Internet-connected Devices Research Articles

Enhancing Cybersecurity Defense of IoT Ecosystem Using Blockchain

The expansion of IoT devices over the past years has led to the development of smart ecosystems susceptible to cyberattacks. These ecosystems include smart industries and smart home security systems. However, when these devices are widely deployed and generate significant amounts of data, this data must be securely stored on the cloud to prevent it from being compromised by serious cyber attacks. Although traditional security methods partly meet the need to protect the privacy and security of smart IoT ecosystems, their centralized structure and limited computing power make them inefficient. However, recent advancements in blockchain technology have made it possible to employ decentralized architectures to enhance the cybersecurity defense mechanisms of IoT ecosystems. The utilization of blockchain-enabled methodologies is considered one of the most auspicious advancements in the domain of the IoT. These techniques offer a secure and decentralized mode of communication among internet-connected devices.
 This study presents a blockchain-based novel framework to enhance the cybersecurity measures of smart IoT ecosystem installations. The proposed framework prioritizes the immutability of devices and users present in such environments and incorporates smart contracts to ensure their overall security. Furthermore, the system includes a module for regularly verifying the firmware of connected devices by comparing them to the valid firmware hashes present in the smart contract's storage. The proposed approach has also come up with the idea of blacklisting the malicious IP addresses trying to connect with IoT ecosystems network.

Comparative Analysis of Multi-Level Algorithm with Different Encryption and Decryption Security Algorithms

This research explores network data encryption and decryption strategies used in communication networks. The majority of network communication systems revolve around information sharing on mobile phones, networked PCs, and other internet-connected electronic devices. As information travels through several networks and is subject to numerous types of assaults, unsecure data can be read, altered, or faked by anybody with access to it. To defend against this form of assault, data encryption / decryption techniques are utilized. To illustrate the effects and evaluate the efficacy of each decoding and encryption method used in network technologies, Visual Basic simulation studies that encrypted and decrypt data were created, developed, and tested. Further study was for comparative analysis of Multi-Level Algorithm with different Security Algorithms for encrypting data approach.

STUDENT ENGAGEMENT AND ACHIEVEMENT IN ONLINE VERSUS TRADITIONAL CLASSROOM INSTRUCTION: AN ANALYSIS

Online teaching has been popular after the pandemic of Covid-19 situation, and it could be delivered with the help of internet-connected devices through images, text, and various kinds of videos. The traditionallearning and online teachingdefinition, along with the background of the study, isthat the short-term impact of COVID-19 has led to the design of the face-to-face challenge, and there was an intended change in the teaching and learning process. The transition of traditional learning to digital learning has faced great changes, mainly in this time of pandemic. The primary data collection process, descriptive method and deductive research approach have been followed. SPSS and ANOVA, frequency analysis, and correlation have been done.Data which have gathered from 55 different students with 13 questions, and their studywas done with the software. The interpretation of data was done with SPSS.A comparison of the literature review and the relation between the collected results are analysed. Thus, it can be concluded that the traditional learning practice created more impact onstudents engagement. The online teaching process has made less engagement compared tothe transitional process.

Development of a Malicious Network Traffic Intrusion Detection System Using Deep Learning

With the exponential surge in the number of internet-connected devices, the attack surface for potential cyber threats has correspondingly expanded. Such a landscape necessitates the evolution of intrusion detection systems to counter the increasingly sophisticated mechanisms employed by cyber attackers. Traditional machine learning methods, coupled with existing deep learning implementations, are observed to exhibit limited proficiency due to their reliance on outdated datasets. Their performance is further compromised by elevated false positive rates, decreased detection rates, and an inability to efficiently detect novel attacks. In an attempt to address these challenges, this study proposes a deep learning-based system specifically designed for the detection of malicious network traffic. Three distinct deep learning models were employed: Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). These models were trained using two contemporary benchmark intrusion detection datasets: the CICIDS 2017 and the Coburg Intrusion Detection Data Sets (CIDDS). A robust preprocessing procedure was conducted to merge these datasets based on common and essential features, creating a comprehensive dataset for model training. Two separate experimental setups were utilized to configure these models. Among the three models, the LSTM displayed superior performance in both experimental configurations. It achieved an accuracy of 98.09%, a precision of 98.14%, an F1-Score of 98.09%, a True Positive Rate (TPR) of 98.05%, a True Negative Rate (TNR) of 99.69%, a False Positive Rate (FPR) of 0.31%, and a False Negative Rate (FNR) of 1.95%.

