Internet of Things. Advances in Information and Communication Technology

The clean and healthy air is essential for all living beings, and air pollution can have a significant impact on human health. It is important to monitor air quality in real time to take action to reduce the pollution level in order to maintain a healthy environment. As cities become more and more congested, the level of pollution is increasing, leading to a localized human health effects such as asthma or bronchitis. Implementation of Internet of Things (IoT) based technology with harmful gas sensors can be an effective solution for continuous monitoring of air quality. By using such a technology, one can gather data on harmful pollutants and take steps to minimize their impact on human health and the environment. In this paper IoT based continuous monitoring of the air pollution level of CO and CO₂ air pollutants carried out including temperature and humidity using different sensors and a micro-controller in Delhi-NCR (National Capital Region) at Greater Noida. The LoRa (Long Range) LPWAN (Low Power Wide Area Network) was used for data communication, and ThingSpeak an IoT analytics cloud service for storing and processing the data to be displayed on the web page. The LoRa is a wireless data communication technology, which is capable to transmit data to large distances using low power transmitter. The air quality monitoring was carried out by storing sensors data in the above cloud, analyzed, and compared with the standard air quality parameters.

As urbanization continues to grow and smart cities are imagined, intelligent transportation systems (ITS) have become increasingly important as a solution to traffic congestion. While ITS focuses on vehicles, pedestrians are important road users who contribute a significant role in influencing traffic, transportation infrastructure, and the design of vehicles, However, roadside assistance is less studied than vehicular. In this paper, we discuss pedestrian-to-infrastructure (P2I) communication, which is an important aspect of ITS that can enhance safety, efficiency, and convenience for pedestrians. Furthermore, P2I systems are examined in terms of their requirements and components, as well as in terms of their potential benefits for safety and mobility issues. Finally, the paper identifies potential future directions for research on pedestrian-to-infrastructure communication in ITS. Providing a comprehensive overview of the current state of research on pedestrian-to-infrastructure communication in ITS, this review highlights the key research areas for the future.

A Smart Parking system has a lot of components, such as an automated parking infrastructure, sensors, and a navigation system. For the implementation of the navigation system in smart parking, a 3D floor map is required. A 3D view of maps is always better than traditional maps, but making a 3D model comes at a cost and requires specialized tools. Infrastructures such as hospitals and offices usually have little luxury when it comes to maintaining their parking spaces, and the proposed system provides a simple yet effective solution for this problem in this paper. Till now, images are two-dimensional, and tools like Lidar or Kinect are used to get the depth element right. However, to make the floor construction handy, portable, and lightweight, a smartphone image-based approach is proposed here to make a 3D model of indoor parking lots. The pillars and the separation walls between parking spaces are easy to identify using deep learning models. A convolution neural network-based architecture was used for object detection. The main problem that remains is to calculate the depth of the objects in the image. Here in this paper, a successful approach is proposed to overcome the problem of finding depth in images.

Visualization of terrain surface finds many applications indifferent area of earth sciences, GIS, and geomatics. With advent of advanced computer graphic algorithms and their HPC implementation through GPU, realistic and high-fidelity visualization of terrain surface is possible. Geographic Information Science (GIS) has made considerable strides since the introduction of 3D terrain visualization facilitating accurate visualization, analysis and measurement of geospatial events and attributes. This paper discusses the significance of different terrain visualization techniques with a special emphasis on 3D visualization.

How 3D terrain visualization is useful in photogrammetry, LiDAR, and aerial imagery, maps can generate detailed models of terrains, buildings, and objects. These techniques enable users to explore geographic features from various angles, zoom in for detailed analysis, and even simulate virtual environments. The applications of 3D maps are diverse and far-reaching. In virtual reality, they can create immersive environments for training, simulations, and gaming. In remote sensing, they aid in understanding natural resources, urban planning, and disaster management. In scientific visualization, they enable researchers to analyze complex spatial data and model real-world phenomena. In GIS, 3D maps enhance spatial analysis, decision-making, and communication of geospatial information.

In conclusion, the evolution of 3D maps in GIS has revolutionized how geographic information is visualized and analyzed, with potential applications in various domains. Advancements in technology continue to drive the development of 3D maps, transforming how we perceive and interact with spatial data, making it more realistic and meaningful.

