Polio remains an extreme health challenge in numerous weak and high-risk regions such as Pakistan, Afghanistan, Nigeria mainly due to ineffective monitoring and the lack of integrated digital systems for immunization organization and management. The current Polio Vaccination System (PVS) of Pakistan faces many problems and issues while eradicating poliovirus because the current system is completely paper based where data is added to sheets manually and there is a big chance of data loss. A system is efficient that make the vaccination campaign successful and remove flaws that we have faced in the current system. This research framework presents a formal approach to design and verification with integration of IoT-enabled digital polio system using Colored Petri Nets (CPNs). Its helps to leverage the IoT devices for real-time data collection, communication, and control of field-level immunization activities. With the help of modeling the system of CPNs, we ensured the formal verification of process correctness, consistency, and deadlock-free process. The model simulates interactions amongst the key mechanisms, including health workers, supervising and observing units to enable an accurate analysis of system behaviour. Formal validation through Colored Petri Nets and state space analysis confirms the reliability, correctness and scalability of the system. The approach proposes a verifiable, adaptable and flexible solution in support of the national polio eradication programs with the help of digitation, transparency, and intelligent management. The proposed system will prove efficient and flawless as compared to the current manual system because it will use a data entry device in place of a hand-held register, the security personnel with the community health workers will move Door-to-Door to vaccinate the children. Results validate the proposed model as robust a good, reliable and complete with IoT enabled smart system along Colured Petri Net and formal method.

The word Internet of Things (IoT) is designed for diverse sensing devices that are considered to capture real-world data and initiate corresponding actions. Sensor nodes consumed their built-in battery capacity allowing them to perform several tasks and act together with each other. Optimizing energy and extending the lifetime of wireless sensors are highly concerned with data transmission in a network. The use of energy at the cluster level to prolong the network’s life, and the sensors’ battery needs a mechanism during the transmission of data in a wireless network. There is also multiple research techniques presented for modelling IoT-based smart systems, but energy savings with efficiency and to validate smart agriculture systems have not been earlier adopted and focused. Our primary purpose is to save, enhance, and optimize the network life of wireless networks in smart agriculture by using an ensemble formal approach with IoT. Our research is based on three phases: firstly, we proposed an algorithm for saving energy consumption and data aggregation in a smart agriculture case to use the NS2 Simulator for experimental results. Secondly, we also developed a model using an activity diagram and transformed it into the formal language of TLA+ (Temporal Logic of Action). Lastly, the correctness properties of a model are verified by using the TLC (Temporal Logic Checker) with the model-checking capability of the TLA+ toolbox. The result of our proposed technique has been evaluated and shows an efficient performance in terms of energy consumption, delay, and network life.

Introduction of smart implantable devices is changing the face of cardiac health monitoring through continuous and real time ECG monitoring useful in early diagnosis of cardiac pathologies. This paper describes CardioHarvest-Net, a newly developed self-powered wireless ECG monitoring system that employs, physiological movements for its power in order to reduce the probability of frequent power replenishment. This self-powered capability eliminates dependency on conventional batteries, thereby offering a viable solution for continuous, long-term cardiac monitoring in real-world conditions. CardioHarvest-Net enables an enhanced machine learning (ML)-based anomaly detection model that learns and adapt to each patient's cardiac behavior to provide high sensitivity in abnormal ECG signs related to diseases like arrhythmia, myocardial infarction, and other diseases of the heart. The CardioHarvest-Net model applies CNN for feature extraction of vital signs such as ECG and uses LSTM for temporal feature extraction for accurate anomaly detection in real-world settings. Evaluation results reveal that gain scores of cardio health phenomena via CardioHarvest-Net is a detection accuracy of 97.2 % and the anomaly recall rate of 95.3 % that qualifies the proposed system as an effective and timely monitoring tool of putting up a signal and cautionary measure on possible event of cardiac occurrences. The average response time for an entire system to detect an anomaly is 10 ms, which makes the system's intervention capacity rather fast. Moreover, they use a power build-up efficiency of 78 % in otherwise low power, real-life in-vivo conditions ranging from acute circumstances to chronic conditions requiring prolonged operation. This ML model is running on an energy-efficient microcontroller suitable for wearable and implantable medical devices along with a feedback adaptation that enhances the accuracy of the predictions based on data that changes over time concerning an individual patient. The outcomes of this study further state the viability of CardioHarvest-Net to transform sustainable cardiac niche by addressing limitations into power independence and facilitating real-time tracking. This development is a breakthrough in moving towards the preventive, long-term approach to cardiac reliability enhancing our method in a manner that offers a solid framework for constant, individualized cardiac monitoring, and timely action in cases of essential occurrences in the heart.

