Hasil untuk "iot"

Shama Siddiqui, Anwar Ahmed Khan, Nicola Marchetti

With the growing applications of the Internet of Things (IoT), a major challenge is to ensure continuous connectivity while providing prioritized access. In dense IoT scenarios, synchronization may be disrupted either by the movement of nodes away from base stations or by the unavailability of reliable Global Navigation Satellite System (GNSS) signals, which can be affected by physical obstructions, multipath fading, or environmental interference, such as such as walls, buildings, moving objects, or electromagnetic noise from surrounding devices. In such contexts, distributed synchronization through Non-Orthogonal Multiple Access (NOMA) offers a promising solution, as it enables simultaneous transmission to multiple users with different power levels, supporting efficient synchronization while minimizing the signaling overhead. Moreover, NOMA also plays a vital role for dynamic priority management in dense and heterogeneous IoT environments. In this article, we proposed a Two-Stage NOMA-Enabled Framework "TSN-IoT" that integrates the mechanisms of conventional Precision Time Protocol (PTP) based synchronization, distributed synchronization and data transmission. The framework is designed as a four-tier architecture that facilitates prioritized data delivery from sensor nodes to the central base station. We demonstrated the performance of "TSN-IoT" through a healthcare use case, where intermittent connectivity and varying data priority levels present key challenges for reliable communication. Synchronization speed and end-to-end delay were evaluated through a series of simulations implemented in Python. Results show that, compared to priority-based Orthogonal Frequency Division Multiple Access (OFDMA), TSN-IoT achieves significantly better performance by offering improved synchronization opportunities and enabling parallel transmissions over the same sub-carrier.

Luisa Schuhmacher, Jimmy Fernandez Landivar, Ihsane Gryech et al.

The Internet of Things (IoT) has become integral to modern technology, enhancing daily life and industrial processes through seamless connectivity. However, the rapid expansion of IoT systems presents significant sustainability challenges, such as high energy consumption and inefficient resource management. Addressing these issues is critical for the long-term viability of IoT networks. Machine learning (ML), with its proven success across various domains, offers promising solutions for optimizing IoT operations. ML algorithms can learn directly from raw data, uncovering hidden patterns and optimizing processes in dynamic environments. Executing ML at the edge of IoT networks can further enhance sustainability by reducing bandwidth usage, enabling real-time decision-making, and improving data privacy. Additionally, testing ML models on actual hardware is essential to ensure satisfactory performance under real-world conditions, as it captures the complexities and constraints of real-world IoT deployments. Combining ML at the edge and actual hardware testing, therefore, increases the reliability of ML models to effectively improve the sustainability of IoT systems. The present systematic literature review explores how ML can be utilized to enhance the sustainability of IoT networks, examining current methodologies, benefits, challenges, and future opportunities. Through our analysis, we aim to provide insights that will drive future innovations in making IoT networks more sustainable.

Sira Yongchareon, Jian Yu, Jing Ma

Internet of Things (IoT) technology has continuously advanced over the past decade. As a result, device-free indoor localization functions have become a crucial part of application areas such as healthcare, safety, and energy management. Passive infrared (PIR) sensors detecting changes in temperature in an environment are one of the suitable options for human localization due to their lower cost, low energy consumption, electromagnetic tolerance, and enhanced private awareness. Although existing localization methods, including machine/deep learning, have been proposed to detect multiple persons based on signal phase and amplitude, they still face challenges regarding signal quality, ambiguity, and interference caused by the complex, interleaving movements of multiple persons. This paper proposes a novel deep learning method for multi-person localization using channel separation and template-matching techniques. The approach is based on a deep CNN-LSTM architecture with ensemble models using a mean bagging technique for achieving higher localization accuracy. Our results show that the proposed method can estimate the locations of two participants simultaneously with a mean distance error of 0.55 m, and 80% of the distance errors are within 0.8 m.

Sridevi Tumula, Y. Ramadevi, M. Rudra Kumar et al.