“Your Protection is in Your Hands Only”: User Awareness and Adoption of Privacy and Security Practices in Five Majority World Countries

Technology companies frequently develop and launch features to protect privacy, reduce user harm, and increase security. But adoption is inconsistent at best. Businesses might increase awareness through prominent placement on user interfaces or in-product promotions, and drive adoption through changes to default user settings, or by guiding users to tutorials that encourage them to engage with a new feature. These solutions can be effective but fail to capture key factors that inform use and adoption – particularly in the majority world. In this study, we conducted qualitative interviews with 24 participants in five majority world countries across three continents: the Philippines, Brazil, India, Egypt, and Nigeria (N = 120). We assess the awareness and use of privacy and security features related to internet usage generally, and internet-connected mobile devices specifically. Participants often cited worries about being victimized by financial scammers, and highlighted how frequent sharing of devices, a common behavior, resulted in consistent concerns about privacy. We identify barriers and concerns around use and adoption for users in these regions and offer a holistic analytical framework that can be leveraged by actors in the space to better understand user behaviors and attitudes, and design accordingly.

ZotCare: a flexible, personalizable, and affordable mhealth service provider.

The proliferation of Internet-connected health devices and the widespread availability of mobile connectivity have resulted in a wealth of reliable digital health data and the potential for delivering just-in-time interventions. However, leveraging these opportunities for health research requires the development and deployment of mobile health (mHealth) applications, which present significant technical challenges for researchers. While existing mHealth solutions have made progress in addressing some of these challenges, they often fall short in terms of time-to-use, affordability, and flexibility for personalization and adaptation. ZotCare aims to address these limitations by offering ready-to-use and flexible services, providing researchers with an accessible, cost-effective, and adaptable solution for their mHealth studies. This article focuses on ZotCare's service orchestration and highlights its capabilities in creating a programmable environment for mHealth research. Additionally, we showcase several successful research use cases that have utilized ZotCare, both in the past and in ongoing projects. Furthermore, we provide resources and information for researchers who are considering ZotCare as their mHealth research solution.

Synergies between Lean and Industry 4.0 for Enhanced Maintenance Management in Sustainable Operations: A Model Proposal

Companies actively seek innovative tools and methodologies to enhance operations and meet customer demands. Maintenance plays a crucial role in achieving such objectives. This study identifies existing models that combine Lean Philosophy and Industry 4.0 principles to enhance decision-making and activities related to maintenance management. A comprehensive literature review on key concepts of Lean Philosophy and Industry 4.0, as well as an in-depth analysis of existing models that integrate these principles, is performed. An innovative model based on the synergies between Lean Philosophy and Industry 4.0, named the Maintenance Management in Sustainable Operations (MMSO) model, is proposed. A pilot test of the application of the MMSO model on a conveyor belt led to an operational time increase from 82.3% to 87.7%, indicating a notable 6.6% improvement. The MMSO model significantly enhanced maintenance management, facilitating the collection, processing, and visualization of data via internet-connected devices. Through this integration, various benefits are achieved, including improved flexibility, efficiency, and effectiveness in addressing market needs. This study highlights the value of integrating Lean Philosophy and Industry 4.0 principles to improve maintenance management practices. The proposed MMSO model effectively leverages these principles, fostering agility, optimized resource utilization, heightened productivity and quality, and reduced energy consumption. The model not only serves as a tool for operational optimization and customer demand enhancement but also aligns with sustainability principles within the energy transition. Its successful application in the pilot test phase further reinforces its potential as a reliable approach for maintenance management and sustainable operations in both production and decision-making processes.

