A Machine Learning-Based Intelligence Approach for Multiple-Input/Multiple-Output Routing in Wireless Sensor Networks

Computational intelligence methods play an important role for supporting smart networks operations, optimization, and management. In wireless sensor networks (WSNs), increasing the number of nodes has a need for transferring large volume of data to remote nodes without any loss. These large amounts of data transmission might lead to exceeding the capacity of WSNs, which results in congestion, latency, and packet loss. Congestion in WSNs not only results in information loss but also burns a significant amount of energy. To tackle this issue, a practical computational intelligence approach for optimizing data transmission while decreasing latency is necessary. In this article, a Softmax-Regressed-Tanimoto-Reweight-Boost-Classification- (SRTRBC-) based machine learning technique is proposed for effective routing in WSNs. It can route packets around busy locations by selecting nodes with higher energy and lower load. The proposed SRTRBC technique is composed of two steps: route path construction and congestion-aware MIMO routing. Prior to constructing the route path, the residual energy of the node is determined. After that, the residual energy level is analyzed using softmax regression to determine whether or not the node is energy efficient. The energy-efficient nodes are located, and numerous paths between the source and sink nodes are established using route request and route reply. Following that, the SRTRBC technique is used for congestion-aware routing based on buffer space and bandwidth capability. The path that requires the least buffer space and has the highest bandwidth capacity is picked as the optimal route path among multiple paths. Finally, congestion-aware data transmission is used to minimize latency and data loss along the route path. The simulation considers a variety of performance metrics, including energy consumption, data delivery rate, data loss rate, throughput, and delay, in relation to the amount of data packets and sensor nodes.publishedVersio

CELL CYCLE REGULATORY MECHANISMS IN SKELETAL MUSCLE CELLS

Ph.DDOCTOR OF PHILOSOPH

Novel optically active lead-free relaxor ferroelectric (Ba0.6Bi0.2Li0.2)TiO3

We discovered a near room temperature lead-free relaxor-ferroelectric (Ba0.6Bi0.2Li0.2)TiO3 (BBLT) having A-site compositional disordered ABO3 perovskite structure. Microstructure-property relations revealed that the chemical inhomogeneities and development of local polar nano regions (PNRs) are responsible for dielectric dispersion as a function of probe frequencies and temperatures. Rietveld analysis indicates mixed crystal structure with 80% tetragonal structure (space group P4mm) and 20% orthorhombic structure (space group Amm2) which is confirmed by the high resolution transmission electron diffraction pattern. Dielectric constant and tangent loss dispersion with and without illumination of light obey nonlinear Vogel-Fulture relation. It shows slim polarization-hysteresis (P-E) loops and excellent displacement coefficients (d33 ~ 233 pm/V) near room temperature, which gradually diminish near the maximum dielectric dispersion temperature (Tm). The underlying physics for light-sensitive dielectric dispersion was probed by X-ray photon spectroscopy (XPS) which strongly suggests that mixed valence of bismuth ions, especially Bi5+ ions, are responsible for most of the optically active centers. Ultraviolet photoemission measurements showed most of the Ti ions are in 4+ states and sit at the centers of the TiO6 octahedra, which along with asymmetric hybridization between O 2p and Bi 6s orbitals appears to be the main driving force for net polarization. This BBLT material may open a new path for environmental friendly lead-free relaxor-ferroelectric research.Comment: 23 pages, 5 figure

Reliability Analysis of Instrumentation and Control System: A Case Study of Nuclear Power Plant

Instrumentation and control system (I&Cs) plays a key role in nuclear power plants (NPP) whose failure may cause the major issue in a form of accidents, hazardous radiations, and environmental loss. That is why importantly ensure the reliability of such system in NPP. In this proposed method, we effectively analyze the reliability of the instrumentation and control system. An isolation condenser system of nuclear power plant is taken as a case study to show the analysis. The methodology includes the dynamic behavior of the system using Petri net. The proposed method is validated on operation data of NPP

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation

Processing of Tungsten Alloy Scrap for the Recovery of Tungsten Metal

Penetrators usedfordefence purposes are prepared by po wder metallurgical technique. The material contains 90% tungsten along with other minor constituents such as iron, nickel, cobalt, chromium, aluminium etc. During the manufacturing process, three forms of scraps are generated whichare, powder, turnings and defective solid. Since the major constituents of the scrap is the costly tungsten metal, attempts were made to recover the metal by four different methods which are described in this paper. Electro-leaching of turnings in a diaphragm cell using chloride electrolyte bath was tried to remove minor elements. The purity of tungsten achieved in thisprocess was 99.9%. In the soda roasting - leaching process of powderliurning scraps, sodium tungstate of 99.85% purity was obtained with 90% yield. Attempt was also made to remove the impurities by acid leaching. 99.8% pure tungsten with 99% yield was achieved by acid leaching. Fine gravity separation and high intensity magnetic separation techniques were also adopted to enhance the tungsten value from the powder scrap, which produced the concentrate containing 96.2% tungsten

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

Ocular manifestations of Hansen's disease

A detailed ophthalmic evaluation including slitlamp biomicroscopy, measurement of corneal sensitivity using Cochet and Bonnet aesthesiometer, Schirmer's test and Goldmann applanation tonometry was carried out in 89 patients of Hansen's disease attending the leprosy clinic with or without ocular symptoms and willing to undergo eye evaluation. Thirty-one patients had lepromatous leprosy (8 with erythema nodosum leprosum), 56 patients had borderline disease (13 with reversal reactions) and 2 had tuberculoid disease. In addition to the well documented changes of lagophthalmos (6.7%), uveitis (7.3%) and cataracts (19%), we noted prominent corneal nerves in 133 eyes (74.7%), beaded corneal nerves in 19 eyes (10.7%), corneal scarring in 10 eyes (5.6%), corneal hypoaesthesia in 51 eyes (28%) and dry eye in 18 eyes (13%). Beaded corneal nerves and/or stomal infiltrates occurred mainly in the lepromatous group (75%). Ocular hypotony (IOP less than 12 mm Hg) was not seen more frequently in Hansen's as compared to age and sex matched controls with refractive errors or cataracts (33.7%, vs. 37.8%,p=0.33). Our study highlights the primary corneal involvement with corneal neuropathy as the predominant feature of Hansen's disease