Routing Protocols

Wireless sensor are scarce resource so therefore Various Algorithms are there which are described in this paper.This paper contains algorithms which are location based ,hierarchical, data centric etc

IoT and Big Data Integration for Real-Time Agricultural Monitoring

The integration of Internet of Things (IoT) and Big Data technologies has emerged as a transformative force in modern agriculture. This review paper provides a comprehensive examination of the implications and applications of this integration for real-time agricultural monitoring. The paper begins by emphasizing the critical role of agriculture in global food security and economic stability, underscoring the need for innovative solutions to address the challenges facing the sector. The review delves into the key components of the integration, starting with a detailed exploration of the diverse range of IoT devices and sensors instrumental in gathering real-time data. It further emphasizes the importance of robust data handling and transmission mechanisms to facilitate timely decision-making. The significance of data fusion and aggregation processes in distilling meaningful insights from the voluminous data generated is thoroughly examined, along with the pivotal role of data analytics in driving data-driven decision-making and optimizing agricultural operations. Acknowledging the challenges associated with the integration, the review highlights the critical need for scalable systems to accommodate the evolving needs of farms. Additionally, it emphasizes the importance of prudent cost assessment for a sustainable and economically viable implementation. This review paper provides a comprehensive overview of the integration of IoT and Big Data in agricultural monitoring. By synthesizing these technologies, farmers are poised to embark on a new era of data-driven agriculture, marked by increased efficiency, resource optimization, and ultimately, enhanced global food security

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Current and prospective pharmacological targets in relation to antimigraine action

Migraine is a recurrent incapacitating neurovascular disorder characterized by unilateral and throbbing headaches associated with photophobia, phonophobia, nausea, and vomiting. Current specific drugs used in the acute treatment of migraine interact with vascular receptors, a fact that has raised concerns about their cardiovascular safety. In the past, α-adrenoceptor agonists (ergotamine, dihydroergotamine, isometheptene) were used. The last two decades have witnessed the advent of 5-HT1B/1D receptor agonists (sumatriptan and second-generation triptans), which have a well-established efficacy in the acute treatment of migraine. Moreover, current prophylactic treatments of migraine include 5-HT2 receptor antagonists, Ca2+ channel blockers, and β-adrenoceptor antagonists. Despite the progress in migraine research and in view of its complex etiology, this disease still remains underdiagnosed, and available therapies are underused. In this review, we have discussed pharmacological targets in migraine, with special emphasis on compounds acting on 5-HT (5-HT1-7), adrenergic (α1, α2, and β), calcitonin gene-related peptide (CGRP 1 and CGRP2), adenosine (A1, A2, and A3), glutamate (NMDA, AMPA, kainate, and metabotropic), dopamine, endothelin, and female hormone (estrogen and progesterone) receptors. In addition, we have considered some other targets, including gamma-aminobutyric acid, angiotensin, bradykinin, histamine, and ionotropic receptors, in relation to antimigraine therapy. Finally, the cardiovascular safety of current and prospective antimigraine therapies is touched upon

Efficient Routing in Wireless Sensor Networks

Wireless Sensors requires energy for communication,sensing and processing. Mechanisms are discovered to reduce the communication between the sensors .Multicasting in Wireless Network is the technique where there is one sender and multiple receivers. That means there is no need of broadcasting . But my proposed model is using mulicasting and it also uses the inherent broadcastingnbsp property of Wireless links.This modelnbsp is based on demand basednbsp protocol strategynbsp where the initiation for receiving the datanbsp starts from the destination.This model assumes that a unique id is given to all the sensors. Another assumption in my model is that there are multiple receivers and only one source node. Each receiver is using a different session for searching and receiving information from the source node.Sink node which hasnbsp interest in receivingnbsp data fromnbsp source will broadcast a search message and then nodes in the communication range of sink will supply a feedback message which contains thenbsp id of sensor node sending the feedbacknbsp message. After this step selection of path or reservation will take place.Path selection is done according to the id of the source node. An id of sensor node is selected which is nearest to the source node id. Along with this an entry is made in the sensor node whose id is selected.Thisnbsp is achieved by utilizing the feedbacks send by sensornbsp nodes.According to my model, reservation means that when a sensor node finds some already present entry in some other sensor node, And if that entry belongs to some other receiver then sensor node will make an entry in other sensor node so that when the information is transmitted from the source node to other receiver then it will be received by the sensor node which has done its registration. Registrations are performed so that only least number of sessions related to other receivers will be active and less number of nodes to be used while sending datanbsp from the source node. Simulation showed less number of nodes are used while the information is coming from source node