FSDA: Framework for Secure Data Aggregation in Wireless Sensor Network for Enhancing Key Management

An effective key management plays a crucial role in imposing a resilient security technique in Wireless Sensor Network (WSN). After reviewing the existing approaches of key management, it is confirmed that existing approachs does not offer good coverage on all potential security breaches in WSN. With WSN being essential part of Internet-of-Things (IoT), the existing approaches of key management can definitely not address such security breaches. Therefore, this paper introduces a Framework for Secure Data Aggregation (FSDA) that hybridizes the public key encryption mechanism in order to obtain a novel key management system. The proposed system does not target any specific attacks but is widely applicable for both internal and external attacks in WSN owing to its design principle. The study outcome exhibits that proposed FSDA offers highly reduced computational burden, minimal delay, less energy consumption, and higher data transmission perforance in contrast to frequency used encryption schemes in WSN

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

A survey of handwritten character recognition with MNIST and EMNIST

This article belongs to the Special Issue Computer Vision and Pattern Recognition in the Era of Deep Learning.This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset for handwritten digit recognition. This dataset has been extensively used to validate novel techniques in computer vision, and in recent years, many authors have explored the performance of convolutional neural networks (CNNs) and other deep learning techniques over this dataset. To the best of our knowledge, this paper is the first exhaustive and updated review of this dataset; there are some online rankings, but they are outdated, and most published papers survey only closely related works, omitting most of the literature. This paper makes a distinction between those works using some kind of data augmentation and works using the original dataset out-of-the-box. Also, works using CNNs are reported separately; as they are becoming the state-of-the-art approach for solving this problem. Nowadays, a significant amount of works have attained a test error rate smaller than 1% on this dataset; which is becoming non-challenging. By mid-2017, a new dataset was introduced: EMNIST, which involves both digits and letters, with a larger amount of data acquired from a database different than MNIST's. In this paper, EMNIST is explained and some results are surveyed

Through Wall Imaging Radar Antenna with a Focus on Opening New Research Avenues

This review paper is an effort to develop insight into the development in antennas for through wall imaging radar application. Review on literature on antennas for use in through wall imaging radar, fulfilling one or more requirements/specifications such as ultrawide bandwidth, stable and high gain, stable unidirectional radiation pattern, wide scanning angle, compactness ensuring portability and facilitating real-time efficient and simple imaging is presented. The review covers variants of Vivaldi, Bow tie, Horn, Spiral, Patch and Magneto-electric dipole antennas demonstrated as suitable antennas for the through wall imaging radar application. With an aim to open new research avenues for making better through wall imaging radar antenna, review on relevant compressive reflector antennas, surface integrated waveguide antennas, plasma antennas, metamaterial antennas and single frequency dynamically configurable meta-surface antennas are incorporated. The review paper brings out possibilities of designing an optimum through wall imaging radar antenna and prospects of future research on the antenna to improve radiation pattern and facilitate overall simple and efficient imaging by the through wall imaging radar

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path

Random forest algorithm use for crop recommendation

The proposed method seeks to assist Indian pleasant in selecting the optimum crop to produce based on the characteristics of the soil as well as external factors like temperature and rainfall by using an intelligent system called Crop Recommender. The Indian economy is significantly impacted by the agricultural sector. Whether publicly or covertly, the bulk of Indians are relying on agriculture for their living. As a result, it is undeniable that agriculture is significant to the country. The majority of Indian farmers believe that they should trust their intuition when deciding on a crop to grow in a particular season or they simply employ the methods they have been doing from the beginning of time. They are more at ease just adhering to conventional agricultural practices and standards than truly appreciating how crop yield is influenced by the present weather and soil conditions. The farmer can unintentionally lose money if he makes one bad decision, which would hurt both him and the surrounding agricultural industry. As the agriculture business is the foundation of the entire lateral system. Using the machine learning algorithm, this problem can be resolved. A crucial perspective for identifying a practical and workable solution to the crop production issue is machine learning (ML). Machine learning (ML) may predict a target or outcome from a set of predictors using supervised learning. A recommendation system is implemented using decision trees. The major goals of this system are to provide farmers with recommendations regarding the best crops to sow based on their soil and local rainfall patterns. We have employed the Random Forest Machine Learning technique to forecast the crop. Crop prediction is assessing the crop based on historical data from the past that includes elements like temperature, humidity, ph, and rainfall. It gives us a broad picture of the best crop that can be raised in light of the current field weather conditions. These predictions can be made by Random Forest, a machine learning technique. The highest level of accuracy, up to 90%, will be possible for crop predictions. The random forest algorithm achieved the accuracy about 99.03%

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

Facilitating higher photovoltaic penetration in residential distribution networks using demand side management and active voltage control

Abstract Future power networks are certain to have high penetrations of renewable distributed generation such as photovoltaics (PV). At times of high PV generation and low customer demand (e.g., summer), network voltage is likely to rise beyond limits mandated by grid codes resulting in a curtailment of PV generation, unless appropriate control means are used. This leads to a reduction in energy yield and consequently reduces the economic viability of PV systems. This work focuses on scenario‐based impact assessments underpinned by a net prosumer load forecasting framework as part of power system planning to aid sustainable energy policymaking. Based on use‐case scenarios, the efficacy of smart grid solutions demand side management (DSM) and Active Voltage Control in maximizing PV energy yield and therefore revenue returns for prosumers and avoided costs for distribution networks between a developed country (the UK) and developing country (India) is analyzed. The results showed that while DSM could be a preferred means because of its potential for deployment via holistic demand response schemes for India and similar developing nations, technically the combination of the weaker low voltage network with significantly higher solar resource meant that it is not effective in preventing PV energy curtailment