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LoRa technology is widely used in the Internet of things network applications. It enables low-volume data transmission via small wireless devices. The principle of LoRa networks (an LPWAN technology: Low Power Wide Area Network) is to transmit data by air from sensors with a short transmission range, about over ten kilometers. These sensors should not be powered by electricity, and batteries power them. Hospital visits are inevitable, but current advances in communication could reduce the burden on hospitals with remote (from home) treatments using these wireless sensors. Thus, using the LoRaWAN protocol could greatly facilitate patient diagnosis by transmitting data between doctors and patients in real time and with minimal energy consumption. The objective of this work is to set up a multihop IoT network containing a large number of sensors based on LoRa for uplink communication. This work evaluates the energy consumption and the packet delivery rate in the multihop IoT network. The simulation result shows that the proposed approach reduces power consumption by 50% and improves the packet delivery rate by 2% compared to the existing state of the art.

The pharmaceutical company is key to having a strong healthcare system, and excellent healthcare is essential for every society and economy. However, there are significant concerns with medication safety and security as a result of fake and inferior medical items, which constitute a significant hazard and harm to consumers’ health. Globally, drug counterfeiting is a severe problem that endangers the public’s health as well as the health of consumers. The global business of manufacturing fake medications generates enormous annual revenue. To have a quality healthcare system, the pharmaceutical industry is of great importance and plays a vital role in medicine and pharmaceutical supplies. With emerging computer technologies like blockchain and IoT cutting across several industries and sectors and revolutionizing the world, a systematic literature review of various articles chosen from different databases was carried out in this study to analyze and evaluate application areas of this technology in the pharmaceutical industry and existing frameworks that have been proposed to solve problems faced in the pharmaceutical industry. The outcome of this review showed that the application of the blockchain and IoT hybrid framework can assist in reducing the drug counterfeit problem and provide solutions to most of the problems faced in the pharmaceutical industry. This study also proposed a framework that addressed the drawbacks of existing frameworks in the pharmaceutical industry.

Secure and reliable identity is essential for identifying a user accurately and providing services. The traditional centralized identity systems have several security weaknesses and do not support user control. Self-sovereign identity (SSI) has been proposed to provide user control and secure identity model. User record privacy and security is a critical factor for any healthcare information system. Healthcare is also a domain where SSI solutions can counter patient information privacy and security threats. This paper reviews the applicability of blockchain-based SSI solution in healthcare, their advantages and requirements. It further provides a model use case for demonstrating the SSI application in healthcare.

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis.

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions.

A collaborative filtering technique has proven to be the preferable approach for personalized recommendations. Traditionally, collaborative filtering recommends target items to those users who have similar tastes. The performance of collaborative filtering degrades significantly when a considerable number of users do not provide ratings on recommended products. In such a scenario, the dataset utilized in recommendation becomes highly sparse, and ratings become very few or none co-rated. To mitigate the problem, as mentioned earlier, and to improve the performance of collaborative filtering, we propose an approach that adopts users' textual reviews and ratings both in the rating prediction. The dataset used is Amazon fine Food Reviews containing rating and text review with 568454 reviews from October 1999 to October 2012. The proposed model is tested on the collected dataset. The experimental results provide the proper evidence that the proposed model outperforms other traditional algorithms of collaborative filtering techniques.

The land registry system is one of the crucial aspects of any government. Proper and reliable land management is essential for economic growth and governance. The traditional land registry model lacks many essential requirements and mired with a different type of malpractices and security concerns. Blockchain technology has been proposed as a possible solution to counter these malpractices and security concerns. Even though there are many advantages of using blockchain in the land registry, still there are some concerns and challenges that need to be reviewed and addressed for successful blockchain implementation in the land registry. This paper reviews the current state of blockchain implementation in the land registry and the existing project being implemented in different countries. It further highlights the requirements, implications and challenges for successfully implementing blockchain in the land registry system. This paper provides a future direction for new implementations.