Code Index Modulation-Aided Spatial Media-Based Modulation System for Future Wireless Networks

Future wireless networks, increment in mobile users, and Internet-connected devices have brought many needs, such as high data rate, low transmission energy, high error performance, and low latency. Index modulation-based systems have been considered as one of the strong candidates for meeting these demands and needs. In this article, a novel wireless communication system that combines code index modulation (CIM)-aided spatial modulation (SM) and media-based modulation (MBM) techniques, briefly called CIM-SMBM, is proposed for future wireless networks. In the CIM-SMBM system, source information bits are carried in transmitted symbols in addition to three indices: active antenna index selected by SM, indices of different channel states that vary depending on the propagation pattern with MBM, and selected spreading code indices determined by CIM. In order to further reduce the complexity of optimal maximum-likelihood (ML) and despreading-based ML detectors for the CIM-SMBM scheme, an enhanced low-complexity detector that can achieve near-ML error performance is proposed. The average bit error rate (ABER), throughput, energy efficiency, and complexity analyses of the CIM-SMBM system are derived over Rayleigh fading channels for the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -quadrature amplitude modulation technique. It has been observed that the ABER and simulation results are quite close to each other.

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

EHealth profile of patients with diabetes.

Digital health technology can be useful to improve the health of patients with diabetes and to support patient-centered care and self-management. In this cross-sectional study, we described the eHealth profile of patients with diabetes, based on their use of digital health technology, and its association with sociodemographic characteristics. We used data from the "Qualité Diabète Valais" cohort study, conducted in one region of Switzerland (Canton Valais) since 2019. Participants with type 1 or type 2 diabetes completed questionnaires on sociodemographic characteristics and on the use of digital health technology. We defined eHealth profiles based on three features, i.e., ownership or use of (1) internet-connected devices (smartphone, tablet, or computer), (2) mHealth applications, and (3) connected health tools (activity sensor, smart weight scale, or connected blood glucose meter). We assessed the association between sociodemographic characteristics and participants' eHealth profiles using stratified analyses and logistic regression models. Some 398 participants (38% women) with a mean age of 65 years (min: 25, max: 92) were included. The vast majority (94%) were Swiss citizens or bi-national and 68% were economically inactive; 14% had a primary level education, 51% a secondary level, and 32% a tertiary level. Some 75% of participants had type 2 diabetes. Some 90% of the participants owned internet-connected devices, 43% used mHealth applications, and 44% owned a connected health tool. Older age and a lower educational level were associated with lower odds of all features of the eHealth profile. To a lesser extent, having type 2 diabetes or not being a Swiss citizen were also associated with a lower use of digital health technology. There was no association with sex. While most participants owned internet-connected devices, only about half of them used mHealth applications or owned connected health tools. Older participants and those with a lower educational level were less likely to use digital health technology. eHealth implementation strategies need to consider these sociodemographic patterns among patients with diabetes.

Perspective of small and medium enterprise (SME's) and their relationship with government in overcoming cybersecurity challenges and barriers in Wales

Small and medium enterprises (SMEs) have adapted their working methods in response to cyber threats, including security checks on internet-connected devices and securing social media accounts. To safeguard their data, SMEs are increasingly adopting intelligent software packages, despite the additional costs and need for skilled personnel. This study examines the perspectives of SMEs in Wales regarding their collaboration with the government to overcome cybersecurity challenges. Through qualitative research involving 34 SMEs, the study explores current cybersecurity practices, challenges faced, and interactions with government agencies. Wales, with its thriving economy and significant number of SMEs, provides a suitable context for the research. The findings will shed light on barriers and challenges encountered by SMEs in implementing effective cybersecurity measures, while also examining their perceptions of government agency involvement. By contributing insights to SME cybersecurity and government policy, the study aims to enhance government initiatives in supporting SMEs to tackle cybersecurity challenges effectively.

