Interconnected Environment Research Articles

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

A decentralized authentication scheme for smart factory based on blockchain

The Industrial Internet of Things (IIoT) is recognized as one of the revolutionary technologies driving smart manufacturing and improving productivity. As manufacturing processes grow increasingly intricate, the entire manufacturing ecosystem encompasses multiple managed IoT domains. Within this highly interconnected environment, devices from diverse domains must collaborate, leading to considerable apprehensions about the security and privacy of device-to-device communications. Current authentication methods encounter several challenges. Traditional authentication schemes overly rely on trusted third parties, rendering them susceptible to external attacks or internal spoofing. This susceptibility gives rise to a range of security and privacy concerns. In response to these challenges, this paper aims to contribute to a more secure and efficient service scheme for smart factories by devising a blockchain-based distributed IoT architecture. The proposed scheme introduces a federated blockchain to establish trust among different domains, thereby enabling secure connections between devices in distinct domains. Through security analysis, it is proved that the proposed authentication scheme has integrity, mutual authentication, scalability, and resistance to four attacks. Furthermore, efficiency analysis experiments show that our scheme is feasible for smart factories, and as the number of peer nodes increases, the performance and efficiency of the blockchain network become better.

The Intersection of Machine Learning and Wireless Sensor Network Security for Cyber-Attack Detection: A Detailed Analysis.

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML's potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.

Redefining Systemic Cybersecurity Risk in Interconnected Environments

Redefining Systemic Cybersecurity Risk in Interconnected Environments

Advancements in Securing Cloud-Stored Data and Managing Sensitive Information: A Comprehensive Review of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) Techniques

Abstract—The exploring comprehensive review of cutting-edge techniques for securing cloud-stored data and managing sensitive information in the context of smart cities through the application of Ciphertext-Policy Attribute-Based Encryption (CP-ABE). It highlights the innovative integration of blockchain technology with CP-ABE, which introduces a decentralized and tamper- resistant key management system, thereby enhancing the overall security framework in cloud environments where data sharing is prevalent. Introducing an online/offline multi-authority CP- ABE scheme, characterized by hidden policies, offers significant advancements in protecting user attributes and access structures, ensuring that sensitive information remains confidential even during encryption and decryption processes. This dual approach not only fortifies security but also optimizes the efficiency of data- sharing mechanisms. Furthermore, the paper delves into imple- menting hidden sensitive policies and keyword search techniques within smart city infrastructures, which are designed to facilitate secure and efficient data retrieval. These techniques ensure that while data remains accessible to authorized users, privacy is rigorously maintained. Collectively, these approaches represent significant strides in bolstering the security and confidentiality of data in both cloud-based and smart city applications, addressing the growing demand for robust and efficient data management solutions in increasingly interconnected environments. Index Terms—Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Blockchain, Key Management, Decentralized Secu- rity, Cloud-Stored Data, Yolo V7, Explainable AI (XAI), On- line/Offline Multi-Authority Scheme, Hidden policies, Privacy Preservation, Data Protection, Secure Data Management

A novel speaker verification approach featuring multidomain acoustics based on the weighted city block Minkowski distance

AbstractAccess control is vital in interconnected environments like the Internet of Things, Industry 4.0, and smart connectivity, ensuring authorized access for security. Biometric‐based access, particularly speaker verification (SV), enhances security with unique vocal features, offering nonintrusive authentication with continuous monitoring. Single‐domain features prove insufficient in distinguishing similar traits, prompting latest SV advancements to adopt multidomain‐based speech features. This paradigm addresses the limitations of single‐domain features by amalgamating the merits of individual domains, establishing a cutting‐edge approach. It utilizes cepstral–frequency–time domain feature fusion, achieved via cepstral mean‐variance normalization for generalizability. The weighted city block Minkowski distance is proposed to compare reference and test speech templates. Parameters are computed based on the confusion matrix, template matching distance functions, dynamic acoustic conditions, and additive white Gaussian noise. A deep convolutional neural network classifier is assessed on open‐source LibriSpeech and Speaker in the Wild corpora, surpassing the current methodologies.