Providing an identity solution is essential for a reliable blockchain-based land registry system. A secure, privacy-preserving, and efficient identity solution is essential but challenging. This paper examines the current literature and provides a systematic literature review in three stages based on the three research questions (RQ) that show the assessment and interpretation process step by step. Based on the parameters and RQ specified in the research methodology section, a total of 43 primary articles have been selected from the 251 articles extracted from various scientific databases. The majority of these articles are concerned with evaluating the existing self-sovereign identity (SSI) solutions and their role in the blockchain-based land registry system to address the compliance issues in the existing SSI solutions with SSI principles and find the best possible SSI solution to address the identity problems in the land registry. The existing digital identity solutions cannot handle the requirements of the identity principle and are prone to various limitations like centralization and dependency on third parties that further augment the chance of security threats. SSI has been designed to overcome these limitations and provide a secure, reliable, and efficient identity solution that gives complete control to the users over their personal identity information (PII). This paper reviews the existing SSI solutions, evaluates them based on the SSI principles, and comes up with the best possible SSI solution for a blockchain-based land registry system. It further provides a detailed investigation of each SSI solution to present its functionalities and limitations for further improvement.

The land registry system is one of the essential components of any governance model required to ascertain the ownership records uniquely. This paper reviews the existing literature and provides a detailed literature review consisting of 3 stages based on three research questions (RQ) that highlight the step by step evaluation and analysis. We selected 48 primary articles out of 477 extracted from different scientific databases based on criteria and RQ defined in the research method section. The majority of these papers focus on assessing the identity issues related to the land registry system and reviewing the existing identity models to find the best possible identity model to resolve the identified identity problems in the land registry. This paper examines the current land registry model and its shortcomings. It explains the various blockchain types and their characteristics. It further evaluates the usability of blockchain technology in different aspects of the land registry. Identity management is one of such weaknesses in the blockchain-based land registry model that has been assessed in detail. Identity issues of blockchain-based models have been further evaluated on defined criteria. The paper ends with a discussion on possible identity models and their comparative analysis to ascertain the most suitable identity model to resolve the identity issues of land registry systems.

The smart medical system is becoming a health policy service that employs wearables, online services and mobile devices to connect to the internet continuously and connect patients, technology and healthcare centers, and then cope efficiently and thoughtfully with the demands of medical ecosystems. This article explores some of the issues facing users to speed up using and accepting intelligent medical technology for access to omnipresent healthcare. The article analyses how Fuzzy Decision Making may be integrated to create better health solutions with an intelligent approach. The smart medical care management system enables the patient to use medical services and services in any place and at any time, including emergency preparedness, medication management and monitoring services. An assessment of this method for managing new concepts comparable to direct health services must be examined. Hence, this paper Smart Healthcare Management Evaluation utilizing Fuzzy Decision Making (SHME-FDM) model, has been proposed to evaluate the technological integration performance. The present paper assesses the security of health data privacy in the intelligent medicare system utilizing the Fuzzy Analytical Hierarchy Process- Preference Technique similar to the ideal solution (Fuzzy AHP-TOPSIS). This paper usage the fuzzy neural network for health care prediction. The test analyses the reliability, error rate accuracy of the fuzzy outcomes. The security hazard analysis data demonstrate that the suggested fuzzy model has the highest risk assessment performance in contrast to the current models.

Understanding evacuees’ responses to dynamic environmental changes, during an emergency evacuation, is of great importance in determining which aspects are ideal and which aspects should be eliminated or corrected. Evacuees differ in their ability to continually plan escape routes and adapt the routes chosen when they become unsafe owing to moving sources of threat. This is because they have different views and perspectives. The perspectives of evacuees are stochastic and are characterized by a high degree of uncertainty and complexity. To reduce the complexity and control of uncertainty, a model is proposed that can test for variant stochastic representations of evacuees’ perspectives. Two extremely realistic perspectives—the most ideal and the least ideal—are proposed to reasonably limit the range of variance. The success of achieving optimal evacuation is tested when different tendencies towards extreme perspectives are adopted. It is concluded that data toward the most ideal perspectives are capable of demonstrating safer evacuation by reducing the number of simulated burnt agents. This study enables crowd managers and fire safety researchers to test guidance systems as well as configuration of buildings using different perspectives of evacuees.