Abstract The Internet of Things (IoT) faces increasing security challenges, as traditional encryption-based methods often fail to prevent cyberattacks while maintaining reliable and cost-effective communication. To overcome these issues, this work introduces a security model that enhances existing frameworks by identifying and analyzing malicious data flows, significantly improving IoT network protection. The proposed approach connects blockchain-based Software-Defined Network (SDN) controllers with SDN switches and IoT applications, creating a highly secure and energy-efficient communication system. This framework detects potential threats, minimizes security risks, and improves overall network performance. To support real-time monitoring, two types of messages are introduced: non-contemporary beacons, which inform SDN controllers about network events through blockchain-SDN switches, and encoded statistical beacons, which provide detailed insights for further security analysis. The proposed method demonstrates substantial improvements over existing technologies, including a 76% reduction in network overhead, 82% lower latency, 73% stronger resistance to malicious data flows, 54% higher throughput, 32% less bandwidth usage, 65% faster algorithm execution, and 41% lower memory consumption. These results highlight the effectiveness of the model in providing faster, safer, and more efficient IoT communication.

Yiqiong Liang, Mingwan Luo

Abstract The Internet of Things (IoT) has been proposed to pose a greater risk of cyberattacks due to the large amounts of data traffic and the diverse range of devices. The main limitations of traditional centralized intrusion detection systems (IDSs) are attributed to privacy risks, high communication costs, and poor scalability. The research presents a distributed, privacy-preserving framework for intrusion detection, which combines Federated Learning (FL) with a new Deep Learning model that performs and optimizes network intrusions to collect and analyze aspects of “federated” augmentation, then improve security in Web usage. The particular method includes Recursive Feature Elimination (RFE) for the reduction in characteristics, the Federated Kalman Filter (FKF) to reduce noise, and an Adaptive Artificial Fish Swarm optimized Long Short-Term Memory (AdapAFS-LSTM) model for accurate detection of multi-type network intrusions. The model parameters are distributed based on IoT model nodes and do not share raw data. Model parameters learn from IoT nodes, which are combined based on the Federated Proximal (FedProx) algorithm and can be applied toward the development of a robust global IDS. Experimental evaluation of the distributed and privacy-preserving intrusion detection framework on the Multi-Type Network Attack Detection (M-TNAD) dataset demonstrated superior performance in achieving 99.79% accuracy, F1-score, precision, and recall, showing low resource consumption in the final execution time and performance metrics. This work demonstrates the potential of implementing a federated, optimization-driven deep learning method to effectively develop an IDS solution against IoT networks through optimization methodology and machine learning.

Yi liu, Yan Li, Nianming Ge

Abstract The swift expansion of the Internet of Things (IoT) has resulted in heightened energy requirements and sustainability issues inside extensive wireless sensor networks. This research presents the Energy-Aware Adaptive Virtualization and Migration (EAVM) protocol to tackle these difficulties in Green IoT-based Wireless Sensor Networks. The technique combines Federated Deep Reinforcement Learning (FDRL) with hybrid solar–RF energy harvesting to facilitate intelligent and sustainable resource management. EAVM allocates and migrates virtual resources dynamically according to real-time energy conditions, ensuring workload balance and extended network stability. A thorough simulation methodology assesses its performance relative to contemporary state-of-the-art techniques, illustrating that EAVM attains enhanced energy efficiency, scalability, and sustainability within dynamic IoT systems.

Hui Wei, Dong Yoon Lee, Shubham Rohal et al.

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security & privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.

Neder Karmous, Mohamed Ould-Elhassen Aoueileyine, Manel Abdelkader et al.

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people’s lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices’ security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

Ayodeji Okubanjo, bashir olufemi odufuwa, Dr, Benjamin Olabisi Akinloye, Dr et al.

The recent growth in car use and population have been identified as potential drivers of municipal traffic congestion, particularly in emerging nations with inadequate road networks. In Nigeria, for example, traffic wardens and traffic lights are prominent traffic control measures used to ease traffic congestion at major road intersections. However, stress, public anger, and rash traffic signal judgments restrict the effectiveness of these tactics, resulting in delayed mobility, decreased transit times, and a climate disaster. Recent solutions have emphasized emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Artificial Neural Network (ANW). Consequently, an efficient use of these technologies can provide a sustainable future for city traffic management in Sub-Saharan African. This model seeks to develop a low cost internet-of-things traffic surveillance system to improve vehicles mobility on a Nigerian closed campus. The goal is to alleviate the academic community's problem of peak-hour traffic congestion by delivering real-time traffic updates

Sukhpal Singh Gill, Minxian Xu, Panos Patros et al.

