ISSN: 2518-3893
Submit Article
Submission Instructions
Open Access Information
Ethics and Malpractice Statement
Most Recent Issue
2023
Issue 4Issue 3Issue 2Issue 1
2022
Issue 4Issue 3Issue 18Issue 17
2021
Issue 16Issue 15Issue 14Issue 13
2020
Issue 12Issue 11Issue 10Issue 9
2018
Issue 8Issue 7
2017
Issue 6Issue 5
2016
Issue 4Issue 3Issue 2Issue 1

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sc/index

Editor(s)-in-Chief: Hiep Xuan Huynh and Fabien Ferrero

EAI Endorsed Transactions on Smart Cities is open access, peer-reviewed scholarly journal focused on applications for Smart Cities with leverage on big-data applications, ICT devices used in the factory of the future, HPC, industrial processes, energy efficiency systems, social platforms, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing.

INDEXING: DOAJ, CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions

Topics include, but are not limited to, the following:

  • Applications for Smart Cities able to leverage Big-Data Applications, ICT devices used in Factory of the Future, HPC, Industrial Processes, Energy Efficiency Systems, Social Platforms, etc.
  • Building Trust-based communication and computing systems for IoT and Clouds applications being Secure for Citizens
  • eHealth applications for Citizens.
  • Connecting in City Buildings for optimizing resources and utilities.
  • Software engineering methodology for the design of Smart Cities Systems
  • Energy Efficient and Green Software-defined Infrastructures aimed at Smart Cities facilities.
  • Re-allocation of resources and services (independently of their location) across distributed computing and geographically separated data storage and processing infrastructures. Inter-Cities along with Federation Services: availability, Disaster recovery, etc.
  • Federation of Services (IaaS, PaaS and SaaS) aimed at Public Administrations
  • Service discovery in Smart Cities - device Discovery and Data Discovery.
  • Easy and Large Deployment and autonomic management of densely interconnected and decentralised Cloud, Networks and IoTs infrastructures aimed at Smart Cities.
  • Control Architectures/Network Programmability in Smart Cities aimed at mobility, traffic and road networks, transport systems, energy, economy, and so on.
  • ICT applications enabling Energy-aware, smart and sustainable mobility solutions for Smart Cities.
  • Verification/Debugging/ Auditing Tools for all Applications in Smart Cities Domains
  • Energy-aware and sustainable computing solutions for Smart Cities applications

Call for Papers: Special issue on: Smart Tourism: Tourism in Smart Cities (Manuscript submission deadline: 2022-03-31; Notification of acceptance: 2022-04-15; Submission of final revised paper: 2022-05-15; Publication of special issue (tentative): 2022-06-15)

Guest Editor: Tiago C. Pereira (IESF's Research, Development and Innovation Centre-CIDI, Portugal)

Call for Papers: Special issue on: Smart Technologies: Applied Technology in Smart Cities (Manuscript submission deadline: 2022-06-30; Notification of acceptance: 2022-07-21; Submission of final revised paper: 2022-08-30; Publication of special issue (tentative): 2022-10-21)

Guest Editor: Tiago C. Pereira (IESF's Research, Development and Innovation Centre-CIDI, Portugal) Guest Editor: Antonio Abreu Silva (Instituto Politécnico do Porto, Portugal) Guest Editor: João Vidal de Carvalho (Polytechnic of Porto, Portugal)

