Internet of Things - Science topic

Respected Editors,

Is there any charges for the accepted article

Hello,

This is a very good question for Research work. I will suggest you an approach, To choose and analyze research papers for publishing your own in IoT (Internet Of Things):

I hope this approach will help you to start the work.

Thank You,

Vaibhav

Dear Mohammad Mominul Hoque,

to clarify this issue, see this information source:

Warren Purcell,Thomas Neubauer: "Digital Twins in Agriculture: A State-of-the-art review"; Smart Agricultural Technology, Volume 3, February 2023

Look here: 4. Applications and use-cases

For general information about DTs see:

Best regards

Anatol Badach

Dear Sunil Gavaskar ,

With the development of the Internet of things (IoT), many tasks are automated. The system aims to make the daily tasks of humans in the kitchen easier. This system provides IoT-based Smart Pantry level monitoring using the Internet of things. The containers are fitted with sensors. These are used to gather data about the stock level of items in the container. This data is collected and stored in the cloud.

Regards,

Shafagat

Automating irrigation in Alternate Wetting and Drying (AWD) rice could indeed be economically feasible for producers. The working hypothesis, based on preliminary research, suggests that integrating automated, IoT-irrigation technologies into an AWD rice production system can lead to several benefits:

The goal is to create productive and profitable low-water-use, row-crop production systems that producers will adopt. Extension programs can be crucial in increasing awareness among the farmers about AWD rice production systems and addressing concerns related to this novel conservation practice.

Further, IoT-based monitoring can provide valuable insights into water requirements, making it easier for farmers to care for their rice fields and reduce irrigation-related problems.

Overall, the integration of IoT technologies holds promise for enhancing rice farming efficiency and sustainability.

EC-GSM-IoT- Extended Coverage GSM IoT- it is an extension of the GSM (Global System for Mobile Communications) standard, specifically designed to provide extended coverage for IoT devices.

LTE-M (LTE for machines) - Low-power wide-area (LPWA) cellular technology operates on LTE networks and infrastructure to provide better coverage and lower latency for IoT devices.

NB-IoT (Narrowband IoT) - another LPWA cellular technology which operates in the narrowband spectrum, enabling efficient use of resources and allowing a number of devices to connect simultaneously.

These technologies are designed to meet the diverse requirements of IoT applications, offering different combinations of coverage, power consumption, data rates, and cost-effectiveness.

Depending on the specific needs of an IoT deployment, one of these technologies may be more suitable than the others.

I hope this is helpful

Thanks

Dear Wilker Rodrigues Zamboni Pereira,

the IoT application protocol CoAP would be well suited for their purposes.

My sources of information about CoAP may be able to help you.

Best regards

Anatol Badach

https://www.researchgate.net/publication/312174308_CoAP_-_Constrained_Application_Protocol Figures: 7932, 7933, 7934, 7935, 7937, 7938,

Figure 009406: Multilayer protocol architecture of devices in the IoT Figure 009408: IoT Access Gateways - structure and functions

Figure 009409: Importance of HTTP-to-CoAP Proxying when connecting Smart Homes to the Internet

Figures 10, 12, 13 and 14

Here are some novel techniques that can be employed:

Behavioral Analysis and Anomaly Detection: Utilize machine learning algorithms and anomaly detection techniques to analyze the behavior of IoT devices. For reference see the following papers.

Traffic Classification and Filtering: Employ deep packet inspection (DPI) techniques to classify incoming traffic based on its characteristics, such as source, destination, payload, and protocol. for reference

Collaborative Defense Mechanisms: Establish collaborative defense mechanisms among IoT devices, gateways, and cloud-based services to share threat intelligence and coordinate response efforts. For reference

Dear,

Anatol Badach,

Thank you for sharing these valuable resources on Digital Twins (DTs) in construction. I'll explore them to deepen my understanding and integrate their insights into my research on IoT and Big Data Fusion in Construction MIS. DTs hold great promise for revolutionizing construction practices, enhancing efficiency, safety, and sustainability.

Best regards,

Mithun Kokare.

IoT devices are inherently vulnerable due to weak security measures, lack of encryption, and insecure default settings, making them easy target iot devices for cyberattacks. Cyber criminals are constantly seeking ways to exploit iot vulnerabilities and gain access to sensitive data, potentially affecting multiple devices connected to both home and corporate networks.

Weak security measures in IoT devices increase the risk of cyberattacks and data breaches. Attackers use a range of techniques to compromise IoT devices, however one of the most popular methods is exploiting weak or hardcoded passwords. Neglecting security measures can leave your device vulnerable to attack. Insecure or outdated components in IoT devices can also increase an organization’s attack surface, making it more susceptible to malicious actors gaining access to the device.

Cyberattacks targeting IoT devices include:

For securing IoT devices, manufacturers and users need to give priority to security measures and proactively address known vulnerabilities. This includes implementing strong authentication mechanisms, ensuring regular software and firmware updates, and utilizing encryption to protect sensitive data.

I will try my best to answer it in terms of Governance, Product organization, or the person tasked with integration, their or his/her commitment in terms of policies and procedures might help showcase their seriousness towards protecting the confidential data of their customers. Compliance with Industry standards and regulations is another step towards trust and faith.

Independent attestations are a piece of excellent evidence.

In terms of technical controls and security, various alternatives can be looked at, starting with access management, data anonymization, encryption, and control/data plane segregation, to name a few.

I am honored to accept your invitation to contribute a chapter to this prestigious publication. The opportunity to share insights and contribute to the exploration of machine learning techniques in enhancing the performance and efficiency of IoT networks with the assistance of unmanned aerial vehicles (UAVs) aligns perfectly with my research interests and expertise.

I am excited to delve into the latest developments, challenges, and opportunities in this emerging field and to contribute to the collective knowledge base through my chapter. I am confident that this collaboration will yield valuable insights and contribute to the advancement of knowledge in the intersection of machine learning and drone-enabled IoT networks.

Dear Romesha Haroon,

the term Industry 4.0 stands for the integration of automation and data exchange in the industrial sector. The use of Artificial Intelligence (AI) in this area and the collaboration between people and intelligent machines when using AI leads to the term Industry 5.0.

However, this does not mean that the introduction of Industry 5.0 will completely replace Industry 4.0. In the future, Industry 5.0 will expand the strengths of Industry 4.0 and make industry even more intelligent.

Best regards

Anatol Badach

Publishing a blueprint paper in the field of pavement digital twin requires identifying journals that specialize in civil engineering, transportation engineering, or related fields. Here are some journals where you can consider submitting your paper:

When selecting a journal, consider factors such as the scope of the journal, its impact factor, the relevance of previous publications to your research, and the target audience. Additionally, review the submission guidelines and requirements of each journal to ensure that your paper meets the specified criteria.

You can also consult with colleagues, advisors, or mentors in your field for recommendations on suitable journals for publishing your blueprint paper in pavement digital twin technology.

Please follow me if it's helpful. All the very best. Regards, Safiul

Important Dates:

➢ Technical Papers: March 31, 2024

➢ Acceptance Notification: May 31, 2024

➢ Camera-Ready Submission: June 20, 2024

Dear Sikander Hans,

I would suggest using the ideas of Digital Twins (DTs) for this. See the added Internet sources.

