Initial boundary value problem of heat conduction equation derived from Cattaneo\u27s law

An initial boundary value problem of hyperbolic partial differential equation derived from Cattaneo’s law for heat conduction is considered. Obtained results consist of the well-posedness, decay estimate of the energy and relaxation limit to the classical heat equation due to Fourier’s law.Moreover, we will consider a finite difference approximation for the problem

Elgar framework: context-aware service orchestration with data Petri net

The Internet of Things is composed of many heterogeneous devices and services. In general, the phase of orchestrating different devices in order to allow interoperability in different environment is difficult. This is because most IoT services are not reusable because of data interpretation and service interoperability problem. In this research, we embrace the concept of design once, deploy anywhere for IoT services. We proposed two major methods which are (i) modeling IoT services with data-aware service model and (ii) semantical approach using context ontology to support service orchestration. Finally, we showed that IoT services with various ontologies can be composed based on our orchestration method

IoT based activity recognition among smart home residents

Activity recognition in smart home environment is actively pursued for accessing changes in physical and behavioral profiles of home dwellers. Various activity recognition solutions have been previously proposed to implement a system with wearable sensors and smartphones. Although such solutions are widely integrated, the availability of the activity data in seamless way still poses interesting research challenges. Internet of Things (IoT) is seen as new paradigm, revolutionizing consumer electronics by extending Internet connectivity to many physical objects associated with consumer's daily life. In this paper, an Internet of Things (IoT) based activity recognition framework is proposed for activity monitoring within consumer home network. Our proposed Elgar framework handles management of activity recognition via IoT services in an IoT environment with multiple devices. The performance evaluation done pointed that the proposed system can robustly identify the activities using IoT in smart home environment with high accuracy. Hence, this system could be reliably deployed into a consumer product for the usage of home dwellers

Semantic interoperability test method for data schema comparison with constrained application protocol

The main objective of this paper is to verify and propose schema compatibility as one of the main interoperability step for devices in the communication level of IoT system. The communication level requires two or more devices that are going to send or received data with each other to have the same data schema. We introduced a test scenario between the devices based on Constrained Application Protocol (CoAP). Then, we considered some scenarios like smart car, smart industry, smart home and smart city of data schema with different versions(core,V1.0,V2.0 and V3.0) of json structure and its comparison method whether they have the same data schema or not. We also proposed an algorithm for schema comparison and tested the processing time for the comparison of data schema

IoT device management using semantics for distinguishing device compatibility

The main objective of this paper is to achieve the device compatibility between multiple devices which are defined in the ontology. For example if a temperature sensor D1 wants to communicate with the actuator fan D2 due to their different data formats and defined properties they are unable to communicate with each other. The interlinking of data derived from different devices used to create semantics for interoperation with a shared meaning. So we are trying to solve the device compatibility problem by using their semantics. To achieve the semantic interoperability we described a ontology module. In this paper we proposed an ontology model using semantics for distinguishing device capability and also the query execution time for the devices. This ontology model is designed for adaptable IoT systems

Sentiment analysis on UTHM issues with big data

Nowadays, social media platform such as Twitter, WhatsApp, Facebook and it Messenger, as well as Instagram plays a very importance role to the society. Twitter is a micro-blogging platform that is able to provide a remarkable amount of data that can be used in several number of sentiment analysis applications such as predictions, reviews, and elections. Sentiment Analysis is a process of extracting information of issues or specific topic from enormous amount of data and categorizes it into different classes. The main target of this project is to classify Twitter data into sentiments value either positive, neutral or negative on data collected regarding Universiti Tun Hussein Onn Malaysia (UTHM) issues. This sentiment was classified using sentiment classifier, while data is trained on a Naïve Bayes Classifier, on TextBlob Python library. Lastly, results were displayed to the user, through a web application using Jupyter Notebook. This study found out that the percentage for positive, neutral and negative tweets regarding UTHM issues were 74%, 26% and 0% in English tweets, meanwhile 17%, 82% and 1 % of Bahasa Melayu tweets, respectively. Positive and neutral sentiments analysis shows positive perception of the products and services, thus promoting and branding UTHM worldwide

A Survey on Multi-Resident Activity Recognition in Smart Environments

Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and improving energy efficiency. However, there are several challenges that must be addressed in order to effectively utilize HAR systems in multi-resident environments. One of the key challenges is accurately associating sensor observations with the identities of the individuals involved, which can be particularly difficult when residents are engaging in complex and collaborative activities. This paper provides a brief overview of the design and implementation of HAR systems, including a summary of the various data collection devices and approaches used for human activity identification. It also reviews previous research on the use of these systems in multi-resident environments and offers conclusions on the current state of the art in the field.Comment: 16 pages, to appear in Evolution of Information, Communication and Computing Systems (EICCS) Book Serie

Sentiment Analysis on UTHM Issues with Big Data

Nowadays, social media platform such as Twitter, WhatsApp, Facebook and it Messenger, as well as Instagram plays a very importance role to the society. Twitter is a micro-blogging platform that is able to provide a remarkable amount of data that can be used in several number of sentiment analysis applications such as predictions, reviews, and elections. Sentiment Analysis is a process of extracting information of issues or specific topic from enormous amount of data and categorizes it into different classes. The main target of this project is to classify Twitter data into sentiments value either positive, neutral or negative on data collected regarding Universiti Tun Hussein Onn Malaysia (UTHM) issues. This sentiment was classified using sentiment classifier, while data is trained on a Naïve Bayes Classifier, on TextBlob Python library. Lastly, results were displayed to the user, through a web application using Jupyter Notebook. This study found out that the percentage for positive, neutral and negative tweets regarding UTHM issues were 74%, 26% and 0% in English tweets, meanwhile 17%, 82% and 1 % of Bahasa Melayu tweets, respectively. Positive and neutral sentiments analysis shows positive perception of the products and services, thus promoting and branding UTHM worldwide