Over the past few years, there has been a notable surge in the integration of Blockchain technology into supply chain management systems. This integration holds the promise of enhanced transparency, security, and efficiency in monitoring the movement of goods and services. This study presents a novel approach aimed at fortifying privacy and accuracy within blockchain-based supply chain management systems. The methodology integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) units with secure multi-party computation (MPC) and differential privacy techniques as a hybrid model. The objective is to safeguard the confidentiality of transaction data while enabling precise detection of media tampering. Performance evaluation revolves around three key aspects: accuracy, privacy preservation, and computational efficiency. In terms of accuracy assessment, the proposed hybrid approach is benchmarked against traditional machine learning algorithms including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Random Forest. Results indicate superior performance, with the proposed hybrid method achieving an accuracy of 0.95, outperforming conventional algorithms. Precision, recall, and F1-score metrics further confirm the effectiveness of the approach in accurately identifying media tampering instances. Privacy preservation capabilities are evaluated through differential privacy techniques, revealing the method’s ability to inject controlled noise into the data to protect individual privacy. Results demonstrate varying levels of privacy preservation across different settings, highlighting the trade-off between privacy and data utility. Computational efficiency is also scrutinized, considering the additional overhead introduced by privacy preservation mechanisms and secure MPC protocols. While there is a slight increase in computational time, the proposed approach maintains reasonable training and inference times, ensuring practical applicability in real-world scenarios.

The study introduces an efficient data aggregation technique for smart agriculture by leveraging Blockchain technology and a novel method referred to as the "cluster head sleep schedule." The primary objective is to enhance the data collection process within a large-scale agricultural setting where multiple sensors continually generate vast amounts of data while monitoring and safeguarding crops from pest attacks. The proposed method involves the segmentation of sensors into clusters, each led by a designated cluster head responsible for collecting data from its constituent members deployed in the field to monitor pest attacks and promptly report any issues to the management. To curtail data redundancy, the study employs a fuzzy matrix to group nodes based on high-similarity data. This approach enables the selective suspension of certain nodes while others remain active. The data received from these nodes undergoes analysis using a fuzzy similarity matrix for clustering, ensuring that only unique data is transmitted to the base station. Redundant nodes from all clusters are identified and placed in a sleep mode, thus conserving energy and prolonging the network’s lifespan. This sleep scheduling mechanism is implemented subsequent to data redundancy reduction, facilitating immediate pest attack control in agriculture. By implementing these techniques, smart agriculture stands to benefit from optimized energy utilization and reduced costs associated with monitoring and pest control, thereby fostering sustainable and efficient operations. The cluster head is responsible for storing the data on a base station positioned at the network’s edge, allowing for local processing and prompt communication of pest attack information to the farmer for immediate action. Moreover, this edge system stores the data on a Blockchain network for future analysis and serves as a guideline for pest attack control in the pesticide industry, thereby enhancing data security and immutability. In addition to these advantages, the research also emphasizes the importance of controlling pest attacks to enhance crop production in the field, ultimately contributing to the country’s economic growth. Simulation results affirm that the proposed approach leads to notable cost reductions, decreased energy consumption, improved crop production, precise crop monitoring to prevent pest attacks, and a prolonged network lifespan. These outcomes underscore the effectiveness of this approach within the context of smart agriculture and its role in enhancing the monitoring system for smart agriculture and bolstering security through Blockchain technology.

Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio.Net framework environment is used for the implementation. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud.

