A Review of Artificial Intelligence in the Internet of Things

Humankind has the ability of learning new things automatically due to the capacities with which we were born. We simply need to have experiences, read, study… live. For these processes, we are capable of acquiring new abilities or modifying those we already have. Another ability we possess is the faculty of thinking, imagine, create our own ideas, and dream. Nevertheless, what occurs when we extrapolate this to machines? Machines can learn. We can teach them. In the last years, considerable advances have been done and we have seen cars that can recognise pedestrians or other cars, systems that distinguish animals, and even, how some artificial intelligences have been able to dream, paint, and compose music by themselves. Despite this, the doubt is the following: Can machines think? Or, in other words, could a machine which is talking to a person and is situated in another room make them believe they are talking with another human? This is a doubt that has been present since Alan Mathison Turing contemplated it and it has not been resolved yet. In this article, we will show the beginnings of what is known as Artificial Intelligence and some branches of it such as Machine Learning, Computer Vision, Fuzzy Logic, and Natural Language Processing. We will talk about each of them, their concepts, how they work, and the related work on the Internet of Things fields

Artificial Intelligence in Chatbot Website Platform

In computing discourse, a Chatbot is a computer programme that is specifically designed to simulate an interactive communication or conversation. The interactive communication is from the machine to the user (human) either through text media, sound media or visual media. Chatbots have been widely used for practical purposes such as online assistance, personalised services, or information acquisition, including in the world of global marketing. The purpose of this research is to describe how a chatbot platform provides its best function in supporting a marketing task of a corporation at a global level. The methods used in this research are: 1) System Analysis, which is the collection of information needed in building the system must be done in detail. Where this information will support all the components needed to obtain results that are in accordance with all the needs related to the design of the system to be input, 2) Preparation of flowcharts, namely the design by entering data on the status of conversations that are commonly carried out by the Help-Desk with customers. Where when the user enters a word or sentence in the column that is already available in the system, a word or sentence search process will be carried out based on the noun, this process is useful for matching whether the input given by the user is in the set of nouns that have been trained in dialogue flow. The result obtained is that the website becomes one of the company's main media in marketing its products because the website already includes all information related to the product and also related to the company. However, when using the website alone, there is no direct communication with potential buyers. Therefore, this research will develop a chatbot that can improve the performance of the Spicering ltd website

Editor’s Note

This special issue has been designed with the primary objective of demonstrating the diversity of fields where AI is used and, consequently, how it is gaining increasing importance as a tool for analysis and research. In this sense, there are works related to the following topics: the use of AI with the IoT, campaign management, topic models and fusion methods, sales forecasting, price forecasting for electricity market, NLP techniques in computational medicine, evaluation of patient triage in hospital emergency settings, algorithms for solving the assignment problem, scheduling strategy for scientific workflow, driver fatigue detection mechanisms, virtual reality and specialized training, image segmentation, web service selection, multimedia documents adaptation, 3D navigation in virtual environments, multi-criteria decision-making methods and emotional states classification

A Holistic Methodology for Improved RFID Network Lifetime by Advanced Cluster Head Selection using Dragonfly Algorithm

Radio Frequency Identification (RFID) networks usually require many tags along with readers and computation facilities. Those networks have limitations with respect to computing power and energy consumption. Thus, for saving energy and to make the best use of the resources, networks should operate and be able to recover in an efficient way. This will also reduce the energy expenditure of RFID readers. In this work, the RFID network life span will be enlarged through an energy-efficient cluster-based protocol used together with the Dragonfly algorithm. There are two stages in the processing of the clustering system: the cluster formation from the whole structure and the election of a cluster leader. After completing those procedures, the cluster leader controls the other nodes that are not leaders. The system works with a large energy node that provides an amount of energy while transmitting aggregated data near a base station

Mamdani Fuzzy Expert System Based Directional Relaying Approach for Six-Phase Transmission Line

Traditional directional relaying methods for 6-phase transmission lines have complex effort, and so there is still a need for novel direction relaying estimation scheme. This study presents a Mamdani-fuzzy expert system (MFES) approach for discriminating faulty section/zone, classifying faults and locating faults in 6-phase transmission lines. The 6-phase fundamental component of currents, voltages and phase angles are captured at single bus and are used in the protection scheme. Simulation results substantiate that the protection scheme is very successful against many parameters such as different fault types, fault resistances, transmission line fault locations and inception angles. A large number of fault case studies have been carried out to evaluate reach setting and % error of proposed method. It provides primary protection to transmission line length and also offers backup protection for a reverse section of transmission line. The experimental results show that the scheme performs better than the other schemes

Awareness of Opportunities and Challenges Related to Artificial Intelligence in Health Sector of Developing Economies: Systematic Review Analysis

