Bhagya Nathali Silva

Internet of Things Based Energy Aware Smart Home Control System

The concept of smart home is widely favored, as it enhances the lifestyle of the residents involving multiple disciplines, i.e., lighting, security, and much more. As the smart home networks continue to grow in size and complexity, it is essential to address a handful among the myriads of challenges related to data loss due to the interference and efficient energy management. In this paper, we propose a smart home control system using a coordinator-based ZigBee networking. The working of the proposed system is three fold: smart interference control system controls the interference caused due to the co-existence of IEEE 802.11x-based wireless local area networks and wireless sensor networks; smart energy control system is developed to integrate sunlight with light source and optimizes the energy consumption of the household appliances by controlling the unnecessary energy demands; and smart management control system to efficiently control the operating time of the electronic appliances. The performance of the proposed smart home is testified through computer simulation. Simulation results show that the proposed smart home system is less affected by the interference and efficient in reducing the energy consumption of the appliances used in a smart home.

A REST-based industrial web of things’ framework for smart warehousing

A Web-oriented architecture using REST framework is proposed to enable warehouse objects to communicate over the Web. In the proposed mechanism, the smart warehouse consists of a data collection module and an administrative module. The former includes a collection of radio-frequency identification sensors to read RFID tags of the products, wireless sensors for operational data collection, and the actuators. It is responsible for the data collection process of products and goods stored in the warehouse as well as the collection of operational parameters, i.e. temperature, humidity, air quality, and pressure. The latter acts as the brain of the smart warehouse by processing data, organizing data, generating events, and executing actions. The warehouse user is provided with the environment to control the functionality of various sensors, things, appliances, and HAVC system of warehouse through the Web. An event decision system is built on top of the Web architecture to control real-time processing of the sensors, things, etc. In the proposed architecture, the smart gateway consists of two layers, namely transport module (TM) and device service module (DSM). The combination of TM and DSM creates the device controller for embedded devices operating on proprietary protocols. The proposed system is simulated and evaluated in various scenarios in context of discovery time, response time, and transmission failure. Its effect is seen in the form of improved performance of warehouse in quick interaction and in high accuracy as well.

A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems

The Web of Things (WoT) plays an important role in the representation of the objects connected to the Internet of Things in a more transparent and effective way. Thus, it enables seamless and ubiquitous web communication between users and the smart things. Considering the importance of WoT, we propose a WoT-based emerging sensor network (WoT-ESN), which collects data from sensors, routes sensor data to the web, and integrate smart things into the web employing a representational state transfer (REST) architecture. A smart home scenario is introduced to evaluate the proposed WoT-ESN architecture. The smart home scenario is tested through computer simulation of the energy consumption of various household appliances, device discovery, and response time performance. The simulation results show that the proposed scheme significantly optimizes the energy consumption of the household appliances and the response time of the appliances.

Internet of Things: A Comprehensive Review of Enabling Technologies, Architecture, and Challenges

ABSTRACT Internet of Things (IoT) has become a continuously growing concept with the advancements of ubiquitous computing, wireless sensor networks, and machine-to-machine (M2M) communication. IoT connects heterogeneous physical devices and enables communication among them over the Internet via uniquely addressable identifiers. This paper delivers an overview of IoT in the context of the architecture and related technologies. However, IoT does not adhere to a universal architecture. Hence, it describes widely accepted architectural designs, further elaborated with the corresponding communication protocols and standards. Moreover, highly prevalent protocols and standards are summarized, so that the reader can gain an overall view of IoT. Furthermore, it describes some identified solutions and future directions towards overcoming the challenges present in the IoT paradigm. Finally, the paper concludes with some applications of IoT, in order to realize the feasibility of IoT concept in real-world scenarios.

Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: data generation and acquisition level collecting heterogeneous data related to city operations, data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.

Load Balancing Integrated Least Slack Time-Based Appliance Scheduling for Smart Home Energy Management

The emergence of smart devices and smart appliances has highly favored the realization of the smart home concept. Modern smart home systems handle a wide range of user requirements. Energy management and energy conservation are in the spotlight when deploying sophisticated smart homes. However, the performance of energy management systems is highly influenced by user behaviors and adopted energy management approaches. Appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption. Hence, we propose a smart home energy management system that reduces unnecessary energy consumption by integrating an automated switching off system with load balancing and appliance scheduling algorithm. The load balancing scheme acts according to defined constraints such that the cumulative energy consumption of the household is managed below the defined maximum threshold. The scheduling of appliances adheres to the least slack time (LST) algorithm while considering user comfort during scheduling. The performance of the proposed scheme has been evaluated against an existing energy management scheme through computer simulation. The simulation results have revealed a significant improvement gained through the proposed LST-based energy management scheme in terms of cost of energy, along with reduced domestic energy consumption facilitated by an automated switching off mechanism.

Topology Configuration and Multihop Routing Protocol for Bluetooth Low Energy Networks

This paper proposes a new cluster-based on-demand routing protocol to support multihop communication in Bluetooth low energy ad hoc networks. The proposed scheme includes the topology configuration procedure, topology recovery scheme, and on-demand routing protocol. The topology configuration procedure consists of node discovery, piconet configuration, and scatternet formation in a randomly distributed environment. The proposed on-demand routing protocol is designed to minimize the number of route request messages by forwarding them to a master and relay nodes in each cluster during the route request procedure. The performance evaluation shows that our proposed scheme substantially reduces energy consumption, which is the most critical issue on energy constrained networks.

Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

You speak, we detect: Quantitative diagnosis of anomic and Wernicke's aphasia using digital signal processing techniques

Aphasia is a common adult language disorder acquired after a stroke, head injury, tumor, etc. Accurate diagnosis influences the prognosis of any speech and language disorder including aphasia. Therefore, in this paper we have proposed a semi-automated Aphasia diagnosis and classification framework employing feature extraction and pattern matching techniques of the digital signal processing (DSP). The proposed scheme evaluates the acoustic properties, time consumed, and speech characteristics for each language component i.e. naming, repetition, and comprehension. The naming and repetition tasks utilize DSP techniques. The proposed solution is highly scalable since it determines the diagnosis based on acoustic properties instead of the language characteristics. Thus, it eases extending into multiple languages. The mathematical relationships calculate the corresponding score for each component. The framework then determines the diagnosis according to the obtained scores. Since it occupies computational analysis of the speech signals, it reduces the subjectivity of the manual diagnosis process, meanwhile increasing the efficiency and accuracy by consistent diagnosis decisions. Finally, it distinguishes two sub types of Aphasia i.e. Anomic Aphasia and Wernickes Aphasia. The results clearly revealed the efficiency improvement achieved by replacing the live auditory model with pre-recorded auditory model.