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported “hallucinations” within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

FL Lino

Objetivo: Relato de caso de tratamento da crise do angioedema hereditário (AEH) com infusão de Plasma Fresco Congelado (PFC). Materiais e métodos: Relato um caso de paciente masculino, 35a, 73Kg, que veio ao PS (D0) com queixa de edema de face, progressivo e iniciado em reg. ocular havia 12h. Referiu diagnóstico de AEH e uma internação anterior por crise. Negou alergias ou outras doenças. No exame físico: edema importante de face e dificuldade para respirar. Exames laboratoriais iniciais normais (hemograma, creatinina, transaminases, eletrólitos e PCR). Optado pela intubação (IOT) e ventilação mecânica pelo rsico de asfixia. Prescrito infusão de 2u PFC de 6/6h e pulsoterapia com Solumedrol. Foi extubado no D3 e suspenso PFC no D4. Teve regressão completa do quadro. Alta hospitalar no D6. Discussão: AEH é uma doença genética rara (1,5 casos/100.000 hab), potencialmente fatal, causada por variantes genéticas do gene (SERPING1) inibidor do complemento 1 (C1) o que leva à deficiência quantitativa (85%) ou funcional (15%) do inibidor de C1 (AEH-C1-INH). A proteína inibidora de C1 é um importante regulador do sist. Complemento e de múltiplas proteases incluindo o fator XII. Sua deficiência leva a hiperativação do sist. Complemento e a produção desregulada do fator XIIa, o que promove à ativação descontrolada da pré-calicreína plasmática em calicreína que pode então clivar o cininogênio de alto peso molecular que finalmente libera a bradicinina (BK), poderoso vasodilatador, e considerado o principal mediador associado às manifestações clínicas. Manifesta-se por crises recorrentes de edema transitório, circunscrito, assimétrico, deformante, não inflamatório, não pruriginoso, as vezes doloroso, e que acomete o tecido subcutâneo e o submucoso, especialmente face, membros, alças intestinais e vias respiratórias. 91% das crises possuem fatores desencadeantes (físicos, psicológicos, infecciosos, medicamentosos ou hormonais). A dosagem do nível sérico de C4 (depleção) pode ser usada na triagem diagnóstica. A avaliação quantitativa e funcional do C1-INH são recomendadas para a confirmação. A dosagem do C1q ou estudo genético são opções para casos específicos (subtipo AEH-nC1- INH) ou sem história familiar (25%). As crises devem ser tratadas o mais cedo possível com o antagonista do receptor de BK (Icatibanto) ou o concentrado do inibidor de C1 derivado do plasma (pdC1-INH). A profilaxia de longo prazo deve ser feita com medicamentos de 1°linha, como pdC1-INH ou o anticorpo monoclonal anti-calicreína (Lanadelumabe). Como 2°linha temos os andrógenos atenuados (AA)-Danazol que aumentam a síntese hepática de C1-INH. Na profilaxia de curto prazo, antes de procedimentos que podem desencadear crises, o uso do pdC1-INH é preconizado. Na falta do pdC1-INH, o PFC pode ser prescrito na dose de 10ml/Kg. Há relato de um caso de possível cura após transplante hepático. Nosso paciente teve uma crise típica revertida com o uso exclusivo de PFC. Conclusão: O uso de PFC no AEH é considerado de 2°linha na profilaxia ou última opção para a crise. Entretanto, é uma alternativa nos locais onde as medicações de 1°linha não são de fácil acesso ou não estão disponíveis. Com o advento de novas drogas, inclusive por via oral, a prevenção e o tratamento de crises poderão se tornar mais eficientes reduzindo o risco de morte, os riscos transfusionais associado ao PFC e melhorando sensivelmente a qualidade de vida desses pacientes.

