This paper proposes a context data management framework for efficient context data collection and evaluation to support context-aware applications. The proposed framework named SCARF leverages two architectures: a server-based centralized architecture and an edge-based architecture to address the problem of scalability of context collection and evaluation. SCARF collects the context data efficiently from resource constrained mobile/edge and multiple in-situ sensors (not resource constrained) deployed in smart environments. In particular, SCARF takes a set of contextual queries from applications and optimizes the rate at which the context data needs to be collected and/or transmitted from the sensor to the server. Furthermore, SCARF decides whether the code for evaluating context should be executed on the server or on the edge. Execution on the edge will result in reduced communication between the server and the edge but can result in low performance due to resource constraints of the edge. On the other hand, evaluation of context on the server, will require SCARF to transmit sensor data from the sensor to the server but can result in high performance due to high resource availability of the server. SCARF is an adaptive framework which generates an optimal context acquisition plan and a context evaluation plan such that the overall latency is minimized.

Occupancy sensors are an integral part of many smart building applications, including energy and space optimizations. However, as more occupancy data is collected at an increasingly fine-grained level, possibilities of potential privacy breaches also increase. In this paper, we propose an attack in which the adversary associates an individual with the occupancy sensor by combining the occupancy data with other public information that could easily be obtained online. We conduct an experiment using real-world data to demonstrate that the proposed attack is not only feasible, but the adversary can efficiently identify a small set of sensor IDs that contains the ID of interest given a relatively small amount of additional information. We also summarize a number of mitigation strategies against the proposed attack.

This demonstration showcases the SemIoTic middleware [2] which provides inhabitants of an IoT space, as well as developers of applications, with a semantic view of the space. Participants will have an opportunity to see how useful IoT applications can be easily developed focusing on describing what information is needed without having to deal with the underlying IoT device infrastructure.

This demonstration showcases the SemIoTic middleware which provides inhabitants of an IoT space, as well as developers of applications, with a semantic view of the space. Participants will have an opportunity to see how useful IoT applications can be easily developed focusing on describing what information is needed without having to deal with the underlying IoT device infrastructure.

Current buildings rely on predefined rules to control the temperature in rooms disregarding their residents' thermal comfort. Multiple approaches have been presented in the literature to tackle this issue (e.g., by enabling occupants to express their feedback using their mobile devices). In general setting, to reach consensus among group members, we apply decision making methods (e.g., majority, mean, trimmed mean, and median) to aggregate the diverse expected comfort levels of the inhabitants. However, such methods might be unfair to some participants. In this paper, we present the first study of the issue of fairness in participatory thermal comfort control. Inspired by the traditional definitions in scheduling, we introduce a definition of fairness that is suitably adapted to the particularities of our scenario. We then present our design of an aggregation method that ensures fairness. Finally, we show how our algorithm behaves compared with traditional aggregation methods in diverse simulated scenarios.

Widespread adoption of smartphones brought significant technical advances in technology in today's world. As a consequence, however, it has almost become impossible for us to separate our personal lives from work. Work-life balance is hard to achieve in the current scenario: our business emails keep arriving on our phones even when we are at home or on the beach. Our research goal is to develop and deploy a location-aware messaging framework that is based on indoor location detection. This framework will only deliver messages when users are at work. The system uses beacon technologies to accurately determine the position of a user inside a building. The users, in return, communicate with the system using an application running on their smartphones. The framework delivers customized messages according to the user (i.e. it can distinguish between the owner and visitor in the same location).

This paper presents a work-in-progress project detailing a novel augmented reality game designed to enhance preschool-aged children's knowledge of print-based literacy. National testing scores suggest children struggle with aspects of literacy in later grades. With the rise of videogames as popular entertainment and the potential of augmented reality to present unique affordances for learning, these factors suggest a confluence deserving of investigation. We present ABC3D, a custom-deigned augmented reality (AR) mobile game that harnesses the motivating power of interest and the affordances of augmented reality to engage children in practicing print-based literacy. ABC3D is bimodal, consisting of 1) a "scanning" mode which allows children to scan drawn letters and view 3D images of content starting with the same letter and 2) a "vehicle" mode which tasks children with collecting objects starting with certain letters as specified by software or instructor. Future work involves further polishing of the software, implementation of audio pronunciation and eventual user testing.

As the size of the web is growing explosively, search engines are becoming increasingly important as the primary means to retrieve information from the Internet. A search engine periodically downloads web pages and stores them in the database to provide readers with up-to-date search results. The web crawler is a program that downloads and stores web pages for this purpose. A large-scale search engines uses a parallel web crawler to retrieve the collection of web pages maximizing the download rate. However, the service architecture or experimental analysis of parallel web crawlers has not been fully discussed in the literature. In this paper, we propose an architecture of the parallel web crawler and discuss implementation issues in detail. The proposed parallel web crawler is based on the coordinator/agent model using multiple machines to download web pages in parallel. The coordinator/agent model consists of multiple agent machines to collect web pages and a single coordinator machine to manage them. The parallel web crawler consists of three components: a crawling module for collecting web pages, a converting module for transforming the web pages into a database-friendly format, a ranking module for rating web pages based on their relative importance. We explain each component of the parallel web crawler and implementation methods in detail. Finally, we conduct extensive experiments to analyze the effectiveness of the parallel web crawler. The experimental results clarify the merit of our architecture in that the proposed parallel web crawler is scalable to the number of web pages to crawl and the number of machines used.