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 aim of this project is to develop a system that uses the Internet of Things (IoT) and machine learning (ML) to help farmers select the best crops for their fields. The geolocations are fetched using the GPS receiver by communicating with the satellite. The system consists of IoT sensors that collect data on soil and environmental conditions, such as pH, temperature, humidity, and rainfall. This data is sent to a cloud platform, where ML algorithms analyze it and provide crop (...) recommendations based on historical data, weather forecasts, and soil properties. The system offers a user-friendly interface that allows farmers to access the recommendations, monitor their crops, and provide feedback. The system aims to enhance agricultural productivity, resource efficiency, and sustainability by using data-driven insights. (shrink)

Monitoring soil pH levels forms the backbone of precision agriculture with regard to maximizing crop health and yield. The paper discusses an IoT-based solution, specifically designed for continuous soil pH testing in digital farming. It has pH sensors placed strategically around the agricultural fields such that the information regarding the acidity or alkalinity of the soil is available in real-time. The pH information will be transmitted to the central IoT gateway. Here, the information is processed, and via communication of cloud-based (...) platforms, it is further stored and analyzed. So, by checking variations in soil pH, the system provides actionable information to farmers. Thus, by adjusting soil treatments like lime or fertilizers better, it enhances crop performance. The response uses mobile and web interfaces that support automated pH testing with automatically designed data visualization. This allows farmers the chance to decide from remote sites about imminent and proper fluctuations in soil pH by the use of historical data analysis and models from machine learning. One part of the effectuality of this proactive approach would be that soil quality is improved while unnecessary chemical use is avoided, therefore reducing the resulting costs of farming with sustainability considerations. By limiting itself to pH value testing, this solution increases the farmland's productivity with less wastage of resources. (shrink)

The paper also discusses the technical architecture, including the network protocols, data management strategies, and user interface design considerations necessary to implement such a system. Additionally, it addresses the challenges related to data security, privacy, and system interoperability. Finally, the paper outlines potential future enhancements, such as the incorporation of AI-driven predictive analytics and advanced AR features, to further elevate the smart home experience.

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)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of Things (...) ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

The Internet of Things (IoT) has revolutionized healthcare by establishing realtime and remote monitoring systems, thereby significantly enhancing patient care and operational efficiency. Traditional monitoring systems, however, focus on basic parameters such as heart rate and body temperature, which can only provide minimal functionality. This work proposes an advanced IoT-enabled solution that makes use of sensors to monitor a variety of health parameters, such as glucose and lactate levels. The system gives real time alerts to ensure timely interventions in critical (...) areas, such as intensive care unit, ICU. The aim should be to optimize patient treatment while using IoT technology such that it does not burden much the healthcare staff but remote monitoring is effective. Of especial importance, this idea can be used in healthcare in rural setups and other settings where pandemics and flu season have rendered traditional monitor setups impractical. The proposed system bridges the gap between patients and providers by providing a scalable and efficient healthcare solution. It ensures better outcomes and streamlined care delivery. Not only does it enhance critical care response times, but it also reduces the need for constant on-site personnel, providing a significant improvement in accessibility, efficiency, and responsiveness in healthcare. Keywords: IoT, Remote Monitoring, Healthcare, Real- Time Alerts, Critical Care, Patient Monitoring Systems, Sensor Technology, Pandemic Response. (shrink)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on applications in transportation, energy, public (...) safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (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)

The integration of Augmented Reality (AR) and the Internet of Things (IoT) within smart homes represents a significant leap forward in home automation and user experience. This paper explores a comprehensive framework that combines these technologies to create an immersive, efficient, and user-friendly smart home environment. By utilizing AR, users can interact with and control IoT-enabled devices in real time through visual and intuitive interfaces. This integration not only simplifies the management of home systems but also enhances the overall user (...) experience by providing a seamless blend of the physical and digital worlds. (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 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)

