Journal of Trends in Computer Science and Smart Technology - September 2019
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Published By Inventive Research Organization

2582-4104
Updated Wednesday, 28 July 2021

Author(s):  
Suma V

The Internet of Things [IoT] is one of the most recent technologies that has influenced the way people communicate. With its growth, IoT encounters a number of challenges, including device heterogeneity, energy construction, comparability, and security. Energy and security are important considerations when transmitting data via edge networks and IoT. Interference with data in an IoT network might occur unintentionally or on purpose by malicious attackers, and it will have a significant impact in real time. To address the security problems, the suggested solution incorporates software defined networking (SDN) and blockchain. In particular, this research work has introduced an energy efficient and secure blockchain-enabled architecture using SDN controllers that are operating on a novel routing methodology in IoT. To establish communication between the IoT devices, private and public blockchain are used for eliminating Proof of Work (POW). This enables blockchain to be a suitable resource-constrained protocol for establishing an efficient communication. Experimental observation indicates that, an algorithm based on routing protocol will have low energy consumption, lower delay and higher throughput, when compared with other classic routing algorithms.


Author(s):  
Joy Iong-Zong Chen ◽  
S Smys

In recent years, both developed and developing countries have witnessed an increase in the number of traffic accidents. Aside from a significant rise in the overall number of on-road commercial and non-commercial vehicles, advancements in transportation infrastructure and on-road technologies may result in road accidents, which generally result in high mortality. More than half of these fatalities are the result of delayed response by medical and rescue personnel. If an accident site receives quick medical treatment, an accident victim's chances of survival may improve considerably. Based on the IoT-based multiple-level vehicle environment, this study proposes a low-cost accident detection and alarm system. Vehicles are equipped with a "Black Box" board unit and an accident location identification module for the Global Positioning System (GPS), in addition to mechanical sensors (accelerometer, gyroscope) for accurate accident detection. This study has evaluated the proposed system with average packet delivery ratio (PDR) vs. relay nodes. Our simulation results have evaluated the evolution of relay nodes in the mobile / sensor node through internet gateway. It has also been demonstrated that the packet delivery ratio is inversely related to the incremental number of relay nodes.


Author(s):  
R Dhaya

The automated captioning of natural images with appropriate descriptions is an intriguing and complicated task in the field of image processing. On the other hand, Deep learning, which combines computer vision with natural language, has emerged in recent years. Image emphasization is a record file representation that allows a computer to understand the visual information of an image in one or more words. When it comes to connecting high-quality images, the expressive process not only requires the credentials of the primary item and scene but also the ability to analyse the status, physical characteristics, and connections. Many traditional algorithms substitute the image to the front image. The image characteristics are dynamic depending on the ambient condition of natural photographs. Image processing techniques fail to extract several characteristics from the specified image. Nonetheless, four properties from the images are accurately described by using our proposed technique. Based on the various filtering layers in the convolutional neural network (CNN), it is an advantage to extract different characteristics. The caption for the image is based on long short term memory (LSTM), which comes under recurrent neural network. In addition, the precise subtitling is compared to current conventional techniques of image processing and different deep learning models. The proposed method is performing well in natural images and web camera based images for traffic analysis. Besides, the proposed algorithm leverages good accuracy and reliable image captioning.


Author(s):  
Kottilingam Kottursamy

The role of facial expression recognition in social science and human-computer interaction has received a lot of attention. Deep learning advancements have resulted in advances in this field, which go beyond human-level accuracy. This article discusses various common deep learning algorithms for emotion recognition, all while utilising the eXnet library for achieving improved accuracy. Memory and computation, on the other hand, have yet to be overcome. Overfitting is an issue with large models. One solution to this challenge is to reduce the generalization error. We employ a novel Convolutional Neural Network (CNN) named eXnet to construct a new CNN model utilising parallel feature extraction. The most recent eXnet (Expression Net) model improves on the previous model's inaccuracy while having many fewer parameters. Data augmentation techniques that have been in use for decades are being utilized with the generalized eXnet. It employs effective ways to reduce overfitting while maintaining overall size under control.


Author(s):  
Akey Sungheetha ◽  
Rajesh Sharma R

Early identification of diabetics using retinopathy images is still a difficult challenge. Many illness diagnosis techniques are accomplished by using the blood vessels present in fundus images. Many conventional methods fail to detect Hard Executes (HE) present in retinopathy images, which are used to determine the severity of diabetes disease. To overcome this challenge, the proposed research work extracts the features by incorporating deep networks through convolution neural networks (CNN). The micro aneurysm may be seen in the early stages of the transformation from normal to sick condition on the images for mild DR. The level of severity of the diabetes condition may be classified by using the confusion matrix detection results. The early detection of the diabetic condition has been achieved through the HE spotted in the blood vessel of an eye by using the proposed CNN framework. The proposed framework is also used to detect a person’s diabetic condition. This article consisting of proof for the accuracy of the proposed framework is higher than other traditional detection algorithms.


Author(s):  
Madhura S ◽  
Disha D ◽  
Deepthi G ◽  
Chinnitaha B

The disasters can be either natural or man-made. Control and management of disaster of any kind is possible in effective and robust way by the implementation of IoT in the system. The objective of implementing IoT into the disaster management system is the quick and effective recovery from the disaster. The various methods that can be deployed after the disaster is outlined through utilization of IoT. This paper gives an insight on the various methods that can be effectively used after the disaster using IoT. The existing techniques are very well monitored and has the ability to react to the situation as per needs, this paper significantly provides the contribution in analyzing these techniques for appropriate disaster management development block.


Author(s):  
Sivaganesan D

Utilization of smart applications in various domains is facilitated pervasively by sensor nodes (SN) that are connected in a wireless manner and a number of smart things. Hazards due to internal and external attacks exist along with the advantages of the smart things and its applications. Security measures are influenced by three main factors namely scalability, latency and network lifespan, without which mitigation of internal attacks is a challenge. The deployment of SN based Internet of things (IoT) is decentralized in nature. However, centralized solutions and security measures are provided by most researchers. A data driven trust mechanism based on blockchain is presented in this paper as a decentralized and energy efficient solution for detection of internal attacks in IoT powered SNs. In grey and black hole attack settings, the message overhead is improved using the proposed model when compared to the existing solutions. In both grey and black hole attacks, the time taken for detection of malicious nodes is also reduced considerably. The network lifetime is improved significantly due to the enhancement of these factors.


Author(s):  
Vijesh Joe C ◽  
Jennifer S. Raj

As the technology revolving around IoT sensors develops in a rapid manner, the subsequent social networks that are essential for the growth of the system will be utilized as a means to filter the objects that are preferred by the consumers. The ultimate purpose of the system is to give the customers personalized recommendations based on their preference. Similarly, the location and orientation will also play a crucial role in identifying the preference of the customer is a more efficient manner. Almost all social networks make use of location information to provide better services to the users based on the research performed. Hence there is a need for developing a recommender system that is dependent on location. In this paper, we have incorporated a recommender system that makes use of recommender algorithm that is personalized to take into consideration the context of the user. The preference of the user is analysed with the help of IoT smart devices like the smart watches, Google home, smart phones, ipads etc. The user preferences are obtained from these devices and will enable the recommender system to gauge the best resources. The results based on evaluation are compared with that of the content-based recommender algorithm and collaborative filtering to enable the recommendation engine’s power.


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