Preference of Internet Usage versus Textbook Usage for Diagnosis and Treatment Planning in Physiotherapy Students: A Cross Sectional Study

Background and need of study: This study aims to evaluate the frequency with which physiotherapy students experience; the need of digital information resources and academic textbooks to diagnose and treat patients. The widespread availability of internet-connected smart devices in the health care setting has the potential to provide physiotherapy students' information needs. Methodology: 140 physiotherapy students, selected by convenience sampling. Students studying in third year, final year and Interns of Physiotherapy students were included. The participants filled a Google self-designed, close-ended questionnaire which was circulated via WhatsApp around different physiotherapist groups. Descriptive analysis was done using Microsoft excel. Results: Out of 140 subjects, 53% use internet for diagnosing and treatment of the patients; 70% find internet useful for understanding the patient’s condition better while 84% find textbooks reliable for diagnosis. 48% think maybe internet is hindering their academic performances, 31% think it’s definitely hindering while 21% think it’s not. Conclusion: Physiotherapy students frequently experience need of internet for diagnosis and treatment for their patients. This also departs a thought that the students are not aware regarding the authenticity of materials given on internet. This study also implies that despite textbooks being safe, internet is being used for the sake of convenience by students. Keywords: Physiotherapy students, Internet, Textbook, Treatment

A Novel and Integrated Digitally Supported System of Care for Depression and Anxiety: Findings From an Open Trial.

The global burden of anxiety and depression has created an urgent need for scalable approaches to increase access to evidence-based mental health care. The Screening and Treatment for Anxiety and Depression (STAND) system of care was developed to meet this need through the use of internet-connected devices for assessment and provision of treatment. STAND triages to level of care (monitoring only, digital therapy with coaches, digital therapy assisted by clinicians in training, and clinical care) and then continuously monitors symptoms to adapt level of care. Triaging and adaptation are based on symptom severity and suicide risk scores obtained from computerized adaptive testing administered remotely. This article discusses how the STAND system of care improves upon current clinical paradigms, and presents preliminary data on feasibility, acceptability, and effectiveness of STAND in a sample of US-based university students. US-based university students were recruited and enrolled in an open trial of the STAND system of care. Participants were triaged based on initial symptom severity derived from a computerized adaptive test and monitored over 40 weeks on anxiety, depression, and suicide risk to inform treatment adaptation and evaluate preliminary effectiveness. Nearly 5000 students were screened and 516 received care. Depression and anxiety severity scores improved across all tiers (P<.001 in all cases). Suicide risk severity improved in the highest tier (ie, clinical care; P<.001). Acceptability and feasibility were demonstrated. STAND is a feasible and acceptable model of care that can reach large numbers of individuals. STAND showed preliminary effectiveness on all primary outcome measures. Current directions to improve STAND are described.

DDoS Attack Detection in IoT-Based Networks Using Machine Learning Models: A Survey and Research Directions

With the emergence of technology, the usage of IoT (Internet of Things) devices is said to be increasing in people’s lives. Such devices can benefit the average individual, who does not necessarily have to have technical knowledge. The IoT can be found in home security and alarm systems, smart fridges, smart televisions, and more. Although small Internet-connected devices have numerous benefits and can help enhance people’s efficiency, they also can pose a security threat. Malicious actors often attempt to find new ways to exploit and utilize certain resources, and IoT devices are a perfect candidate for such exploitation due to the huge volume of active devices. This is particularly true for Distributed Denial of Service (DDoS) attacks, which involve the exploitation of a massive number of devices, such as IoT devices, to act as bots and send fraudulent requests to services, thus obstructing them. To identify and detect whether such attacks have occurred or not in a network, there must be a reliable mechanism of detection based on adequate techniques. The most common technique for this purpose is artificial intelligence, which involves the use of Machine Learning (ML) and Deep Learning (DL) to help identify cyberattacks. ML models involve algorithms that use structured data to learn from, predict outcomes from, and identify patterns. The goal of this paper is to review selected studies and publications relevant to the topic of DDoS detection in IoT-based networks using machine-learning-relevant publications. It offers a wealth of references for academics looking to define or expand the scope of their research in this area.