The emergence of wearable Internet of Things (IoT) devices has revolutionized the Life Insurance industry by enabling dynamic, personalized pricing models. These devices collect continuous streams of health and lifestyle data, offering insurers rich insights for risk assessment and actuarial analysis. However, ensuring the reliability, accuracy, and ethical handling of this data presents significant challenges. This paper proposes a comprehensive validation framework that integrates AI-driven anomaly detection, federated learning for privacy preservation, and blockchain for secure data traceability. AI-based anomaly detection (...) algorithms identify irregularities in the data to ensure its accuracy and consistency. Federated learning allows the model to learn from the data on the device, preserving privacy by never transmitting sensitive information. Blockchain technology ensures the integrity of the data by recording it in an immutable ledger, providing transparency and preventing fraud. Together, these technologies enable the secure and ethical use of wearable IoT data, ensuring reliable and transparent dynamic pricing models in Life Insurance. (shrink)

Anamoly detection in videos plays an important role in various real-life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Nowadays, there has been a rise in the amount of disruptive and offensive activities that have been happening. Due to this, security has been given principal significance. Public places like shopping centers, avenues, banks, etc. are increasingly being equipped with CCTVs to guarantee the security of individuals. Subsequently, this inconvenience is making (...) a need to computerize this system with high accuracy. Since constant observation of these surveillance cameras by humans is a near-impossible task. It requires workforces and their constant attention to judge if the captured activities are anomalous or suspicious. Hence, this drawback is creating a need to automate this process with high accuracy. Moreover, there is a need to display which frame and which parts of the recording contain the uncommon activity which helps the quicker judgment of that unordinary action being unusual or suspicious. Therefore, to reduce the wastage of time and labour, we are utilizing deep learning algorithms for Automating Threat Recognition System. Its goal is to automatically identify signs of aggression and violence in real-time, which filters out irregularities from normal patterns. We intend to utilize different Deep Learning models (CNN and RNN) to identify and classify levels of high movement in the frame. From there, we can raise a detection alert for the situation of a threat, indicating the suspicious activities at an instance of time and spray the smoke spray. (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 data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. Future work (...) will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

Air pollution is a significant environmental concern that affects human health, ecosystems, and climate change. Effective monitoring and management of outdoor air quality are crucial for mitigating its adverse effects. This paper presents an advanced approach to outdoor pollution measurement utilizing Internet of Things (IoT) technology, combined with optimization techniques to enhance system efficiency and data accuracy. The proposed framework integrates a network of IoT sensors that continuously monitor various air pollutants, such as particulate matter (PM), carbon monoxide (CO), sulfur (...) dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃), across different geographic locations. The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. (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)

The food business has typically trailed behind other industries in embracing new technology, and plant automation is no exception. However, fast improvements in computer technology, as well as increased customer and regulatory agency demands for enhanced food quality and safety, have prompted the food sector to contemplate automation of most industrial operations. Though the food business presents numerous specific hurdles to complete automation, the industry has been successful in implementing several automatic operations Electronic sensors, Arduino, computer vision, expert systems, computer (...) integrated manufacturing, flexible manufacturing systems, systems engineering, and other new technological tools have made it possible to integrate many batch operations into an overall manufacturing system design, allowing for on-line and continuous control capability. In this study, we conducted a literature survey and analysed data from automation in food processing for a variety of applications utilizing Arduino, electronic sensors, and other modules. Then we discovered an Arduino based system that works as a Fruit Sorter machine, an Automatic Bottle Filling Machine, a Bean Cooker, a Food Waste Management System, and A Food Cooking Machine, sorting and grading agricultural products, controlling and monitoring atmospheric conditions or the quality of fruits and vegetables during storage, and so on. This work has improved quality control throughout the manufacturing process. Keep your employees safe, and boost your end- to-end traceability to unrivalled heights. Increase productivity and output rates, safeguard your brand from potentially negative product recalls, increase adaptability and flexibility, and improve supply and demand management to reduce waste. (shrink)

The Internet of Things (IoT) is transforming industries by enabling devices to gather and share data. However, IoT devices often face limitations in processing power, storage, and energy consumption, restricting their ability to make complex decisions in real time. To address these challenges, cloud-assisted edge AI combines the advantages of edge computing and cloud-powered machine learning models, enabling IoT devices to make intelligent decisions at the edge while leveraging cloud resources for more complex processing tasks. This paper explores the integration (...) of edge AI with cloud assistance, demonstrating how this hybrid approach enhances decision-making capabilities in IoT devices. The paper examines key concepts, the architecture of cloud-assisted edge AI systems, application areas, and the benefits of combining edge and cloud computing. Furthermore, challenges such as latency, data privacy, and integration issues are discussed, alongside future directions for this technology in IoT applications. (shrink)