Muhammad Zubair Islam, A. S. M. Sharifuzzaman Sagar, Hyung Seok Kim

Over the past few years, life expectancy has increased significantly. However, elderly individuals living independently often require assistance due to mobility issues, symptoms of dementia, or other health-related challenges. In these situations, high-quality elderly care systems for the aging population require innovative approaches to guarantee Quality of Service (QoS) and Quality of Experience (QoE). Traditional remote elderly care methods face several challenges, including high latency and poor service quality, which affect their transparency and stability. This paper proposes an Edge Computational Intelligence (ECI)-based haptic-driven ECI-TeleCaring system for the remote caring and monitoring of elderly people. It utilizes a Software-Defined Network (SDN) and Mobile Edge Computing (MEC) to reduce latency and enhance responsiveness. Dual Long Short-Term Memory (LSTM) models are deployed at the edge to enable real-time location-aware activity prediction to ensure QoS and QoE. The results from the simulation demonstrate that the proposed system is proficient in managing the transmission of data in real time without and with an activity recognition and location-aware model by communication latency under 2.5 ms (more than 60%) and from 11∼12 ms (60∼95%) for 10 to 1000 data packets, respectively. The results also show that the proposed system ensures a trade-off between the transparency and stability of the system from the QoS and QoE perspectives. Moreover, the proposed system serves as a testbed for implementing, investigating, and managing elder telecaring services for QoS/QoE provisioning. It facilitates real-time monitoring of the deployed technological parameters along with network delay and packet loss, and it oversees data exchange between the master domain (human operator) and slave domain (telerobot).

Kyuseok Kim, Yeonkyeong Kim, Young Sam Kim et al.

A ventilator central monitoring system (VCMS) that can efficiently respond to and treat patients’ respiratory issues in intensive care units (ICUs) is critical. Using Internet of Things (IoT) technology without loss or delay in patient monitoring data, clinical staff can overcome spatial constraints in patient respiratory management by integrated monitoring of multiple ventilators and providing real-time information through remote mobile applications. This study aimed to establish a VCMS and assess its effectiveness in an ICU setting. A VCMS comprises central monitoring and mobile applications, with significant real-time information from multiple patient monitors and ventilator devices stored and managed through the VCMS server, establishing an integrated monitoring environment on a web-based platform. The developed VCMS was analyzed in terms of real-time display and data transmission. Twenty-one respiratory physicians and staff members participated in usability and satisfaction surveys on the developed VCMS. The data transfer capacity derived an error of approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula>, and the difference in data transmission capacity was approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mn>1.99</mn><mo>×</mo><mn>10</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup><mo>±</mo><msup><mrow><mn>9.97</mn><mo>×</mo><mn>10</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup></mrow></semantics></math></inline-formula> with a 95% confidence interval of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mo>−</mo><mn>1.16</mn><mo>×</mo><mn>10</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula> to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mn>5.13</mn><mo>×</mo><mn>10</mn></mrow><mrow><mo>−</mo><mn>7</mn></mrow></msup></mrow></semantics></math></inline-formula> among 18 ventilators and patient monitors. The proposed VCMS could transmit data from various devices without loss of information within the ICU. The medical software validation, consisting of 37 tasks and 9 scenarios, showed a task completion rate of approximately 92%, with a 95% confidence interval of 88.81–90.43. The satisfaction survey consisted of 23 items and showed results of approximately 4.66 points out of 5. These results demonstrated that the VCMS can be readily used by clinical ICU staff, confirming its clinical utility and applicability. The proposed VCMS can help clinical staff quickly respond to the alarm of abnormal events and diagnose and treat based on longitudinal patient data. The mobile applications overcame space constraints, such as isolation to prevent respiratory infection transmission of clinical staff for continuous monitoring of respiratory patients and enabled rapid consultation, ensuring consistent care.

