Subramanian Shivashankar

A REST based design for Web of Things in smart environments

Any physical Thing is a smart object, if it is embedded with sensor(s), actuator(s), a microcontroller and a low power radio. More often these components can be fabricated in chip, as small as the size of a human thumb finger. The main vision of “Web of Things” (WoTs) is to realize connectivity of the Web to billions of such smart objects. Applications can be built on top of standards like IPv6 over Low Power Wireless Personal Area Networks (6LoWPAN) at the L3 layer and IEEE 802.15.4 at the L2 layer of the network stack. Some of the popular applications include smart metering, building and home automation, healthcare, asset management, environmental monitoring and industrial automation. One of the main challenges for application developers is interoperability among the services provided by smart objects of disparate environments. This paper deals with interoperability at the application layer. We present an approach using the REST principles, for smart plant-watering application. The proposed approach can be adapted to any kind of applications involving smart objects. The paper also specifies an efficient syndication algorithm for event notifications from Things. We empirically show that our proposed approach achieves interoperability as well as performs efficiently.

An Ontology and Pattern Clustering Approach for Activity Recognition in Smart Environments

Activity recognition aims at modeling the occupants’ behavior by analyzing the sensor data collected from the smart environment. Though most of the activity recognition systems use supervised learning techniques for building such models there is a shift towards the unsupervised learning paradigm as the process of annotating and labeling the data is prone to errors. This paper proposes an Event Pattern Activity Modeling Framework (EPAM) to identify the occupant activity pattern from the sensor data by using an unsupervised machine learning approach and further analysis is done with a knowledge driven approach. In the context of smart environments, an activity is considered as a sequence of events that are generated continuously from the sensor data. The segmentation algorithm proposed in EPAM is used to identify appropriate event patterns for an activity that are then grouped together using a pattern clustering algorithm that presents a hierarchy of activities. The set of activities of the occupant, observed in a smart environment is not always sequential but is highly interleaved and discontinuous. The proposed algorithm accommodates this valid factor by an innovative use of the Jaro Winkler similarity measure. The hierarchy of activity generated by the pattern clustering approach is used for activity modeling. Ontology based activity modeling is preferred over other modeling techniques because of its unified modeling, representation and semantically clear reasoning. The experimental results show that the proposed EPAM framework of segmentation, pattern clustering and ontological modeling is efficient and more effective than the existing approach of activity modeling.

Composite Activity Recognition in Smart Homes Using Markov Logic Network

Smart environments have progressed and evolved into a significant research area with development of sensor technology, wireless communication and machine learning strategies. Ambient Intelligence incorporated into smart environment assists in resolving many social related applications to facilitate the future society. The initiative of modeling Activity of Daily Living (ADL) and Ambient Assisted Living (AAL) in smart homes have helped in the deployment of applications to various domains like elderly care, health care etc. Activity recognition is the task involved in reasoning within smart homes with the aim of recognizing the ongoing activity of the occupant. Constructing an activity model is essential to carry out recognition and is achieved through various machine learning and artificial intelligence techniques. Data driven approach constructs activity model through statistical machine learning mechanisms while knowledge driven approach constructs activity model through knowledge representation and modeling strategies. Uncertainty and temporal data are better handled by data driven approach while re-usability and context based analysis is handled better by knowledge driven approach of activity modeling. To combine the features of data driven and knowledge driven approaches, a hybrid activity modeling technique is required. The proposed system performs activity modeling via Markov Logic Network, a machine learning strategy that combines probabilistic reasoning and logical reasoning with a single framework. Activities in a smart home are categorized as simple and composite activities, wherein composite activities are defined as related simple activities within a given time interval. The proposed system models both simple and composite activity using soft and hard rules of MLN. Experiments carried over the proposed system shows the effectiveness of the proposed work for recognizing simple and composite activity.