The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular (...) focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, (...) network security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

The Internet of Things (IoT) is revolutionizing industries by connecting everyday objects to the internet, enabling improved functionality and convenience. However, the rapid adoption of IoT devices has raised significant cybersecurity concerns, as the vast number of connected devices presents new vulnerabilities that can be exploited by cybercriminals. These vulnerabilities are often the result of insecure device configurations, weak authentication mechanisms, and inadequate data protection. This paper explores the cybersecurity challenges associated with IoT, such as device security, (...) network security, and data privacy, and discusses the solutions that can help mitigate these risks. The paper further evaluates the role of standardization, encryption techniques, and intrusion detection systems in enhancing the security of IoT ecosystems. Finally, it proposes a framework for IoT security based on best practices and emerging technologies. (shrink)

The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and (...) consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols. (shrink)

The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research (...) data collection. H-IoT promises many benefits for health and healthcare. However, it also raises a host of ethical problems stemming from the inherent risks of Internet enabled devices, the sensitivity of health-related data, and their impact on the delivery of healthcare. This paper maps the main ethical problems that have been identified by the relevant literature and identifies key themes in the on-going debate on ethical problems concerning H-IoT. (shrink)

The Internet-of-Things (IoT) is gradually being established as the new computing paradigm, which is bound to change the ways of our everyday working and living. IoT emphasizes the interconnection of virtually all types of physical objects (e.g., cell phones, wearables, smart meters, sensors, coffee machines and more) towards enabling them to exchange data and services among themselves, while also interacting with humans as well. Few years following the introduction of the IoT concept, significant hype was generated as a (...) result of the proliferating number of IoT-enabled devices, which (according to many projections) are expected to amount to several billion in the next years. During recent years, this hype has been turning to reality, as a wave of IoT applications with significant social and economic has been emerging. Data analytics is the process of deriving knowledge from data, generating value like actionable insights from them. This article reviews work in the IoT and big data analytics from the perspective of their utility in creating efficient, effective and innovative applications and services for a wide spectrum of domains. We review the broad vision for the IoT as it is shaped in various communities, examine the application of data analytics across IoT domains, provide a categorization of analytic approaches and propose a layered taxonomy from IoT data to analytics. IoT data analysis is an integral element of any non- trivial IoT system. Nevertheless, IoT analytics are still in their infancy, as IoT data still remain largely unexploited. (shrink)

With the advent of Internet of Things (IoT) and data convergence using rich cloud services, data computing has been pushed to new horizons. However, much of the data generated at the edge of the network leading to the requirement of high response time. A new computing paradigm, edge computing, processing the data at the edge of the network is the need of the time. In this paper, we discuss the IoT architecture, predominant application protocols, definition of edge computing (...) and its research opportunities. (shrink)

Security and privacy are the major concerns in the internet of things (IoT) which are uncertain and unpredictable. Trust aware routing is one of the recent and effective strategies which ensure better resilience for IoT nodes from different security threats. Towards such concern, this paper proposes a new strategy called independent onlooker withstanding trust aware routing (IOWTAR) for IoT. IOWTAR introduced a new compound trust metric by combining three individual metrics namely independent trust, onlooker trust, and withstanding trust (...) (a combination of resilient trust and immovability trust). Independent trust and onlooker trust are assessed based on direct and indirect experiences of nodes about their neighbor nodes. Withstanding trust is assessed based on the stability and resilience of nodes towards dynamic topological changes and network failures respectively. Further, this work adapted the Firefly algorithm (FFA) to optimize the weights of individual trusts and establishes a secure path. Simulation experiments carried out over the proposed method had shown its superiority in terms of packet delivery ratio, delay, and throughput. The proposed method has gained an average improvement in the throughput is of 23.71%, 20.18%, 17.27%, and 2.88% from PSO, GSA, WOA, and CBBMOR-TSM-IOT methods respectively. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the (...) proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

