Challenges in Blockchain as a Solution for IoT Ecosystem Threats and Access Control: A Survey

The Internet of Things (IoT) is increasingly influencing and transforming various aspects of our daily lives. Contrary to popular belief, it raises security and privacy issues as it is used to collect data from consumers or automated systems. Numerous articles are published that discuss issues like centralised control systems and potential alternatives like integration with blockchain. Although a few recent surveys focused on the challenges and solutions facing the IoT ecosystem, most of them did not concentrate on the threats, difficulties, or blockchain-based solutions. Additionally, none of them focused on blockchain and IoT integration challenges and attacks. In the context of the IoT ecosystem, overall security measures are very important to understand the overall challenges. This article summarises difficulties that have been outlined in numerous recent articles and articulates various attacks and security challenges in a variety of approaches, including blockchain-based solutions and so on. More clearly, this contribution consolidates threats, access control issues, and remedies in brief. In addition, this research has listed some attacks on public blockchain protocols with some real-life examples that can guide researchers in taking preventive measures for IoT use cases. Finally, a future research direction concludes the research gaps by analysing contemporary research contributions

Groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of anfis models

Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one-and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HAANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs

Impact Learning: A Learning Method from Features Impact and Competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm

Impact learning : A learning method from feature’s impact and competition

Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of well-known machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of impact learning over the conventional machine learning algorithm

TNF-α promoter polymorphism: a factor contributing to the different immunological and clinical phenotypes in Japanese encephalitis

<p>Abstract</p> <p>Background</p> <p>More than three billion populations are living under the threat of Japanese encephalitis in South East Asian (SEA) countries including India. The pathogenesis of this disease is not clearly understood and is probably attributed to genomic variations in viral strains as well as the host genetic makeup. The present study is to determine the role of polymorphism of TNF-alpha promoter regions at positions -238G/A, -308G/A, -857C/T and -863C/A in the severity of Japanese encephalitis patients.</p> <p>Methods</p> <p>Total of 142 patients including 66 encephalitis case (IgM/RT-PCR positive), 16 fever cases (IgM positive) without encephalitis and 60 apparently healthy individuals (IgG positive) were included in the study. Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) using site specific restriction enzymes were implemented for polymorphism study of TNF alpha promoter.</p> <p>Results</p> <p>Following the analysis of the digestion patterns of four polymorphic sites of the TNF- alpha promoter region, a significant association was observed between the allele -308A and -863C with the patients of Japanese encephalitis.</p> <p>Conclusions</p> <p>TNF- alpha 308 G/A has been shown to be associated with elevated TNF- alpha transcriptional activity. On the other hand, polymorphism at position -863C/A in the promoter region has been reported to be associated with reduced TNF- alpha promoter activity and lower plasma TNF levels. As per the literature search, this is the first study to identify the role of TNF- alpha promoter in JE infection. Our results show that subjects with - 308A and -863C alleles are more vulnerable to the severe form of JE infection.</p

Integrative Analysis Applying the Delta Dynamic Integrated Emulator Model in South-West Coastal Bangladesh

A flexible meta-model, the Delta Dynamic Integrated Emulator Model (ΔDIEM), is developed to capture the socio-biophysical system of coastal Bangladesh as simply and efficiently as possible. Operating at the local scale, calculations occur efficiently using a variety of methods, including linear statistical emulators, which capture the behaviour of more complex models, internal process-based models and statistical associations. All components are tightly coupled, tested and validated, and their behaviour is explored with sensitivity tests. Using input data, the integrated model approximates the spatial and temporal change in ecosystem services and a number of livelihood, well-being, poverty and health indicators of archetypal households. Through the use of climate, socio-economic and governance scenarios plausible trajectories and futures of coastal Bangladesh can be explored

A simple firing delay control circuit for bridge converters

A simple ramp control firing circuit, suitable for use with fully controlled, line-commutated thyristor bridge circuits, is discussed here. This circuit uses very few components and generates the synchronized firing pulses in a simple way. It operates from a single 15 V Supply and has an inherent pulse inhibit facility. This circuit provides the synchronized firing pulses for both thyristors of the same limb in a bridge. To ensure reliability, wide triggering pulses are used, which are modulated to pass through the pulse transformers1 and demodulated before being fed to the thyristor gates. The use of throe such circuits only for a three-phase bridge is discussed

A linear ramp control circuit using two LM 556 dual timers

A simple linear ramp control circuit, suitable for use with force-commutated thyrister circuits is discussed here. The circuit is based on only two IM 558 dual timer iCs, operating from a single 15 V supply. The reset terminals facilitate inhibition of the output of any stage. The use of this circuit in a thyristor chopper operating at 400 Hz 13 described

A simple firing delay control circuit for bridge converters

A simple ramp control firing circuit, suitable for use with fully controlled, line-commutated thyristor bridge circuits, is discussed here. This circuit uses very few components and generates the synchronized firing pulses in a simple way. It operates from a single 15 V Supply and has an inherent pulse inhibit facility. This circuit provides the synchronized firing pulses for both thyristors of the same limb in a bridge. To ensure reliability, wide triggering pulses are used, which are modulated to pass through the pulse transformers1 and demodulated before being fed to the thyristor gates. The use of throe such circuits only for a three-phase bridge is discussed

A Simple Sector Independent Space Vector Modulation Technique Implemented using a DSP Processor

This paper presents the software implementation of a simple sector independent Space Vector Modulation (SVPWM) technique for simple motor drive applications using DSP processor. The closed loop control scheme with Volts/Hz principle, suitably regulates the switching period and operates with a fixed set of compare register values. The Space Vector Pulse Width Modulation hardware module on the TI TMS320LF2407A is used to produce the Pulse Width Modulated (PWM) pulses with four switching states in one PWM period. In contrast to the conventional scheme, the algorithm developed here, eliminates the computational burden involved in determining the magnitude and phase of the voltage vector, the corresponding sector and the decomposition matrices for calculating the switching time segments. A 1hp, 3 phase induction motor fed from an IGBT based inverter module is tested with the control pulses generated from the DSP processor and the performance is found to be satisfactory.DOI: http://dx.doi.org/10.11591/ijpeds.v2i3.34