The dense deployment of the wireless sensor networks has caused enormous data flow leading to a Big-Data generation. To enable a continuous transmission for the huge volume of data packets, it becomes necessary to readapt the routing protocol to facilitate the routing in handling the Big-data scenario. Since energy is the main constraint in the wireless sensor network, as the sensor are battery powered, and the routing methods consuming enormous of the energy for route discovery thereby reducing the network life time, many conventional methods were developed to address the problem of energy consumption with the, increased network latency, overhead, security issues and delay. So the paper proposes the energy-aware routing protocol that could handle the security issues, arising in the enormous data generation satisfying the QOS requirements. The node with substantial resources are selected to overcome the problems of energy consumption and the network life time handling the security issues , delay, network latency and the overhead problem. Further validation of the proposed method in the Network Simulator-3 is performed to evaluate the network latency, packet delivery ratio, throughput, power consumption, network lifetime and Cost.

https://www.irojournals.com/iroismac/V1/I1/04.pdf

Energy-aware security routing protocol for wsn in big-data applications

Iris recognition is considered as the most secure biometric application. A robust iris recognition system involves many crucial steps in implementation. Edge estimation of iris biometric image is the initial critical step for any iris recognition technique for iris localization. Many algorithms have already been suggested for iris localization; but for better accuracy, robustness and freedom to extract the desired edges quickly, still requires novel iris recognition approaches. This paper presents a distinct approach for iris recognition by extraction of edges using fuzzy logic approach followed by iris localization using Circular Hough Transform (CHT). The proposed approach results into improved robustness of iris detection due to the efficient edge detection. It also improves localization due to ability of CHT to detect even partially visible iris. Computational speed is also improved using the simpler iris feature extraction and template matching. The proposed approach was experimented on MMU1, IITD, and UTIRIS iris biometric databases. Results show iris localization is achieved with greater accuracy at lower computational time.

A Robust Iris Recognition Approach Using Fuzzy Edge Processing Technique

Social Internet of Things (SIoT) is a paradigm of IoT where in objects are able to build social relationship among each other based on user preferences there by creating social platform. To establish relationship, diversified IoT devices interact and setup a connection between them. The relationship is built by considering same features, attribute, device type etc. The data generated by the heterogeneous devices of SIoT devices are huge and it has to be efficiently used. There exist few works related to data aggregation in SIoT in the literature. Hence, in this work a method is proposed to aggregate the SIoT data and conditions used to classify the relationship between the devices. The performance of Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Artificial Neural Network (ANN) machine learning algorithms are tested on the dataset. The experimental results show that DT and ANN algorithms performs well compared to other ML algorithms.

Performance comparison of machine learning algorithms for data aggregation in social internet of things

Continuous monitoring of chemical signals in plants under stress

Iris image segmentation process based on graphical user interface (GUI) to accurately localize the iris structure is presented in this paper. The major challenge confronting the precision of an iris recognition model is how to determine the accuracy of the iris segmentation and localization. There are varying parameters that introduce constraints during feature extraction and these greatly affect the matching performance during iris localization. To this end, the Integro-differential operator, which involves the detection of inner and outer regions of the iris, and the circular hough transform, which is capable of detecting the circular boundary from the edge mapping were investigated, and an active contour model was evolved. In the evolved model, an emerging curve mapped with the zeros of the data set function is experimentally exploited. To demonstrate the suitability of the model for precise iris recognition, its parameters were compared against other related models. Simulation results show that the model has higher flexibility of substitution of images, and the images could be analyzed more accurately with less false rejections (FR) and false acceptance (FA) in comparison with the integro-differential operator. This implies that images could be analyzed faster using the evolved model, and easily substituted especially in situations where the need to care for numerous eye patients occur.

/pdf/investigation-of-iris-segmentation-techniques-using-active-ql0gplq7y3.pdf

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. 

Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms

Iris Recognition is one of the most challenging and fastest growing areas in the field of biometrics. This paper focuses about the brief summary of iris recognition system for identification and verification of iris images. This paper also provides the Comparative analysis of various preprocessing approaches such as Adaptive Histogram Equalization (AHE), Histogram Equalization (HE), Gamma, Log etc. Feature Extraction algorithms namely Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP), HAAR transform and so on., for extracting the iris features and classification algorithms such as Support Vector Machine (SVM), Euclidean Distance (ED), Nearest Neighbor (NN) and so on. The popular public databases such as UPOL and CASIA were available to carry out the experiments. The results obtained by various algorithms/methods provide better accuracy in terms of their recognition rate.

Comparative survey of iris recognition

The use of wireless sensor networks has been increasing tremendously in the past decades mainly because of its applications in military, medicine, under water survey and various other fields. Depending on the applications the sensor nodes are placed in different areas and the data sensed will be sent to the base station. The process of transmitting and receiving data sensed by the sensor nodes continues till the sensors have battery life. This leads to generate data redundancy and reduces efficiency of the network. In order to overcome the limitations faced by the wireless sensor networks, the fusion of data known as data aggregation is introduced. In data aggregation, the data sensed by the various nodes are aggregated and sent to the base station as a single data packet. In this paper, a brief review on various data aggregation methods, challenges and issues are addressed. In addition to this, performance parameters of various data fusion methods to measure the efficiency of the network are discussed. The design of single aggregator models are easy compared to the multiple aggregator models. However, the security to most of the data fusion schemes is provided by using message authentication code. It also uses public keys and symmetric to achieve end to end or hop by hop encryptions.

/pdf/comprehensive-study-of-data-aggregation-models-challenges-3m4ls503gv.pdf

Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks

Technical Challenges, Performance Metrics and Advancements in Face Recognition System

The proposed approach uses ResNet-18 for feature extraction and with the help of temporal interest proposals generated for the video sequences, generates a video summary. The ResNet-18 is a convolutional neural network with eighteen layers. The existing methods don't address the problem of the summary being temporally consistent. The proposed work aims to create a temporally consistent summary. The classification and regression module are implemented to get fixed length inputs of the combined features. After this, the non-maximum suppression algorithm is applied to reduce the redundancy and remove the video segments having poor quality and low confidence-scores. Video summaries are generated using the kernel temporal segmentation (KTS) algorithm which converts a given video segment into video shots. The two standard datasets TVSum and SumMe are used to evaluate the proposed model. It is seen that the F-score obtained on TVSum and SumMe dataset is 56.13 and 45.06 respectively. 

Veena I Puranikmath

Papers

Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms

Comparative survey of iris recognition

Comprehensive Study of Data Aggregation Models, Challenges and Security Issues in Wireless Sensor Networks

Technical Challenges, Performance Metrics and Advancements in Face Recognition System

Robust Video Summarization Algorithm using Supervised Machine Learning