This paper discusses the epistemology of the Internet of Things [IoT] by focusing on the topic of trust. It presents various frameworks of trust, and argues that the ethical framework of trust is what constitutes our responsibility to reveal desired norms and standards and embed them in other frameworks of trust. The first section briefly presents the IoT and scrutinizes the scarce philosophical work that has been done on this subject so far. The second section suggests that the (...) field of epistemology is not sufficiently capable of dealing with technologies, and presents a possible solution to this problem. It is argued that knowledge is not only social phenomena, but also a technological one, and that in order to address epistemological issues in technology, we need to carefully depart from traditional epistemic analysis and form a new approach that is technological (termed here Techno-Epistemology). The third and fourth sections engage in an epistemic analysis of trust by dividing it in to various frameworks. The last section argues that these various frameworks of trust can be understood to form a trustworthy large-scale socio-technological system, emphasizing the place of ethical trust as constituting our commitment to give proper accounts for all of the other frameworks. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

The Internet of Things (IoT) wireless LAN in healthcare has moved away from traditional methods that include hospital visits and continuous monitoring. The Internet of Things allows the use of certain means, including the detection, processing and transmission of physical and biomedical parameters. With powerful algorithms and intelligent systems, it will be available to provide unprecedented levels of critical data for real-time life that are collected and analyzed to guide people in research, management and emergency care. (...) This chapter provides a quick overview of IoT features and how they relate to wireless discovery and technology to deploy the medical applications you need. In the world, the revolution in any industry is to connect your products and devices to the Internet and make them independent and remotely connected, so that anyone can use and view them from anywhere and anytime. The Internet of Things provides us with a home automation system that uses smart devices to overcome this obstacle, allowing us to easily manage our appliances. A smart city is a vision to integrate a variety of information and communication solutions for residents with essential services, such as smart parking on all streets. The main motivation for using the Internet for parking objects is simply collecting data to get free parking. The IoT-based RTSSPS architecture is divided into three parts: a WSN-based smart street parking module, an IoT-based smart street parking module, and an IoT-based smart street parking module. IoT-based cloud with street parking algorithm, rating and future directions. (shrink)

Internet, a revolutionary invention, is always transforming into some new kind of hardware and software making it unpreventable for anyone. The type of communication that we see today is either human-to-human or human-to-device, but the Internet of Things (IoT) promises a great future for the internet where the type of communication is machine-to-machine (M2M). The Internet of Things (IoT) is defined as a paradigm in which objects provide with sensors, actuators, and processors communicate with (...) each other to serve a meaningful purpose. In this paper we discussed IoT and its architecture. Further we explained different applications of IoT for users, IoT advantages and disadvantages. (shrink)

This article analyses, defines, and refines the concepts of ownership and personal data to explore their compatibility in the context of EU law. It critically examines the traditional dividing line between personal and non-personal data and argues for a strict conceptual separation of personal data from personal information. The article also considers whether, and to what extent, the concept of ownership can be applied to personal data in the context of the Internet of Things (IoT). This consideration is (...) framed around two main approaches shaping all ownership theories: a bottom-up and top-down approach. Via these dual lenses, the article reviews existing debates relating to four elements supporting introduction of ownership of personal data, namely the elements of control, protection, valuation, and allocation of personal data. It then explores the explanatory advantages and disadvantages of the two approaches in relation to each of these elements as well as to ownership of personal data in IoT at large. Lastly, the article outlines a revised approach to ownership of personal data in IoT that may serve as a blueprint for future work in this area and inform regulatory and policy debates. (shrink)

Vibration, especially basic vibration, may cause loose contacts, open circuits, or other contact problems, which account for a large proportion of system failure causes. Due to the complexity of the chassis structure used in the biomedical motors, the vibration analysis of a single component cannot solve the vibration problems encountered in the work of the system. Therefore, it is necessary to use system simulation and experiments to monitor and enhance the health of the structure. Finite element method is used to (...) carry out the dynamic analysis from the component to the system, and calculate the natural frequency, modal shape and harmonic response. Then, the effectiveness and accuracy of the analysis results are verified by experiments through simulation and experiments. The results show that the measures taken have effectively reduced the vibration of the chassis equipment and improved its safety for better health safety of patients. There is just a 2% variation between the natural frequencies acquired from the experiment and the simulation computation. In terms of natural frequency, the first 6-order natural frequencies all have errors of less than 10%, and the natural frequency error is comparable to that of fireproof board. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. (...) This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