For general idea of DTs see my presentation at the address:

Best regards

Anatol Badach

The part „Use cases of a digital twin network“ in „Y.3090 : Digital twin network - Requirements and architecture“ at the address: https://www.itu.int/rec/T-REC-Y.3090-202202-I

Wei Yang; Wei Xiang; Yuan Yang; Peng Cheng: „Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT“ https://ieeexplore.ieee.org/document/9815106

Kangde Liu, Zheng Yan, Xueqin Liang, Raimo Kantola, Chuangyue Hu: A survey on blockchain-enabled federated learning and its prospects with digital twin https://www.sciencedirect.com/science/article/pii/S2352864822001626

Selvarajan Shitharth, Hariprasath Manoharan , Achyut Shankar, et al.: Federated learning optimization: A computational blockchain process with offloading analysis to enhance security https://www.sciencedirect.com/science/article/pii/S1110866523000622

Dear Sergey Alexandrovich Shoydin ,only English manuscripts are accepted in the conference.

Yes, there are research and developments in service discovery mechanisms in IoT that support the mobility of nodes. One approach is to leverage AI-based solutions to enhance traditional service discovery methods, considering the dynamic nature of node mobility in IoT environments.

@@@ AI-based solutions for service discovery in IoT that consider node mobility include:

However, these AI-based solutions aim to improve service discovery mechanisms' efficiency, scalability, and adaptability in IoT environments with mobile nodes. They enable intelligent decision-making and resource allocation to support dynamic changes in node mobility and network conditions.@Samia Haboussi

I believe my research aligns well with the themes of the session, particularly in exploring innovative approaches to enhancing cybersecurity within IoT ecosystems using blockchain technology. I am more than happy to provide further details or discuss how my work could complement the session's objectives.

Interdisciplinary collaboration between researchers in computer science, engineering, and social sciences can significantly contribute to advancing IoT research by bringing together diverse expertise and perspectives to address complex challenges and opportunities in the field. Here are some ways in which interdisciplinary collaboration can enhance IoT research:

In conclusion, interdisciplinary collaboration between researchers in computer science, engineering, and social sciences is essential for advancing IoT research. By leveraging the diverse expertise and perspectives of different disciplines, researchers can develop innovative, user-centered, and ethically responsible IoT technologies that have a positive impact on society. Collaborative efforts across disciplines can lead to breakthroughs in IoT research and drive the evolution of connected technologies towards a more sustainable and inclusive future.

To build an environment for computation inside Mobile Edge Computing (MEC) based on the benefits of Software-Defined Networking (SDN) to manage the network, you can follow these steps:

By combining MEC, SDN, and custom controller development, you can create a powerful and efficient environment for computation at the edge of the network, enabling seamless interaction between IoT devices and MEC resources. Using appropriate tools for task generation from IoT devices will ensure smooth communication and task offloading to the MEC servers for processing.

Dear Doctor

"10 Critical Steps to Enhance IoT Cybersecurity

In order to enhance IoT cybersecurity, there are 10 critical steps that should be followed: identify and understand your network and connected devices; regularly assess the IoT devices on your network; implement strong password policies; segregate your IoT devices on a separate network; customize default device settings; employ firewalls and IoT security solutions; utilize strong encryption and secure networks; disconnect devices when not in use; disable Universal Plug and Play (UPnP); ensure physical security of devices."

the choice depends on the specific IoT application, the level of privacy required, and the trade-off between privacy and utility. A hybrid approach, combining elements of both LDP and CDP, might also be considered based on the unique needs of the IoT deployment.

Dr Safi Muqdad Sadeq thank you for your contribution to the discussion

Thank you so much

Edge computing in IoT networks is like having a security guard right at your doorstep. Here's how it helps:

Faster Response: With edge devices processing data locally, you get quicker threat detection and response. No need to wait for data to travel to a central server.

Reduced Data Exposure: Less data needs to be sent to the cloud, so there's less chance of sensitive info being intercepted during transmission.

Offline Security: Even if the internet goes down, your edge devices can still monitor and secure your IoT network.

It's like having a security team on the ground, ready to act fast and keep your IoT network safe and sound.

First up, blockchain. It's like the trusty sidekick ensuring data integrity. By nature, it's transparent and tamper-proof. So, when you have a bunch of IoT devices communicating, blockchain can help keep that data exchange secure and verifiable. It's like having a digital ledger that says, "Yep, this data is legit and hasn't been messed with."

Then, enter machine learning. ML is the brains of the operation, constantly learning and adapting. It can analyze data patterns from IoT devices to spot anything unusual. Think of it as a detective that's always on the lookout for anomalies or suspicious activities.

And finally, there's NLP. It's a bit like the communicator of the group. In this context, NLP can be used to sift through tons of textual data from IoT devices or networks, helping to identify potential security threats or unusual patterns that might not be obvious at first glance.

Put them all together, and you've got a powerful team. Blockchain keeps the data trustworthy, ML hunts down anomalies, and NLP digs deeper into the data narrative. This combo can seriously level up cybersecurity in IoT, making it harder for bad actors to sneak in and cause havoc. Cool, right?

Combining image-trained neural networks, bioinformatics breaches, and quantum-resistant backdoors has major limitations.

Moving from image-trained neural networks to bioinformatics data requires significant domain transfer, which is not straightforward due to the distinct nature of these data types and tasks.

Secure IoT medical devices are designed with robust security features in mind and deployed. Successful attacks requires exploiting a specific vulnerability in the implementation of security measures, rather than the reliance on neural network capabilities.

Deliberately inserting backdoors and to the extent, even quantum-resistant ones, poses ethical and legal questions that would go against norms and standards of cybersecurity practitioners. The actions would violate privacy rights on the federal level, ethical standards and codes of conduct and pose severe legal consequences. Those would be the domestic ones; assuming we're keeping the products in the US.

Quantum computers with sufficient power to break current cryptographic systems are not yet available. Developing quantum-resistant backdoors knowingly anticipates a future scenario to be truth that is still today largely theoretical, without being proven or true.

Dear Maral Moradi Falah ,

In today's fast-paced world, emerging technologies have become catalysts for digital transformation, reshaping industries and challenging traditional business models. The potential of technologies such as artificial intelligence (AI), blockchain, and virtual reality (VR) is huge.

Regards,

Shafagat

The Growing Importance of Cybersecurity in the IoT Era - Infosec Resources

IoT cybersecurity: How trust can unlock value | McKinsey

Internet of Things (IoT) Cybersecurity: Literature Review and … - MDPI

IOT Security and the Role of AI/ML to Combat Emerging Cyber Threats in …

How to Secure IoT Devices & Protect Them From Cyber Attacks - EC-Council

In the rapidly evolving landscape of the digital age, insurance companies face a myriad of strategic challenges that demand innovative responses to stay competitive and relevant. The ongoing process of digitalization has ushered in a new era, transforming the industry in profound ways. Understanding and navigating these changes is crucial for insurance companies to thrive in an increasingly digital world.