  • Editors-in-Chief
  • Hiep Xuan Huynh, Can Tho University, Vietnam
  • Fabien Ferrero, Université de Côte d'Azur (UCA), France
  • Associate Editors            
  • Vincent Rodin, Université de Bretagne Occidentale (UBO), France
  • Serge Stinckwich, United Nations University (UNU), Macau
  • Thanh-Nghi Do, Can Tho University (CTU), Vietnam
  • Jean-François Dorville, The University of the West Indies, Jamaica / Université des Antilles Guyanes, Guadeloupe
  • Mahamadou Traore, Université Gaston Berger Saint-Louis, Senegal
  • Onil Goubier, CIRELA, Indonesia
  • Tsimitomby Briand, Institut Supérieur de Technologie d'Antsiranana (IST-ANTSIRANANA), Madagascar
  • Nghia Duong-Trung, German Research Center for Artificial Intelligence (DFKI), Germany
  • Mansha Swami, Morgan State University, USA
  • Area Editors      
  • Sergei Gorlatch, University of Muenster, Germany
  • Bao Hoai Lam, Can Tho University, Vietnam
  • Dung Van Hoang, Ho Chi Minh City University of Technology and Education (HCMUTE), Vietnam
  • Nguyen Nhat Vo, University of Michigan, USA
  • Nhat Minh Viet Vo, Hue University, Vietnam
  • Angelo Martella, University of Salento, Italy
  • Khoi Tan Nguyen, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Marco Zappatore, University of Salento, Italy
  • Thang Cong Pham, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Duy Khanh Ninh, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Thuy Thu Thi Pham, Nha Trang University (NTU), Vietnam
  • Yen-Wen Chen, National Central University, Taiwan
  • Anh Viet Nguyen, Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
  • Nghia Quoc Phan, Tra Vinh University, Vietnam
  • Vu Tran Pham, Ho Chi Minh City University of Technology, Vietnam National University (Ho Chi Minh City), Vietnam
  • Xia Wang, German Research Center for Artificial Intelligence (DFKI), Germany
  • Anand Nayyar, Duy Tan University, Viet Nam
  • Sheikh Faisal Rashid, German Research Center for Artificial Intelligence (DFKI), Germany
  • Tung Kieu, Aalborg University, Denmark
  • Chinh Quoc Bui, Elsevier, Netherlands
  • Binh Thanh Nguyen, Monash University, Australia
  • Viet Xuan Le, Quy Nhon University, Vietnam
  • Lang Van Tran, Vietnam Academy of Science and Technology, Vietnam
  • Son Hoang Le, Information Technology Institute, Vietnam National University (Hanoi), Vietnam
  • Vinh Quoc Tran Nguyen, University of Science and Education, The University of Da Nang (DUT-UDN), Vietnam
  • Toan Nang Do, Vietnam National University - Hanoi, Vietnam
  • Son Xuan Ha, RMIT University Vietnam
  • Hanh Hong Thi Nguyen, Can Tho University of Technology, Vietnam
  • Eloi Keita, Université de Bretagne Occidentale (UBO), France
  • Congduc Pham, Université de Pau et des Pays de l’Adour (UPPA), France
  • An Cong Tran, Can Tho University (CTU), Vietnam
  • Duong Ngoc Vo, University of Science and Technology - The University of Da Nang (DUT-UDN), Vietnam
  • Hanh Huu Hoang, Ministry of Information and Communications, Vietnam

  • Enhancing precision agriculture: An IoT-based smart monitoring system integrated LoRaWAN, ML and AR

    Appears in:
    sc 24(4):

    Authors:
    Nguyen Thi Hang Duy, Do Thanh Huong, Kou Yamada, Pham Vu Minh Tu, Huu Hoang Hanh

    Published:
    20th Nov 2024

    Abstract:
    Effective crop production and harvesting decisions rely on proper farm monitoring and management. Each region has distinct needs for farm oversight, but the primary focus remains on collecting and evaluating environmental data such as temperature, soil moisture, air humidity, all of which are vital to plant growth. Gathering this data on a large scale requires significant effort and is often based on intuition or simple measurement tools. This paper proposes a novel solution for farming data collection using an IoT platform integrated Long-Range Wide Area Networks (LoRaWAN) network application with Augmented Reality (AR) technology and Machine Learning (ML) algorithms to predict key environmental daily indexes. In a pilot study in Quang Tho, Vietnam, the system accurately predicted environmental conditions, reduced the risk of crop failure, and improved farm management efficiency. This approach enhances real-time data interaction and offers predictive analytics, supporting sustainable agriculture.

  • Multimodal Sentiment Analysis in Natural Disaster Data on Social Media

    Appears in:
    sc 24(4):

    Authors:
    Sefa Dursun, Süleyman Eken

    Published:
    14th Nov 2024

    Abstract:
    INTRODUCTION: With the development of the Internet, users tend to express their opinions and emotions through text, visual and/or audio content. This has increased the interest in multimodal analysis methods. OBJECTIVES: This study addresses multimodal sentiment analysis on tweets related to natural disasters by combining textual and visual embeddings. METHODS: The use of textual representations together with the emotional expressions of the visual content provides a more comprehensive analysis. To investigate the impact of high-level visual and texual features, a three-layer neural network is used in the study, where the first two layers collect features from different modalities and the third layer is used to analyze sentiments. RESULTS: According to experimental tests on our dataset, the highest performance values (77% Accuracy, 71% F1-score) are achieved by using the CLIP model in the image and the RoBERTa model in the text. CONCLUSION: Such analyzes can be used in different application areas such as agencies, advertising, social/digital media content producers, humanitarian aid organizations and can provide important information in terms of social awareness.