This paper presents the implementation of a Generic Medicine Recommender System (GMRS) that incorporates user feedback as a solution to streamline the selection process of appropriate generic medication and improve overall quality of healthcare. The study evaluates the suggestions given by the Generic Medicine Recommender System (GMRS), enhance the suggestions based on user input, and comprehend the influence of these suggestions on patient outcomes following the usage of generic medications. The results imply that the GMRS not only has the potential to enhance patient outcomes but can also give important information about the efficacy of various generic medications. The study also emphasizes how critical it is to incorporate user input into system recommendations in order to maximize performance and the system’s effect on healthcare expenses. Recommending generic alternatives to costly prescribed medicines is significant because in addition to ensuring the provision of more broadly accessible drugs for treatment, it assists patients in saving money on their prescription pharmaceuticals. Generic medications have the same amounts of active ingredients as branded drugs. As a result, when it comes to treating sickness, generic medications are often as effective as branded medications.

This paper presents an innovative approach to address the pressing concern of fall incidents among the elderly by developing an accurate fall detection system. Our proposed system combines state-of-the-art technologies, including accelerometer and gyroscope sensors, with deep learning models, specifically Long Short-Term Memory (LSTM) networks. Real-time execution capabilities are achieved through the integration of Raspberry Pi hardware. We introduce pruning techniques that strategically fine-tune the LSTM model’s architecture and parameters to optimize the system’s performance. We prioritize recall over precision, aiming to accurately identify falls and minimize false negatives for timely intervention. Extensive experimentation and meticulous evaluation demonstrate remarkable performance metrics, emphasizing a high recall rate while maintaining a specificity of 96%. Our research culminates in a state-of-the-art fall detection system that promptly sends notifications, ensuring vulnerable individuals receive timely assistance and improve their overall well-being. Applying LSTM models and incorporating pruning techniques represent a significant advancement in fall detection technology, offering an effective and reliable fall prevention and intervention solution.

Procurement is an important governance tool that is used by the government and development agencies globally to manage and fulfil their complex development plans. However, corruption is a persistent and pervasive issue that hinders ethical progress and it can have a detrimental effect on the outcome of the proposed project that involves more than a few stakeholders. The implementation of Blockchain based e-procurement has been suggested as the potential solution due to its underlying characteristics of immutability and disintermediation. Blockchain technology utilizes distributed consensus, offering evident advantages to procurement by using the digital identities of the stakeholders in the bidding process and maintaining the privacy and security of the contract. This paper systematically maps and implements the existing literature to comprehend the utilization of Blockchain technology in the procurement domain and its potential to mitigate corruption in tender-based environments in smart cities by using digital identities.

An imperative issue that impedes economic, social, and environmental progress is the involvement of the middle tier in public institutions. Due to inadequate transparency and clandestineness in establishments, the trust in procurement process has become a predominant concern that affects many nations around the world. Governments across the globe have begun exploring cutting-edge technologies to improve procurement transparency and integrity. Blockchain is an emerging technology that comprises the potential for generating substantial advancement in the public procurement domain by establishing transparent, immutable, and autonomous processes. Even though contributions are constantly rising, adequate research and implementation are pivotal in order to fully comprehend the technology to attain these advantages. In this study, the typical procurement process was examined to evaluate the associated issues and how the digitization of the process through Blockchain technology can mitigate them by using digital identities towards maintaining the security and privacy of the entity.

Early adoption of Internet of Medical Things (IoMT) are enhancing the healthcare sector in all directions. Though the advances are adding advantages to the existing systems, the security and privacy of medical data remain a challenge. The increase in IoMT and mobile healthcare devices presence on untrusted networks can make the situation more complicated for healthcare system users. Moreover, they are pushing critical data to centralized locations like cloud, where the patient lacking control on his data. In this regard, a secure IoT framework is desirable which is capable of preserving the integrity and confidentiality of the medical data. Due to this, we proposed a novel architecture which leverages blockchain, IPFS, zero-knowledge protocols, and functional encryption technologies to provide decentralised healthcare system privacy and security. The proposed system helps the healthcare system administrators maintain data confidentiality, availability, integrity, and transparency over an untrusted peer-to-peer network without any human interference. Moreover, the system eliminates the requirement for a centralised server for functional encryption operations using hybrid computing paradigms. Finally, the proposed system suggests a novel mechanism to minimise the latency in data sharing over the network without compromising data security and privacy. To describe the working principle of this architecture a logical analysis is carried out which shows that the system is capable of providing the desired security and privacy.