One of the primary challenges is the shift in customer expectations. As digital technologies continue to redefine the way individuals interact with businesses, insurance customers now expect seamless and personalized experiences. The challenge for insurance companies lies in meeting these expectations, from the initial policy purchase to the handling of claims. Achieving a balance between the traditional aspects of the industry, such as risk assessment, and the demand for a digital-first approach is a strategic imperative.

Moreover, the rise of InsurTech startups introduces a competitive dynamic that can disrupt established business models. These newcomers leverage cutting-edge technologies, such as artificial intelligence and data analytics, to streamline processes, enhance customer experiences, and offer more tailored products. Incumbent insurance companies must adapt to these advancements to remain competitive, either by collaborating with InsurTech firms or by developing their own technological capabilities.

Data privacy and cybersecurity are critical challenges that have gained prominence in the digital era. Insurance companies handle vast amounts of sensitive information, making them attractive targets for cyberattacks. Ensuring the security of customer data is not only a legal and ethical obligation but also a key factor in maintaining trust. Developing robust cybersecurity measures and staying abreast of evolving threats is an ongoing strategic challenge for insurance companies.

The digital transformation also necessitates a cultural shift within insurance organizations. Embracing a culture of innovation and agility is essential for adapting to the fast-paced nature of technological advancements. This involves not only investing in employee training and development but also fostering a mindset that values experimentation and learning from failures.

Additionally, regulatory challenges are amplified in the digital realm. As technology continues to outpace regulatory frameworks, insurance companies must navigate complex and evolving compliance requirements. Staying ahead of regulatory changes and proactively adapting policies and procedures is essential to avoid legal issues and maintain the trust of customers and stakeholders.

The implementation of new technologies in agribusiness, also known as Agro 4.0, is influenced by various factors that can be categorized into four groups: institutional factors, market factors, technology factors, and stakeholder perception factors.

- Institutional factors refer to the policies, regulations, standards, and incentives that affect the adoption and diffusion of new technologies in the agricultural sector. For example, the availability of subsidies, loans, tax breaks, or grants for farmers who invest in new technologies can facilitate their implementation. Conversely, the lack of clear rules, guidelines, or protocols for data sharing, privacy, security, or ownership can hinder their adoption.

- Market factors refer to the supply and demand conditions, prices, costs, and competitiveness of the agricultural products and services that are affected by new technologies. For example, the demand for high-quality, traceable, and sustainable food products can drive the adoption of new technologies that enable precision farming, smart irrigation, or blockchain-based traceability. On the other hand, the high costs of new technologies, the lack of infrastructure, or the low profitability of some crops can discourage their implementation.

- Technology factors refer to the characteristics, performance, availability, and accessibility of the new technologies themselves. For example, the compatibility, interoperability, reliability, scalability, and user-friendliness of new technologies can influence their adoption by farmers and other stakeholders. Moreover, the availability of technical support, training, maintenance, and updates can also affect their implementation.

- Stakeholder perception factors refer to the knowledge, attitudes, beliefs, preferences, and expectations of the farmers and other actors involved in the agricultural value chain regarding new technologies. For example, the awareness, understanding, and perception of the benefits, risks, challenges, and opportunities of new technologies can affect their adoption. Additionally, the trust, confidence, and satisfaction of the stakeholders with the new technologies can also influence their implementation.

These factors are interrelated and can have positive or negative effects on the implementation of new technologies in agribusiness. Therefore, it is important to consider them holistically and address the potential barriers and enablers for Agro 4.0.

The description of Humane's Ai Pin wearable technology paints a vivid picture of the advancements in wearable tech, highlighting how far the field has come and the exciting possibilities that lie ahead. Here are a few key points to consider:

Overall, the Ai Pin is a testament to the rapidly evolving world of wearable technology, where the convergence of AI, cloud computing, and innovative design is creating new possibilities for how we interact with technology in our everyday lives.

The implementation of a supply chain management system that integrates state-of-the-art technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and real-time data analytics epitomizes a paradigm shift towards digital metamorphosis in supply chain operations. This avant-garde approach facilitates an unprecedented level of end-to-end visibility and orchestration across the entire value chain.

By leveraging AI's predictive analytics and machine learning algorithms, stakeholders can prognosticate market vicissitudes, optimize inventory levels, and anticipate potential disruptions with greater acuity, thereby enabling proactive remediation strategies. The IoT ecosystem, replete with sensor-generated data, fosters a granular, real-time observability that underpins dynamic decision-making processes and augments operational efficiency. Concurrently, real-time data analytics transmute colossal data troves into actionable insights, ensuring a nimble and responsive supply chain apparatus.

The confluence of these advanced technologies engenders a robust, resilient supply chain framework that not only mitigates latency and attenuates bottlenecks but also drives value creation through cost-effective and customer-centric operations. Moreover, this tech-centric modus operandi propels the transition from a traditionally reactive supply chain to a strategic, forward-looking entity that is pivotal in achieving a competitive advantage in the tumultuous global market.

The aim of this question is to define global features to tend to make analysis partially comparable between any type of processor. In my opinion, it is possible to do this by focusing on programing language and program structure (AST, Graph Analysis...). However, that's could lead to decrease in case of your goal is to produce categorization by any means (ML, DL...). I focus my work on program architecture especially on central node detection. It is hard to produce a malware detection system with good accuracy, but it is too interesting.

The integration of blockchain and the Internet of Things (IoT) is a relatively new concept with the potential to revolutionize many industries and aspects of our lives.

Blockchain is a distributed ledger technology that allows for secure, transparent, and tamper-proof transactions. IoT is a network of physical devices that are connected to the internet and can collect and exchange data.

The integration of blockchain and IoT can provide a number of benefits, including:

The integration of blockchain and IoT is still in its early stages of development, but there are a number of real-world applications that are already emerging. For example:

There are a number of challenges that need to be addressed before blockchain and IoT integration can be widely adopted. These challenges include:

Despite these challenges, the integration of blockchain and IoT has the potential to revolutionize many industries and aspects of our lives. As the technology continues to develop and mature, we can expect to see even more innovative and groundbreaking applications emerge.

Here are some examples of real-world projects that are integrating blockchain and IoT:

These are just a few examples of the many ways that blockchain and IoT are being integrated. As these technologies continue to develop, we can expect to see even more innovative and groundbreaking applications emerge.

Collecting sensor data from IoT (Internet of Things) devices and transferring it to a local server involves several methods and protocols to ensure efficient and secure data transmission. Here are common data collection methods and protocols for IoT devices:

The choice of data collection methods and protocols depends on factors such as the nature of the IoT devices, the required data transfer rate, security considerations, and the capabilities of the local server. A combination of methods and protocols may be used in a comprehensive IoT data collection and transmission strategy.

There are a few public datasets with textual security requirements data generated from IoT systems that you can use in academic research. Here are a few examples:

In addition to these datasets, you may also be able to find public datasets of textual security requirements for IoT systems in specific domains, such as healthcare, transportation, and manufacturing. For example, the Healthcare IoT Security Requirements Dataset (HC-IoTSRD) is a collection of textual security requirements for healthcare IoT systems. The dataset is available on GitHub and is licensed under the Creative Commons Zero (CC0) license.