  • Development of a Smart Waste Management System with Automatic Bin Lid Control for Smart City Environment

    Appears in:
    sc 24(3):

    Authors:
    Abdussalam El-Suleiman, Teng David Chollom, Rabiu B. Ahmad, Ameer Mohammed, Mathias Usman Bonet, Abbas Abdullahi

    Published:
    18th Apr 2024

    Abstract:
    As cities worldwide transform into smart, interconnected urban environments, the management of municipal waste emerges as a pressing challenge. This paper, titled "Development of a Smart Waste Management System with Automatic Bin Lid Control," offers a sophisticated solution that integrates seamlessly within the concept of smart cities. This system harnesses the power of the Internet of Things (IoT) to optimize waste collection and enhance urban cleanliness. The primary aim of this project is to create a smart waste management system that extends beyond traditional waste bins. It introduces a network of intelligent waste containers equipped with automatic lid control mechanisms that operate based on real-time waste level data. When approaching these bins, the lid control mechanism automatically opens the bin only if the waste level is not full, thereby facilitating convenient waste disposal. In the context of smart cities, this innovative approach presents several advantages. It optimizes waste collection efficiency by prioritizing bins in need of immediate attention and ensures that waste containers are not prematurely emptied, reducing unnecessary waste disposal trips. Moreover, the system enables city authorities to gain insights into waste level trends, fostering data-driven and proactive waste management strategies for a cleaner, more sustainable urban environment.

  • Vehicle counting application utilizing background subtraction method with large-scale camera data

    Appears in:
    sc 24(3):

    Authors:
    Mien Puoc Doan, Vu The Tran, Sy Ngo Van

    Published:
    17th Apr 2024

    Abstract:
    In modern society, people are increasingly using cameras at home, in shops, and on the streets. Traffic systems have also invested in building more surveillance camera systems. The data collected by cameras contains valuable information for traffic regulation and recording traffic violations. The challenge is how to effectively use this data. In this article, we will discuss the use of real-time data from surveillance cameras on some roads in Da Nang City for vehicle counting using background subtraction methods. Additionally, we also tested the detection of red light violations to contribute to the development of a smart traffic system. So, the use of background subtraction in analyzing real-time data from surveillance cameras can greatly improve traffic management

  • A Contemporary Approach to Designing and Implementing Electronic Voting Systems (EVS)

    Appears in:
    sc 24(3):

    Authors:
    Benedict C. Nwachuku, Samuel Chris Quist, Adams Addison Kobla Azameti, Godfred Koi-Akrofi

    Published:
    14th Mar 2024

    Abstract:
    This study delves into the potential of Electronic Voting Systems (EVS) in Ghana, to enhance transparent and trustworthy electoral processes. We presented a comprehensive framework highlighting trust, diaspora engagement, and human factors in voting. The study proposes a robust EVS framework for Ghana, emphasizing trust and accountability, preventing electoral fraud, and encouraging African governments to invest in IT and collaborate with experts in e-government and e-voting systems. We commence with a detailed systems analysis, identifying specific electoral challenges in Ghana. An artifact is designed and developed, and the artifact's effectiveness is demonstrated through Design Science Research Methodology (DSRM). We evaluated its alignment with the desired solution for Ghana's electoral issues. We emphasize the potential of EVS to address electoral challenges in Ghana and underscore the importance of proactive government policies, IT investments, and collaboration with IT experts. User acceptance testing achieved a remarkable 98% approval rate, showcasing the feasibility of implementing EVS at the national level. This research underlines the pivotal role of EVS in Ghana and advocates for visionary government policies and investments in IT. These measures can modernize electoral systems, align them with international standards, and promote democratic progress while preventing electoral fraud in Ghana and other African nations to avoid condemnation and punishment of unconstitutional transfers of power that are being challenged by meticulously planned takeovers.  

  • Evaluation of Frameworks for MLOps and Microservices

    Appears in:
    sc 23(3):

    Authors:
    Rogério Rossi, Igor Urias

    Published:
    13rd Nov 2023

    Abstract:
    Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration.