In the era of digital communication, social media platforms have experienced exponential growth, becoming primary channels for information exchange. However, this surge has also amplified the rapid spread of hate speech, prompting extensive research efforts for effective mitigation. These efforts have prominently featured advanced natural language processing techniques, particularly emphasizing deep learning methods that have shown promising outcomes. This article presents a novel approach to address this pressing issue, combining a comprehensive dataset of 18 sources. It includes 0.45 million comments sourced from various digital platforms spanning different time frames. There were two models utilized to address the diversity in the data and leverage distinct strengths found within deep learning frameworks: CNN and BiLSTM with an attention mechanism. These models were tailored to handle specific subsets of the data, allowing for a more targeted approach. The unique outputs from both models were then fused into a unified model. This methodology outperformed recent models, showcasing enhanced generalization capabilities even when tested on the largest and most diverse dataset. Our model achieved an impressive accuracy of 89%, while maintaining a high precision of 0.88 and recall of 0.91.

Message-transmission energy expenditure dominates battery lifetime in a Wireless Sensor Network (WSN). This paper newly combines network coding with a brokered WSN architecture to decrease the number of messages by means of message aggregation. It also facilitates low-latency delivery of critical messages and improves the overall energy efficiency of a WSN. Sensor nodes are arranged into subgroups, in each of which a broker separates messages into High Priority (HP) and Best Effort (BE) queues. Both arriving HP and BE messages are separately aggregated through network coding and, according to priority, are forwarded to the next broker or eventually to a data sink, where they are decoded. Service differentiation, together with network coding, prolongs the lifetime of the network by reducing the amount of energy consumed in brokers and increases message throughput by reducing waiting times at intermediate brokers. Best-effort message latency was reduced message as well as for high-priority messages. Without network coding all WSN nodes had run out of energy, whereas with the network-coded approach, twenty percent of the sensor nodes were still alive. This compares with some prior research which provides one or more of low message latency, increased message throughput, reduced WSN energy consumption, and prioritized queueing but not all these features together.

This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem. In our proposed system, patients’ health data is securely managed using a combination of elliptic curve cryptography-based identity-based cryptosystems and edge nodes, ensuring both privacy and integrity. These edge nodes, designed for low-power and short-range communication, play a pivotal role in in-vivo data collection and monitoring within the human body. The DLT model at the core of our framework utilizes peer-to-peer networks, enabling seamless information exchange while eliminating the need for centralized servers. We emphasize public edge DLTs, such as Ethereum, to ensure accessibility and data ownership for all stakeholders. Furthermore, our system incorporates a hybrid machine learning model for early detection and prediction of security threats, enhancing overall system efficiency. Our findings demonstrate a remarkable 99.7% accuracy in classification using this approach. In conclusion, this framework’s multidisciplinary approach bridges the gap between healthcare, edge computing, and DLT, promising real-time data processing, enhanced security, and privacy preservation. With the rise of the Internet of Things, this innovation holds the potential to transform the future of healthcare technology.

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

Recently researchers and companies have shown significant interest in merging blockchain and the Internet of Things (IoT) to create a safe, reliable, and resilient communication platform. However, determining the proper role of blockchain in existing IoT contexts with minimum implications is a challenge. This work suggests a message schedule for a blockchain-based architecture with two access-level setting filters for incoming messages: critical and non-critical. The proposed work of the researchers divides the fog layer into two parts: action clusters and blockchain fog clusters. Similar to the three-layered IoT architecture, the action cluster and the main cloud data center work together for critical message requests. The blockchain fog cluster is dedicated to only the blockchain application's requirements. In the fog layer, a fog broker is used to schedule critical and non-critical messages in the action and blockchain fog clusters, respectively. The proposed technique is compared to the existing Dual Fog-IoT architecture. The solution is also tested for fog and cloud computing resource utilization. The findings demonstrate that this architecture is feasible for varying percentages of receiving critical and non-critical messages. In addition to the inherent benefits of blockchain, the suggested paradigm reduces the system loss rate and offloads the cloud data center with minimal changes to the existing IoT ecosystem.