Broad Perspective of Smart Home Technology in 2024

This paper presents a thorough introduction to the rapidly evolving field of smart home technology, highlighting the transition of homes from traditional living spaces to dynamic, interconnected environments. As the world delves deeper into the digital age, smart home technology stands at the forefront of this transformation, offering unprecedented convenience, security, and energy efficiency. Through a comprehensive exploration, the authors discuss the benefits and challenges of smart home integration. The research design, which forms the foundation of this study, includes a comparative analysis of hub-based and hub-free systems, evaluating their respective advantages and disadvantages through systematic examination. The authors also explore emerging trends and innovations in smart homes, such as the integration of healthcare monitoring, energy management in smart grids, and the application of augmented and virtual reality, underlining the potential of artificial intelligence in enhancing smart home capabilities.

Global Quality of Service (QoX) Management for Wireless Networks

In the fast-changing technological landscape, novel applications are emerging with the potential to reshape the world. These applications, while promising, impose stringent requirements in terms of quality of service (QoS). The advent of wireless networks like 5G, 6G and Wi-Fi 6 brings about resource management solutions to ensure these requirements while meeting the user expectations within the interconnected environment. Nevertheless, user behaviors are also evolving, highlighting the importance of satisfaction and quality of experience (QoE). Furthermore, changes in user behavior trigger shifts in business models, where the quality of business (QoBiz) takes on a pivotal role. This evolving ecosystem, encompassing QoS, QoE, and QoBiz, demands a comprehensive and adaptable approach that conventional QoS management frameworks fail to perform. This paper introduces an implementation methodology for a global QoS management model named QoXphere. The implementation methodology is grounded in machine learning techniques and addresses the multifaceted aspects of quality of service (QoX) and their interconnections within wireless networks. The objective is to facilitate dynamic resource management that not only elevates user satisfaction but also optimizes provider benefits. Real-world examples illustrate the methodology’s applicability in widely deployed networks, complemented by simulated scenarios of modern network environments that further validate the approach.

Development and Control of Virtual Industrial Process using Factory IO and MATLAB

In today's rapidly evolving business landscape, the strategic adoption of virtual manufacturing methods has emerged as a key driver for companies seeking to streamline operations and expedite product launches in a cost-effective manner. This progressive approach involves the creation of a synthetic and interconnected environment, empowered by advanced software tools and systems, including Virtual Reality and Simulation technologies, tailored to optimize industrial processes. Our methodology employs a unique combination of two simulation software tools: Factory I/O for process development and MATLAB for control program implementation. Furthermore, we explore the use of the Modbus TCP/IP communication protocol as the framework for seamless interaction between these software tools during simulation. This research presents practical insights into the transformative potential of virtual manufacturing, showcasing its real-world application in enhancing operational efficiency and agility within industrial settings.

Evaluating Sensitivity and Stability in Secure Deep Learning Based Channel Estimation for NextG Networks with Defensive Distillation

In the domain of wireless networks, including 5G and beyond, envisions a highly interconnected environment, enabling swift data transmission, expanded cell capacity, and minimal latency to support various applications and advanced manufacturing. Next Generation (NextG) networks seek to optimize and enhance network functionalities through Artificial intelligence (AI) integration, although concerns persist regarding the security of AI based models, particularly regarding model poisoning. Protecting these networks against cybersecurity threats, especially adversarial attacks, is vital. Hence, effective mitigation techniques and secure solutions employing AI-based methods are crucial. This study delves into the pivotal aspects of stability and sensitivity in AI-driven models tailored for NextG networks, focusing on Deep learning (DL) based channel estimation models. Utilizing data from MATLAB's 5G toolbox, we scrutinize these models' susceptibility to adversarial attacks and evaluate the efficacy of defensive distillation techniques in mitigating vulnerabilities. The research highlights the urgent necessity to fortify DL-based models against potential threats within NextG networks. Through a comprehensive evaluation of defensive distillation strategies, we showcase their ability to bolster both the stability and sensitivity of channel estimation models. By addressing these critical aspects, the study aims to enhance the resilience of AI-driven solutions within NextG environments, strengthening defenses against cybersecurity risks and safeguarding network integrity. Ultimately, the findings underscore the importance of prioritizing sensitivity and stability in the development and deployment of AI-based models for NextG networks. Through proactive measures and advanced mitigation methods, efforts are made to mitigate the risks posed by adversarial attacks and foster a more secure and resilient network infrastructure for the future.

