Iota Tangle

Purpose: The purpose of the research is to investigate the role of machine learning (ML) and artificial intelligence (AI) in the growth of smart cities. It aims to understand how these technologies are being used to manage expanding metropolitan areas, boost economies, reduce energy consumption, and improve the living standards of residents. The study also aims to analyze the information flow associated with ICT in smart cities. Methodology: The methodology involves conducting a survey to identify the typical technologies used to support communication in smart cities. It also involves a systematic evaluation of current patterns in publications related to ICT in smart cities. The research utilizes ML and AI techniques to analyze and interpret the collected data. Findings: The findings of the study indicate that ML and AI play a significant role in various aspects of smart cities, particularly in the field of intelligent transportation systems. These technologies are utilized for tasks such as modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. The research also reveals the application of ML and AI in other forms of transportation like air, rail, and road travel. Recommendations: Based on the findings, the study suggests that the agent computing paradigm is a powerful technology for the development of large-scale distributed systems, particularly in the context of geographically dispersed and dynamic transport systems. The research emphasizes the interoperability, flexibility, and extendibility of agent-based traffic control and management systems. It concludes by suggesting potential future research directions to effectively integrate agent technology into traffic and transportation systems.

The use of Electric Vehicles (EV) will promote urban sustainability, decrease air pollution, and reduce noise pollution. In this landscape a new mobility concept termed shared electric mobility-as-a-service (eMaaS) has emerged over the years. Shared eMaaS comprises the seamless integration of various forms of electric transport services available via one single digital platform. Although, the current shared eMaaS solutions are based mostly on fragmented and siloed systems which has resulted to issues related to the exchange of data and services from different eMaaS providers. Therefore, there is need for integrators and enablers to achieve an inter-operable and intra-operable seamless shared eMaaS. To this end, Distributed Ledger Technologies (DLT) is proposed in this study to enable new business models for shared electric mobility solutions. As compared to conventional approaches DLT offers a transparent, cost-efficient, and decentralized services both for managing the supply and demand sides of shared eMaaS to improve public transportation. Accordingly, this article presents a DLT based business models grounded on the literature to decentralize shared eMaaS. Qualitative data is collected from Scopus and Web of Science database, and descriptive analysis is employed to analyze the collected data. Findings from this study presents use case scenarios of how IOTA tangle as a DLT using smart contracts and IOTA wallet/ tokens are deployed to design novel business models for managing seamless travel experience for electric car sharing and leasing to improve public transportation.

Due to the numerous components in various domains that need to be identified in digital ecosystems, the
design of digital platforms spans a variety of areas that complicate the development process. Misalignment
between the various domains, such as business-IT, may result from the intricacy and decisions made.
We found that the primary cause of the complexity is the ignorance of those who develop digital platforms. In
order to convey knowledge about the motivation, business, application, and data domains, we developed a
reference model. We developed Enterprise Architecture (EA) patterns in each area using the ArchiMate language
after identifying pertinent digital platform principles with the help of literature. These trends outline the decisions
that must be made while developing a digital platform for service exchange. We also concentrate on the application
domain on how business and IT are in sync. We developed a visualization tool that allows natural language to
generate perspectives using the reference model in order to get around the problem of ArchiMate's lack of
popularity. In order to verify the reference model's generalizability, we examined an existing digital platform and
conducted a case study at Steel and Tube, a firm that aims to develop a digital platform to offer a casting ondemand
service.

This paper analyzes the problems associated with the implementation of international agreements in social security institutions. International social security agreements aim at protecting social rights of migrant workers by enabling the portability of social benefits, which involve managing billions of dollars paid worldwide by the signatory countries. This involves significant cross-border data exchange and back-office information processing. The effective and reliable implementation of agreements, therefore, requires an intensive application of information and communication technology (ICT) to ensure the integrity of the process. In this paper, a series of enterprise architectures were presented that will help the designers of the systems of social security institutions to carry out the international agreements.

This study highlights the necessity for developing smart building and cities across the globe in order to conserve natural resources
and energy. The aim of the study is to investigate the efficacy of 5G technologies in the architecture and construction industry. The
study benefits from an extensive literature review to discuss the advantages and challenges in incorporating 5G in smart buildings.
5G is found to be needed to maximize the use of artificial intelligence (AI) and IoT in architecture in order to be able to efficiently
enhance the features of smart buildings by managing energy and waste.

Presently to achieve enterprise digitalization technologies such as Distributed Ledger Technologies (DLT) has now been deployed to support digital services provided by enterprises. But several challenges in DLTs remain to be addressed, including the interoperability, standardization, and integration. Therefore, this study provides theoretical and practical understanding of DLT interoperability and identified the factors that influence the interoperability of DLTs. Also, an architecture is designed to shows how interoperability can be achieved in DLTs and legacy systems supported by Application Programming Interface (API). A case study is presented to illustrate the applicability of the architecture to support a digital energy marketplace.