Naigen Li, Xiaohu Liu

The aim of this research is to devise and assess a holistic approach for optimizing the performance of the Internet of Things Sensor Network (IoTSN), thereby enabling it to better adapt to diverse and unpredictable environments. Initially, this study delves into the self-organizing network, achieving intelligent nodal cooperation and allowing the network to autonomously reshape its topology. Subsequently, an adaptive approach is incorporated to intelligently modulate the operational modes and task allocations of nodes by continuously monitoring network load, energy usage, and data transfer efficiency. To validate the efficacy of our method, we utilized the SensorScope Dataset, composed of real-time data from various IoT devices, encompassing diverse environmental metrics such as temperature, humidity, and lighting. Experimental findings reveal that, in contrast to conventional strategies, our integrated self-organization and adaptive approach offer substantial benefits in terms of load balancing, energy consumption, and data transfer latency. More specifically, we recorded a 30&#x0025; drop in average load, a 25&#x0025; decrease in energy use, and a 20&#x0025; reduction in average transmission delay. These outcomes strongly support the effectiveness of our comprehensive strategy in boosting IoTSN performance when juxtaposed with alternative methods. Additionally, we offer an in-depth exploration of the experimental data and identify potential areas for refinement and future research avenues, opening the door to fresh perspectives and advancements in IoTSN intelligence and efficiency. This investigation is pivotal in advancing IoT technology and bolstering the adaptability and performance of IoTSN in a wide range of practical scenarios.

Omar Alruwaili, Fan Wu, Wael Mobarak et al.

The technology&#x2019;s integration into factories has accelerated automation&#x2019;s growth, creating autonomous working conditions and cutting-edge capacity for production. Modern and smart factories provide consumers with time-saving solutions and reliable outcomes. The present paper presents the concept of Event-Dependent Process Planning (EDPP), which seeks to improve the time-effectiveness of smart factories. The suggested approach automatically arranges planned and queued activities according to previous results, matching them with customer demands. Before process planning, essential data are provided by intelligent measuring equipment in the factories. Recurrent learning ensures the integrated process planning is successful and aligned with customers&#x2019; needs. The efficiency with which the planning method exceeded customer expectations in earlier years is used to instruct this learning process. Applications of the technique are made to the manufacturing automation process&#x2019;s delivery and production layers. Essential metrics like processing time, response ratio, delivery delay, and backlogs are evaluated in an experimental analysis to validate the suggested process strategy. The proposed EDPP achieves 11.38% less processing time, 5.43% high response ratio, 10.18% less delivery delay, and 3.8% less backlog rate.

Gaith Rjoub, Saidul Islam, Jamal Bentahar et al.

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents' capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decision-making efficiency and adaptability. Our contributions include a detailed exploration of the transformer's role in processing heterogeneous IoT data, a comprehensive evaluation of the framework's performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

Qusai Bshiwa

6G is envisioned to connect everything and yet to be a hundred times more energy efficient than the 5G. Thanks for its ability to use sources of ambient energy, energy harvesting (EH) is promising in alleviating the challenge of meeting such conflicting demands. Moreover, when it comes to the Internet of things (IoT), one of the foundations for enabling connecting everything, the need for EH may become inevitable. IoT involves connecting not only devices that are large in number, but also hard to reach. The good news, nevertheless, is that the unmanned aerial vehicle (UAV), owning to its flexibility and ease of deployment is emerging to offer communication services when infrastructure is lacking. Merging the UAV and IoT is of quite interest as the former could not just enable flexible connectivity for the IoT but also powering them in spite of any restrictions. However, managing the UAV assisted IoT resources to meet certain data communications and EH quality measures while keeping the UAV consumed energy minimized is a major challenge as this corresponds to a non-convex optimization problem. Things, obviously, become even worse when the IoT network devices are mobile. Owing to the success of artificial intelligence (AI) in solving complicated problems, in this project we rely on the deep deterministic policy gradient (DDPG) technique, to manage the UAV assisted IoT resources. Our results show that DDPG achieves joint optimization of three objectives, namely sum data rate and harvested energy maximization, and energy consumption minimization, while out performing traditional mathematical schemes. The code of this project is made publicly accessible at https://github.com/QusaiBshiwa/Smart-6G- Sky-for-Green-Mobile-IOT-Networks