In the field of human computer interaction (HCI), the usability assessment of m-learning (mobile-learning) applications is a real challenge. Such assessment typically involves extraction of best features of an application like efficiency, effectiveness, learnability, cognition, memorability, etc., and further ranking of those features for overall assessment of the quality of the mobile application. In the previous literature, it is found that there is neither any theory nor any tool available to measure or assess a user’s perception and assessment of usability features of a m-learning application for the sake of ranking of the graphical user interface of a mobile application in terms of a user’s acceptance and satisfaction. In this paper, a novel approach is presented by performing a mobile application’s quantitative and qualitative analysis. Based on the user’s requirements and perception, a criterion is defined based on a set of important features. Afterwards, for the qualitative analysis, genetic algorithm (GA) is used to score prescribed features for usability assessment of a mobile application. The used approach assigns a score to each usability feature according to the user’s requirement and weight of each feature. GA performs the rank assessment process initially by performing feature selection and scoring the best features of the application. A comparison of assessment analysis of GA and various machine learning models, i.e., K-nearest neigbors, Naïve Bayes, and Random Forests is performed. It was found that GA-based support vector machine (SVM) provides more accuracy in the extraction of best features of a mobile application and further ranking of those features.

The Internet of Things (IoT) is getting important and interconnected technologies of the world, consisting of sensor devices. The internet is smoothly changing from an internet of people towards an Internet of Things, which permits various objects to connect to another wirelessly. The energy consumption of the IoT routing protocol can affect the network life span. In addition, the high volume of data produced by IoT will result in transmission collision, security issues, and energy dissipation due to increased data redundancy because tiny sensors are usually hard to recharge after they are deployed. Generally, to save energy, data aggregation reduces data redundancy at each node by turning some nodes into sleep mode and others into wake mode. Therefore, it is important to group the nodes with high data similarity using the fuzzy matrix. Then, the data received from the member nodes at the Cluster Head (CH) are analyzed using a fuzzy similarity matrix for clustering. In the next step, after clustering, some nodes are chosen from all groups as redundant nodes. The sleep scheduling mechanism is then applied to reduce data redundancy, network traffic jamming, and transmission costs. We have proposed an Energy-Efficient Data Aggregation Mechanism (EEDAM) secured by blockchain, which uses a data aggregation mechanism at the cluster level to save energy. As edge computing is used to provide on-demand trusted services to IoT with minimum delay, blockchain is integrated inside a cloud server, so the edge is validated by the blockchain to provide secure services to IoT. Finally, we performed simulations to calculate the performance of the proposed mechanism and compared it with the conventional energy-efficient algorithms. The simulation results show that the proposed structural design can successfully reduce the amount of data, provide proper security to the IoT, and extend the wireless sensor network (WSN).

Real-time tracking and surveillance of patients' health has become ubiquitous in the healthcare sector as a result of the development of fog, cloud computing, and Internet of Things (IoT) technologies. Medical IoT (MIoT) equipment often transfers health data to a pharmaceutical data center, where it is saved, evaluated, and made available to relevant stakeholders or users. Fog layers have been utilized to increase the scalability and flexibility of IoT-based healthcare services, by providing quick response times and low latency. Our proposed solution focuses on an electronic healthcare system that manages both critical and non-critical patients simultaneously. Fog layer is distributed into two halves: critical fog cluster and non-critical fog cluster. Critical patients are handled at critical fog clusters for quick response, while non-critical patients are handled using blockchain technology at non-critical fog cluster, which protects the privacy of patient health records. The suggested solution requires little modification to the current IoT ecosystem while decrease the response time for critical messages and offloading the cloud infrastructure. Reduced storage requirements for cloud data centers benefit users in addition to saving money on construction and operating expenses. In addition, we examined the proposed work for recall, accuracy, precision, and F-score. The results show that the suggested approach is successful in protecting privacy while retaining standard network settings. Moreover, suggested system and benchmark are evaluated in terms of system response time, drop rate, throughput, fog, and cloud utilization. Evaluated results clearly indicate the performance of proposed system is better than benchmark.