Distributed ledger technologies (DLTs) are considered one of the foremost emerging technologies which can contribute to transform cities to smarter cities. DLT play important role in municipalities to accelerate the digitalization process toward changing the roles and services of enterprises in sustainable smart cities. Standardization of DLTs aids to reduce data and digital assets silos while decreasing vendor lock-in across distributed applications enabling a digital urban ecosystem that supports migration capabilities making it possible for cities to seamlessly achieve interoperability among DLTs and centralized digital platforms, although a few standards such as IEEE 2418, IEEE P2418.5, and ISO/TC 307 have been developed. The alignment and integration mechanisms required to support standardization of DLT for interoperable services in smart cities is lacking. Therefore, this study presents an understanding on current and open issues on standardization of DLTs in sustainable smart cities with a specific focus on data integration and alignment efforts related to interoperable DLTs. A framework is developed to promote standardization of DLTs to support integration and alignment for interoperability in smart cities. Design science research methodology was adopted based on three use case scenarios which illustrates how IOTA tangle is employs as a DLT for secured standardized communication between physical sensors, devices, and digital platforms in smart city environment. Findings from this article provide exploratory evidence demonstrating the potential uses of IOTA tangle through the developed framework applied for decentralized and centralized digital services. Based on this evidence, this study provides interface integration and alignment strategies to better exploit distributed applications full potential by improving DLT standardization in urban environment.

In this paper, we present the design, implementation, and evaluation of a secure sensing data processing and logging system. The system is inspired and enabled by the blockchain technology. In this system, a public blockchain is used as immutable datastore to log the most critical data needed to secure the system. Furthermore, several innovative blockchain-inspired mechanisms have been incorporated into the system to provide additional security for the system's operations. The first priority in securing sensing data processing and logging is admission control, i.e., only legitimate sensing data are accepted for processing and logging. This is achieved via a sensor identification and authentication mechanism. The second priority is to ensure that the logged data remain intact overtime. This is achieved by storing a small amount of data condensed from the raw sensing data on a public blockchain. A Merkel-tree based mechanism is devised to link the raw sensing data stored off-chain to the condensed data placed on public blockchain. This mechanism passes the data immutability property of a public blockchain to the raw sensing data stored off-chain. Third, the raw sensing data stored off-chain are secured with a self-protection mechanism where the raw sensing data are grouped into chained blocks with a moderate amount of proof-of-work. This scheme prevents an adversary from making arbitrary changes to the logged data within a short period of time. Fourth, mechanisms are developed to facilitate the search of the condensed data placed on the public blockchain and the verification of the raw sensing data using the condensed data placed on the public blockchain. The system is implemented in Python except the graphical user interface, which is developed using C#. The functionality and feasibility of the system have been evaluated locally and with two public blockchain systems, one is the IOTA Shimmer test network, and the other is Ethereum.

Accidents have consistently ranked as the
major cause of death in India. More than eighty percent
of the fatalities that occur as a result of accidents are not
directly attributable to the accident itself; rather, they
are the result of victims not receiving prompt assistance.
It is possible for an accident victim to be left unattended
for a significant amount of time on routes that have very
light and quick traffic. The objective is to design a system
that is able to determine whether or not an accident has
occurred based on the video input received by the
system. It is the intention to run each frame of a video
through a convolutional neural network and BILSTM
models that have been trained to identify video frames as
either accident or non-accident frames. The
Convolutional Neural Network and the BiLSTM models
have been shown to be a method that is both quick and
accurate when it comes to identifying photographs. CNN-
based image classifiers have attained an accuracy of
greater than 95% with fewer datasets, and they require
less preprocessing than other image classification
techniques.

Artificial Intelligence/Machine learning [1] [2] [3] (AI/ML) and its application to various fields are growing at rapid speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning and long-term remote monitoring problems. AI refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as "learning" and "problem solving". AI systems are gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning and deep learning). AI is the field of study focused on machine learning & deep learning [4][5][6][7] (ML\DL) algorithms being used by computers to perform specific tasks without using explicit instructions.

The increasing integration of distributed energy resources (DERs) into power grid makes it significant to forecast solar irradiance for power system planning. With the advent of deep learning techniques, it is possible to forecast solar irradiance accurately for a longer time. In this paper, day-ahead solar irradiance is forecasted using encoder-decoder sequence-to-sequence models with attention mechanism. This study formulates the problem as structured multivariate forecasting and comprehensive experiments are made with the data collected from National Solar Radiation Database (NSRDB). Two error metrics are adopted to measure the errors of encoder-decoder sequence-to-sequence model and compared with smart persistence (SP), back propagation neural network (BPNN), recurrent neural network (RNN), long short term memory (LSTM) and encoder-decoder sequence-to-sequence LSTM with attention mechanism (Enc-Dec-LSTM). Compared with SP, BPNN and RNN, Enc-Dec-LSTM is more accurate and has reduced forecast error of 31.1%, 19.3% and 8.5% respectively for day-ahead solar irradiance forecast with 31.07% as forecast skill. This is an open access article under the CC BY-SA license.