Abstract: Reports suggests that total amount of data generated everyday reaches 2.5 quintillion bytes [9], annual global IP traffic run rate in 2016 was 1.2 zettabytes and will reach 3.3 zettabytes by 2021 [12]. According to Gartner [25], Internet of Things excluding personal computers, tablets and smartphones will grow to 26 billion units of installed devices in year 2020. This results from penetration of digital applications which highly motivated by smart societies which can be defined as to when (...) a society deploys light and advanced computer technologies to aid provision and or supply chain value of social, cultural, governance and economic utilities for efficiency. Smart society is equipped with mobile, ubiquitous computing facilities, sensors and cyber-physical systems aims at exploring economies of scale; and to large extent it has been made possible with Internet of Things (IoT). This survey paper discusses status of big data in Internet of Things; how IoT generates big data, nature of data generated and dynamics in IoT as influenced by big data. (shrink)

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond delay, and (...) inefficient use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods. (shrink)

The integration of Internet Protocol and Embedded Systems can enhance the communication platform. This paper describes the emerging smart technologies based on Internet of Things (IOT) and internet protocols along with embedded systems for monitoring and controlling smart devices with the help of WiFi technology and web applications. The internet protocol (IP) address has been assigned to the things to control and operate the devices via remote network that facilitates the interoperability and end-to-end communication (...) among various devices c,onnected over a network. The HTTP POST and HTTP GET command that supports the RESTful service have been used to ensure the transmission and reception of packets between the IOT Gateway and Cloud Database. The emerging smart technologies based on the Internet of Things (IoT) facilitated features like automation, controllability, interconnectivity, reliability which in turn turn paved the way for a wide range of acceptance amongst the masses. The Internet of Things (IoT) has brought in many new emerging technologies into varoius field like our daily lives, industry, agricultural sector, and many more. The world is experiencing the explosive growth with the advent of Internet of Things (IoT) these years. The potential growth of IoT is enoromous which is evidenced by all the human beings in our day to day life. (shrink)

Autonomous agricultural equipment represents a transformative innovation, combining cutting-edge technologies such as artificial intelligence (AI), robotics, and the Internet of Things (IoT). These machines address challenges such as labor shortages, environmental sustainability, and rising food demand. This paper explores the concept, key technologies, applications, and benefits of autonomous farming equipment. A comprehensive literature review highlights current advancements and challenges in the field. An experimental study evaluates the efficiency and impact of autonomous machinery in farming, providing results that demonstrate (...) their potential. (shrink)

The management of waste has become an increasingly critical issue as urbanization and industrial activities surge globally. Traditional waste management practices are struggling to keep pace with the rising quantities and complexities of waste. This paper delves into the integration of intelligent technologies, such as Artificial Intelligence (AI), the Internet of Things (IoT), and data analytics, into waste management systems. By examining current practices, methodologies, experiments, and results, this study aims to highlight the transformative potential of intelligent technologies (...) in optimizing waste management processes, enhancing efficiency, and reducing environmental impact. (shrink)

The quick expansion of the Internet of Things (IoT) has produced exponential data production requiring efficient processing solutions. Because of too high latency, limited bandwidth, and security concerns, real-time applications find conventional cloud-based architectures less suitable. Edge computing addresses these restrictions by processing data nearer the source, thus reducing latency, improving response times, and so raising overall system efficiency. Edge computing—by leveraging localized computation—allows real-time decision-making for significant IoT purposes like smart cities, industrial automation, healthcare, and autonomous autos. (...) Filtering and evaluating data at the edge before passing relevant information to the cloud helps to clear network congestion as well. This study addresses the importance of edge computing in IoT, its primary benefits, challenges, and future prospects in providing intelligent, real-time, scalable IoT systems. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...) confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. (shrink)

The increasing sophistication of cyber threats necessitates innovative and proactive cybersecurity measures. This paper explores the latest trends in cybersecurity, focusing on the role of Artificial Intelligence (AI), Zero Trust security, and blockchain technology. A review of the literature highlights significant advancements and persistent challenges, including the security of Internet of Things (IoT) ecosystems and human-centric vulnerabilities. Experiments were conducted to evaluate the efficacy of machine learning-based intrusion detection systems and Zero Trust implementation in a simulated environment. Results (...) demonstrate improved detection rates and reduced insider threat risks. This paper concludes by emphasizing future directions, such as quantum-resistant cryptography and collaborative cybersecurity frameworks, to secure the digital future. (shrink)

