Internet of things have many applications, one of them is smart homes. The main drawback of IoT that depends on a centralized cloud. The Blockchain approach with a decentralized technique will solve the centralized cloud approach. Smart homes have many security issues, threats, attacks, user privacy, integrity, etc. Blockchain is playing role in many IoT applications also the solution of these security problems. This paper explains the Blockchain in smart home with the main three tiers, how Blockchain can help to secure the data, transaction, also describe the security analysis in IoT smart homes.

Advanced survey of Blockchain for the Internet of Things Smart Home

The term Internet of Things (IoT) represents all communicating countless heterogeneous devices to share data and resources via the internet. The speedy advance of IoT devices proposes limitless benefits, but it also brings new challenges regarding security and forensics. Likewise, IoT devices can generate a massive amount of data that desires integrity and security during its handling and processing in an efficient way. IoT devices and data can be vulnerable to various types of cyber-crimes at each IoT layer. For combating these cyber-crimes in IoT infrastructure, IoT forensic term has shown up. The IoT forensic is the process of performing digital forensic investigation in the IoT environment in a forensically sound and timely fashion manner. Sundry challenges face the IoT forensics that requires urgent solutions and mitigation methods; digital evidence needs to be collected, preserved, analyzed, processed, and reported in a trusted manner to be acceptable for presenting in the court of law. Preserving the evidence unchanged or tampered with is the most critical challenge in digital forensics. Authentication is another challenge facing digital forensics; who is allowed to deal with the evidence? One of the most recent solutions for supporting IoT forensics is the use of Blockchain. Using Blockchain in digital forensics guarantees data integrity, immutability, scalability, and security. Therefore, this paper presents a comprehensive review of IoT security and forensics with the integration with Blockchain technology. It begins by providing an inclusive discussion of IoT security, as well as the need for IoT forensics, and the concepts of Blockchain. Then, a review of Blockchain-based IoT security and forensics issues is presented. Finally, a discussion of open research directions is provided.

A review study on blockchain-based IoT security and forensics

Towards energy-aware tinyML on battery-less IoT devices

A low-cost real-time IoT human activity recognition system based on wearable sensor and the supervised learning algorithms

—The taxi services are growing rapidly as reliable services. The demand and competition between service providers is so high. A billion trip records need to be analyzed to raise the spirit of competition, understand the service users, and improve the business. Although decision tree classification is a common algorithm which generates rules that are easy to understand, there is no implementation for classification on taxi dataset. This research applies the decision tree classification model on taxi dataset to classify instances correctly, build a decision tree, and calculate accuracy. This experiment collected decision tree algorithm with Spark framework to present the good performance and high accuracy when predicting payment type. Applied decision tree algorithm with different aspects on NYC taxi dataset results in high accuracy.

Using Decision Tree Classification Model to Predict Payment Type in NYC Yellow Taxi

Human Activity Recognition has been a favorite topic for the scholars not only because of its wide scale acceptance in the industry but areas which may help in medical and in our normal household works as well. Since to make this technology available to the last person standing in the queue it is important that models compiled and trained in this field are not just high performing but optimized as such with incurs the least overhead. And thus bringing TinyML into the picture which has specialty in the field of optimizing the model w.r.t. the size of the model, energy consumption, network bandwidth usage etc. Thus this work includes using optimizing techniques such as pruning and quantization on the pre-proposed models and analyze the changes it causes in such models w.r.t accuracy and size. Our work is able infer that by using both Pruning and Quantization techniques on a human activity recognition model we can compress a model up to 10 time without hampering severe diversion to the accuracy of the model. We have taken three models and UCI-HAR dataset and compare the outcomes of the experiment.