ANALISIS PERBANDINGAN TOLERANSI KESALAHAN (FAULT TOLERANCE) ROUTING STATIC DAN OSPF PADA JARINGAN HYBRID UNUD

A reliable and failure-resistant computer network is an important prerequisite in the modern interconnected environment. This research aims to examine and compare the effectiveness of fault tolerance between two commonly used routing protocols, namely static routing and OSPF (Open Shortest Path First), in the context of hybrid networks at Udayana University (UNUD). This research uses GNS3 to simulate the UNUD network environment. The research method is carried out by implementing static and OSPF routing separately in the GNS3 simulation. After the routing implementation is carried out, the network is tested against various error scenarios to observe how each routing method handles the situation.In this research, the analysis results provide a deeper understanding of the advantages and disadvantages of each routing method. Our findings illustrate that OSPF significantly improves fault tolerance and network availability compared to static routing in complex hybrid network environments.

“Kauaka e kōrero mō te Awa, kōrero ki te Awa: An Awa-Led Research Methodology” (Don’t Talk about the Awa, Talk with the Awa)

Indigenous people continue to develop methods to strengthen and empower genealogical knowledge as a means of conveying histories, illuminating current and past values, and providing important cultural frameworks for understanding their nuanced identities and worlds across time and space. Genealogies are more than simply a record of a family tree; they are a rich tapestry of ancestral links, representing a tradition of thought and connection to entities beyond the human. This article proposes an Iwi-specific methodological approach to conducting research based on the specific paradigms (ontological and epistemological) of Māori (Indigenous peoples of New Zealand) from the region of Te Awa Tupua in the North Island of Aotearoa, New Zealand. A Whanganui world view can be actioned as an operating system within research by developing a bespoke place-based methodology drawing on kōrero tuku iho (ancestral wisdom) to conduct research amongst a genealogical group with whakapapa (genealogical connection) to a distinct geographic locale. This methodological shift allows the inclusion of human research participants and more-than-human, including Te Awa Tupua (an interconnected environment around the Whanganui River) and Te Kāhui Maunga (ancestral mountains that feed the Whanganui river) as living ancestors. Whanganui ways of knowing, doing, and being underpin a worldview that situates Te Awa Tupua and tāngata (people) as inter-related beings that cannot maintain their health and wellbeing without the support of one another.

Applied Artificial Intelligence for Sustainability

In the contemporary era, modern civilization is immersed in a technologically interconnected environment, where numerous applications within the digital ecosystem harness advanced artificial intelligence (AI) techniques [...]

Energy Management of a Low-Cost Power Meter using ESP8266 and PZEM-016


 
 
 The burgeoning significance of the Internet of Things (IoT) lies in its capacity to configure interconnected environments and facilitate human-object interactions through collaborative services. This study proposes an efficient energy management approach leveraging cost-effective technologies like the ESP8266 microcontroller and the PZEM-016 Modbus RTU energy monitoring module. Tailored towards wireless connectivity, this solution is purposefully crafted for diverse sectors operating within constrained budgets, obviating the need for intricate infrastructure. A systematic deployment of the forward engineering research methodology is undertaken to discern the requisites and hurdles inherent in energy management. The amalgamation of ESP8266, PZEM-016, and the MQTT protocol, with RabbitMQ serving as a message broker, forges an efficacious framework for inter-device information exchange. The solution's instantiation entails the interconnection of power meter devices using the MQTT protocol, transmitting data in JSON format. The PZEM-016 sensor constitutes the crux, adeptly measuring voltage, current, frequency, and power with precision. Furthermore, the solution encompasses a prototype Smart Meter fortified with Wi-Fi connectivity to the internet, thus extending network coverage ubiquitously. Economic scrutiny reveals that the resultant power meter device costs less than 100 USD, competitively positioning it against analogous market offerings. This economically optimized design advocates for widespread adoption across multifarious sectors constrained by budgetary limitations, assuaging the complexities inherent in energy management through a trifecta of efficiency, reliability, and affordability.
 