Long ties, disruptive life events, and economic prosperity

Social networks shape and reflect economic life. Prior studies have identified long ties, which connect people who lack mutual contacts, as a correlate of individuals' success within firms and places' economic prosperity. However, we lack population-scale evidence of the individual-level link between long ties and economic prosperity, and why some people have more long ties remains obscure. Here, using a social network constructed from interactions on Facebook, we establish a robust association between long ties and economic outcomes and study disruptive life events hypothesized to cause formation of long ties. Consistent with prior aggregated results, administrative units with a higher fraction of long ties tend to have higher-income and economic mobility. Individuals with more long ties live in higher-income places and have higher values of proxies for economic prosperity (e.g., using more Internet-connected devices and making more donations). Furthermore, having stronger long ties (i.e., with higher intensity of interaction) is associated with better outcomes, consistent with an advantage from the structural diversity constituted by long ties, rather than them being weak ties per se. We then study the role of disruptive life events in the formation of long ties. Individuals who have migrated between US states, have transferred between high schools, or have attended college out-of-state have a higher fraction of long ties among their contacts many years after the event. Overall, these results suggest that long ties are robustly associated with economic prosperity and highlight roles for important life experiences in developing and maintaining long ties.

Innovations in Multicore Network Processor Design for Enhanced Performance

The rapid expansion of network traffic, driven by the proliferation of internet-connected devices and the growing demand for high-speed data transmission, has intensified the need for advanced network processing capabilities. Multicore network processors have emerged as a pivotal solution to address these challenges, offering significant enhancements in performance, scalability, and efficiency. This paper explores the innovations in multicore network processor design, focusing on the architectural advancements and optimization techniques that have been instrumental in elevating their performance. One of the key innovations in multicore network processor design is the shift from traditional single-core processors to multicore architectures. This transition has allowed for parallel processing, where multiple cores can simultaneously execute different tasks, significantly increasing throughput and reducing latency. The adoption of multicore architectures has also facilitated the handling of diverse and complex workloads, which is essential in modern networking environments that demand high performance and low power consumption. A major focus of recent innovations is the optimization of core interconnects and memory hierarchies. Efficient inter-core communication is critical for maintaining high performance in multicore processors. The development of advanced interconnect technologies, such as network-on-chip (NoC) and high-bandwidth interconnects, has minimized communication bottlenecks, enabling faster data exchange between cores. Additionally, improvements in memory hierarchies, including the integration of larger caches and the use of intelligent memory management techniques, have further enhanced data access speeds and reduced memory latency, contributing to overall performance gains. Another significant area of innovation is the implementation of specialized cores within multicore processors. These specialized cores are designed to handle specific network functions, such as encryption, compression, and deep packet inspection, more efficiently than general-purpose cores. By offloading these tasks to specialized cores, the overall processing load is balanced, leading to better performance and energy efficiency. Furthermore, the integration of hardware accelerators, such as field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs), has been a critical development, providing dedicated processing power for complex tasks and further enhancing the performance of multicore network processors. Power efficiency has also been a major consideration in the design of multicore network processors. Innovations in dynamic voltage and frequency scaling (DVFS) and power gating have enabled processors to adjust their power consumption based on workload demands, reducing energy usage without compromising performance. Additionally, advances in thermal management techniques, such as improved heat dissipation methods and adaptive cooling technologies, have ensured that multicore processors can operate at peak performance levels without overheating. The integration of machine learning (ML) and artificial intelligence (AI) in multicore network processor design represents another frontier of innovation. ML algorithms can optimize resource allocation, predict traffic patterns, and dynamically adjust processing tasks to enhance performance and efficiency. AI-driven management of network processors allows for more intelligent decision-making, enabling the processors to adapt to changing network conditions in real-time, which is crucial for maintaining high performance in dynamic environments. Moreover, the increasing complexity of network security has driven innovations in multicore network processor design. Enhancements in security features, such as hardware-based encryption and real-time threat detection, have been integrated into modern processors to safeguard against evolving cyber threats. The ability to handle security tasks at the hardware level not only improves performance but also provides a robust defense mechanism against attacks, ensuring the integrity and confidentiality of data. In conclusion, the ongoing innovations in multicore network processor design are pivotal in meeting the growing demands of modern networking environments. By advancing processor architectures, optimizing interconnects and memory hierarchies, integrating specialized cores, enhancing power efficiency, and incorporating AI and security features, these processors are well-equipped to deliver superior performance and efficiency. As network demands continue to evolve, these innovations will play a crucial role in shaping the future of network processing, enabling faster, more reliable, and secure data transmission across increasingly complex networks.