Distributed Ledger Technology (DLT) brings a set of opportunities for the Internet of Things (IoT), which leads to innovative solutions for existing components at all levels of existing architectures. IOTA Tangle has the potential to overcome current technical challenges identified for the IoT domain, such as data processing, infrastructure scalability, security, and privacy. Scaling is a serious challenge that influences the deployment of IoT applications. We propose a Scalable Distributed Intelligence Tangle-based approach (SDIT), which aims to address the scalability problem in IoT by adapting the IOTA Tangle architecture. It allows the seamless integration of new IoT devices across different applications. In addition, we describe an offloading mechanism to perform proof-of-work (PoW) computation in an energy-efficient way. A set of experiments has been conducted to prove the feasibility of the Tangle in achieving better scalability, while maintaining energy efficiency. The results indicate that our proposed solution provides highly-scalable and energy efficient transaction processing for IoT DLT applications, when compared with an existing DAG-based distributed ledger approach.

The utility companies are heavily investing in the production of renewable energy to meet the current demand for energy consumption by the commercial industries and the households. Renewable energy is derived from the earth's natural sources. The most common renewable energy resources are Solar, Biogas, Geothermal, Hydroelectric, Wind, Solar Thermal, etc. Conventional energy sources are making a negative impact on the environment and the economy; hence it's a need of an hour to have a blend of sources of energy with a majority percentage of sustainable energy sources. Renewable energy is an alternative to conventional energy, and it tends to be much less harmful to the environment. The prediction of energy generation from sustainable sources is required to gauge the amount of energy produced in the future to meets higher energy consumption needs. In this paper, we provide an approach for predicting the geothermal energy production using a LSTM (Long Short Term-Memory) Sequence to Sequence Encoder-Decoder Neural Network architecture.

Due to the tremendous population growth in the country, the use of vehicles and other transportation means has increased which has led to traffic congestion and road accidents. Hence, there is a demand for intelligent transportation systems in the country that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This paper focuses on providing an overview and applications of how Artificial intelligence (AI) and Machine Learning (ML) can be applied to develop an Intelligent Transportation system that can address the issues of traffic congestion and road safety to prevent accidents. We will then review various ML approaches to detect road anomalies for avoiding obstacles, predict real-time traffic flow to achieve smart and efficient transportation, detect and prevent road accidents to ensure safety, using smart city lights to save energy, and smart infrastructure to achieve efficient transportation. Next, we review various AI approaches such as safety and emergency management system to provide safety to the public, autonomous vehicles to provide economical and reliable transportation. We then propose smart parking management and how it can be used to find parking spaces or spots conveniently, incident detection which detects the traffic incidents or accidents in real-time provides a report. Finally, we conclude with predictive models and how the algorithms utilize sensor data to develop an Intelligent Transportation System.

With the rise of the Internet of Things (IoT), applications have become smarter and connected devices give rise to their exploitation in all aspects of a modern city. As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application. The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques. In this review, smart transportation is considered to be an umbrella term that covers route optimization, parking, street lights, accident prevention/detection, road anomalies, and infrastructure applications. The purpose of this paper is to make a self-contained review of ML techniques and IoT applications in Intelligent Transportation Systems (ITS) and obtain a clear view of the trends in the aforementioned fields and spot possible coverage needs. From the reviewed articles it becomes profound that there is a possible lack of ML ...

Due to the tremendous population growth in the country, the use of vehicles and other transportation means has increased which has led to traffic congestion and road accidents. Hence, there is a demand for intelligent transportation systems in the country that can provide safe and reliable transportation while maintaining environmental conditions such as pollution, CO2 emission, and energy consumption. This paper focuses on providing an overview and applications of how Artificial intelligence (AI) and Machine Learning (ML) can be applied to develop an Intelligent Transportation system that can address the issues of traffic congestion and road safety to prevent accidents. We will then review various ML approaches to detect road anomalies for avoiding obstacles, predict real-time traffic flow to achieve smart and efficient transportation, detect and prevent road accidents to ensure safety, using smart city lights to save energy, and smart infrastructure to achieve efficient transportation. Next, we review various AI approaches such as safety and emergency management system to provide safety to the public, autonomous vehicles to provide economical and reliable transportation. We then propose smart parking management and how it can be used to find parking spaces or spots conveniently, incident detection which detects the traffic incidents or accidents in real-time provides a report. Finally, we conclude with predictive models and how the algorithms utilize sensor data to develop an Intelligent Transportation System.