Purpose: Given the proper implementation of Artificial Intelligence (AI) technology, administrative and medical processes in the health sector of countries with low incomes can change quickly. This modification highlights the crucial influence of AI on a variety of health sector processes. Previous research indicates that AI may improve the standard of medical treatments. According to reports, AI technologies make life better for people by making it simpler, safer, and more productive. This study sought to identify the most significant potential and difficulties related to the application of AI in the health sector of emerging economies. Method: A thorough systematic literature review analysis was conducted using a total of 6 databases (Web of Science, ACM Digital, Science Direct, Emerald, IEEE, and Scopus). The selection was narrowed down to 49 articles after careful consideration in order to complete the review on potential AI possibilities and challenges that the health sector of developing economies need to be aware of.Results: The study found five major obstacles connected with AI adoption that requires attention in developing nations' health sectors: a lack of infrastructure, a lack of AI capabilities and skills, data integration, security, privacy, and legal concerns, as well as patient safety. The research also revealed six AI potentials that can aid the developing economy's health sector, including data exchange and availability, workflow management, cost reduction, resource management, professional training, and autonomous decision-making. It was discovered that AI has the ability to significantly outperform humans in terms of accuracy, efficiency, and timeliness of medical and associated administrative activities. Keywords: Artificial Intelligence, Opportunities, Challenges, Health Sector, Developing Economies, PRISMA DOI: 10.7176/CEIS/14-3-03 Publication date:August 31st 2023

Comparative Study on Ant Colony Optimization (ACO) and K-Means Clustering Approaches for Jobs Scheduling and Energy Optimization Model in Internet of Things (IoT)

The concept of Internet of Things (IoT) was proposed by Professor Kevin Ashton of the Massachusetts Institute of Technology (MIT) in 1999. IoT is an environment that people understand in many different ways depending on their requirement, point of view and purpose. When transmitting data in IoT environment, distribution of network traffic fluctuates frequently. If links of the network or nodes fail randomly, then automatically new nodes get added frequently. Heavy network traffic affects the response time of all system and it consumes more energy continuously. Minimization the network traffic/ by finding the shortest path from source to destination minimizes the response time of all system and also reduces the energy consumption cost. The ant colony optimization (ACO) and K-Means clustering algorithms characteristics conform to the auto-activator and optimistic response mechanism of the shortest route searching from source to destination. In this article, ACO and K-Means clustering algorithms are studied to search the shortest route path from source to destination by optimizing the Quality of Service (QoS) constraints. Resources are assumed in the active and varied IoT network atmosphere for these two algorithms. This work includes the study and comparison between ant colony optimization (ACO) and K-Means algorithms to plan a response time aware scheduling model for IoT. It is proposed to divide the IoT environment into various areas and a various number of clusters depending on the types of networks. It is noticed that this model is more efficient for the suggested routing algorithm in terms of response time, point-to-point delay, throughput and overhead of control bits

The quest for effective fundamental labour rights in the European post-pandemic scenario:Introducing principles of explainability and understanding for surveillance through AI algorithms and IoT devices.

Internet of Things’ devices (IoT) and Artificial Intelligence’s algorithms (AI) make workplaces’ surveillance unprecedentedly meticulous. Tracking workforce traits, behaviours and results is increasingly the new normal, also in light of the pandemic scenario, where working from home and workplace surveillance to prevent contagion have been (and will be) a necessity. This meticulousness indeed allows for a data-driven reorganisation of workplaces, significantly rewriting the way hierarchies are articulated in subordinate employment. Along this, the workforce and unions are confronted with often obscure surveillance, due to the high level of technical skills needed to understand how AI and IoT networks work. Fundamental labour rights such equal treatment, freedom of thought, assembly, association and prohibition of forced labour risk being affected by the ability – available nowadays to employers and third-party services – to gather important amounts of data relating to workforce and working environment, opening for potential lacks in fundamental freedoms’ enforcement. The first elaborations of the mentioned essential labour rights developed at the dawn of modern industrial society towards decent working conditions. Still today, part of the legal theory describes labour regulation as the countervailing power against the inherent inequality in subordinate employment, due to the hierarchies it entails. The role of those essential rights, again, shall take on a protective function for workers in the labour market. Some scholars claim the right to data protection and private life (art. 7, 8 CFREU, art. 8 ECHR) as rights 'enabling' the information symmetry, opening ‘black boxes’, safeguarding trade union freedoms, equal treatment and protection against forced labour conditions. Nevertheless, European jurisprudence is still divided. The jurisprudence ranges from identifying requirements of legitimacy, legality, necessity and proportionality with a duty of notification on employers under art. 8 ECHR (ECtHR, Bărbulescu v. Romania), to denying that duty as a necessary condition for workplace surveillance (ECtHR, Lopez Ribalda v. Spain) thus emphasizing the employer's free economic initiative. The proposed theoretical analysis aims at deriving from European jurisprudence on art. 4, 8-11 ECHR and 5,7-8,10-12 CFREU (e.g.: ECtHR Demir and Baykara v Turkey, Wilson and Palmer v UK) and the available literature two converging principles. The explainability of the technologies involved and their understanding, of which a definition will be provided from the existing literature relating to labour studies and privacy. This pair of principles would allow for a holistic perspective on privacy and private life as enablers of fundamental labour freedoms. In fact, these principles should be aimed not only as employees’ rights or employers’ obligations, but above all as compliance principles with fundamental labour rights applicable to third parties providing software and hardware. Explainability and understanding would act transversally, balancing at its source (providers or employers) developments and implementations of surveillance impacting fundamental labour rights