The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge (...) data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization algorithm (GWO) is a developed bio-inspired algorithm that improves the efficacy of the IDS in detecting both regular and abnormal congestion in the network. The smart initialization step integrates the different pre-processing strategies to make sure that informative features are incorporated in the early development stages, has been improved. Researchers pick multi-source material in a big data environment for the identification and verification of index components and present a parallel reduction approach based on the classification significance matrix to decrease data underlying data characteristics. For the simulation of this situation, grey wolf optimization and whale optimization were combined to detect the attack prevention and the deep learning approach was presented. Utilizing system software plagiarism, the TensorFlow deep neural network is intended to classify stolen software. To reduce the noise from the signal and to zoom the significance of each word in the perspective of open-source plagiarism, the tokenization and weighting feature approaches are utilized. Malware specimens have been collected from the Mailing database for testing purposes. The experimental findings show that the suggested technique for measuring cyber security hazards in IoT has superior classification results to existing methods. Hence to detect the attack prevention in IoT process Whale with Grey wolf optimization (WGWO) and deep convolution network is used. (shrink)

Traditional project risk management techniques are becoming less and less effective in the age of Industry 4.0, which is defined by sophisticated automation, data interchange, and cyber-physical systems. It suggests a new framework that improves risk detection, assessment, and mitigation techniques by utilising technology like artificial intelligence (AI), big data analytics, and the Internet of Things (IoT). This paper illustrates how Industry 4.0 is revolutionising risk management procedures by looking at actual case studies and industry implementations. The framework (...) offers practical insights for practitioners looking to adjust to the changing technological landscape by addressing important possibilities and obstacles. According to the research, implementing Industry 4.0 technologies can greatly enhance project results and risk management effectiveness, providing a means for businesses to handle the challenges of contemporary project environments. (shrink)

Agriculture is critical to the growth of an agricultural country. Many industries, including agricultural, have been altered as a result of the fast rise of Internet of Things-based technology. In India, farming employs over 70% of the people and generates one-third of the nation's capital. Agriculture-related issues have traditionally hampered the country's progress. The only answer to this challenge is smart agriculture, which involves upgrading conventional agricultural processes. Solutions based on IoT are being developed to autonomously manage and (...) monitor agricultural crops with minimum human intervention. Many elements of technology engaged in the realm of IoT in agriculture are covered in this article. It describes the essential components of IoT-based smart farming. Farmers benefit from IoTbased Agriculture by collecting live knowledge (Temperature, Soil Moisture, and Humidity) of agricultural data. These real-time measurements enable farmers to experiment with new agricultural techniques in order to boost average crop yields as well as plant quality. All of these actions will be controlled by any remote smart device or computer linked to the Internet, and they will be carried out by connecting sensors, Wi-Fi or ZigBee modules, cameras, and actuators with a Raspberry Pi and a microcontroller. (shrink)

The main objective of our project is to construct a fully functional voice-based home automation system that uses the Internet of Things and Natural Language Processing. The home automation system is user-friendly to smartphones and laptops. A set of relays is used to connect the Node MCU to homes under controlled appliances. The user sends a command through the speech to the mobile devices, which interprets the message and sends the appropriate command to the specific appliance. The voice (...) command given by the user is interpreted by the mobile device using Natural Language Processing. The system utilizes a node microcontroller unit (Node MCU) as a Wi-Fi-based gateway to connect to the Google Assistant. It determines operation must be completed by which the appliance to fulfill the user’s request. The user increased mobile capabilities we will be using a smart phone in this project. A smartphone application as nearly all of the data transferred can be processed by cloud. The user increased mobile capabilities we will be using a smart phone in this project. The IoT -based system for home automation can easily and efficiently control appliances. The Node MCU is interfaced with the appliances and programmed in a manner that they respond to mobile inputs. (shrink)

Our rapidly increasing reliance on frictionless AI interactions may increase cognitive and emotional distance, thereby letting our adaptive resilience slacken and our ethical virtues atrophy from disuse. Many trends already well underway involve the offloading of cognitive, emotional, and ethical labor to AI software in myriad social, civil, personal, and professional contexts. Gradually, we may lose the inclination and capacity to engage in critically reflective thought, making us more cognitively and emotionally vulnerable and thus more anxious and prone to manipulation (...) from false news, deceptive advertising, and political rhetoric. In this article, I consider the overarching features of this problem and provide a framework to help AI designers tackle it through system enhancements in smartphones and other products and services in the burgeoning internet of things (IoT) marketplace. The framework is informed by two ideas: psychologist Daniel Kahneman’s cognitive dual process theory and moral self-awareness theory, a four-level model of moral identity that I developed with Benjamin M. Cole. (shrink)