Globalization has led to complex, cloud-centric supply chains that require transparency and traceability in the manufacturing process. However, traditional supply chain models are vulnerable to single points of failure and lack a people-centric approach. To address these challenges, our proposed work presents an innovative healthcare supply chain model that utilizes blockchain technology combined with Zero Knowledge Proofs (zk-SNARKs) and role-based access control (RBAC) mechanisms. The addition of RBAC to the proposed model ensures that only authorized users can access certain data and functionalities within the system, while improving the security and access control. This approach guarantees secure storage of business-sensitive data while enabling real-time product tracking and traceability. The proposed model was tested using an Ethereum-based decentralized application (DApp), demonstrating the preservation of digital record integrity, availability, and scalability by eliminating single points of failure. The system also offers privacy and security for sensitive data through the use of zk-SNARKs. In case of product faults, the model enables error tracing without disclosing the entire data set through the use of document hashes. By incorporating RBAC access control mechanisms, our proposed solution offers an effective, secure, and privacy-preserving mechanism for handling sensitive data, also benefiting stakeholders in the supply chain ecosystem.

Post Pandemic there has been a boost in smart health management. Internet-of-Medical-Things (IoMT) has given an end-to-end control system from the diagnosis of the disease to the cure. Security breaches in this type of hardware security can have fatal effects. The paper discusses Insulin delivery system which includes a security model of glucose measurement device along with an automated insulin pump of IoMT framework. The proposed model is used to monitor and control the glucose levels of a diabetes patient. The blockchain-based security solution is developed for the non-invasive glucometer and insulin pump for safe insulin secretion. It is helpful to mitigate challenges which are present in automatic insulin delivery. With the help of machine learning models, several results can be produced with a futuristic approach along with better understanding of the insulin to be pumped accurately.

Internet of Things (IoT) devices are frequently utilized to collect information around humans’ daily routine, producing a need for them to regularly pair with each other. With the increasing interest in digitizing human’s natural environment and evolution of advanced application scenarios, the wireless communication networks have turned into a key player for IoT devices. As IoT devices are resource-constrained and transmit the perceived information regularly to its corresponding participant, it is mandatory for the devices to adopt a lightweight authentication scheme to overcome their limited energy availability and avoid security and privacy issues in ubiquitous sustainable computing system. Researchers have proposed protocols for IoT devices in wireless communication networks, many of which neglect numerous serious security weaknesses such as loss of identity preservation, vulnerability to replay, Man-in-the-Middle, eavesdropping attacks, and loss of key secrecy. Additionally, various security and efficiency threats in the proximity-based device authentication scheme, such as device cloning and identity loss, have a large signaling overhead. Furthermore, we evaluate the performance of the proposed schemes in terms of computation, communication and storage overhead. The results illustrate the implementation advantages and suitability of the proposed schemes for low-powered devices compared to existing protocols.

Access control is one of the most important measures for protecting information and system resources because it prevents unauthorized users from gaining access to protected objects and legitimate users from exceeding their access rights. This paper provides an in-depth exploration of the security challenges posed by the confluence of Internet of Things (IoT) networks and cloud-based architectures, with a particular focus on Access Control Models (ACMs). As the integration of IoT devices with cloud services becomes more pervasive, securing access to resources and data has emerged as a critical area of concern. To address this, we delve into the principles of Access Control and their applications within a Cloud-IoT Architecture. The paper dissects popular ACMs, exploring their strengths, limitations, and suitability for securing Cloud-IoT networks. Along with these the comprehensive analysis of the prevalent Cloud Security Challenges are presented, highlighting the vulnerabilities in current ACMs and proposing potential mitigations. In addition, open research challenges are identified, underlining the need for further investigation and development in this area. The goal of this work is to provide a thorough understanding of the issues and threats in this domain and contribute to the advancement of robust, secure, and efficient access control mechanisms for the evolving landscape of Cloud-IoT networks.

The expansion of the Internet of Things (IoT) has made it possible for numerous widespread objects to connect to one another and communicate with one another, leading to unprecedented data releases. However, regardless of the fact that cloud computing has been a useful tool for processing and storing these data, problems like the growing demand for real-time applications and the constrained availability of network bandwidth cannot yet be resolved solely through the use of cloud computing. As a supplement to the cloud solution, a new approach to computing called the fog computing paradigm has been developed. By moving processing, connectivity, and capacity nearer to edge gadgets and end clients and taking cloud computing to the network’s edge, fog computing aims to increase low latency, activity, data traffic, reliability, and privacy. The present paper discusses the framework of the fog computing model and presents a literature survey of various works that is carried out on fog computing. It also covers the current problems and difficulties in fog computing and opportunities for research in the area of Fog computing for the Internet of Things.

