The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet [1]. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between the things. This paper proposes a gateway and Semantic Web enabled IoT architecture to provide interoperability between systems, which utilizes established communication and data standards. The Semantic Gateway as Service (SGS) allows translation between messaging protocols such as XMPP, CoAP and MQTT via a multi-protocol proxy architecture. Utilization of broadly accepted specifications such as W3C's Semantic Sensor Network (SSN) ontology for semantic annotations of sensor data provide semantic interoperability between messages and support semantic reasoning to obtain higher-level actionable knowledge from low-level sensor data.Note to the reviewers: Unlike traditional academic journal publications, IEEE IC has a preference to limit number of references, so we welcome any suggestions on removing references, especially if additional references are suggested for inclusions. We have also included some introductions to communication technologies and an overview on current IoT ecosystem to make this manuscript as self contained as possible, especially for IC's wider audience. However, if needed, some of these can be removed if we want to assume that readers will be; actionable suggestions and recommendations on these matters will be valuable.

insight generation, and just about anything that humans, as intelligent beings, seek to do. We've used the term computing for human experience (CHE) 1 to capture technology's human-centric role. CHE emphasizes the unobtrusive, supportive, and assistive part technology plays in improving human experience; here, technology "takes into account the human world and allows computers themselves to disappear in the background." 2 We can distinguish this from Licklider's vision of human-computer collaboration, Eglebert's vision of augmenting human intellect and-more recently-ambient intelligence, and Vannever Bush's and McCarthy's machine-centric vision of making computing more intelligent so that it thinks and behaves like humans.Here, we present an emerging paradigm called physical-cyber-social (PCS) computing. It encompasses a holistic treatment of data, information, and knowledge from the PCS worlds to integrate, correlate, interpret, and provide contextually relevant abstractions to humans. We view PCS as the next phase of computing systems, building on current progress in cyber-physical systems, sociotechnical systems, and cyber-social systems to support CHE. PCS incorporates deeper and richer semantic interdependence and interplay between sensors and devices at physical layers; richer technology-mediated social interactions; and the gathering and application of collective intelligence characterized by massive and contextually relevant background knowledge and advanced reasoning to bridge machine and human perceptions.PCS computing requires that we move away from traditional data processing to multitier computation along the data-information-knowledge-wisdom (DIKW) dimension, which supports reasoning to convert data into abstractions that are more familiar, accessible, and understandable to humans.We illustrate PCS computing for healthcare with a focus on semantic perception, 3 which converts low-level, heterogeneous, multimodal, and contextually relevant data into higher-level abstractions that can provide insights and assist humans in making complex decisions. Case StudyConsider the case of Ram, a 60-year-old Asian male, who receives a blood pressure screening from his doctor and discovers that the reading is slightly higher than expected (90 diastolic, measured in mmHg). Let's look at two questions that Ram might have: What is the normal blood pressure of an Asian male of his age? What is the best way to manage a diastolic blood pressure of 90? To answer these questions, we need access to physiological observations obtained from other people with similar characteristics and demographics (physical). We should also consider the ethnic, social, cultural, and economic background for similarity (social). Moreover, in addition to expert knowledge, the knowledge and experience of similar people dealing with the same health issue are important (cyber). Neither an average doctor nor current cyber-physical systems can answer these questions, but PCS computing can address them in a holistic manner. Patient empowerment and p...

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology-enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services, such as traffic, public transport, water supply, weather, sewage, and public safety, as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance-level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over 4 months from the San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.

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