Search

The emerging field of the Internet of Vehicles (IoV) has garnered significant attention due to its potential to revolutionize transportation and mobility. IoV enables the development of innovative services and applications that can enhance the efficiency, safety, and sustainability of transportation systems. However, ensuring secure and reliable communication among different components of an IoV system poses a critical challenge. This study proposes a blockchain-based communication framework for secure and trustworthy IoV applications. The framework utilizes blockchain technology's decentralization and security features to create secure communication channels between IoV system components, including vehicles, infrastructure, and service providers. An identity management system is also integrated into the framework to authenticate and authorize users and devices, thereby preventing unauthorized access and data breaches. To assess the proposed framework's effectiveness, real-world IoV scenarios were used to conduct experiments, and the results demonstrate that the framework can provide secure and trustworthy communication for IoV applications. The proposed blockchain-enabled communication framework provides a promising solution for addressing security and trust challenges in IoV communication systems. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) paves way into Industry 5.0, which incorporates human–machine collaboration, thereby making manufacturing industry efficient. As 5G architecture supports massive IoT connectivity and has higher spectrum efficiency, device-to-device (D2D) communication is favorable at 28 GHz. While transmitting data from sensors to end user through IoT network, interference affects the system. Thus, an efficient resource allocation scheme is needed for minimizing interference and increasing data rate. Here, formulated problem is divided into two subproblems, channel assignment and power optimization in order to lower computational complexity. A partial resource multiplexing scheme is proposed that will allocate channels to available D2D users. Later, power optimization problem is formulated which is determined through Lagrangian dual optimization technique. Dynamic sectorization overcomes issue of increase in user traffic. Stability factor, fairness index (FI), and energy efficiency depict the performance superiority of proposed scheme over existing schemes. Simulation results prove efficacy of proposed system. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel approach for detecting patients’ position using the radial basis function of the neural network. This new approach aims to continuously monitor the patients’ health statistics and real-time prediction, even when they are outside of cellular coverage. Our research is driven by an initiative to innovate a novel healthcare system of significant importance for intelligent and efficient medical services. For example, doctors need to remotely monitor any patient’s health with the provided health statistics derived from data collected from battery-powered Internet of Things sensors. To this end, our proposed method has been quantified with a holistic mathematical analysis and extensive simulations considering realistic network situations. Our results have accredited the efficiency in the prediction of localization for the patients’ position for the anticipated intelligent healthcare system. [ABSTRACT FROM AUTHOR]

The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city‐based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera‐enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%–10% of more classification accuracy than existing systems. [ABSTRACT FROM AUTHOR]

Encryption can secure sensitive data, access control mechanisms ensure only authorized access [[22]], authentication verifies identities, data minimization limits data collection, anonymization removes personal identifiers, blockchain technology provides tamper-proof and decentralized records [[23]], and threat modeling identifies potential security threats [[24]]. To achieve these objectives, technical solutions such as data encryption, access control, authentication, data minimization, anonymization, blockchain technology, and threat modeling can be employed [[21]]. The world is currently at the dawn of a new era characterized by a global transformation reshaping how we interact with our surroundings and each other. This global transformation is being fueled by rapid advancements in intelligent technologies [[1]], such as artificial intelligence (AI), big data, and the Internet of Things (IoT) [[3]]. [Extracted from the article]

This study contributes to our understanding of how the emergence of the COVID‐19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID‐19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID‐19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID‐19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real‐world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID‐19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances. [ABSTRACT FROM AUTHOR]

In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. Often, the tumors are predecessors to cancers, and the survival rates are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare and Harvard medical repository datasets. The experimental results are superior to the other existing models. Performance measures such as accuracy, specificity, and F1 score are examined to evaluate the performances of the proposed model. [ABSTRACT FROM AUTHOR]

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification. [ABSTRACT FROM AUTHOR]

The internet-of-things(IoT) extends the traditional Internet and realizes the interconnection of all things in a smart way. With the rapid development of 5G and beyond communication technology, the number of users and demands for IoT applications has increased significantly along with resource constraints networking in the communication systems. The next generation IoT applications challenges these issue using smarter cooperative communication with large heterogeneous clusters along in par with traditional IoT systems. The proposed work illustrates a hybrid neural network (NN) model for dynamic clustering for efficient next generation IoT applications. A dynamic cluster model based on hybrid NN optimization is proposed along with the Gaussian copula technique for realizing the correlation between the clusters for efficient cooperative communication. The mathematical analysis and simulation results show that the model maximizes the amount of information in the cluster and balances the resource allocation among nodes to improve the life of the entire network. [ABSTRACT FROM AUTHOR]

Optical cloud radio access network (O-CRAN) is an emerging solution for IIoT, where numerous different devices/nodes are networked together. O-CRAN provides pool of shareable computing facility, equipped with hundreds of general-purpose processor (GPP). The GPPs process massive big data exerted by nodes via remote radio heads (RRHs), regarded as RRH-requests, which are bandwidth-intensive and deadline-constrained digitized base-band signals. Computing resource (CR) optimization has been widely investigated in O-CRAN. However, the existing optimizations may not guarantee workload and thermal balance among the active GPPs while satisfying RRH-request's deadline, which are necessary to efficiently leverage virtualization GPP capacity in a manner that provides the greatest uniform CR utilization (CRU). Due to varying network-load a single optimal solution does not exist. Therefore, in this article, we propose a modified-first-fit decreasing (MFFD) algorithm to obtain a suboptimal solution for each time_stage. The MFFD evenly assigns RRH-requests among GPPs that maximizes individual CRU uniformly contrasting with FFD. [ABSTRACT FROM AUTHOR]