Currently, entire government and private organizations using technologies such as structured databases But, when the data increases, the noted databases cannot handle big data efficiently and effectively. This research wants to use unstructured database management system technologies to satisfy user’s demands. We compared CRUD operations based on key-value approach by No-SQL and SQL databases. Also, we presented the performance analysis of unstructured and structured technologies. Moreover, this paper studied the importance of the sharding and replication in the unstructured technology paradigm. This strength makes an environment for manageability, schema-less development, horizontal scalability etc. that the developer would be helped while taking decision wisely and leads to cover the weaknesses of MySQL.

Cryptography and associated technologies have existed for a long time. This field is advancing at a remarkable speed. Since the inception of its initial application, blockchain has come a long way. Bitcoin is a cryptocurrency based on blockchain, also known as distributed ledger technology (DLT). The most well-known cryptocurrency for everyday use is Bitcoin, which debuted in 2008. Its success ushered in a digital revolution, and it currently provides security, decentralization, and a reliable data transport and storage mechanism to various industries and companies. Governments and developing enterprises seeking a competitive edge have expressed interest in Bitcoin and other cryptocurrencies due to the rapid growth of this recent technology. For computer experts and individuals looking for a method to supplement their income, cryptocurrency mining has become a big source of anxiety. Mining is a way of resolving mathematical problems based on the processing capacity and speed of the computers employed to solve them in return for the digital currency incentives. Herein, we have illustrated benefits of utilizing GPUs (graphical processing units) for cryptocurrency mining and compare two methods, namely overclocking and undervolting, which are the superior techniques when it comes to GPU optimization. The techniques we have used in this paper will not only help the miners to gain profits while mining cryptocurrency but also solve a major flaw; in order to mitigate the energy and resources that are consumed by the mining hardware, we have designed the mining hardware to simultaneously run longer and consume much less electricity. We have also compared our techniques with other popular techniques that are already in existence with respect to GPU mining.

The Internet of Things (IoT) and blockchain (BC) are reliable technologies widely employed in various contexts. IoT devices have a lot of potential for data sensing and recording without human intervention, but they also have processing and security issues. Due to their limited computing power, IoT devices cannot use specialized cryptographic security mechanisms. There are various challenges when using traditional cryptographic techniques to transport and store medical records securely. The general public’s health depends on having an electronic health record (EHR) system that is current. In the era of e-health and m-health, problems with integrating data from various EHRs, preserving data interoperability, and ensuring that all data access is in the patient’s hands are all obstacles to creating a dependable EHR system. If health records get into the wrong hands, they could endanger the lives of patients and their right to privacy. BC technology has become a potent tool for ensuring recorded data’s immutability, validity, and confidentiality while enabling decentralized storage. This study focuses on EHR and other types of e-healthcare, evaluating the advantages of complementary technologies and the underlying functional principles. The major BC consensus mechanisms for BC-based EHR systems are analyzed in this study. It also examines several IoT-EHR frameworks’ current infrastructures. A breakdown of BC integration’s benefits with the IoT-EHR framework is also offered. A BC-based IoT-EHR architecture has been developed to enable the automated sensing of patient records and to store and retrieve these records in a secure and reliable environment. Finally, we conduct a security study to demonstrate the security of our suggested EHR framework.

FANET (flying ad-hoc networks) is currently a trending research topic. Unmanned aerial vehicles (UAVs) have two significant challenges: short flight times and inefficient routing due to low battery power and high mobility. Due to these topological restrictions, FANETS routing is considered more complicated than MANETs or VANETs. Clustering approaches based on artificial intelligence (AI) approaches can be used to solve complex routing issues when static and dynamic routings fail. Evolutionary algorithm-based clustering techniques, such as moth flame optimization, and ant colony optimization, can be used to solve these kinds of problems with routes. Moth flame optimization gives excellent coverage while consuming little energy and requiring a minimum number of cluster heads (CHs) for routing. This paper employs a moth flame optimization algorithm for network building and node deployment. Then, we employ a variation of the K-Means Density clustering approach to choosing the cluster head. Choosing the right cluster heads increases the cluster’s lifespan and reduces routing traffic. Moreover, it lowers the number of routing overheads. This step is followed by MRCQ image-based compression techniques to reduce the amount of data that must be transmitted. Finally, the reference point group mobility model is used to send data by the most optimal path. Particle swarm optimization (PSO), ant colony optimization (ACO), and grey wolf optimization (GWO) were put to the test against our proposed EECP-MFO. Several metrics are used to gauge the efficiency of our proposed method, including the number of clusters, cluster construction time, cluster lifespan, consistency of cluster heads, and energy consumption. This paper demonstrates that our proposed algorithm performance is superior to the current state-of-the-art approaches using experimental results.