  • Welcome to the second issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities

    Appears in:
    sc 23(2):

    Authors:
    Dario Vieira

    Published:
    9th Oct 2023

    Abstract:

  • Intelligent Aircraft Hangar Fire Detection and Location System Based on Wireless Sensor Network

    Appears in:
    sc 23(2): e5

    Authors:
    Ameer Muhammed, Ubadike Obunike Arinze, Ubadike Osichinaka Chiedu, Mathias Usman Bonet, Abbas Abdullahi

    Published:
    5th Oct 2023

    Abstract:
    Aircraft hangar fire detection systems are essential for protecting both the facility's assets and the contents of an aircraft. In terms of predicting a fire outbreak at an aircraft hangar, the Intelligent Aircraft Hangar Fire detection is considered as a high-performance system that is designed based on the principle of a wireless sensor network (WSN), which operates by employing three sensor nodes at different locations inside the aircraft hangar to transmit gas concentrations in the air to a base station (BS) and send the resulting data from the sensor nodes to a server for analysis and visualization of the risk level. The server uses Machine Learning (ML) techniques to analyze the acquired data along with the sample gas data and displays the report in real time. When the smoke (gas) concentration is high, the server predicts a fire outbreak by displaying a high concentration zone on the Graphic User Interface (GUI). By this, the server automatically issues a warning and identifies the potential fire location. The technology is built to protect aircraft assets, hangar buildings, and human (personnel) life. A crucial part in the early detection of fire is played by the intelligent system

  • Applied Design and Methodology of Delivery Robots Based on Human–Robot Interaction in Smart Cities

    Appears in:
    sc 23(2):

    Authors:
    Tiande Mo, Kam Wah Fan, Ki Sing Li, Wing Ting LAW

    Published:
    26th Jun 2023

    Abstract:
    This paper proposes an optimised design of an autonomous delivery robot while adopting the latest technologies from the different branching fields of robotics, artificial intelligence, and tele-communication. As a prospective representation of a user-centric robot design, the proposal is design with the major focus on maximizing users’ satisfaction throughout every human–robot interaction (HRI) touchpoints. By the use of sensor fusion techniques along with the deployment of an image-detection-based technique accompanying the point-cloud-detection-based path-planning methodology, the robot delivery would be optimised with effective path-planning and obstacle avoidance capability. With the extension of 5G connectivity, it is proposed that the real-time status update and video stream would enable greater efficiency in terms of remote monitoring and centralised robot administration.

  • Editorial: Welcome to the first issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities

    Appears in:
    sc 23(1): e1

    Authors:
    Mohammad Derawi

    Published:
    30th Mar 2023

    Abstract:

  • Smart Tourism Ecosystem perspective on the Tourism Experience: A conceptual approach

    Appears in:
    sc 22(4): e3

    Authors:
    Cláudia Seabra, Pedro Vaz Serra, Ana Caldeira

    Downloads:
    804

    Abstract:
    The smart tourism ecosystem concept, in addition to integrating various components, processes, and actions in the design of a place, advocates certain results through the convergence of technological resources, business environments, and value-inducing experiences. This conceptual paper should result in theoretical contributions regarding the specificity of the tourist experience within the framework of a smart tourism ecosystem, with a view to the competitiveness and sustainability of accommodation and destinations. From the perspective of a smart tourism ecosystem, the production and consumption of tourist value − which, hopefully, should be socially, culturally, environmentally, and economically sustainable −, is shared, and generate distinctive experiences, and the corresponding interactions are promoted by technology, through the collection, processing, and communication of data. The suggested approach has relevant implications at the management level, given the need to obtain differentiating factors, mediated by technology, with the incorporation of added value for the stakeholders.  

  • Medical Data Analytics and Wearable Devices

    Appears in:
    sc 22(4): e2

    Authors:
    John Amose , Sowmiya E C , Iswarya Manoharan, Jeslin Libisha J, Harishma S.

    Downloads:
    474

    Abstract:
    Clinical decision-making may be directly impacted by wearable application. Some people think that wearable technologies, such as patient rehabilitation outside of hospitals, could boost patient care quality while lowering costs. The big data produced by wearable technology presents researchers with both a challenge and an opportunity to expand the use of artificial intelligence (AI) techniques on these data. By establishing new healthcare service systems, it is possible to organise diverse information and communications technologies into service linkages. This includes emerging smart systems, cloud computing, social networks, and enhanced sensing and data analysis techniques. The characteristics and features of big data, the significance of big data analytics in the healthcare industry, and a discussion of the effectiveness of several machine learning algorithms employed in big data analytics served as our conclusion.