When choosing a public dataset to use in your research, it is important to consider the following factors:

Here are some project topics related to IoT (Internet of Things) and cybersecurity, with a focus on security and privacy, that can be implemented with relatively non-challenging artifacts:

IoT Device Security Assessment: Evaluate the security of a specific IoT device (e.g., a smart thermostat or a home security camera). Identify vulnerabilities, suggest improvements, and create a report on enhancing its security.

IoT Home Network Security Setup: Design a secure IoT network at home by implementing best practices like network segmentation, strong passwords, and firmware updates. Create a guide for users to follow to secure their IoT devices.

Privacy-Focused IoT Data Collection: Develop a simple IoT project that collects data (e.g., temperature or light levels) but focuses on privacy protection. Implement techniques like data anonymization and encryption for data transmission.

IoT Security Awareness Training: Create an educational program or presentation on IoT security and privacy for non-technical users. Address common risks and offer practical tips for securing IoT devices.

IoT Device Authentication: Build a basic system for authenticating IoT devices on a network. Use techniques like device certificates or secure tokens to ensure that only trusted devices can connect.

IoT Firmware Analysis: Analyze the firmware of a common IoT device, such as a smart bulb or smart plug. Identify security vulnerabilities or privacy concerns in the firmware and suggest improvements.

IoT Communication Protocol Analysis: Study and compare the security of different IoT communication protocols (e.g., MQTT, CoAP, HTTP). Create a report on their strengths and weaknesses.

IoT Penetration Testing Lab: Set up a controlled environment for testing the security of IoT devices. Perform penetration testing on various devices and document the vulnerabilities found.

IoT Privacy Policy Evaluation: Evaluate the privacy policies of popular IoT device manufacturers. Analyze how they handle user data and make recommendations for better privacy practices.

IoT Security Dashboard: Develop a basic security dashboard that displays the status of IoT devices on a network, including information about security patches and potential vulnerabilities.

IoT Secure Boot Implementation: Implement secure boot mechanisms on a Raspberry Pi or Arduino-based IoT device. Ensure that the device only runs authorized firmware.

IoT Device Access Control: Create a user-friendly access control system for IoT devices. Users can define who can control or access their devices, enhancing privacy and security.

IoT Threat Modeling: Develop a threat model for a specific IoT ecosystem, such as a smart home or industrial IoT network. Identify potential threats and propose countermeasures.

IoT Privacy Preservation for Wearables: Build a simple IoT wearable device (e.g., a fitness tracker) that collects user data while preserving privacy through techniques like data aggregation.

IoT Password Management: Design a secure password management system for IoT devices, ensuring that users can easily manage and update passwords for multiple devices.

These project ideas cover a range of aspects within IoT and cybersecurity, and they can be adapted to different levels of technical complexity depending on your skill level and resources. Always prioritize security and privacy when working on IoT projects to contribute to a safer and more privacy-aware IoT ecosystem.

Ambient Intelligence (AmI) and Internet of Things (IoT) are two concepts that have gained significant attention in the field of technology. While they share some similarities, there are also distinct differences between the two.

Ambient Intelligence refers to a computing environment that is sensitive and responsive to human presence. It aims to create an intelligent and intuitive system that can adapt to users' needs without explicit instructions. AmI systems utilize sensors, data analysis, and machine learning algorithms to provide personalized services in a seamless manner. For example, smart homes equipped with AmI technology can adjust lighting, temperature, and music preferences based on individual preferences.

On the other hand, the Internet of Things refers to a network of physical objects embedded with sensors, software, and connectivity capabilities. IoT enables these objects to collect and exchange data over the internet without human intervention. The main goal of IoT is to connect various devices for efficient communication and automation. For instance, IoT can be seen in applications like smart cities where streetlights automatically adjust their brightness based on real-time traffic conditions.

Although both AmI and IoT involve interconnected devices and rely on data collection for decision-making processes, there are key differences between them. Firstly, while AmI focuses on creating an intelligent environment that adapts to humans' needs seamlessly, IoT emphasizes connecting devices for efficient communication without direct human involvement.

Secondly, AmI systems primarily rely on local processing capabilities within the environment itself. This means that most of the data processing occurs within the immediate vicinity of users or devices. In contrast, IoT systems often rely on cloud computing for storing and analyzing large amounts of data collected from multiple sources.

Lastly, another difference lies in their scope of application. Ambient Intelligence has a more personal focus as it aims at providing personalized services tailored specifically for individuals or small groups. On the other hand, IoT has broader applications ranging from industrial automation to healthcare monitoring systems.

In conclusion, Ambient Intelligence (AmI) and Internet of Things (IoT) are two distinct concepts in the field of technology. While they share similarities in terms of interconnected devices and data collection, their focus, processing capabilities, and scope of application differ significantly. Both concepts have the potential to revolutionize various industries and improve our daily lives.

Reference:

Kidd, C.D., Orr, R.J., Abowd, G.D., Atkeson, C.G., Essa, I.A., MacIntyre, B., Mynatt E.D. & Starner T.E. (1999). The Aware Home: A Living Laboratory for Ubiquitous Computing Research. In Proceedings of the Second International Workshop on Cooperative Buildings (CoBuild '99), 191-198.

Hi,

Please take a look at my papers in the IoT. In the first paper I used Machine Learning with IoT:

■ A. Abusukhon Intelligent Shoes for Detecting Blind Falls Using the Internet of Things. KSII Transactions on Internet and Information Systems. Vol. 17, Issue 9. 2023

■ A. Abusukhon, A. Al-Fuqaha, B. Hawashin, A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things. Journal of Universal Computer Science (JUCS). Vol. 29, No. 8.

■ A. Abusukhon, IOT Bracelets for Guiding Blind People in an Indoor Environment, in Journal of Communications Software and Systems, vol. 19, no. 2, pp. 114-125, April 2023, doi: 10.24138/jcomss-2022-0160.

■ A. Abusukhon (2021) Towards Achieving a Balance between the User Satisfaction and the Power Conservation in the Internet of Things, IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3051764. impact factor 9.936. Published by IEEE. https://ieeexplore.ieee.org/document/9326414. [Science Citation Index].

■ Ahmad Abusukhon, Bilal Hawashin and Mohammad Lafi (2021) An Efficient Algorithm for Reducing the Power Consumption in Offices Using the Internet of Things, International Journal of Advances in Soft Computing and its Applications (IJASCA). http://ijasca.zuj.edu.jo/Volumes.aspx

■ A. Abusukhon, Z. Mohammad, A. Al-Thaher (2021) An authenticated, secure, and mutable multiple-session-keys protocol based on elliptic curve cryptography and text_to-image encryption algorithm. Concurrency and computation practice and experience. [Science Citation Index].

■ B. Hawashin, A. Abusukhon An Efficient Course Recommender Using Deep Enriched Hidden Student Aptitudes. ICIC Express Letters, Part B: Applications, 2022.