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)

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)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices further augments the decision-making (...) process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (shrink)

Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing (...) possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. (shrink)

Accident occur as public do not follow the traffic rules, like not maintaining a proper braking distance between two vehicles, they also fail to follow the traffic signals when it is ‘RED’, also due to the fast life everyone is in a hurry of reaching the destination in time. In this paper, propose system implementing an IOT based Vehicle Tracking, Accident Detection, Accident Avoidance, Drunk Driver Detection and Fire Detection System. This system using Vibration sensor, Ultrasonic sensor, Alcohol Sensor, Smoke (...) Sensor, Web camera, GPS, Arduino Microcontroller, Android App. Our application focus on to providing them more convenience with vehicle safety. It provides Real-time vehicle location information also integrated Google Maps in the application. (shrink)

The Internet of Things (IoT), Artificial Intelligence (AI), and Big Data are shaping the new evolving customer experience landscape. This transition points to the very important role that Salesforce, the world’s leading Customer Relationship Management (CRM) platform, will play. This paper talks about how Salesforce uses these technologies to reinvent customer engagement, personalize and predict. And by connecting devices and systems in real time, IoT lets businesses collect actionable data about their customer’s behavior and preferences. With the combination of AI (...) inside Salesforce, delivered via tools like Einstein, processes can be automated and predictive analytics; recommendations are provided by this data. It also allows us to integrate big data and analyze massive datasets to find trends, improve decision making, and deliver hyper personalization experiences. The practical applications considered in the paper include predictive maintenance, real time customer support, and targeted marketing campaigns. Second, it discusses the problems of including IoT, AI, and Big Data in existing systems, including data privacy issues, technological complexities and organizational blockades. This paper features key trends, takes stock of innovations and serves as a roadmap for organizations looking to redefine customer experiences in the hyper digital world. It’s no wonder Salesforce plays a pivotal marketing role in this future of customer-centric strategies. (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)

: 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)

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)

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 India most of the diseases caused by food borne illness, resulting in more number of hospitalizations and deaths are happened. For the reason we are designing a project called food detection system using Internet Of Things (IOT). It is used to test a freshness food like meat, fruits. Different sensor are used to testing a food quality like, pH sensor is used to test a salt content of the food and normal pH value is stored in IOT server and (...) compared to that food. The colour food is checked or abnormal condition. The fruit freshness is tested using image processing testing colour of food and testing food freshness. In this way we are checking food quality in an effective way to avoid food borne illness and buy a healthy food and make a human wealthy. (shrink)

Farmers are often concerned about plant disease since it can greatly affect crop productivity and quality. Expert manual inspection is required in traditional techniques of identifying plant diseases, which can be time- and money-consuming. Deep learning algorithms have made automated plant disease detection systems more practical. Convolutional neural networks (CNNs) are used in our proposed deep learning- based technique for the diagnosis of plant diseases. The suggested system uses plant photos as input to determine the presence of illnesses in the (...) plants and then provides treatment recommendations. The CNN can learn the visual patterns connected to various diseases because it is trained using a sizable dataset of photos of healthy and diseased plants. This system also includes IOT so that after the result is acquired, it will communicate via IOT. Several publicly accessible datasets of plant photos were used in our studies to gauge the performance of the suggested approach. The experimental results show that the suggested approach detects several plant diseases, such as tomato bacterial spot, potato early blight, and apple black rot, with high accuracy. Overall, our approach shows the promise of deep learning methods for automatically detecting plant diseases, which can assist farmers in identifying and treating plant diseases more quickly and successfully. (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)

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)

Each wagon is equipped with sensors that continuously capture and transmit weight data to the central server via Node MCUs, enabling real-time analysis through the Blynk app. By monitoring the load conditions, the system ensures wagons remain within safe weight limits, preventing overloading, which can cause damage and safety hazards, or under-loading, which reduces operational efficiency. A key feature of the system is its use of machine learning algorithms to detect patterns and anomalies related to load distribution. When potential risks (...) are identified, the system generates automated alerts, prompting interventions such as load adjustments, wagon rerouting, or even halting operations when necessary. (shrink)