  • Securing Data using Audio Steganography for the Internet of Things

    Appears in:
    sc 23(4): e5

    Authors:
    Anju Gera, Dr. Vaibhav Vyas

    Downloads:
    471

    Abstract:
    The Internet of Things (IoT) is prevalent in today's world and is part of our everyday life. While the residential district gains in several respects, numerous problems are developed, such as data confidentiality and privacy. The community is worried, in reality, about what information might leak through IoT. Therefore, the need for a protected environment is necessary if data transmission from devices across the network is to be protected. As a consequence, this paper proposes a secure scheme for using audio steganography to secure data from Laptop, which is distributed as an IoT device to other devices, or on LAN or WAN networks, as an alternative protection strategy along with a home server. The outcome of the developed system shows that the amount of distortion exposed by the Signal to Noise Ratio (SNR) is low.  

  • An Efficient Substitution Box design with a chaotic logistic map and Linear Congruential Generator for secure communication in Smart cities

    Appears in:
    sc 23(1): e6

    Authors:
    Noshina Tariq , Muhammad Asim Hashmi

    Downloads:
    469

    Abstract:
    The study provides a unique method for creating an efficient substitution box (S-box) for advanced encryption standards using a Chaotic Logistic Map (CLM) and a Linear Congruential Generator (LCG) (AES) for secure communications in a smart city. The Pseudo-Random Number Generator (PRNG), which is further examined, is constructed using an extensive search of reasonable possibilities for the initial seed and set parameters. Using statistical testing, the performance analysis of the new S-box is assessed. Additionally, the resilience of differential, as well as linear cryptanalysis, is shown. It is derived using other features, including nonlinearity, the Bit Independence Criterion (BIC), and the Strict Avalanche Criterion (SAC). The suggested S-box has good potential and is usable for symmetric key cryptography, according to the features of the new S-cryptographic box.

  • Evaluation of Frameworks for MLOps and Microservices

    Appears in:
    sc 23(3):

    Authors:
    Rogério Rossi, Igor Urias

    Downloads:
    425

    Abstract:
    Information Technology involves solutions for many kinds of industries and organizations, offering conditions for solving problems of different types and complexities. Artificial Intelligence, and more specifically applications that considers Machine Learning (ML) and Software Technology are part of these solutions for solving problems, including solutions for solving problems that involve smart cities approach. In order to present frameworks that deal with the operationalization of Machine Learning and Software technology, this article is based on the study and evaluation of frameworks that involve Machine Learning Operations (MLOps) and microservices. Specifically, three frameworks that integrate ML algorithms with microservices are evaluated based on a bibliographical review in scientific journals of relevance to the area. From an exploratory analysis of these frameworks, it was possible to highlight their main objectives, their benefits, and their ability to offer solutions that favor the large-scale use of Machine Learning algorithms in problem solving. The main results are highlighted in the article through a qualitative analysis that considers six evaluation criteria, such as: capacity for sharing resources, scope of use by users, and use in a cloud environment. The results achieved are satisfactory since the work allows, through a qualitative view of the evaluated frameworks, a perspective of how the integration of MLOps and microservices has been carried out, its benefits and possible results achieved through this integration.

  • An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks

    Appears in:
    sc 23(1): e4

    Authors:
    Abdul Basit, Muhammad Tehmasib Ali Tashfeen, Hina Shoukat, Ali Haider, Usman Haider, Muhammad Yaseen Ayub

    Downloads:
    422

    Abstract:
    INTRODUCTION: Internet of Things (IoT) along with Cloud based systems are opening a new domain of development. They have several applications from smart homes, Smart farming, Smart cities, smart grid etc. Due to IoT sensors operating in such close proximity to humans and critical infrastructure, there arises privacy and security issues. Securing an IoT network is very essential and is a hot research topic. Different types of Intrusion Detection Systems (IDS) have been developed to detect and prevent an unauthorized intrusion into the network. OBJECTIVES: The paper presents a Machine Learning based light, fast and reliable Intrusion Detection System (IDS). METHODS: Multiple Supervised machine learning algorithms are applied and their results are compared. Algorithms applied include Linear Discriminant analysis, Quadratic Discriminant Analysis, XG Boost, KNN and Decision Tree. RESULTS: Simulation results showed that KNN Algorithm gives us the highest accuracy, followed by XG Boost and Decision Tree which are not far behind. CONCLUSION: A fast, secure and intelligent IDS is developed using machine learning algorithms. The resulting IDS can be used in various types of networks especially in IoT based networks.

