Thinagaran Perumal

A rule-based framework for heterogeneous subsystems management in smart home environment

Recent advancements in computing and communication technologies have increased the growth of heterogeneous subsystems in smart home environment. However, many of these heterogeneous systems are standalone and do not adapt towards joint execution of tasks. Hence, it is rather difficult to perform interoperation especially to realize desired services preferred by home dwellers. In this paper, we propose a new rule-based framework for heterogeneous systems management as well as coordinating them by means of federated manner in smart home environment. The proposed framework is based on event-condition-action (ECA) rule mechanism with SOAP technology that provides interoperability among those systems. We have implemented the framework with several subsystems to demonstrate their effectiveness for interoperation using ECA rule mechanism. The performance of the framework was tested in LAN environment and proves to be reliable in smart home setting.

Interoperability among Heterogeneous Systems in Smart Home Environment

The smart home environment is highly characterized by heterogeneity with many systems that need to interoperate and perform their tasks efficiently. With rapid growth of services, applications and devices in smart home environment, the interoperability factor seems still elusive. This is due to the nature of smart home as distributed architecture that needs certain degree of interoperability and interoperation for managing heterogeneous systems comprising of different platforms. These heterogeneous systems are developed in isolation and consist of different operating systems, different programming platform and different tier of services. There is need for a mechanism that could make the heterogeneous systems `talk¿ each other and interoperate in an efficient manner regardless of operating platform. Web services seems to be state-of-the art technology that could be one potential solution in providing greater interoperability. In this paper we describe interoperability issues that need to be considered and we present a solution based on Simple Object Access Protocol (SOAP) technology to solve the interoperability problem in smart home environment.

Design and implementation of SOAP-based residential management for smart home systems

This paper presents the design and implementation of SOAP-based residential management for smart home systems appliances control. An appliance control module based on SOAP and Web Services developed to solve the interoperation of various home appliances in smart home systems. 15 feedback based control channels implemented with residential management system through Web Services. If the residential management system experiences server downtime, the home appliances can still be controlled using alternate control mechanism with GSM network via SMS Module locally and remotely. This system offers a complete, bi-directional real-time control and monitoring of smart home systems.

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 setting1.

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.

Proactive architecture for Internet of Things (IoTs) management in smart homes

Smart homes are driven by heterogeneity in nature and consist of diverse components that promote user comfort and security. In recent times, tremendous growth of Internet of Things (IoTs) applications is seen in smart homes. The huge diversity of various IoTs applications generally leads to interoperability requirements that need to be fulfilled. Current IoTs management is achieved using physical platforms that lack intelligence on decision making. A proactive architecture that deploys Event-Condition-Action (ECA) method is proposed to resolve the management of heterogeneous IoTs in smart homes. The proactive architecture, developed with a core repository stores persistent data of IoTs schema, proved to be an ideal solution in solving interoperability in smart homes.

Activity recognition based on accelerometer sensor using combinational classifiers

In recent years, people nowadays easily to contact each other by using smartphone. Most of the smartphone now embedded with inertial sensors such accelerometer, gyroscope, magnetic sensors, GPS and vision sensors. Furthermore, various researchers now dealing with this kind of sensors to recognize human activities incorporate with machine learning algorithm not only in the field of medical diagnosis, forecasting, security and for better live being as well. Activity recognition using various smartphone sensors can be considered as a one of the crucial tasks that needs to be studied. In this paper, we proposed various combination classifiers models consists of J48, Multi-layer Perceptron and Logistic Regression to capture the smoothest activity with higher frequency of the result using vote algorithmn. The aim of this study is to evaluate the performance of recognition the six activities using ensemble approach. Publicly accelerometer dataset obtained from Wireless Sensor Data Mining (WISDM) lab has been used in this study. The result of classification was validated using 10-fold cross validation algorithm in order to make sure all the experiments perform well.

A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification

Class imbalanced data set is a state where each class of the given data set is not evenly distributed. When such case happens, most standard classifiers fail to recognize examples that belong to a minority class. Hence, several methods have been proposed to solve this problem such as resampling, modification on classifier optimization problem or introducing a new optimization task on top of the classifier. This work proposes a new optimization task based on genetic programming, built on top of support vector machine, in order to improve the classification rate for minority class without significant reduction on accuracy metric. The experimentation carried out on wilt disease data set shows the new classifier, support vector based on genetic programming machine, gives a more balanced accuracy between classes compared to various classification techniques in solving the imbalanced classification problem.

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.

Improving Anomalous Rare Attack Detection Rate for Intrusion Detection System Using Support Vector Machine and Genetic Programming

Commonly addressed problem in intrusion detection system (IDS) research works that employed NSL-KDD dataset is to improve the rare attacks detection rate. However, some of the rare attacks are hard to be recognized by the IDS model due to their patterns are totally missing from the training set, hence, reducing the rare attacks detection rate. This problem of missing rare attacks can be defined as anomalous rare attacks and hardly been solved in IDS literature. Hence, in this letter, we proposed a new classifier to improve the anomalous attacks detection rate based on support vector machine (SVM) and genetic programming (GP). Based on the experimental results, our classifier, GPSVM, managed to get higher detection rate on the anomalous rare attacks, without significant reduction on the overall accuracy. This is because, GPSVM optimization task is to ensure the accuracy is balanced between classes without reducing the generalization property of SVM.