Natural disasters such as earthquakes, floods, and wildfires have devastating consequences, necessitating efficient early warning systems. This paper presents a real-time disaster detection system leveraging IoT sensors to monitor environmental parameters, including temperature, humidity, seismic activity, and air quality. The system collects and processes sensor data using machine learning algorithms to detect anomalies and predict potential disasters. A cloud-based architecture ensures seamless data transmission and storage, enabling real-time monitoring and quick decision-making. The system issues automatic alerts to authorities and residents (...) through mobile notifications, SMS, and sirens, enhancing preparedness and minimizing losses. Experimental results demonstrate the system's high accuracy in detecting disasters, significantly reducing response time compared to traditional methods. The integration of IoT with artificial intelligence improves disaster prediction capabilities, making it a reliable solution for mitigating risks. Future enhancements will focus on refining sensor accuracy, expanding disaster coverage, and incorporating blockchain for secure data handling. (shrink)

Nowadays, remote monitoring systems have developed gradually to respond for particular needs in healthcare sector, which is an essential pillar in the modern concept of smart living, we propose a smart health monitoring system to monitor patient health conditions, as a smart healthcare system based on the widely spread and evolved technologies. Statistics show that severe Falls, hypertensive heart disease and blood pressure are risk factors for high death rate. To decrease it a preventive measure should be applied providing a (...) real-time health monitoring system, to save patients life at acceptable time. The objectives of this project are to provide an effective system model that will track, trace, and monitor patient’s movement, location, vital readings like heartbeat pulses, body temperature in order to provide efficient medical services in time. In proposed system data will be captured using sensors and compared with a predefined threshold. After processing it will send the Message for support to Relatives/Caretakers which will contain the Location, Heartbeat Values and Body temperature. (shrink)

In the modern business environment, characterized by rapid technological advancements and globalization, abetted by IoT and Industry 5.0 phenomenon, innovation is indispensable for competitive advantage and economic growth. However, many organizations are facing problems in its true implementation due to the absence of a practical innovation management framework, which has made the implementation of the concept elusive instead of persuasive. The present study has proposed a new innovation management framework labeled as “Absolute Innovation Management (AIM)” to make innovation more understandable, (...) implementable, and part of the organization’s everyday routine by synergizing the innovation ecosystem, design thinking, and corporate strategy to achieve competitive advantage and economic growth. The current study used an integrative literature review methodology to develop the “Absolute Innovation Management” framework. The absolute innovation management framework links the innovation ecosystem with the corporate strategy of the firm by adopting innovation management as a strategy through design thinking. Thus, making innovation more user/human-centered that is desirable by the customer, viable for business and technically feasible, creating both entrepreneurial and customer value, and boosting corporate venturing and corporate entrepreneurship to achieve competitive advantage and economic growth while addressing the needs of IoT and Industry 5.0 era. In sum, it synergizes innovation, design thinking, and strategy to make businesses future-ready for IoT and the industry 5.0 revolution. The present study is significant, as it not only makes considerable contributions to the existing literature on innovation management by developing a new framework but also makes the concept more practical, implementable and part of an organization’s everyday routine. (shrink)

The data collected by these sensors are transmitted to a centralized system where optimization algorithms, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), are applied to optimize sensor placement, data transmission, and processing efficiency. This ensures accurate, real-time pollution monitoring and data analysis, providing actionable insights for policymakers, environmental agencies, and the general public. The system's performance is evaluated through simulations and real-world experiments, demonstrating its capability to deliver reliable and timely pollution data. Future work (...) will explore the integration of machine learning techniques for predictive analytics and the expansion of the sensor network for broader coverage. (shrink)

The EdTech revolution in India has emerged as a transformative force, particularly during and after the COVID-19 pandemic, when traditional education systems faced unprecedented disruptions. While digital technologies have unlocked new opportunities for teaching and learning, they have also exposed systemic inequities and deepened the existing digital divide. This paper examines how EdTech is reshaping India's education landscape by addressing these challenges, with a focus on both the opportunities it presents and the barriers it creates.

