A study of existing Ontologies in the IoT-domain

Several domains have adopted the increasing use of IoT-based devices to collect sensor data for generating abstractions and perceptions of the real world. This sensor data is multi-modal and heterogeneous in nature. This heterogeneity induces interoperability issues while developing cross-domain applications, thereby restricting the possibility of reusing sensor data to develop new applications. As a solution to this, semantic approaches have been proposed in the literature to tackle problems related to interoperability of sensor data. Several ontologies have been proposed to handle different aspects of IoT-based sensor data collection, ranging from discovering the IoT sensors for data collection to applying reasoning on the collected sensor data for drawing inferences. In this paper, we survey these existing semantic ontologies to provide an overview of the recent developments in this field. We highlight the fundamental ontological concepts (e.g., sensor-capabilities and context-awareness) required for an IoT-based application, and survey the existing ontologies which include these concepts. Based on our study, we also identify the shortcomings of currently available ontologies, which serves as a stepping stone to state the need for a common unified ontology for the IoT domain.Comment: Submitted to Elsevier JWS SI on Web semantics for the Internet/Web of Thing

4W1H in IoT semantics

International audienceIoT systems are now being deployed worldwide to sense phenomena of interest. The existing IoT systems are often independent which limits the use of sensor data to only one application. Semantic solutions have been proposed to support reuse of sensor data across IoT systems and applications. This allows integration of IoT systems for increased productivity by solving challenges associated with their interoperability and heterogeneity. Several ontologies have been proposed to handle different aspects of sensor data collection in IoT systems, ranging from sensor discovery to applying reasoning on collected sensor data for drawing inferences. In this paper, we study and categorise the existing ontologies based on the fundamental ontological concepts (e.g., sensors, context, location, and more) required for annotating different aspects of data collection and data access in an IoT application. We identify these fundamental concepts by answering the 4Ws (What, When, Who, Where) and 1H (How) identified using the 4W1H methodology

Towards Building Real-Time, Convenient Route Recommendation System for Public Transit

International audiencePublic transportation is essential for sustainable and economical development of cities. Several transport organizations aim to provide service information to commuters through web and mobile apps. This information includes possible routes between two stations, estimated travel and arrival times, and real-time updates about traffic conditions. However, this information is currently not personalized according to commuter preferences. In this work, we emphasize the need for personalized transit service information to commuters and present a vision of our work in this direction. Our final goal is to develop a fully-functional personalized route recommendation system for public transit commuters. This involves identifying commuter preferences and suitable recommendation techniques, and developing a platform to communicate this information to the commuters. We identify the requirements for the development of this platform, and propose an architecture for our system. As a proof of concept, we present an Android participatory sensing application - MetroCognition, which acquires feedback on convenience experienced by commuters in public transit

Toward Enabling Convenient Urban Transit through Mobile Crowdsensing

International audienceThe smart cities of the future are expected to be serviced by advanced, personalized multimodal transit systems, charged with timely transport of citizens. Optimizing routes on such networks is a complex problem, in part due to the fact that simple metrics such as latency by themselves are not sufficient to find the best routes. In this paper, we focus on the problem of providing commuters with personalized routes with the most convenience. We present our mathematical model of user convenience during a multi-leg journey, and the overview of a middleware for enabling convenient transit (including ensuring acceptable network connectivity to mobile apps) by using crowdsourcing. We also report on initial insights obtained through empirical studies on network connectivity and user-perception of convenience in Delhi, India, and Paris, France