Technology has the power to break the limitations of traditional passive learning and innovate almost all aspects of everyday life with the power of connecting things of the world to the Internet, “Internet of Things (IoT).” IoT is no longer a phenomenon, but it has become a prevalent system in which people, processes, data, and things connect to the Internet and each other. This paper ‘Enhanced Campus Automation System and the emerging need of IoT (...) integration: an automation perspective’ [eCAS-IoT] is an outcome of case study undertaken at St. Aloysius’ College, Jabalpur, India, wherein the automation challenges are taken into consideration that has been integrated in the existing ERP and related tools such as RFID based attendance and monitoring system in real-time, digital library, API integration and implementation of Optical Network. In this paper we have tried to bring out ideas to achieve the campus automation challenges and awareness of greater need of technical expertise, promotion and training on the effective use of ICT and related devices to make teaching, learning and administration more impacting. (shrink)

In today’s world, technology seems to touch every aspect of our lives. Our processes for so many things have become automated and digitalized. The use of networked computational devices by both end users and corporations has become, in a word, ubiquitous. In many ways, the era of rapid technological growth we find ourselves in currently can be traced back to the work of the pioneering computer scientist, Mark Weiser. It is widely accepted among technologists today that his original vision (...) of “ubiquitous computing” played a large part in setting these events into motion in the first place. Yet the emergence of our technologies and the ways we use them has turned out to be something radically different from what Weiser had in mind. -/- As John Tinnell carefully explains in The Philosopher of Palo Alto, Weiser had in fact hoped for a future that would place computation all around us. Devices would perform specialized tasks regularly to enhance and improve our quality of life. The goal, however, according to Weiser’s view, was for these technologies to function always on the periphery of human perception. They were never meant to place any unreasonable demand on our attention. Operating in this manner, the devices designed to support human affairs could do so, and at the same time leave space for us to connect with the world and each other in essentially human ways. (shrink)

With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time processing of large data volumes, traditional cloud-based systems struggle to meet latency and bandwidth requirements. Edge-Cloud convergence has emerged as a solution, combining the computational power of cloud data centers with the low-latency and high-throughput capabilities of edge devices. This paper explores the architecture, design principles, and best practices for building hybrid systems that integrate edge computing and cloud infrastructure. We investigate various (...) methods to optimize latency, improve real-time data processing capabilities, and ensure seamless communication across the hybrid environment. Additionally, the paper highlights challenges such as data consistency, scalability, security, and fault tolerance in Edge-Cloud systems. The performance of Edge-Cloud hybrid systems is analyzed through simulation, showcasing key advantages such as reduced latency, bandwidth optimization, and fault tolerance in real-time applications. The paper concludes by providing recommendations for future work and areas of research to advance the development of more robust and efficient Edge-Cloud systems. (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be (...) able to resist a wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random (...) Forest, XGBoost, Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

The increasing demand for reliable power sources has made diesel generators essential in various industries. However, traditional monitoring methods often rely on manual inspections, resulting in inefficiencies, unplanned downtimes, and higher operational costs. This project proposes a digital monitoring system for diesel generators that leverages advanced sensor technologies and real-time data analytics to overcome these challenges. The system will utilize Internet of Things (IoT) devices to collect key operational parameters, such as fuel levels, temperature, vibration, and performance metrics, (...) enabling comprehensive monitoring of generator health. By integrating machine learning algorithms, the system will analyze historical and real-time data to predict potential failures and optimize maintenance schedules, shifting from reactive to proactive management. A user-friendly interface will allow operators to visualize generator performance and receive instant alerts for detected anomalies, enhancing decision- making processes, reducing downtime, and lowering maintenance costs—ultimately increasing the reliability and efficiency of power supply. Additionally, the system facilitates remote monitoring, providing continuous oversight regardless of location. This project not only aims to enhance the operational efficiency of diesel generators but also lays the groundwork for smarter energy management solutions in the future. The successful implementation of this digital monitoring system could serve as a model for similar applications across various sectors, contributing to sustainability and operational excellence in energy management. Through this innovative approach, we aim to ensure diesel generators operate at optimal performance levels while minimizing environmental impact. Keywords. Diesel Generators, Digital Monitoring, IoT Devices, Real-time Data Analytics, Machine Learning. (shrink)

Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer (...) ethics content to be included in Computer Science curricula. In this paper we present the results of a survey that polled faculty from Computer Science and related disciplines about teaching practices for computer ethics at their institutions. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not computer ethics is taught to Computer Science students at each institution, the reasons why computer ethics is or is not taught, how computer ethics is taught, the background of staff who teach computer ethics and the scope of computer ethics curricula. This paper presents and discusses the results of the survey. (shrink)

The goal of this project is to create a prototype smart farm using Internet of Things (IoT) technology. Developing an Internet of Things feeding control system and designing and implementing environmental monitoring and control systems for farm housing are the primary objectives. The development method uses open-source hardware and software and is conducted in pig farms in the Thai region of Nakhon Si Thammara. An ESP8266 Wi-Fi microcontroller is crucial to the system design, allowing several sensors (...) to be connected to the server and actuators to be controlled. The MQTT protocol is used to transmit data over a secure wireless local network. The data flow is designed using Node-RED, and storage is handled using a NoSQL database such as InFluxDB. The feeding control system achieved a 100% compliance rate by following instructions continuously. End customers can utilise a web application on their PCs or cellphones to obtain real-time data and operate the system. Furthermore, the collected data is processed and analysed as big data, which facilitates the analysis of climate change trends in the vicinity of the study site and forecasting. The overall performance rating of the system received great marks from users, averaging 4.40 out of 5 with a 0.46 standard deviation. As a result, the created IoT system functions as a prototype that may be further enhanced for use in bigger farms or other agricultural environments. (shrink)

Internet of Things (IoT) conceptualizes the idea of remotely connecting and monitoring real world objects (things) through the Internet [1]. When it comes to our house, this concept can be aptly incorporated to make it smarter, safer and automated. This IoT project focuses on building a smart wireless home security system which sends alerts to the owner by using Internet in case of any trespass and raises an alarm optionally. Besides, the same can also be (...) utilized for home automation by making use of the same set of sensors. The leverage obtained by preferring this system over the similar kinds of existing systems is that the alerts and the status sent by the Wi-Fi connected microcontroller managed system can be received by the user on his phone from any distance irrespective of whether his mobile phone is connected to the internet. The microcontrolleri used in the current prototype is the TI-CC3200 Launchpad board which comes with an embedded micro-controller and an onboard Wi- Fi shield making use of which all the electrical appliances inside the home can be controlled and managed. (shrink)

Mobile Edge Computing (MEC) is an emerging technology that enables computing directly at the edge of the cloud computing network. Therefore, it is important that MEC is applied with reliable transmission. The problem of reaching consensus in the distributed system is one of the most important issues in designing a reliable transmission network. However, all previous protocols for the consensus problem are not suitable for an MEC paradigm. It is the first time an optimal protocol of reaching consensus is pro- (...) posed for MEC paradigm. The protocol makes all fault-free nodes communicate with each other and collect the exchanged messages to decide a common value. Based on the common value, the protocol ensures all fault-free nodes reach consensus without the influence of unreliable transmission. Finally, we proved theoretically that the proposed protocol can tolerate the maximum number of faulty components and using only two rounds of message exchanges. (shrink)

Ambient assisted living technologies are increasingly presented and sold as essential smart additions to daily life and home environments that will radically transform the healthcare and wellness markets of the future. An ethical approach and a thorough understanding of all ethics in surveillance/monitoring architectures are therefore pressing. AAL poses many ethical challenges raising questions that will affect immediate acceptance and long-term usage. Furthermore, ethical issues emerge from social inequalities and their potential exacerbation by AAL, accentuating the existing access gap between (...) high-income countries and low and middle-income countries. Legal aspects mainly refer to the adherence to existing legal frameworks and cover issues related to product safety, data protection, cybersecurity, intellectual property, and access to data by public, private, and government bodies. Successful privacy-friendly AAL applications are needed, as the pressure to bring Internet of Things devices and ones equipped with artificial intelligence quickly to market cannot overlook the fact that the environments in which AAL will operate are mostly private. The social issues focus on the impact of AAL technologies before and after their adoption. Future AAL technologies need to consider all aspects of equality such as gender, race, age and social disadvantages and avoid increasing loneliness and isolation among, e.g. older and frail people. Finally, the current power asymmetries between the target and general populations should not be underestimated nor should the discrepant needs and motivations of the target group and those developing and deploying AAL systems. Whilst AAL technologies provide promising solutions for the health and social care challenges, they are not exempt from ethical, legal and social issues. A set of ELSI guidelines is needed to integrate these factors at the research and development stage. (shrink)

