tower / VWT initiative (www.virtualwatchtower.org). In 2022, RISE and Singapore Maritime Institute signed a collaboration agreement focused on innovation in shipping. The VWT initiative is the first collaborative project under the umbrella of this partnership. The VWT is led by RISE, A*STAR, and VTT. VWT is a community-driven, digitally empowered initiative, a cargo owner-driven, and terminal-centric approach for improved supply chain management. It is the users themselves who co-create and co-evolve the solution that they need. The initiative aims to create a community that shapes the digital tool (VWTnet) they need to reach the required higher levels of visibility. This, through primary data sharing across the supply chain ecosystem and between actors (VWT Users) participating in individual end-to-end transports (VWT Shipments). The VWT serves as an object of research to hone the new thinking and understand the implications of its implementation.

Enhancing hotel efficiency and environmental health with biosensors and big data analytics

The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

A NOVEL COST-EFFECTIVE PRESSURE SENSOR BASED FLOOD MONITORING SYSTEM WITH IOT

Floods are one of the most frequent and destructive natural hazards in the world. Early warning and flood monitoring is beneficial in the response and preparedness for this hazard. This paper presents a novel, low-cost flood monitoring system for disaster prevention, based on a pressure sensor. The system comprises a state-of-the-art microcontroller, a pressure sensor, and a security element. It is connected to an Internet of Things (IoT) cloud service, allowing for further data processing, interaction, and storage. The sensor data is processed to provide real-time flood measurements. The calibration data obtained from the designed system demonstrates a linear correlation between the water level and the sensor output. Additionally, the system was able to provide flood level measurements with an average error of 4.08% using the calibration equation, whereas a 4.40% error was obtained when using theoretical equations to determine the flood height.

A ranking framework for the selection of IoT cloud platforms using hybrid multi-attribute decision-making method

PurposeThe Internet of Things (IoT) cloud platforms provide end-to-end solutions that integrate various capabilities such as application development, device and connectivity management, data storage, data analysis and data visualization. The high use of these platforms results in their huge availability provided by different capabilities. Therefore, choosing the optimal IoT cloud platform to develop IoT applications successfully has become crucial. The key purpose of the present study is to implement a hybrid multi-attribute decision-making approach (MADM) to evaluate and select IoT cloud platforms.Design/methodology/approachThe optimal selection of the IoT cloud platforms seems to be dependent on multiple attributes. Hence, the optimal selection of IoT cloud platforms problem is modeled as a MADM problem, and a hybrid approach named neutrosophic fuzzy set-Euclidean taxicab distance-based approach (NFS-ETDBA) is implemented to solve the same. NFS-ETDBA works on the calculation of assessment score for each alternative, i.e. IoT cloud platforms, by combining two different measures: Euclidean and taxicab distance.FindingsA case study to illustrate the working of the proposed NFS-ETDBA for optimal selection of IoT cloud platforms is given. The results obtained on the basis of calculated assessment scores depict that “Azure IoT suite” is the most preferable IoT cloud platform, whereas “Salesman IoT cloud” is the least preferable.Originality/valueThe proposed NFS-ETDBA methodology for the IoT cloud platform selection is implemented for the first time in this field. ETDBA is highly capable of handling the large number of alternatives and the selection attributes involved in any decision-making process. Further, the use of fuzzy set theory (FST) makes it very easy to handle the impreciseness that may occur during the data collection through a questionnaire from a group of experts.

Research on jujube storage and preservation system based on Internet of Things (IoT)

With the surge in Xinjiang jujube production, there has been a concurrent increase in storage losses, posing an ongoing challenge for farmers to preserve the freshness of the jujubes. To tackle this issue, a novel storage and freshness control system for jujubes, integrating Internet of Things technology, has been developed. The control system is centered around a programmable logic controller (PLC) and utilizes an HMI as the interface for human–computer interaction. It is structured with distinct layers, including equipment, transportation, and application layers. By integrating with the Internet of Things cloud platform, data transmission between the device layer and the cloud server is ensured through the use of the TCP/IP protocol over a 4G network. A mobile app enables remote control functionality for monitoring and managing the storage and preservation environment. Experimental results demonstrate the system's stable performance, enabling remote monitoring and convenient operation by management personnel during the jujube storage and preservation process. This contributes to enhancing automation levels in jujube storage, extending its storage cycle while maintaining quality standards, thus providing valuable insights into intelligent fruit and vegetable storage practices in Xinjiang.