 

Deciphering variations in the surficial bacterial compositions and functional profiles in the intersection between North and South Yellow Sea

The coastal ocean systems play paramount role in the nutrient biogeochemistry because of its interconnected environment. To gain a novel insight into coupling relationships between bacterial community, functioning properties and nutrient metabolism, we conducted analysis on the patterns and driving factors of planktonic bacterial functional community across subsurface water of marine ranching near the Yellow Sea in both summer and winter. Illumina HiSeq Sequencing and a corresponding set of biogeochemical data were used to assess distribution patterns of taxa, adaptive mechanism and metabolic function. Results demonstrated that Proteobacteria, Cyanobacteria, Actinobacteriota and Bacteroidota were dominant phyla both in summer and winter. Taxonomic profiles related to nutrient variation were found to be highly correlated with Dissolved Oxygen (DO) and Chlorophyll fluorescence (FLUO), and distinct diversity differences were also found between summer and winter samples. Functional activity in summer associated with the relative abundance of phototrophy and photoautotrophy were the highest in the subsurface water, while in winter the dominant functional properties were mainly include chemoheterotrophy and aerobic_ chemoheterotrophy. A significant difference related to functional activity between summer and winter, mainly representing ligninolysis and iron_respiration. In general, our study provides a framework for understanding the relative importance of environmental factors, temperature variation and nutrient availability in shaping the metabolic processes of aquatic microorganisms, particularly in ocean mariculture systems.

IoT-AD: A Framework to Detect Anomalies Among Interconnected IoT Devices

In an Internet of Things (IoT) environment (e.g. smart home), several IoT devices may be available that are interconnected with each other. In such interconnected environments, a faulty or compromised IoT device could impact the operation of other IoT devices. In other words, anomalous behavior exhibited by an IoT device could propagate to other devices in an IoT environment. In this paper, we argue that mitigating the propagation of the anomalous behavior exhibited by a device to other devices is equally important to detecting this behavior in the first place. In line with this observation, we present a framework, called IoT Anomaly Detector (), that can not only detect the anomalous behavior of IoT devices, but also limit and recover from anomalous behavior that might have affected other devices. We implemented a prototype of, which we evaluated based on open-source IoT device datasets as well as through real-world deployment on a small-scale IoT testbed we have built. We have further evaluated in comparison to prior relevant approaches. Our evaluation results show that can identify anomalous behavior of IoT devices in less than 2.12 milliseconds and with up to 98% of accuracy.

ВЛИЯНИЕ ЦИФРОВИЗАЦИИ НА СОЗДАНИЕ УСТОЙЧИВЫХ ГЛОБАЛЬНЫХ ЦЕПЕЙ ПОСТАВОК

A key trend that has emerged in response to modern challenges and instability in the global economy has been the accelerated introduction of digital technologies as a means of ensuring sustainability, which in this context is understood as the ability of organizations to consciously build a complex interconnected environment and quickly cope with key vulnerabilities. The paper analyzes current approaches to strengthening the role of digital transformation tools in modern scientific literature: from the Internet of Things and blockchain to artificial intelligence (AI) and big data analysis as ways to increase transparency, flexibility and adaptability in global supply chains. The key patterns and trends in the digital transformation of supply chains identified by the analysis of international regulations and extensive data sets (McKinsey&Company, IDC, KBV, HFS Research, Deloitte Consulting, JPMorgan, PwC) are listed to disclose the degree of influence of instruments on financial performance, identify the best strategies and practices. The advantages and risks of implementing digital tools in companies related to the introduction of the Internet of Things, artificial intelligence and data analysis are assessed. A number of recommendations have been formulated for international organizations to find new guidelines in the changing environment of global supply chains. This improves the reliability of planning by allowing you to anticipate and mitigate potential failures and problems.

The Impact of Scenario-based Services in an Intelligent Interconnected Environment on Value Co-creation

The Impact of Scenario-based Services in an Intelligent Interconnected Environment on Value Co-creation

Securing the skies: A comprehensive survey on internet of drones security challenges and solutions

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) in the Internet of Things (IoT) era has given rise to the Internet of Drones (IoD), introducing a myriad of security challenges. This survey paper provides a comprehensive examination of the security landscape within the IoD ecosystem. Delving into communication security, authentication mechanisms, data integrity safeguards, firmware and software vulnerabilities, counter-drone measures, and regulatory compliance, the paper explores the multifaceted dimensions of securing UAVs in interconnected environments. By synthesizing current research findings, industry developments, and regulatory frameworks, this survey not only highlights the evolving threat landscape but also presents an overview of state-of-the-art security solutions. The objective is to offer a holistic understanding of IoD security, fostering awareness and providing a foundation for further research and practical implementations. As the integration of drones into various domains becomes increasingly pervasive, this survey aims to contribute to the ongoing discourse on ensuring the safe and responsible utilization of UAV technology within the broader IoT landscape.

Development and Validation of a Cyber-Physical System Leveraging EFDPN for Enhanced WSN-IoT Network Security.

In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F3S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.