■ A. Abusukhon, N. Anwar, M. Mohammad, Z., Alghanam, B. (2019) A hybrid network security algorithm based on Diffie Hellman and Text-to-Image Encryption algorithm. Journal of Discrete Mathematical Sciences and Cryptography. 22(1) pp. 65- 81. (SCOPUS). https://www.tandfonline.com/doi/abs/10.1080/09720529.2019.1569821

■ A. Abusukhon, B.Wawashin, B. (2015) A secure network communication protocol based on text to barcode encryption algorithm. International Journal of Advanced Computer Science and Applications (IJACSA). (ISI indexing). https://thesai.org/Publications/ViewPaper?Volume=6&Issue=12&Code=IJACSA&Seri alNo=9

■ A. Abusukhon, Talib, M., and Almimi, H. (2014) Distributed Text-to-Image Encryption Algorithm. International Journal of Computer Applications (IJCA), 106 (1). [ available online at : https://www.semanticscholar.org/paper/Distributed-Text-to-Image-Encryption-Algorithm-Ahmad-Mohammad/0764b3bd89e820afc6007b048dac159d98ba5326]

■ A. Abusukhon (2013) Block Cipher Encryption for Text-to-Image Algorithm. International Journal of Computer Engineering and Technology (IJCET). 4(3) , 50-59. http://www.zuj.edu.jo/portal/ahmad-abu-alsokhon/wpcontent/uploads/sites/15/BLOCK-CIPHER-ENCRYPTION-FOR-TEXT-TO-IMAGE ALGORITHM.pdf

■ A. Abusukhon, Talib, M. and Nabulsi, M. (2012) Analyzing the Efficiency of Text-to-Image Encryption Algorithm. International Journal of Advanced Computer Science and Applications ( IJACSA )(ISI indexing) , 3(11), 35 – 38. https://thesai.org/Publications/ViewPaper?Volume=3&Issue=11&Code=IJACSA&Seri alNo=6

■ A. Abusukhon, Talib M., Issa, O. (2012) Secure Network Communication Based on Text to Image Encryption. International Journal of Cyber-Security and Digital Forensics (IJCSDF), 1(4). The Society of Digital Information and Wireless Communications (SDIWC) 2012. https://www.semanticscholar.org/paper/SECURE NETWORK-COMMUNICATION-BASED-ON-TEXT-TO-IMAGE-Abusukhon-Talib/1d122f280e0d390263971842cc54f1b044df8161

Suneel Kumar Ji

Dr Aniket Sunil Gaikwad thank you for your contribution to the discussion

Dr Kamel Khedhiri thank you for your contribution to the discussion

In fact, thanks to it, farmers can see and manage all data and equipment using one device in real-time without going on the field. This smart farming approach improves overall plant productivity, reduces waste, and optimizes electricity, fuel, water, and fertilizer use. IoT smart agriculture products are designed to help monitor crop fields using sensors and by automating irrigation systems. As a result, farmers and associated brands can easily monitor the field conditions from anywhere without any hassle. IoT-based smart agriculture refers to applying IoT technology in the agricultural sector for optimized farming processes. It integrates sensors, actuators, and other smart devices to enable data collection, analysis, and automated decision-making in farming operations. By using IoT sensors to collect environmental and machine metrics, farmers can make informed decisions, and improve just about every aspect of their work – from livestock to crop farming. Digital technologies such as satellite imaging, drones, and sensors can continue to help farmers optimize their use of resources such as water, fertilizers, and pesticides. This can help to increase productivity, reduce costs, and minimize environmental impacts. Increasing control over production leads to better cost management and waste reduction. The ability to trace anomalies in crop growth or livestock health, for instance, helps eliminate the risk of losing yields. Additionally, automation boosts efficiency. Climate-smart agriculture (CSA) is a part of India's and the G20 countries' Sustainable Development Goals (SDGs) vision. The objective of CSA is to optimize a country's agricultural productivity, resilience and emissions in response to climate change using real-time, localized information. With the right IoT technologies, farmers can make better decisions about fertilizer use, harvesting, and livestock health monitoring, reducing post-harvest losses. Additionally, the IoT in agriculture will help farms reduce their energy usage and repair costs. The Future of IoT in agriculture in India will enable growers to grow better crops at lower costs. The husbandry IoT devices can help them identify herd health, prognosticate crop water conditions, and collect environmental and machine criteria. IoT and AI based systems are capable of enhancing input use efficiency on the farm. Smart farming leverages digital technologies to automate agricultural operations in real-time. Deep learning based solutions can solve numerous day to day agricultural problems.

Hi thank you so much, currently the Silos and the sheds are used as grain facilities. Routine fumigation is conducted after three months for stocks in storage and all doors and windows (if any) are opened for storage sheds to allow fresh air to circulate through the stocks.

In fact, AI enables farmers to cultivate in a smart and sustainable manner, reaping higher and better yields while using fewer resources. If adopted at scale, AI definitely has the potential to power the next agricultural revolution. For this, farmers, governments and AI experts need to work in tandem to ease AI adoption. By collecting data on plant growth, AI can help produce crops that are less prone to disease and better adapted to weather conditions. With the help of AI, scientists can identify the best-performing plant varieties and crossbreed them to create even better hybrids. AI can help detect field boundaries and bodies of water to enable sustainable farming practices, improve crop yields, and support India's 1.4 billion people and the rest of the world. AI can be appropriate and efficacious in agriculture sector as it optimizes the resource use and efficiency and solves the scarcity of resources and labor to a large extent. Artificial intelligence can be technological revolution and boom in agriculture to feed the increasing amount of human population in the world. AI systems are helping improve the harvest quality and accuracy, which is known as precision agriculture. AI technology assist's in detecting the diseases in plants, pests, poor plant nutrition, etc. It also allows the farmers to monitor the health of the crops and the soil.PwC numbers estimate that the use of AI can reduce worldwide greenhouse gas (GHG) emissions by 4%. Use of AI for environmental purposes can also contribute up to $5.2 trillion USD to the global economy in 2030. Artificial intelligence (AI) is playing a critical role in the agriculture sector as it helps farmers to increase crop yields, improve crop quality, and reduce production costs. In addition, AI can help farmers to optimize irrigation systems, predict weather patterns, and forecast market prices.IoT and AI based systems are capable of enhancing input use efficiency on the farm. Smart farming leverages digital technologies to automate agricultural operations in real-time. Deep learning based solutions can solve numerous day to day agricultural problems. By collecting IoT data, smart sensors can enable real-time monitoring of “what is happening on the ground.” Farming can be made more efficient by knowing when to harvest, the amount of water used and whether irrigation is needed, soil health, and fertilizer requirements. I oT in agriculture uses robots, drones, remote sensors, and computer imaging combined with continuously progressing machine learning and analytical tools for monitoring crops, surveying, and mapping the fields, and provide data to farmers for rational farm management plans to save both time and money. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making. Using the deep learning and ML techniques, the crop productivity in the agriculture can be improvised. By using the sensor data, the accurate data are predicted by evolving the artificial intelligence, which allows a smart way in farmer decision making.

Yes, Internet of Things (IoT) devices and embedded systems can be classified within Flynn's taxonomy. Flynn's taxonomy is a classification scheme for computer architectures based on the number of instructions and data streams that can be processed simultaneously. The four categories of Flynn's taxonomy are:

IoT devices and embedded systems can be classified into all four categories of Flynn's taxonomy. For example, a simple IoT device such as a smart thermostat is typically an SISD architecture. A more complex IoT device such as a self-driving car is likely to be an MIMD architecture.