Attack detection model for BCoT based on contrastive variational autoencoder and metric learning

With development of blockchain technology, clouding computing and Internet of Things (IoT), blockchain and cloud of things (BCoT) has become development tendency. But the security has become the most development hinder of BCoT. Attack detection model is a crucial part of attack revelation mechanism for BCoT. As a consequence, attack detection model has received more concerned. Due to the great diversity and variation of network attacks aiming to BCoT, tradition attack detection models are not suitable for BCoT. In this paper, we propose a novel attack detection model for BCoT, denoted as cVAE-DML. The novel model is based on contrastive variational autoencoder (cVAE) and deep metric learning (DML). By training the cVAE, the proposed model generates private features for attack traffic information as well as shared features between attack traffic information and normal traffic information. Based on those generated features, the proposed model can generate representative new samples to balance the training dataset. At last, the decoder of cVAE is connected to the deep metric learning network to detect attack aiming to BCoT. The efficiency of cVAE-DML is verified using the CIC-IDS 2017 dataset and CSE-CIC-IDS 2018 dataset. The results show that cVAE-DML can improve attack detection efficiency even under the condition of unbalanced samples.

دمج انترنت الاشياء والحوسبة السحابية مع ضمان جودة الخدمة

Now a days the cloud computing and Internet of Things (IoT) become the most importance technologies that are both used in our life. As we know the Internet of Things (IoT) is generally characterized by real world small things,. It allows billions of devices to be connected and communicate with each other to share information that improves the quality of our daily lives. in the other ways, Cloud Computing has virtually unlimited capabilities in terms of storage and processing power, is a much more mature technology, and has most of the IoT issues at least partially solved. That is why their adoption and use are expected to be more extensive and more importance, thus, a novel IT paradigm in which Cloud and IOT are two complementary technologies merged together is expected to offer the best quality of services, and this new paradigm called Cloud Iot paradigm. Therefore, the integration of cloud and Internet of Things to providing the best service to users and have the highest level of customer satisfaction should always provide quality of service can be guaranteed. The purpose and the aim of this paper is to evaluate the integrity requirements of cloud services and Internet of Things, and to achieve this aim we used descriptive methodology to describe and to recognize about the cloud computing and the internet of things; so that quality of service in these services must be guaranteed. Keywords: Cloud Computing, Internet of Things, Cloud based IoT, Integration and quality of services.

An auto-encoder bio medical signal transmission through custom convolutional neural network

The transmission of biomedical signals in real-time is extremely difficult and necessitates the use of cloud and internet of things (IoT) infrastructure. Security is also an important consideration, however, to achieve this, a reconstruction method is developed where the entire signal is fed as an input, just the primary portion, the entire signal is taken then encoded, and then deliver to the destination. It is unlocked using a reconstruction technique without any signal attenuation. The key difficulty is how to manage the sensor network once the input is prepared for transmission. This has problems with extremely high network energy consumption and accurate data collection. The accuracy of data reconstruction through is improved by compressive sensing. The lifespan of the network as a whole could be extended, in this study; the proposed proposed system convolutional neural network (PS-CNN) is an integrated model that combines feature selection and auto-encoder. In order to produce the most useful features for particular tasks, our algorithm can eventually separate the appropriate task units from the irrelevant tasks.

Research on Task Scheduling for Internet of Things Cloud Computing Based on Improved Chicken Swarm Optimization Algorithm

Aiming at the shortcomings of long completion time and high consumption cost of cloud computing batch task scheduling in IoT, an Improved Chicken Swarm Optimization Algorithm (ICSO) for task scheduling in cloud computing scenarios is proposed. Specifically, in order to solve the problems of slow convergence and falling into local optimum of the chicken swarm optimization algorithm, we adopt the nonlinear decreasing technique of the rooster and the weighting technique of the hen, optimize the following coefficients of the chicks, and apply ICSO to cloud computing task scheduling. In simulation experiments, we conducted a large number of experiments using four standard benchmark functions with different number of tasks and the results show that ICSO algorithm reduces 25.8%, 9.3%, 8.8%, 7.5% in small task time compared to CSO, DCSO, GCSO, ABCSO in large task time by 30.8%, 8.3%, 7.8%, 6.3%, 11.8%, 10.3%, 8.8%, 7.5% savings in small task cost and 25.8%, 11.2%, 10.8%, 9.3% savings in large task cost. This method effectively reduces task scheduling time and cost consumption. Meanwhile, we tested it in combination with an IoT-based cloud platform and achieved very satisfying Results.

A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation

Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.