https://iopscience.iop.org/article/10.1088/1742-6596/2273/1/012025/pdf

A TinyML Approach to Human Activity Recognition

Internet of Things aims to build a new future of computing by taking every smart object viz. sensors, actuators, RFID tags etc., into a network that is connected globally and which can sense, communicate and share information. Earlier only few devices could be connected to the internet or intranet but the recent developments in the past few years in RFID, NFC, sensors and IPV6 has led to connection of a very large number of heterogeneous devices to the internet. However the number of devices being connected to the network is proportional to the security risks associated with it. Providing security features in IoT has become the concern of various researchers. Home automation system is one of the many applications of IoT. It allows controlling and accessing the home appliances from any corner of the world through Internet. Several security issues are associated with smart home because of its dynamic and heterogeneous nature. In this paper we have presented some security challenges and issues in the home automation system that we analyzed from various research papers.

A Survey on Security Issues and Challenges in IoT Based Smart Home

With the improvement of technology, internet shopping has expanded tremendously across the world. Customers may now buy products from anywhere and at any time due to the online shopping revolution. Virtual Trial Room is a 2D implementation of an e-commerce shopping experience in which the customer may try on clothing in front of the camera before purchasing it. It creates a virtual system in which ecommerce consumers may try on clothing items before making purchases without having to put them on in person. During the pandemic, online purchasing has just seen a significant increase. When all offline purchasing came to a standstill. The virtual trial room is the most recent innovation that has enhanced the customer's hopping experience to a larger extent. Customers imagine themselves when trying on several outfits. It provides a clearer picture of the clothing the customer wants to purchase, resulting in higher customer satisfaction.

A Review on Virtual Reality for 3D Virtual Trial Room

AbstractThis paper investigates the application of a set of machine learning algorithms to predict the time that will be spent inside a vehicle between any two locations. The ride duration is estimated by analyzing data collected from historical traces of taxis, i.e., Jan 2015 New York City (NYC) Yellow Cab trip record data. Moreover, the taxi data is integrated with Uber dataset to estimate time accurately taking into account a set of semantic variables. Nevertheless, these semantic variables are selected through outlier detection and feature selection using Chi-Square scores. Features such as pick-up latitude and longitude, drop-off latitude and longitude, pick-up date, pick-up time, etc., are considered for prediction purposes in NYC dataset. Mainly, the forecasting effectiveness is compared for three machine learning models, namely, Decision Tree Regression (DTR), Random Forest Regression (RFR), and K-Nearest Neighbor Regression (KNNR). It is found that RFR and KNNR are the favorites for this travel time prediction. Also, our result supersedes the best performance of Kaggle competition.KeywordsTravel timeRegressionNYC Yellow CabMobilityFeature selection 

Predicting Taxi Travel Time Using Machine Learning Techniques Considering Weekend and Holidays

Image based virtual trial room technologies are used for integrating modern in-store clothes into a person image which have caught the interest of research as well as representatives of the multimedia and computer vision communities. However, it’s indeed challenging. However, most existing image-based virtual trial room techniques combine both person and in-store clothes images without taking into consideration of mutual relation. An ideal process will not only change the target clothing into the best suitable shape but it will maintain the cloth uniqueness in the resulting image like color, shade, logos and texture of the material that represent the primary clothes. Prior Generative Adversarial Network (GAN) approaches failed to achieve the above essential performance requirements for realistic virtual trial room performance because they do-not manage considerable spatial misalignment between the primary image and targeted cloth. We present a novel fully-learn-able 3D grid virtual trial room for overcoming all significant barriers in this project. In the first stage, it performs an affine transformation and then thin plate spline transformation for matching the in-store clothes to the target person’s body shape using the Geometry Matching Component. As a result, the warped clothes in the shop appear more realistic. We use Transformation Guided Component that generates a composition mask to blend the warped garments and the image produced is guarantee smoothness, which reduces borderline distortions of warped clothes and creates its outcomes more realistic. Numerous trials on the fashion data-set show that our model delivers decent performance in both qualitative and quantitative terms.

3D Grid Based Virtual Trial Room

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