A Wireless Sensor Network (WSN) is of paramount importance in facilitating smart Agricultural Internet of Things (Agri-IoT). It connects numerous sensor nodes or devices to develop a robust framework for efficient and seamless communication with improved throughput for intelligent networking. Such enhancement has to be facilitated by an adequate and smart machine learning-based resource allocation approach. With the ensuing surge in the volume of devices being deployed from the smart Agri-IoT, applications such as intelligent irrigation, smart crop monitoring and smart fishery would be largely benefited. However, the existing resource allocation techniques would be inefficient for such anticipated energy-efficient networking. To this end, we develop a distributed artificial intelligence approach that applies efficient multi-agent learning over the WSN scenario for intelligent resource allocation. The approach is based on dynamic clustering which coupled tightly with the Back-Propagation Neural Network and empowered by the Particle Swarm Optimization (BPNN-PSO). We implement the overall framework using a Bayesian Neural Network, where the outputs from BPNN-PSO are supplied as weights to the underlying neuron layer. We observe that the cost function and energy consumption demonstrate a substantial improvement in terms of cooperative networking and efficient resource allocation. The approach is validated with simulations under realistic assumptions. [ABSTRACT FROM AUTHOR]

Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors' objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time-varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well-known techniques. [ABSTRACT FROM AUTHOR]

Recently, underwater acoustic sensor networks (UASNs) have gained wide attention due to their numerous applications in underwater surveillance, oil leakage detection, assisted navigation, and disaster prevention. With unique characteristics like increased propagation delay, constant mobility of sensor nodes, high error rate, and limitations in energy and interference, efficient routing of data packets from the source node to the destination is a major challenge in UASNs. Most of the protocols proposed for traditional sensor networks do not work well in UASNs. Although many protocols have been specifically proposed for underwater environments, the aim of most of them is to improve only the quality of service (QoS) in the network. The security of the transmitted data, energy efficiency of the participating nodes, and handling of communication voids are three significant challenges that need to be adequately addressed in UASNs. In this research work, a secure and energy-efficient opportunistic routing protocol with void avoidance (SEEORVA) is proposed. This protocol uses the latest opportunistic routing strategy for reliable data delivery in the network and also provides priority to the nodes having energy above a specific threshold in the forwarding process, thereby increasing the lifetime and energy efficiency in the network. The transmitted messages are encrypted using a secure lightweight encryption technique. The protocol is also integrated with a strategy to handle the communication voids in the network. Simulation results with Aqua-Sim validate the better performance of the proposed system compared to the existing ones. [ABSTRACT FROM AUTHOR]

In this Letter, a novel approach is used for improving the Bit Error Rate (BER) for non-stationary environment based spectrum sensing. Here, we have considered Converged Welch's Power Spectral Density (PSD) for analyzing of power. The signal of interest is thus further studied in terms of BER keeping the antenna and channel parameters constant. The proposed method yields an improvement of 34.6% as compared with the conventional method. [ABSTRACT FROM AUTHOR]

This letter addresses the problem of the fusion center (FC) positioning based on the novel approach of hierarchical maximum likelihood clustering for cooperative communication. In a cooperative communication system, the power allocation for all the nodes, including FC, should not be the same all the time, as decision of all the nodes is not required. Using the proposed scheme, the location of the FC based on the cluster formation in hierarchical approach can be made closer to the dense clusters. [ABSTRACT FROM PUBLISHER]

This Letter addresses the problem of dynamic power allocation based on spectrum sensing outputs for non-ergodic systems. On such type of systems, the power allocation for each corresponding nodes to be sensed by cognitive transmitter may not require all the time. The dynamic power allocation in nodes of each channel in cognitive radio (CR) network using vector quantization based on autocorrelation error is proposed. [ABSTRACT FROM AUTHOR]

Patient health record analysis models assist the medical field to understand the current stands and medical needs. Similarly, collecting and analyzing the disease features are the best practice for encouraging medical researchers to understand the research problems. Various research works evolve the way of medical data analysis schemes to know the actual challenges against the diseases. The computer-based diagnosis models and medical data analysis models are widely applied to have a better understanding of different diseases. Particularly, the field of medical electronics needs appropriate health indicator extraction models in near future. The existing medical schemes support baseline solutions but lack optimal hypothesis-based solutions. This work describes the optimal hypothesis model and Akin procedures for health record users, to aid health sectors in clinical decision-making on health indications. This work proposes Medical Hypothesis and Health Indicators Extraction from Electronic Medical Records (EMR) and International Classification of Diseases (ICD-10) patient examination database using the Akin Method and Friendship method. In this Health Indicators and Disease Symptoms Extraction (HIDSE), the evidence checking procedures find and collect all possible medical evidence from the existing patient examination report. Akin Method is making the hypothesis decision from count-based evidence principles. The health indicators extraction scheme extracts all relevant information based on the health indicators query and partial input. Similarly, the friendship method is used for making information associations between medical data attributes. This Akin-Friendship model helps to build hypothesis structures and trait-based feature extraction principles. This is called as Composite Akin Friendship Model (CAFM). This proposed model consists of various test cases for developing the medical hypothesis systems. On the other hand, it provides limited accuracy in disease classification. In this regard, the proposed HIDSE implements Deep Learning (DL) based Akin Friendship Method (DLAFM) for improving the accuracy of this medical hypothesis model. The proposed DLAFM, Convolutional Neural Networks (CNN) associated Legacy Prediction Model for Health Indicator (LPHI) is developed to tune the CAFM principles. The results show the proposed health indicator extraction scheme has 8-10% of better system performance than other existing techniques., Competing Interests: Conflict of interestThe submitted work is unpublished and the authors confirm no conflict of interest associated with this., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)