Here are some examples of IoT devices and embedded systems classified within Flynn's taxonomy:

It is important to note that Flynn's taxonomy is a simplified classification scheme and does not capture all of the nuances of modern computer architectures. For example, many modern processors can support both SISD and SIMD instructions. Additionally, some IoT devices and embedded systems may use hybrid architectures that combine elements of multiple categories from Flynn's taxonomy.

Thank you Antharaju Kalyan Chakravarthy sir.

Industry 5.0 expands upon and completes Industry 4.0. It emphasizes elements that will determine factors for the position of industry in the future European society, not just economic or technological ones. These factors also have aspects related to the environment, society, and fundamental rights. Industry 5.0 should not be viewed as a replacement for the current Industry 4.0 paradigm or as a chronological continuation of it. It is the outcome of an exercise that looked towards the future to help frame how European industry and new societal trends and needs could coexist. The report contributes to the larger discussion on industrial change.

There is no better time to introduce the new idea of Industry 5.0. In order to remain the solution provider for all Europeans, many European industries are reinventing themselves, adjusting to the new COVID reality, and increasingly embracing digital and green technologies. The time is now to implement more sustainable production practices, create more resilient supply chains, and make workplaces more inclusive.

The society will change as a result of a transformed industry. This is particularly true for industry workers, whose roles might change and call for new skills. Action in a variety of areas will be required to make the transition to Industry 5.0.

LoRaWAN Class A possesses bi-directional communication capability and is power efficient. It only initiates uplink communication and this makes it suitable for event-driven applications like alarms, sensors, etc.

LoRaWAN Class B devices can open extra receive windows at scheduled intervals and these devices are also able to synchronize with a network beacon to better coordinate downlink communication.

LoRaWAN Class C devices are open to receive but at the expense of higher power consumption.

Since TOA is critical in LoRaWAN because it depends on power efficiency, network capacity and regulatory compliance. So, the spreading factor is a trade-off that needs to be carefully considered based on the specific requirements of a LoRaWAN deployment.

TOA = preamble time + payload time

Both preamble time and payload time are dependent on the spreading factor, among other things. As the spreading factor increases, these times increase, leading to a higher TOA.

Implementation of cluster-based routing can be done with the following:

iRecently, one very popular topic of research that joins together IoT, security and machine learning is detection of anomalies (malicious or technical) from network traffic. In the past anomaly detection was more restricted to malware, and used mostly signature-based and behavior-based methods. Current, machine-learning methos have taken over the state of the art and are not restricted to malware or malicious attacks, but can detect operational problems and other physical failures of the system. It is not restricted to IoT, but as many network topics, IoT scalated existing problems with sheer numbers and network heterogeneity.

If you look for papers with any combination of the following keywords, you will find an increasingly big number of studies:

Anomaly detection, network traffic, ids, ddos, machine-learning, classification and mitigation.

Finally, a very popular survey on the topic can be found below:

FFFFFinally,

The Harvard architecture in embedded systems architecture is a computer architecture that has separately placed memory areas of data and instructions of the system. The CPU of this architecture includes separate storage and memory that are accessed differently. Embedded systems architecture types can be classified into four groups: simple, monolithic, microkernel, and modular. Simple architecture is the simplest of all architectures. It contains a single processor and a small amount of memory. The architecture of an embedded system is centered around its microcontroller, also sometimes referred to as the microcontroller unit (MCU), typically a single integrated circuit containing the processor, RAM, flash memory, serial receivers and transmitters, and other core components. These systems are so scalable and reliable. Works on wide variety of sectors and environments. Improve product quality and enhance performance. The internet of things, or IoT, is a network of interrelated devices that connect and exchange data with other IoT devices and the cloud. IoT devices are typically embedded with technology such as sensors and software and can include mechanical and digital machines and consumer objects. The Internet of Things (IoT) describes the network of physical objects—“things”—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. The bottom layer has container management, platform management, and IoT orchestration elements. Messaging to connected devices is facilitated through the communication layer, while the core layer handles configuration management, OTA services, and messaging.

Dear Bakhit Amine Adoum,

this ETSI document may help you:

ETSI White Paper No. 59:

Enabling Multi-access Edge Computing in Internet-of- Things: how to deploy ETSI MEC and oneM2M; June 2023

Best regards

Anatol Badach

Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. This sensor provides information such as air temperature, soil temperature at various depths, rainfall, leaf wetness, chlorophyll, wind speed, dew point temperature, wind direction, relative humidity, solar radiation, and atmospheric pressure is measured and recorded at scheduled intervals. Precision agriculture sensors are very efficient in agriculture because they transmit data that helps farmers not only to monitor but also to improve their products and keep abreast of changes in the field and ecosystem. It can minimize the use of pesticides, effectively control weeds and pests, and achieve efficient green precision agriculture. WSN can sense and collect real-time data of various information changes in the process of agricultural production and provide timely feedback to the users. In IoT, sensors are used to collect data from various sources and send it to cloud-based platforms for analysis. The data collected by sensors are used to monitor and control various systems, including environmental conditions, traffic patterns, and equipment performance. Microcontroller on Arduino uno and Node MCU ESP8266 platform is used to implement the control unit. The setup uses soil moisture sensors which measure the exact moisture level in soil & also it contains Humidity and Temperature Sensor DHT11 for Online monitoring of system. CropSpec sensors measure plant reflectance to determine chlorophyll content, which correlates to nitrogen concentration in the leaf.n IoT-based smart farming, a system is built for monitoring the crop field with the help of sensors (light, humidity, temperature, soil moisture, etc.) and automating the irrigation system. The farmers can monitor the field conditions from anywhere. These precision agriculture sensors are used to determine the variety, distance, and height of any position within the required area. They take the help of GPS satellites for this purpose. They are installed on tractors and other field equipment to check equipment operations. The data collected by the IoT sensors can provide a real-time picture of what's going on in the field. This means that farmers will be able to know when their crops are ripe, how much water is being used and if an irrigation system is needed, soil health, and whether they need more fertilizer or any other input. This smart agriculture using IOT system is powered by Arduino, it consists of Temperature sensor, Moisture sensor, water level sensor, DC motor and GPRS module. When the IOT based agriculture monitoring system starts it checks the water level, humidity and moisture level. IoT agricultural solutions consist of multiple monitoring, controlling, and tracking applications that measure several types of variables such as air monitoring, temperature monitoring, humidity monitoring, soil monitoring, water monitoring, fertilization, pest control, illumination control, and location tracking. One can connect IoT-based agriculture sensors, such as temperature and moisture sensors in agriculture for environmental monitoring applications. The sensors can ensure fine dust, high-pressure spray, submersion in water, and extreme temperatures. Precision agriculture sensors are very efficient in agriculture because they transmit data that helps farmers not only to monitor but also to improve their products and keep abreast of changes in the field and ecosystem. Smart sensors in agriculture collect data to help farmers in monitoring and optimize their crops while being kept updated on the changing environmental and ecosystem factors. Smart sensors in agriculture collect data to help farmers in monitoring and optimize their crops while being kept updated on the changing environmental and ecosystem factors. The system uses various sensors to monitor environmental conditions in real-time. The data collected is processed by a microcontroller and transmitted wirelessly to a web application that provides farmers with visualized information about their crops. This system uses different components like DHT11 sensor, Soil Moisture sensor, Gsm Modem, ultrasonic sensor etc. and according to this sensor parameters farmer are provided an automated way to irrigate their fields and monitor the tank.To predict production rate of the crop artificial network use information collected by sensors from the farm. This information includes parameters such as soil, temperature, pressure, rainfall, and humidity. The farmers can get an accurate soil data either by the dashboard or a customized mobile application.

