Development of an Embedded Smart Home System

Smart home systems are expected to become key research area for ubiquitous and embedded system computing in coming years. In this thesis, a new scheme in smart home systems technology using embedded system for providing intelligent control of home appliances is proposed. An embedded system act as protocol glue that incorporates wired and wireless option such as Short Message Service (SMS) router with wireless local area network (WI-FI) for intelligent automation and higher speed of home appliances connectivity. The system is implemented in 2 tier models. First-tier model consist of incorporated design of SMS Router and Wireless Access Point. Wireless local area network (WI-FI) is selected as mechanism due to its transmission range within 100m which suits the smart home requirement for automation and control, justifies the Personal Area Network (PAN) for mobile device connectivity. Second tier model consist of remote application server systems, which cater a conceptual model between embedded hardware and software integration of appliances in smart home. This interface model will be between in house networks and external communication environment, whereas embedded system acts as storage media and server for information interchange between systems especially with mobile devices within a smart home. Embedded system sits at the core of the home network, acts as residential gateway and enables bi-directional communication and data transfer channel among networked appliances in the home and across the Internet. On the other hand, client-side application provides a user-friendly Graphic User Interface (GUI) to enhance the usability of the system. The proposed embedded system has been implemented and verified that the system can be a core device for smart home environment functionality

FPGA implementation of handwritten number recognition using artificial neural network

Implementation of Deep Learning and Machine Learning Algorithms is always a challenge as they consume a lot of resources and power. In this paper, we have presented a very simple yet efficient way for creating an IP (intellectual property) core for Handwritten Number Recognition for FPGAs. The proposed ANN was verified and compared with several ANN networks on MATLAB, which gave the accuracy of about 99.38%. This network was implemented on Xilinx Zybo board XC7Z010CLG400-1. The total area covered by the IP block is 27.9%. The IP created is efficient and uses fewer resources thus suitable for other embedded applications

Random k-labelsets method for human activity recognition with multi-sensor data in smart home

In today's world, we are surrounded by ambient sensors everywhere that record our data of activities of daily living. Moreover, the solutions to various applications such as health care, surveillance, home monitoring, and so on are possible by inferring this data. Thus, human activity recognition, especially in the smart home environment, has been a very actively researched problem. Multiple residents in a single home environment pose several challenges making multi-resident activity recognition a daunting task. Therefore, in this paper, we model the Random k-Labelsets method of the Multi-Label Classification to tackle this activity recognition problem. The proposed method not only takes label dependencies into account which is essential for multi-resident activity recognition but also overcomes the drawbacks of other problem transformation methods. Experiments are carried on a real smart home dataset and accuracy, precision and hamming loss are selected as metrics for evaluating the results of the proposed method

Enhancing road safety through accurate detection of hazardous driving behaviors with graph convolutional recurrent networks

Car accidents remain a significant public safety issue worldwide, with the majority of them attributed to driver errors stemming from inadequate driving knowledge, non-compliance with regulations, and poor driving habits. To increase road safety, several studies proposed Driving Behavior Detection (DBD) systems that can differentiate between safe and unsafe driving behavior. Many of these papers used the sensor information retrieved from the CAN (Controller Area Network) bus to construct their models. According to the existing literature, using public sensors reduces the detection model's accuracy while adding vendor-specific sensors into the data increases the accuracy. However, the earlier techniques' utility is limited by the use of non-public sensors. As a result, this paper presents a reliable DBD system based on Graph Convolutional Long Short-Term Memory networks in order to improve the detection model's precision and practical usability for public sensors. Additionally, non-public sensors were utilized to assess the model's effectiveness. The proposed model achieved an accuracy of 97.5% for public sensors and an average accuracy of 98.1% for non-public sensors, which shows that the proposed model can produce consistent and accurate results for both scenarios. The proposed DBD system deployed on Raspberry Pi at the network edge to analyze the driver's driving behavior locally. Drivers can access daily driving condition reports, sensor data, and prediction results from the DBD system through the monitoring dashboard. A voice warning from the dashboard also warns drivers of hazardous driving conditions.</p

IoT device management framework for smart home scenarios

The paradigm of the Internet of Things (IoT) requires pervasive connectivity to billions of heterogeneous devices. In recent time, rapid growth of IoT devices in smart home environment envisioned a wide range of novel services and applications. However, due to the inherent heterogeneity, home environment is becoming complex making device management extremely difficult. This paper proposes a lightweight IoT device management framework for smart home services. The framework can be deployed at home gateways and consumer smart devices. A prototype implementation and performance evaluation results are also presented

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

Deep learning for multi-resident activity recognition in ambient sensing smart homes

Advances in smart home technology and IoT devices has enabled us for monitoring of human activities for their health status and efficient energy consumption. Machine learning has been a great tool for the prediction of human activities. However, Multi-resident activity recognition is still a challenge as there is no direct correlation between sensor values and resident activities. In this paper, we have displayed the state of art deep learning algorithms on the real-world ARAS multi-resident dataset, which consists of data from two houses each with two residents. We have used different variations of RNN on the dataset and measured their performance with fewer data and more data and with data generated with GAN

FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud

Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability

Internet of Things (IoT) enabled water monitoring system

Water is always a crucial part of everyday life. Due to global environmental situation, water management and conservation is vital for human survival. In recent times, there were huge needs of consumer based humanitarian projects that could be rapidly developed using Internet of Things (IoT) technology. In this paper, we propose an IoT based water monitoring system that measures water level in real-time. Our prototype is based on idea that the level of the water can be very important parameter when it comes to the flood occurrences especially in disaster prone areas. A water level sensor is used to detect the desired parameter, and if the water level reaches the parameter, the signal will be feed in realtime to social network like Twitter. A cloud server was configured as data repository. The measurement of the water levels are displayed in remote dashboard

Interoperability Framework for Smart Home Systems.

Recent advancements in smart home systems have increased the utilization of consumer devices and appliances in home environment. However, many of these devices and appliances exhibit certain degree of heterogeneity and do not adapt towards joint execution of operation. Hence, it is rather difficult to perform interoperation especially to realize desired services preferred by home users. In this paper, we propose a new intelligent interoperability framework for smart home systems execution as well as coordinating them in a federated manner. The framework core is based on Simple Object Access Protocol (SOAP) technology that provides platform independent interoperation among heterogeneous systems. We have implemented the interoperability framework with several home devices to demonstrate their effectiveness for interoperation. The performance of the framework was tested in Local Area Network (LAN) environment and proves to be reliable in smart home settin