Blockchain: Game Changer for IoT Data

Potentially game-changing innovations that could impact mechanical advertising agencies in the next decade include blockchain technology and the Internet of Things (IoT). Soon, the Internet of objects (IoT) will have an effect on the objects we use on a daily basis and, more importantly, on our way of life. This is only the beginning. Intelligent buildings and smart cities are two sectors that will feel the full impact of the Internet of Things (IoT) foundation. internet of things (IoT) For the purpose of managing data and assets distributed throughout a city, a Smart City setup is analogous to an Internet of Things (IoT) cloud system. By connecting once isolated devices to the internet, the Internet of Things (IoT) is quickly becoming a target for cyberattacks on homes and businesses. The current state of security is inadequate to handle this problem. When the time comes, blockchain technology has emerged as a potential solution for creating more secure IoT frameworks.

Typical Applications of IoT Cloud Technology in the HVAC Industry

(Objective) Use the Internet of Things and cloud data technology to build intelligent HVAC environment, form the whole ecosystem with cloud technology as the core, running through designers, builders and managers. The application of virtualization based on cloud technology can integrate environmental resources, provide virtual resources such as intelligent management and control and intelligent construction, and provide an intelligent cloud platform for the whole HVAC environment. With the help of big data technology, we can effectively integrate the information of various building structures to reduce the duplication of labor in the design, construction and use process. In this way, we can create an intelligent HVAC environment, and can carry out interactive construction in the new interactive mode, but also can build a cloud platform of HVAC resource sharing, as well as a centralized control of intelligent management center, to achieve multi-dimensional construction and intelligent management. (Conclusion) The intelligent HVAC environment integrated with the Internet of Things cloud technology can realize the effective sharing of resources through the integration of various material resources, thus greatly improving the efficiency of subsequent maintenance.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Emerging wireless communication technologies in Iraqi government: Exploring cloud, edge, and fog computing

This study aims to structure the implementation of a governmental cloud of things (CoT), edge computing (EC), and fog computing in Iraq in the context of sustainable wireless communication. A base of literature was built that included any challenges, opportunities, and best practices relevant to these innovative technologies to set up the background for this paper. A concept model was created that included core components (cognitive technologies and fog computing), key processes (resource analysis, infrastructure design), and stakeholders (governments, industry, community). A strategic methodology made up of stakeholder involvement, capacity building, and pilot projects was used in the project. Concerning IoT planned deployment and services provision, network infrastructure was put in place to support the devices and a higher level of security measures were recommended. Using scenario hypothesis, MATLAB simulator was employed to simulate data value distribution as well as received power distribution based on different institutions for 12 months. Monitoring and evaluation should be followed to measure performance indicators and effects on this process. Continuously improvement strategies were the highlight of the session which further stimulated innovations. Acquainted projects will be put in the function to extend the range of activities by including additional government agencies, regions, or sectors. Reporting of the collected data and funding will be done with stakeholders to share and pool knowledge.

IoT Technology for Monitoring and Control of Smart Greenhouses

A greenhouse is a covered structured area which protect the plants from extreme weather condition, providing a controlled environment for their growth and cultivation. The innovative Internet of Things (IoT) technology uses a series of sensors connected to a central computer to control the greenhouse environment. Greenhouse sensor systems include elements that monitor and control temperature, humidity, soil moisture, lighting, and external weather conditions. The research aims to design a greenhouse monitoring and control system based on the Internet of Things (IoT). In smart greenhouses, IoT involves sensors, devices, and information and communication infrastructure for real-time monitoring, data collection, and processing to control the environment inside the greenhouse unit. Controlled greenhouses, supported by computer technology, can enhance quality and increase crop yields. This research involves three greenhouses that are independently monitored and controlled through an Internet of Things (IoT) cloud platform. Cloud monitoring facilitates better integration of devices across different geographical locations.

Biomedical signal compression using deep learning based multi-task compressed sensing

<span>Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.</span>

Balancing the Load and Scheduling the Tasks using Zebra Optimizer in IoT based Cloud Computing for Big-data Applications.