Isabela F. Rocha , Kumiko O. Kissimoto

Abstract Purpose: Based on the context of digital transformation and the evolution of digital technologies, this research sought to understand how artificial intelligence (AI) and internet of things (IoT) collaborate to improve the efficiency of operations management (OM). Originality/value: Digital transformation and the use of new technologies, such as AI and IoT, have impacted the management of the companies’ operation. A preliminary survey carried out in the Web of Science (WoS) database, analyzing data through the VOSviewer bibliometric software, identified an important relationship between AI, IoT, and OM through industry 4.0 (i4.0), which has as one of its main objectives the improvement in OM. The results of this research bring a practical contribution to business managers, such as the identification of the main barriers and expected benefits when adopting AI and IoT in their operations. For researchers, this study differs from studies already published by conducting a systematic review of the literature that investigates the relationship of OM with technological tools, such as AI and IoT. Design/methodology/approach: A systematic review of the literature was carried out with the objective of analyzing all articles that brought some contribution to a better understanding of how AI and IoT collaborate to improve the efficiency of operations. Findings: The results demonstrated how AI and IoT were being incorporated into OM, identifying the main barriers of its use, as well as indications of research gaps that may lead to further investigations to advance on this topic. / Resumo Objetivo: Tomando como base o contexto de transformação digital e a evolução das tecnologias digitais, esta pesquisa buscou compreender como a inteligência artificial (IA) e a internet das coisas (internet of things – IoT) colaboram para melhorar a eficiência da gestão da operação (GO). Originalidade/valor: A transformação digital e o uso de novas tecnologias, como a IA e a IoT, têm impactado a gestão da operação das empresas. Um levantamento feito na base de dados Web of Science (WoS) e a análise deles, realizadas pelo software bibiliométrico VOSviewer, identificaram uma importante relação entre IA, IoT e GO por meio da indústria 4.0 (i4.0), que tem como um de seus principais objetivos a melhora na gestão da operação. Os resultados da presente pesquisa trazem uma contribuição prática aos gestores de negócios, como a identificação das principais barreiras e benefícios esperados ao adotarem a IA e a IoT em suas operações. Para os pesquisadores, este estudo difere de pesquisas já publicadas ao realizar uma revisão sistemática da literatura que investiga a relação da GO com as ferramentas tecnológicas IA e IoT. Design/metodologia/abordagem: Foi realizada uma revisão sistemática da literatura com o objetivo de analisar todos os artigos que trouxessem alguma contribuição no sentido de fornecer uma melhor compreensão de como a IA e a IoT colaboram para melhorar a eficiência das operações. Resultados: Os resultados demonstraram de que forma a IA e a IoT foram sendo incorporadas na gestão da operação, com destaque às barreiras e aos benefícios de seu uso. Verificaram-se ainda as indicações de lacunas de pesquisa que podem levar a novas investigações para avançar no tema.

Ruihua Nai

This study aims to improve the efficiency of modern college English teaching. With interactive teaching as the core element, smart classrooms as technical support, and informationization, automation, and interaction as the main body, a smart system for college English teaching is established based on cloud computing and Internet of Things (IoT). The system is built using the B/S architecture and verified by specific example data, to prove the effectiveness of the proposed smart system for college English teaching based on the IoT. It is found that the smart platform for English teaching based on the IoT not only effectively improves the stability of the system, but also enhances the personal experience of students. The coordinated operation of the various modules reduces the response time of the system. When the number of users reaches 500, the average response time of the system is 3.65 seconds, and the memory and occupancy rate of the system are reduced. Students who receive smart classrooms for teaching have a greater improvement in the test results of various aspects of English without teacher intervention. The proposed model can significantly improve the performance of poor students and reduce the gap in learning performance in the class, which provides reliable research ideas for smart teaching in modern colleges and universities.

Abdullah Waqas, Nasir Saeed, Hasan Mahmood et al.

Fifth-generation and beyond networks target multiple distributed network application such as Internet of Things (IoT), connected robotics, and massive Machine Type Communication (mMTC). In the absence of a central management unit, the device need to search and establish a route towards the destination before initializing data transmission. In this paper, we proposes a destination search and routing method for distributed 5G and beyond networks. In the proposed method, the source node makes multiple attempts to search for a route towards the destination by expanding disk-like patterns originating from the source node. The source node increases the search area in each attempt, accommodating more nodes in the search process. As a result, the probability of finding the destination increases, which reduces energy consumption and time delay of routing. We propose three variants of routing for high, medium, and low-density network scenarios and analyze their performance for various network configurations. The results demonstrate that the performance of the proposed solution is better than previously proposed techniques in terms of time latency and reduced energy consumption, making it applicable for 5G and beyond networks.