The Routing Protocol for low-power and lossy Networks (RPL) is the established standard for packet-level communications in the Internet of Things (IoT). The efficiency of point-to-point (P2P) communications in RPL relies on the mode of operation employed by the network nodes. Hybrid modes of operation (MOP) have gained attention as they combine the advantages of both standard non-storing and storing operation modes. However, existing hybrid MOPs have struggled to achieve a balance between reduced communication overhead and storage overhead. To address this, we propose the application of the 2-colorable graph property to enable a hybrid mode of operation among IoT network nodes. By mapping the two-colorable property to the standard MOPs, we conduct experiments comparing the proposed hybrid MOP with existing approaches. Results demonstrate that our proposed hybrid MOP achieves a significant balance between storing and non-storing nodes, while also exhibiting improved average path length compared to existing hybrid MOPs. This research contributes to optimizing P2P communications in RPL-based IoT networks and highlights the potential benefits of utilizing the 2-colorable graph property in achieving an efficient hybrid mode of operation.

Role-based access control (RBAC) is a mechanism that controls access to resources within an organization based on the roles of individual users. This RBAC can be used in the context of an IoT-integrated smart contract for a private blockchain to govern access to smart contract functions and data based on the responsibilities of the system’s participants. By preventing unauthorized access to vital functions and data, RBAC can help assure the security and integrity of an IoT-integrated smart contract. In this study, we investigate novel methods to devise a smart contract process that enables data sharing among stakeholders for IoT-based applications to provide complete access control implementation in a private blockchain environment. Here, we have developed and verified our proposed access control mechanism with an added layer of machine learning-based security for an Ethereum-based private blockchain to securely handle IoT-based application data.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. Several authentication systems are used to safeguard the data and authenticate the devices, but some of them are inefficient and some of them have some limitations. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80% was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF. A further study was evaluated the uniformity of (m,n,p)-OAN-XOR-PUF, and a result of 43.75% was found, which is close to the ideal value of arbitrary PUF response.

With the rapid growth of new technological paradigms such as the Internet of Things (IoT), it opens new doors for many applications in the modern era for the betterment of human life. One of the recent applications of the IoT is the Internet of Vehicles (IoV) which helps to see unprecedented growth of connected vehicles on the roads. The IoV is gaining attention due to enhancing traffic safety and providing low route information. One of the most important and major requirements of the IoV is preserving security and privacy under strict latency. Moreover, vehicles are required to be authenticated frequently and fast considering limited bandwidth, high mobility, and density of the vehicles. To address the security vulnerabilities and data integrity, an ultralight authentication scheme has been proposed in this article. Physical Unclonable Function (PUF) and XOR function are used to authenticate both server and vehicle in two message flow which makes the proposed scheme ultralight, and less computation is required. The proposed Easy-Sec can authenticate vehicles maintaining low latency and resisting known security threats. Furthermore, the proposed Easy-Sec needs low overhead so that it does not increase the burden of the IoV network. Computational (around 4 ms) and Communication (32 bytes) overhead shows the feasibility, efficiency, and also security features are depicted using formal analysis, Burrows, Abadi, and Needham (BAN) logic, and informal analysis to show the robustness of the proposed mechanisms against security threats.

A paper-based prescription signed by the prescriber to authorize dispensing of medication is typically used in traditional healthcare. Such systems are prone to many issues like medication errors, latency, and lack of integration with other healthcare systems. Hence, Electronic prescription (E-prescription) systems are being used as alternatives to overcome these issues. Even though E-prescription systems provide the advantage of recording and maintaining patient medication history but still face issues such as system crashes, latency due to their centralized architectures, prone to many security threats like identity theft and unauthorized patient record access and modifications. Lack of standardization can also make such E-prescription systems not interoperable, which may lead to information fragmentation or delays in the processing of prescriptions. Hence, there is still a need for making these E-prescription systems more secure, reliable, and cost-effective for wide-range adaptation. Blockchain is one such technology that can add additional layers of security to the existing E-prescription systems by providing tamper-proof records of all transactions which will help in ensuring the authenticity and integrity of prescriptions. Blockchain can also help in better management of patients’ privacy while patients still have full control over their health data. Blockchain usage can also enhance interoperability and reduce prescription abuse. The proposed application FortiRx makes use of the Ethereum blockchain platform and leverages smart contracts for implementing business logic. Cyphertext-Policy Attribute-Based Encryption (CP-ABE) is used in the proposed application to create and manage access-control mechanisms and ensure Health Insurance Portability and Accountability Act (HIPPA) compliance. The proposed system has been implemented and analyzed for security, reliability, and adaptability in a real-time environment.