In consideration of the reduced chaotic range and susceptibility of a single chaotic map, we exploit the 4D-hyperchaotic system for creating three S-boxes i.e., red, green and blue S-boxes and a logistic map to transform a plain image into DNA strands. Afterwards, a logistic map based fake image is also generated which is also mapped to deoxyribonucleic acid (DNA) strands. Then DNA operations based on logistic map sequence are performed among the DNA strands and the resultant strands are decoded. The decoded strands are substituted by three substitution-boxes (s-boxes) to create an encrypted image. In this research, a cryptanalysis driven design approach is used to prove the security of a proposed encryption scheme. The proposed scheme operates on numerous image dimensions N $\times $ M and different image file sizes and formats. Experimental results and analysis are completed for visual analysis, key space, key sensitivity, energy analysis, homogeneity analysis, contrast analysis, entropy analysis, histogram analysis, correlation analysis, chosen-plaintext attacks, number of pixels change rate (NPCR), universal average changing intensity (UACI), mean absolute error, robustness against noises and occlusion attacks and encryption efficiency analysis. The visual as well as numerical simulations demonstrate that the proposed algorithm is safe and reliable.

COVID-19 epidemic second wave is affecting the world severely. It is a gigantic challenge for governments of all countries to protect their citizens from this virus and put effected ones in quarantine centers so that these can't cause of spreading Covid-19 virus any more. There is no trustworthy treatment of this disease till now (16 November 2020). Complete Lockdown is not solution for this pandemic because this can lead heavy loss of economy and can cause enhance in poverty and hunger in society. In this study we proposed a smart method of detection and prevention the people from COVID-19 with help of IoT and Blockchain technologies. Now a day's sensor is a cheap technology and different devices can be thru sensors. Each person in COVID-19 suspected area has a smart corona belt in his wrest along with face mask. This belt consists of different hardware modules like sensors, tiny battery and transvers. Sensors collect the different symptoms of a person for COVID-19 then this information will be communicated to other entities like government database, quarantine center and rescue office. This corona belt can detect a person as safe, suspected, high Suspected and positive. A person is safe if he has no COVID-19 symptoms and also not met someone having COVID-19 positive. If someone has COVID-19 symptoms then he is suspected case and if someone has COVID-19 symptoms and also met with COVID-19 positive he will be high Suspected case. All smart corona belts communicate with cellular phone running COVID-19 application for processing and sending messages to government database, corona centre and rescue centers for different actions. In this proposed solution three layered architecture is used to enhance the flexibility and effectiveness of system. The whole process is enabled by IoT, fog and cloud technologies. All information is stored using blockchain for the sake of data privacy, Integrity and security.

Mobile multimedia communication requires considerable resources such as bandwidth and efficiency to support Quality-of-Service (QoS) and user Quality-of-Experience (QoE). To increase the available bandwidth, 5G network designers have incorporated Cognitive Radio (CR), which can adjust communication parameters according to the needs of an application. The transmission errors occur in wireless networks, which, without remedial action, will result in degraded video quality. Secure transmission is also a challenge for such channels. Therefore, this paper’s innovative scheme “VQProtect” focuses on the visual quality protection of compressed videos by detecting and correcting channel errors while at the same time maintaining video end-to-end confidentiality so that the content remains unwatchable. For the purpose, a two-round secure process is implemented on selected syntax elements of the compressed H.264/AVC bitstreams. To uphold the visual quality of data affected by channel errors, a computationally efficient Forward Error Correction (FEC) method using Random Linear Block coding (with complexity of O(k(n−1)) is implemented to correct the erroneous data bits, effectively eliminating the need for retransmission. Errors affecting an average of 7–10% of the video data bits were simulated with the Gilbert–Elliot model when experimental results demonstrated that 90% of the resulting channel errors were observed to be recoverable by correctly inferring the values of erroneous bits. The proposed solution’s effectiveness over selectively encrypted and error-prone video has been validated through a range of Video Quality Assessment (VQA) metrics.

Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.