Health data processing fields face ethical and legal problems regarding fundamental rights. As we know, patients can benefit in the Digital Era from having health or medical information available, and medical decisions can be more effective with a better understanding of clinical histories, medical and health data thanks to the development of Artificial Intelligence, Internet of Things and other Digital technologies. However, at the same time, we need to guarantee fundamental rights, including privacy ones. The complaint about ethical (...) and legal requirements – including constitutional ones – is particularly relevant in the processing of health data. This paper is focused on the problem of the consent required to the processing of health data and the exceptions established in the new European Union General Data Regulation, which cover the processing of this special data -within other aims- for scientific, historical and statistical research as legitimate purposes, which include biomedical research. The conclusion is that these open concepts are problematic both for the protection of privacy rights and for the legal security/certainty of research. On one hand there are several interpretation problems, regarding the processing of health data and the protection of information privacy. On the other hand, professionals must follow the Multilevel legal framework and to guarantee fundamental rights in the processing of health data, so there are also problems of interpretation for researchers. Therefore, we need a clearer legal framework for biomedical research. (shrink)

According to the forecast that billions of devices will get connected to the Internet by 2020. All these devices will produce a huge amount of data that will have to be handled rapidly and in a feasible manner. It will become a challenge for real-time applications to handle this huge data while considering security issues as well as time constraints. The main highlights of cloud computing are on-demand service and scalability; therefore the data generated from IoT devices are generally (...) handled in cloud infrastructure. Though, dealing with IoT application requests on the cloud exclusively is not a proficient result for some IoT applications particularly time-sensitive ones. These issues can be settled by utilizing another idea called, Fog computing. Fog computing has become one of the major fields of research from both academia and industry perspectives. The ongoing research commitments on few issues in fog computing are figuring out in this paper. At long last, this paper also highlights some open issues in fog with IoT, which will determine the future research direction for implementing Fog computing paradigm. (shrink)

The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to (...) identify potential cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve (...) the accuracy and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

Contemporary Information Systems management incorporates the need to make explicit the links between semiotics, meaning-making and the digital age. This focus addresses, at its core, pure rationality, that is, the capacity of human interpretation and of human inscription upon reality. Creating the new real, that is the motto. Humans are intrinsically semiotic creatures. Consequently, semiotics is not a choice or an option but something that works like a second skin, establishing limits and permeable linkages between: human thought and human's infinite (...) world of imagination; and human action, with its correspondent infinite world of intentionality, of desire and of unexplored possibilities. Two instances are contrasted as two reading lenses of current business reality: IS governance and industry 4.0. These phenomena correspond to the need to take accountability, transparency and responsibility into account, when designing IS and when using such systems through the ecology of connectivity, Big Data and the Internet of Things. Political, social and cultural dimensions are brought into the equation, when addressing the question of the relevance and adequateness of IS theory and practice to respond to contemporary challenges. The message is that what has already been achieved is but a shadow, a pale vision, of what might be achieved in the age of the new Renaissance. (shrink)

The proliferation of Internet of Things (IoT) technologies presents a promising avenue for fostering inclusive societies through community development and education initiatives. This paper explores the potential of leveraging IoT to address societal inequalities and empower marginalized communities. Through a multidisciplinary lens, the paper examines the intersection of IoT, community development, and education, elucidating how IoT-enabled solutions can contribute to building more resilient, connected, and equitable societies. By harnessing IoT devices, sensor networks, and data analytics, community development efforts (...) can be augmented, enabling better resource allocation, infrastructure management, and service delivery. Additionally, IoT-enabled educational interventions offer opportunities to enhance learning experiences, improve access to educational resources, and promote lifelong learning for individuals of all backgrounds. However, alongside the promise of IoT, ethical considerations surrounding data privacy, security, and digital divide must be addressed to ensure equitable access and participation. Through case studies and best practices, the study highlights successful examples of IoT implementations in community development and education, while also identifying key challenges and opportunities for future research and practice. Finally, the study advocates for a collaborative and inclusive approach to leveraging IoT for community development and education, emphasizing the importance of empowering individuals and communities to participate actively in shaping their futures. Keywords: Internet of Things, Community development, Education, Inclusive societies, Sensor. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...) confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...) confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)