Embedded systems are standalone devices that have usually been designed to do one specific thing. An IoT embedded system is an embedded system that also has connectivity to the internet and can therefore communicate with other IoT embedded systems. Embedded systems are ubiquitous in IoT devices. Combined with software, dedicated systems for IoT usage employ microcontrollers and microprocessors to enable the networked devices to communicate. The other difference between an embedded system and IoT is that IoT refers more to a class of devices that represent the newly connected world. But an embedded system refers very specifically to the hardware used in these devices. Embedded systems are commonly found in consumer, industrial, automotive, home appliances, medical, and telecommunication, commercial, aerospace and military applications. Generally, an embedded system comprises power supply, processor, memory, timers, serial communication ports and system application specific circuits. Embedded systems can be classified into different types based on performance, functional requirements and performance of the microcontroller. Embedded systems are rapidly changing the future of technology, and their growth is set to continue at a fast pace in India. The integration of embedded systems with new technologies such as IoT, AI, and automation is expected to lead to a new era of innovation and disruption in various industries. An embedded system is a microprocessor-based computer hardware system with software that is designed to perform a dedicated function, either as an independent system or as a part of a large system. At the core is an integrated circuit designed to carry out computation for real-time operations. The processor is the main part of embedded systems hardware architecture, and architecture defines how the hardware and software components must interact with each other. A well-designed architecture enables the creation of energy-efficient systems capable of executing real-time applications.

Generally, an embedded system comprises power supply, processor, memory, timers, serial communication ports and system application specific circuits. Embedded systems can be classified into different types based on performance, functional requirements and performance of the microcontroller. This type of system makes sure that all critical processes are completed within the given time frame. This means that all the delays in the system are strictly time bound. Also, there is little to no secondary memory and data is stored in short term memory or read only memory. So we can define an embedded system as a Microcontroller based, software driven, and reliable, real-time control system. An IoT embedded system is an embedded system that has internet connectivity. Another word for what is IoT embedded system is a "smart" device. A touch screen and a keyboard are not necessary to define a device as an IoT embedded system, although these peripherals can also be attached. Four-layer architecture is the standard and most widely accepted format. As you can see from the above image, there are four layers present i.e., the Perception Layer, Network Layer, Processing Layer, and Application Layer. IoT architecture can comprise up to seven layers, which are known as the perception, transport, edge, processing, application, business, and security layers.

Blockchain technology can be used in IoT to provide a secure and transparent platform for storing and sharing IoT data, and to enable secure and authenticated transactions between IoT devices. An IoT-enabled blockchain can store the temperatures, position, arrival times, and status of shipping containers as they move. Immutable blockchain transactions help ensure that all parties can trust the data and take action to move products quickly and efficiently. A blockchain is a database ledger, which is decentralized, shared, distributed, and immutable. All transactions or data are stored in chronological order, in a set of participating computer memories (called nodes) that are tamper-proof. Blockchain is ideal for delivering that information because it provides immediate, shared and completely transparent information stored on an immutable ledger that can be accessed only by permission network members. A blockchain network can track orders, payments, accounts, production and much more. One of the most significant advantages of blockchain technology in securing IoT data is its decentralized nature. Unlike traditional centralized systems, where data is stored on a single server, blockchain data is distributed across a network of computers, making it more resilient to attacks. The future of IoT depends on blockchain because it can keep a record of each transaction mode on IoT. It can also make transactions secure in such a way that data cannot be altered. Blockchain technology is the most ground-breaking and revolutionary technology in recent years. Blockchain can potentially change how we use the internet by offering a secure, decentralized platform for performing transactions and storing data. Blockchain technology can improve the security of Internet of Things (IoT) devices by providing enhanced data privacy. Its decentralized nature also makes it difficult for hackers to target a single point of vulnerability. Blockchain is a distributed ledger technology that combines with IoT to make machine-to-machine transactions possible. It uses a set of transactions that are recorded in a database, verified by multiple sources and entered in a common ledger distributed across every node. Blockchain and IoT can improve supply chain efficiency by eliminating the middleman, increasing transaction speed, and lowering costs. Fees are paid with each hop in a typical supply chain transaction, which takes four or five hops to validate. For IoT safety, the blockchain is able to monitor the information collected by the sensors, without allowing them to be duplicated by any wrong data. Sensors can also transfer data using Blockchain technology, without the need for a trusted third party.

IoT technology in the manufacturing industry lets businesses implement digital transformation. The technology includes advanced sensor devices, gateway connectivity, and a dashboard for the managers to simplify their workload and improve all aspects of industrial production. Enhanced Productivity IoT devices can monitor equipment and processes in real-time, providing valuable data that can be used to streamline operations, reduce waste, and increase output. According to a study by the MPI Group, factories implementing IoT solutions have seen a 72% increase in productivity. Automation technologies can improve manufacturing productivity by streamlining processes and increasing efficiency. As, robotics and automation can reduce the risk of human error and omissions resulting in product defects. IoT sensors are paramount for collecting essential information and sending data to the cloud for analysis in manufacturing. By analyzing data compiled from the sensors, businesses can create solutions that improve productivity, avoid costly unplanned downtime, and reduce the expense of manufacturing. IoT enables machines to complete tedious tasks without human intervention. Companies can automate processes, reduce labor costs, cut down on waste and improve service delivery. IoT helps make it less expensive to manufacture and deliver goods, and offers transparency into customer transactions. Technology can automate the workflow of almost any function in a business, such as finance, marketing, operations, and workplace management. Technology can turn inefficient, tedious tasks into a seamless and automated process. This frees up time for your team and allows them to be more productive. In economics, it is widely accepted that technology is the key driver of economic growth of countries, regions and cities. Technological progress allows for the more efficient production of more and better goods and services, which is what prosperity depends on. Technology can play an important role in creating lean and efficient processes. It can help you reduce or eliminate duplications and delays in the workflow, as well as help you speed up by automating specific tasks. Here are three areas where efficiencies can be created by using technology. Information technology has enabled businesses to attain a greater reach. Now more than ever, it's easier for companies to do business across the world. Emails, text, instant messaging, websites and applications have made global communication quicker and more effective than ever.