Providing a system user with a unique and secure identity is a prerequisite for authentication and authorization aspects of a security system. It is generally understood that the existing digital identity systems store the identity details in centralized databases, and users store the identity details in centralized databases in which users do not have any control over them. These vulnerabilities in the traditional digital identities make them susceptible to various malicious assaults and modifications. Users’ personally identifiable information (PII) may leak through these identity solutions that can consequently affect other applications being used by the users, and they have no control over them. Land registration is a major domain of governance that defines civilians’ well-being and needs to be handled properly to avoid conflict and to support Environmental Sustainability. These traditional land registry applications also lack identity parameters due to weaknesses in identity solutions. A secure and reliable digital identity solution is the need of the hour. Self-sovereign identity (SSI), a new concept, is becoming more popular as a secure and reliable identity solution for users based on identity principles. SSI provides users with a way to control their personal information and consent for it to be used in various ways. In addition, the user’s identity details are stored in a decentralized manner, which helps to overcome the problems with digital identity solutions. This article reviews existing SSI solutions and analyzes them using SSI principles. It also assesses the SSI components required for constructing SSI frameworks that adhere to the SSI principles. Furthermore, it defines the procedures for establishing an SSI ecosystem, explores the laws governing digital identity that governments have adopted, and identifies SSI applications in several fields. Finally, a review of SSI applications in the domain of land registry systems is given in order to propose an SSI-based land registry framework for a secure and reliable land registry system.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.

Early detection of vessels from fundus images can effectively prevent the permanent retinal damages caused by retinopathies such as glaucoma, hyperextension, and diabetes. Concerning the red color of both retinal vessels and background and the vessel's morphological variations, the current vessel detection methodologies fail to segment thin vessels and discriminate them in the regions where permanent retinopathies mainly occur. This research aims to suggest a novel approach to take the benefit of both traditional template-matching methods with recent deep learning (DL) solutions. These two methods are combined in which the response of a Cauchy matched filter is used to replace the noisy red channel of the fundus images. Consequently, a U-shaped fully connected convolutional neural network (U-net) is employed to train end-to-end segmentation of pixels into vessel and background classes. Each preprocessed image is divided into several patches to provide enough training images and speed up the training per each instance. The DRIVE public database has been analyzed to test the proposed method, and metrics such as Accuracy, Precision, Sensitivity and Specificity have been measured for evaluation. The evaluation indicates that the average extraction accuracy of the proposed model is 0.9640 on the employed dataset.

Major problems and issues in Internet of Things (IoT) systems include load balancing, lowering operational expenses, and power usage. IoT devices typically run on batteries because they lack direct access to a power source. Geographical conditions that make it difficult to access the electrical network are a common cause. Finding ways to ensure that IoT devices consume the least amount of energy possible is essential. When the network is experiencing high traffic, locating and interacting with the next hop is critical. Finding the best route to load balance by switching to a less crowded channel is hence crucial in network congestion. Due to the restrictions indicated above, this study analyzes three significant issues—load balancing, energy utilization, and computation cost—and offers a solution. To address these resource allocation issues in the IoT, we suggest a reliable method in this study termed Dynamic Energy-Efficient Load Balancing (DEELB). We conducted several experiments, such as bandwidth analysis, in which the DEELB method used 990.65 kbps of bandwidth for 50 operations, while other existing techniques, such as EEFO (Energy-Efficient Opportunistic), DEERA (Dynamic Energy-Efficient Resource Allocation), ELBS (Efficient Load-Balancing Security), and DEBTS (Delay Energy Balanced Task Scheduling), used 1700.91 kbps, 1500.82 kbps, 1300.65 kbps, and 1200.15 kbps of bandwidth, respectively. The experiment’s numerical analysis showed that our method was superior to other ways in terms of effectiveness and efficiency.

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (S-C) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies.

Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.