Multi sensor data fusion allows large scale deployments for environmental monitoring applications especially in the areas like homes, other buildings and infrastructures and industries. In such areas early fire detection is of great importance as the consequences of a fire are catastrophic. This project predicts the possibility of catching fire using a multi-sensor scheme of data fusion to enhance the performance of the early fire detection process by detecting the concentration of inflammable gases in the atmosphere and by measuring the (...) atmospheric temperature. (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)

The proposed system allows users to monitor and control various home appliances, security systems, and environmental controls through AR interfaces, which overlay digital information on the physical environment. IoT devices communicate with each other and with the AR system, providing real-time data and enabling automated responses based on user preferences or environmental conditions. This synergy between AR and IoT facilitates a more responsive and intelligent home environment that adapts to the needs of its occupants.

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)

Smart home has become more and more popular in recent years. Due to the rapid development in the field of the Automation industry, human life is becoming more advanced and better in all aspects. In the present scenario, Automated systems are being preferred over the non-automated system. With the rapid growth in the number of consumers using the internet over the past years, the Internet has become an important part of life, and IoT is the newest and emerging internet technology. (...) Internet of things plays an important role in human life as well as in the educational field because they are able to provide information and complete the given tasks while we are busy doing some other work. In this project, a prototype and implementation of Smart Home Automation with Wi-Fi technology are demonstrated. ESP8266 is used as a Wi-Fi technology. The proposed system consists of a hardware interface and software interface. In the hardware interface, the integration of ESP8266 Wi-Fi technology for controlling home appliances, door lock and unlock and sensors is manifested, and an application is provided for controlling to multiple users of home, with smart phones, tablets, and laptops. This system is one of the best methods for controlling home devices with ease with multiple users and one of the best methods for an energy management system. The access to the whole system is given by its admin only to different users. This system is also expandable for controlling various appliances used at home and also for the security and safety purpose of the home through sensors as long as it exists on Wi-Fi network coverage. (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)

Natural disasters such as earthquakes, floods, wildfires, and landslides pose significant threats to human life, property, and infrastructure. Early detection and timely response are critical in minimizing the damage caused by such events. This paper presents the design and implementation of an IoT-based real-time disaster detection and alert system that leverages a network of low-cost sensors, microcontrollers, and cloud connectivity to monitor environmental parameters and detect potential disasters. The proposed system integrates various IoT sensors— including temperature, humidity, water level, gas, (...) vibration, and smoke sensors—to detect abnormal conditions indicative of natural hazards. Data collected from the sensors are transmitted to a cloud server via Wi-Fi or GSM modules, where it is processed and analyzed in real time. Once a threshold is crossed or a disaster is detected, the system triggers instant alerts via SMS, email, and mobile notifications to relevant authorities and users in the affected region. The system is designed to be modular, scalable, and energy-efficient, making it suitable for deployment in both urban and remote areas. A prototype has been developed and tested under simulated disaster scenarios to validate its effectiveness, response time, and reliability. The results demonstrate that the system can serve as an efficient early warning mechanism, contributing to disaster preparedness and risk reduction through the integration of smart technology. (shrink)

Gas leakages results a serious problem in household and other areas where household gas is used, therefore the proposed gas leakage detection and monitoring system is developed. There are many methods available for booking a Gas Refill, methods include online booking, telephonic booking etc. It will be difficult situation for the one who uses LPG gas for cooking regularly. The aim of this paper is to present a new system automatically books a cylinder when the gas is about to empty (...) is by sending a notification to the gas agency using wifi using Internet of Things approach. In addition to that sensor is used to detect gas leakage at home. If the gas leakage is sensed automatically it will send SMS to the user. wifi is one of the most used networks across the world. Hence, load cell has been used to monitor the weight of the LPG gas regularly. The values are next fed to the microcontroller. If the gas in the cylinder indicates a value where the remaining percentage level is crossed below the threshold levelset for gas to be indicated as getting emptied, then a notification will be delivered to gas enterprise automatically to book the new cylinder. Subsequently, reply notification will be sent to the customer about the booking status. At the same time, application software is developed in the gas enterprise to inform and record the booking. This, work this helps the society to specifically indicate gas leakage and also helps both customers and the agency to get the gas booking made automatically using the IOT technique. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine Learning Algorithms (...) designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)