  • Intelligent Dashboards to Monitor the Occurrences in Smart Cities – A Portuguese Case Study

    Appears in:
    sc 22(4): e4

    Authors:
    Filipe Portela, Maria Silva, Rita Silva, Henrique Santos, Gustavo Caldas

    Downloads:
    370

    Abstract:
    This article concerns the needed response by the Professional Fire Brigade Regiment (FBR) in the city of Lisbon. To solve and answer the question "How to improve FBR intervention requests when an emergency is detected?" the project aims to create a functional prototype containing interactive dashboards allowing the analysis of indicators that improve decision capacity. As results attest, 58% of false alarms are cancelled even after the emergency and rescue means have been activated to the location. About 97% of the suspended requests are not cancelled before the means are sent. The number of records of occurrences tends to increase over the 8 years of study. Sunday is the weekday with the highest number of associated records, with 23.33%, specifically at 9 am and 8 pm. Autumn is the season with more occurrences, with 26.51%. More than 50% of the occurrences are in the administrative services closing time and more than 50% of the registrations send only one vehicle to the place. These indicators aim to understand if these variables are probabilistically associated with requests for interventions to be able to anticipate these scenarios and help in decision-making whenever necessary.

  • AI for Healthy Meal Preparation in Smart Cities

    Appears in:
    sc 22(4): e1

    Authors:
    Bhuvana Namasivayam

    Downloads:
    370

    Abstract:
    INTRODUCTION: ‘Food is medicine’. Eating healthy fresh cooked foods is increasingly becoming a challenge, especially among working professionals, elderly people, people in care homes and those getting medical care, as they find it difficult to cook everyday meals and to make sure they take in all necessary nutrients regularly. OBJECTIVES: With the intervention of Robotics and AI, food preparation and delivery can be made efficient in a way it supports overall health and wellbeing. METHODS: The proposed idea is a smart city AI scheme with robots engaged in food preparation tasks such as chopping, grating etc, robotic kitchens assembled to prepare foods as per the dietary needs of various groups of people and delivery bots and drones to effectively deliver meals, fruits and necessary supplements on a daily basis and also pick up leftovers for effective waste management. This can also be extended to smart hospitals for providing nutritious meals to patients to aid in faster recovery and also avoid the carelessness and haste in food preparation when human workers are involved.

  • Investigation of Blockchain for COVID-19: A Systematic Review, Applications and Possible Challenges

    Appears in:
    sc 23(1): e5

    Authors:
    Syed Haider Ali , Sana Ullah , Farhatullah , Shah Hussain Badshah , Muhammad Abul Hassan , Naimullah , Muhammad Imad , Shabir khan

    Downloads:
    349

    Abstract:
    Smart city is emerging application in which many Internet of Things (IoT) devices are embedded to perform overall monitoring and perform processing automatically. In smart city the authenticity is key problem and many users in the in smart city has faced challenges during COVID-19. The COVID-19 epidemic, a deadly virus, first appeared in the globe in 2019. The World Health Organization (WHO) states that it is almost certainly feasible to contain this virus in its early phases if some precautions are taken. To contain the infection, most nations declared emergencies both inside and outside their borders and prohibited travel. Artificial intelligence and blockchain are being used in smart city applications to monitor the general condition in the nation and reduce the mortality rate. Blockchain has also made it possible to safeguard patient medical histories and provide epidemic tracking. AI also offers the ideal, wanted answer for correctly identifying the signs. The primary goal of this study is to fully investigate blockchain technology and artificial intelligence (AI) in relation to COVID-19. A case study that was recently developed to identify and networked pathogens acquired important knowledge and data. Additionally, AI that can handle massive quantities of medical data and perform difficult jobs will be able to reduce the likelihood of intricacy in data analysis. Lastly, we highlight the present difficulties and suggest potential paths for addressing the 19 diseases in future circumstances.

  • Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review

    Appears in:
    sc 22(1): e2

    Authors:
    Farhatullah , Aimen Shoukat, Muhammad Abul Hassan, Muhammad Rizwan, Syed Haider Ali, Sana Ullah, Muhammad Imad

    Downloads:
    343

    Abstract:
    The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications.
Publisher
EAI
ISSN
2518-3893
Number of Volumes
7
Last Published
2024-11-13
EAI Logo

About EAI

Community

Publish with EAI