In 2020, nearly one hundred thousand deaths were drug related overdoses. In addition, forty to sixty percent of recovering addicts relapsed. A top contributor to relapsing is stress, as high levels of hormones are released within the brain. Individuals that could benefit from a rehabilitation center have many varied reasons as to why they do not seek one, proximity to a center being a primary reason. Rehabilitation facilities are a key factor in the recovery of an addict, as the probability of drug use decreases fifty to seventy percent after treatment. The hardest step in recovery is recognizing the need for help, however finding a treatment center can be a stressful experience, potentially leading to relapse. To reduce the stress of finding the closest and most qualified center, the goal of this research is to create a website that contains a database of rehabilitation centers. This could later be connected to a mobile application that would track the GPS location of the user and return the geographical location of the closest treatment center, as well as the name of the facility and contacting phone number. The software will have the ability to connect to wearable devices, allowing an opportunity to track physiological factors affected during an overdose.

Heart disease is a term used to describe a range of conditions that affect the heart, such as coronary artery disease, heart failure, and arrhythmias. It is a leading cause of death globally and can be prevented or managed through lifestyle changes and medical treatments, such as medication and surgery. The use of federated learning and edge computing has become increasingly popular for machine learning tasks, especially in the healthcare domain. However, privacy and security concerns remain major challenges in the adoption of such technologies. In this article, we propose a blockchain-enabled federated edge-cloud framework for heart disease risk prediction to address these challenges. The proposed framework involves the use of blockchain to secure the data sharing and model aggregation process, while edge devices are utilized for data preprocessing and feature extraction, and cloud servers are used for model training and validation. The federated learning approach ensures data privacy, while the use of blockchain provides immutability, transparency, and accountability to the system.

In Smart grid (SG), cyber-physical attacks (CPA) are the most critical hurdles to the use and development. False data injection attack (FDIA) is a main group among these threats, with a broad range of methods and consequences that have been widely documented in recent years. To overcome this challenge, several recognition processes have been developed in current years. These algorithms are mainly classified into model-based algorithms or data-driven algorithms. By categorizing these algorithms and discussing the advantages and disadvantages of each group, this analysis provides an intensive overview of them. The Chapter begins by introducing different types of CPA as well as the major stated incidents history. In addition, the chapter describes the use of Machine Learning (ML) techniques to distinguish false injection attacks in Smart Grids. A few remarks are made in the conclusion as to what should be considered when developing forthcoming recognition algorithms for fake data injection attacks.

With the increasing reliance on computer networks in our daily lives, the threat of network layer DoS (Denial of Service) attacks has become more prevalent. Attackers use various techniques to disrupt network services and cause loss of data, revenue, and reputation. Recent development in machine learning approaches have shown promise in prevention and detection of such types of attacks by several orders of magnitude. In this paper a thorough overview of machine learning approaches for detecting and preventing network layer DoS attacks is presented. Firstly, the basics of network layer DoS attacks, their classification, and the impact of these attacks is discussed. Then, different machine learning techniques and the ways in which they can be utilized for attack detection and prevention is explored. Additionally, analysis on the strengths and limitations of each approach, and provide a comparative study of the most relevant works in this field is done. Finally, some obstacles in research and potential avenues for future exploration is presented. in the field of machine learning-based defense mechanisms against network layer DoS attacks is discussed. In this paper a detailed summary of the most up-to-date advancements or developments in machine learning-based defense mechanisms against network layer DoS attacks is shown and serve as a reference for one and all who are involved in this field.

Systems that use secret keys or personal details are seriously at risk from side-channel attacks, especially if they rely on power analysis. Attackers can use unintentional sources like power consumption and electromagnetic waves to extract sensitive information. Recently, machine learning has become a promising approach for executing power side-channel attacks that are efficient and effective for single and cross-device environments. This paper reviews various machine learning-based power side-channel attacks, including feature extraction techniques, classification methods, and countermeasures. This survey investigates same-device and cross-device attacks that use multiple devices for training an artificial intelligence model for this purpose. It examines the strengths and limitations of various machine learning algorithms and suggests areas for future research to address challenges.