One can connect IoT-based agriculture sensors, such as temperature and moisture sensors in agriculture for environmental monitoring applications. The sensors can ensure fine dust, high-pressure spray, submersion in water, and extreme temperatures. The technology consists of a sensor or series of sensors that measure the level and moisture content in grains, such as corn. This information is then relayed back to an app on a tablet or computer where it can be viewed by the farmer for analysis. Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. In addition to monitoring the plants that are harvested, temperature sensors observe the equipment that gathers these plants. Temperature sensors send out alerts whenever an equipment system requires minor maintenance, is underperforming, or is critically failing.Agriculture through precision agriculture implements IoT through the use of robots, drones, sensors, and computer imaging integrated with analytical tools for getting insights and monitoring the farms. Placement of physical equipment on farms monitors and records data, which is then used to get valuable insights. Sensors play a crucial role by detecting and measuring a variety of parameters such as temperature, pressure, humidity, flow rate, motion, and position. They convert physical signals into electric signals and provide information in real-time to the control system, thereby making production intelligent and automated.

The IoT-based Smart Plant Monitoring System is designed to monitor and maintain the growth and health of plants. The system works by collecting data from various sensors and then sending that data to a mobile application through the internet.This system is very used in few areas like nursery farms and in agriculture. In this system a mechanism is established to find the moisture content in the soil with the help of soil moisture sensor and depending upon the condition of the sensor the water is controlled. The set up uses the temperature sensor, moisture sensor and humidity sensor which measure the approximate temperature, moisture and humidity in the soil. This value enables the system to use appropriate quantity of water which avoids over/under irrigation. Monitoring systems are responsible for supervising the technology a company makes use of in order to analyze its performance, and to detect and alert about possible errors. The applications of IoT in environmental monitoring are broad − environmental protection, extreme weather monitoring, water safety, endangered species protection, commercial farming, and more. In these applications, sensors detect and measure every type of environmental change. Environmental monitoring is to manage and minimize the impact an organization's activities have on an environment, either to ensure compliance with laws and regulations or to mitigate risks of harmful effects on the natural environment and protect the health of human beings.A combination of sensors in different capacities throughout the city for various tasks such as managing the traffic, handling waste management, optimizing streetlights, saving water, monitoring energy expenditure, creating smart buildings, and more. IoT is used in various applications like smart home, smart parking, smart water level monitoring, smart healthcare, smart traffic lighting, smart waste management, smart solar panels, etc.

The set up uses the temperature sensor, moisture sensor and humidity sensor which measure the approximate temperature, moisture and humidity in the soil. This value enables the system to use appropriate quantity of water which avoids over/under irrigation. Smart irrigation technology uses weather data or soil moisture data to determine the irrigation need of the landscape. Smart irrigation technology includes: These products maximize irrigation efficiency by reducing water waste, while maintaining plant health and quality. The smart irrigation system uses this information to adjust the amount of water it delivers and when it delivers it. System sensors range in type and purpose. They include rain sensors, soil moisture sensors, weather sensors, flow sensors, and others. In smart irrigation system using IOT it contains solar panel, charge controller, battery, Arduino, Ethernet shield, relay, soil moisture sensor, humidity sensor and DC pump. The solar which acts as a source and generates electricity. These charges are stored in the battery via charge controller. Electrochemical Sensors provide key information required in precision agriculture: pH and soil nutrient levels. Sensor electrodes work by detecting specific ions in the soil. Currently, sensors mounted to specially designed “sleds” help gather, process, and map soil chemical data. These include a smart controller, rain sensors, flow sensors, and moisture sensors. As a property manager, you can use all four together to get the most out of your irrigation system. The proposed irrigation system pumps the water to the crop based on the type, area, and date of the plantation of the crop, and these parameters are registered by the farmer through the IoT-based Android App. Temperature, proximity, gas, smoke, and water and air quality sensors provide the input data for environmental monitoring applications. A sensor is a physical device for example a motion detector or a light switch that converts physical events or characteristics. Rain sensors comprise discs that stop or delay the irrigation cycle when they get wet. The primary benefit is water-saving, as the rain will substitute the next irrigation cycle. This automated irrigation system is easily controlled using a computer. It behaves as an intelligent switching system that detects the soil moisture level and irrigates the plant if necessary. This will also save time and energy, as well as minimize energy loss. The IoT-based Smart Plant Monitoring System is designed to monitor and maintain the growth and health of plants. The system works by collecting data from various sensors and then sending that data to a mobile application through the internet.The plant monitoring system is helpful for watering the plants and to monitor few parameters for growth of plants. This system is very used in few areas like nursery farms and in agriculture. Granular crop-level monitoring can help growers identify and remedy problematic and potentially destructive conditions. Smart sensing helps 30MHz customers prevent sunscald, optimize nutrient and water delivery, and prevent moisture-borne diseases by tracking dew point and soil moisture. The IoT-based Smart Plant Monitoring System is designed to monitor and maintain the growth and health of plants. The system works by collecting data from various sensors and then sending that data to a mobile application through the internet.

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Dear Anatol,

You can submit in the book series, using my reference id that is mentioned. We will check & revert you

Dear Manvitha Gali,

an interesting way of delivering Healthcare based on IoT using Human Health Digital Twins (HHDTs) illustrated Fig. 9 (Healthcare Scenario with Patient Digital Twin and Mobile Phone as Healthcare Assistant)

under the address

Best regards

Anatol Badach

You can set up digital twins using IoT simulators like NS3 or NetSim and then use ML for things like: a) Ad hoc routing: Dynamic route selection, load balancing, fault tolerance and recovery b) Energy consumption: Adaptive power management, communication protocol optimization c) Enhanced security: Modeling attacks and developing countermeasures, intrusion detection and response

The emerging threats and challenges in securing Internet of Things (IoT) devices and networks revolve around the increasing complexity and scale of interconnected devices. As IoT adoption grows, the expanding attack surface provides opportunities for cybercriminals to exploit vulnerabilities, compromising the privacy, integrity, and availability of devices and networks. Common challenges include weak device authentication and authorization mechanisms, inadequate encryption and data protection practices, lack of timely security updates and patch management, and the absence of standardized security protocols across different IoT platforms. Additionally, the proliferation of heterogeneous IoT ecosystems exacerbates interoperability issues and complicates security management. Addressing these challenges requires a holistic approach, incorporating robust security measures at every stage of the IoT lifecycle, fostering collaboration among stakeholders to establish industry standards, promoting security awareness among consumers and manufacturers, and investing in research and development for innovative security technologies.

Daer Rajwinder Kaur,

I would suggest a topic for your research integrating SD-IoT (Software Defined Internet of Things) and the idea of “Digital Twins in IoT”.

Some of my figures can provide ideas for the use of SD-IoT and Digital Twins in Smart Sities. See for this:

Fig. 8: Example for Application of Digital Twins in Smart Cities to Monitor Building Safety usingvirtual Sensors and virtual Actuators

Fig.10: Example of using Digital Twins in Smart Cities to monitor Building Safety with Help ofvirtual Sensors and virtual Actuators

Fig. 11: Important Application Areas of Digital Twins and Examples for their Use

Fig. 9771: Smart City Parking System as a regional SD-IoT system

Fig. 9773: Proxy-based SD-IoT Architecture for Realizing SD-IoT Services

Fig. 9773: SDSH City Services logical Architecture with proxy-based Control of IoT Devices

See also:

Fig. 04_Logical Model of Human Cognition

Fig. 05_Model of IIoT Cognition

Fig. 10_Autonomous Driving with Def Computing

Best regards and much success Anatol Badach