Task scheduling is one of the major problems with Internet of Things (IoT) cloud computing. The need for cloud storage has skyrocketed due to recent advancements in IoT-based technology. Sophisticated planning approaches are needed to load the IoT services onto cloud resources professionally while meeting application necessities. This is significant because, in order to optimise resource utilisation and reduce waiting times, several procedures must be properly configured on various virtual machines. Because of the diverse nature of IoT, scheduling various IoT application activities in a cloud-based computing architecture can be challenging. Fog cloud computing is projected for the integration of fog besides cloud networks to address these expectations, given the proliferation of IoT sensors and the requirement for fast and dependable information access. Given the complexity of job scheduling, it can be difficult to determine the best course of action, particularly for big data systems. The behaviour of zebras in the wild serves as the primary basis of stimulus for the development of the Zebra Optimisation Algorithm (ZOA), a novel bio-inspired metaheuristic procedure presented in this study. ZOA mimics zebras' feeding habits and their defence mechanisms against predators. Various activities are analysed and processed using an optimised scheduling model based on ZOA to minimise energy expenditures and end-to-end delay. To reduce makespan and increase resource consumption, the technique uses a multi-objective strategy. By using a regional exploratory search strategy, the optimisation algorithm may better utilise data and stays out of local optimisation ruts. The analysis revealed that the suggested ZOA outperformed other well-known algorithms. It was advantageous for big data task scheduling scenarios since it converged more quickly than other techniques. It also produced improvements of 18.43% in several outcomes, including resource utilisation, energy consumption, and make span.

Cloud of Things (CoT) based Diabetes Risk Prediction System using BiRNN

The introduction of Internet of Things (IoT) technology witnesses the continuous and distributed connectivity between different objects and people. Currently, with the emerging technological advances, IoT integrates with the cloud and evolves into a new term called “Cloud of Things” to further enhance human lives. Using predictive analytics and Artificial Intelligence (AI) approaches in the healthcare area allows for the development of more reactive and smart healthcare solutions. As a subfield of AI, the Deep Learning (DL) technique has the potential to analyse the given data accurately, provide valuable insights, and solve complex challenges with its ability to train the model continuously. This study intends to implement a deep learning model – Bidirectional Recurrent Neural Networks (Bi-RNN) to obtain a timely and accurate prediction of diabetes risk without requiring any clinical diagnosis. This method of processing the time series data will highly assist in ensuring preventive care and early disease intervention. The proposed model collects real-time data from IoT devices along with the medical data stored in Electronic Health Records (EHR) to perform predictive analytics. The proposed Bi-RNN based diabetes prediction model results in an accuracy of 97.75%, which is comparatively higher than other existing diabetes risk prediction models.

Exploring the applications and security threats of Internet of Thing in the cloud computing paradigm: A comprehensive study on the cloud of things

AbstractThe term “Internet of Things” (IoT) represents a vast interconnected network comprising ordinary objects enhanced with electronics like sensors, actuators, and wireless connectivity. These augmentations enable seamless communication and data sharing among devices. IoT constitutes an ecosystem of programs, systems, and technologies that operate across diverse communication services. In our rapidly evolving world, cutting‐edge technologies are swiftly gaining prominence. IoT, in particular, strives to proliferate innovative applications, programs, and communication amalgamating the virtual and physical realms. The bedrock of this communication is the machine‐to‐machine communication paradigm. IoT encompasses a spectrum of technologies encompassing smart vehicles, efficient vehicle parking management systems, and roadway‐embedded sensors, culminating in the vision of a “smart city.” Such integration has the potential to alleviate traffic congestion and curb energy consumption. The impact of IoT extends to power generation and business operations, promising transformative outcomes. Furthermore, the synergy between IoT and cloud computing plays a pivotal role in the wireless communication domain. This paper offers a comprehensive insight into IoT's functionalities and underscores the associated security threats. The study aims to equip academia with an enhanced understanding of diverse IoT applications, particularly their integration with cloud computing, referred to as the “Cloud of Things.”

Cloud of Things and fog computing in Iraq: Potential applications and sustainability

This paper depicts the principles of Cloud of Things and fog computing and discusses its possible uses in Iraq with sustainability measures. The capacity of cloud computing to supply elastic, as-needed computer resources has garnered widespread interest worldwide. However, fog computing and a Cloud of Things enhance the Internet of Things by relocating computation to devices on the network's periphery. This study looks at how the Cloud of Things and fog computing are used now in Iraq, the obstacles, and the future uses of these technologies in various fields. To fully reap the benefits of the Cloud of Things and fog computing in Iraq, the study also emphasizes the significance of infrastructure development, policy design, cybersecurity, and other measures. This study will discuss the use of questionnaires in research. There are two distinct components to this. The first section includes questions regarding the respondents' affiliations, including their roles, departments, organization sizes, and ministries. The rest of the study's factors are discussed with inquiries in line with issues of cyber security, privacy, sustainability, cost of implementation, feasibility, trust, IT infrastructure, and government support. The survey's final open-ended inquiry will help us to compile a wide range of perspectives on what kinds of Cloud of Things and fog computing services based on the Iraqi government's needs.