Optimizing horizontal scalability in cloud computing using simulated annealing for Internet of Things

The Internet of Things (IoTs) is a technology that connects sensor devices to the Internet to enable smarter and more intelligent communication. Today, many industries are using various IoT devices to create smart and intelligent environments. However, the sudden increase in demand has created a major challenge for IoT connections, known as scalability. Scalability refers to increasing and expanding the number of internet-connected devices for a specific application. To address this issue, we propose simulated annealing-based horizontal scaling to achieve faster and more efficient scaling to accommodate IoT devices. We explore different horizontal scaling methods and propose a Markov chain process to model the scaling. We then use simulated annealing to optimize the scaling visualized by the Markov chain process. Our goal is to focus on the flexible nature of horizontal scalability for adding various IoT devices and resources as needed. We have compared our proposed horizontal scalability optimization with vertical scalability, which has a built-in feature of elasticity. We have evaluated several parameters, such as cost, service rate, and transfer rate, and found that our proposal outperforms existing methods.

LED Control System Using Arduino Wemos D1 R1 Based on Web Server Communication Via Internet of Things (IoT)

The Internet of Things (IoT) is a concept that connects physical devices to an Internet network, enabling data exchange and remote control. In this study, an LED control system was developed using the Arduino Wemos D1 R1, which is based on communication via a web server using IoT. This system uses a web server as a user interface to remotely control the LEDs. Users can access the web server via internet-connected devices, such as smartphones or laptops. They can set the LED status (on or off) by clicking the button provided on the web server interface. This research can be used as a basis for developing other device control systems using the Arduino Wemos D1 R1 and the IoT concept. In further development, this system can be improved by adding sensors or other additional features for more complex applications.

From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management

The advancement in technology has led to the integration of internet-connected devices and systems into emergency management and response, known as the Internet of Emergency Services (IoES). This integration has the potential to revolutionize the way in which emergency services are provided, by allowing for real-time data collection and analysis, and improving coordination among various agencies involved in emergency response. This paper aims to explore the use of IoES in emergency response and disaster management, with an emphasis on the role of sensors and IoT devices in providing real-time information to emergency responders. We will also examine the challenges and opportunities associated with the implementation of IoES, and discuss the potential impact of this technology on public safety and crisis management. The integration of IoES into emergency management holds great promise for improving the speed and efficiency of emergency response, as well as enhancing the overall safety and well-being of citizens in emergency situations. However, it is important to understand the possible limitations and potential risks associated with this technology, in order to ensure its effective and responsible use. This paper aims to provide a comprehensive understanding of the Internet of Emergency Services and its implications for emergency response and disaster management.

An Exploratory Study on Netflix Inc. Current Strategy by Case Study Methods

Netflix offered special service that allows members to watch TV movies and shows on internet-connected devices, while Netflix movies &amp; TV shows online or stream right to your smart TV, game console. This paper revealed that participants' gender identities were kept confidential through case study. This paper found that Netflix is expected to put out the same shows, with it ensuring that movies available for people in the UK, while it may need to be the same for Individuals in the USA to increase the number of viewership.