The Lite-Agro study aims to deploy deep learning neural network models for pear disease identification through tree leaf image analysis on TinyML device. A case study on pear leaves is conducted with publicly available pear disease dataset. Quantitative comparisons are made between different datasets. Lite-Agro is a light-duty image computing detection solution that is tested for deployment on a microcontroller. The novelty of Lite-Agro, lies in the export of a lightweight TinyML, Tensorflow Lite model that is geared for low power applications on battery powered hardware. The goal is to find the best model that is custom selected for the application and achieves the highest accuracy. The study emphasizes finding a balance between size, accuracy and performance. In future iterations of the study, Lite-Agro is to be mounted on an unmanned aerial vehicle to be powered with solar panels. Modern low powered microcontroller devices are to be a staple implementation in Smart Villages.

Farmer uses traditional crop insurance to protect their farms against crop loss and natural risks. However, farmers are concerned about crop insurance claims due to delays in processing claims that cost significantly. Insurance fraud is another problem in crop insurance which costs significantly for insurance companies. The proposed FarmIns framework uses blockchain technology, allowing farmers to create and manage insurance agreements with insurance providers through smart contracts and creating a verifiable log of farm monitoring parameters to help insurance providers verify and approve claims promptly. FarmIns uses the Internet of Agro-Things (IoAT), and video surveillance technologies like Closed-Circuit Television (CCTV) to monitor and provide reliable farm data to process claims. FarmIns also acts as Decision Support Tool (DST) for both the insurer and the insured.

This is an extended abstract for a Research Demo Session based on our published work [1]. PTSD has been a major problem in our society and much research has been done along the line to predict and diagnose PTSD. Our method helps to predict PTSD in its early stage with the help of physiological markers which combined with prior information about the patient like PTSD history, exposure to trauma, substance abuse disorder and other information helps to create a risk score with more accuracy. Due to the lack of a public dataset on this domain, we used different uni variate relationships of physiological markers with PTSD to create a multi-modal model using a slight modification of the naive Bayes algorithm. Implementation of a micro-controller along with the cloud IoT platform and a mobile app is created to demo the possibility of the system which helps healthcare providers and users to timely track and monitor PTSD risks with background information and priors accurately.

The paper presents development of signal conditioning circuit with integrated potentiostat for glucose measurement. The programmable transimpedance amplifier (PTIA) offers 94% linearity of output voltage. The whole architecture consumes 2.33 mW of total power. The reference potential of 0.6 V has been used for measurements. Three electrode arrangement with Ag/AgCl as reference electrode, the Pt foil as the counter electrode and a CuO/Cu_0.76CO_2.25O₄ (copper cobaltite) coated glassy carbon electrode (GCE) filled-in used as the working electrode. The working feasibility of proposed glucose sensing architecture is tested via an emulated circuit. The second-generation current conveyor (CCII-) is implemented with the help of two AD844 ICs. The two TAs are implemented by using IC LM13700 and I-V conversion is obtained with op-amp IC LM741 and feedback resistance. The voltage ranges from 1.19 to 1.67 V has been measured corresponding to glucose concentration ranges from 18 mg/dl to 180 mg/dl.

The automatic identification of various spatial entities within satellite imagery is a crucial undertaking for interpreting such images. Numerous research papers have explored the segregation, identification, and geolocation of objects of interest, such as airplanes, vehicles, and human elements, within satellite images. The detection of aircraft from satellite imagery is particularly significant for gathering operational intelligence. Detecting aircraft within the environment is achieved through active remote sensing methods, such as RADAR and LASER. Various algorithms have been devised and developed specifically for aircraft detection in satellite imagery. The advent of AI-based techniques has brought about a transformative shift in object detection within remotely sensed images. This paper proposes a methodology employing a convolutional neural network (CNN) for the detection of aircraft within satellite imagery. Initially, an image dataset is generated using QGIS software, which is then partitioned into training and testing datasets. A multi-layered CNN model is employed to train and evaluate the dataset. Subsequently, the trained CNN is applied to remotely sensed images to detect the presence of aircraft within the scene. The aircraft detection accuracy from randomly selected satellite images is reported to be 95%.