CEIoT: A Framework for Interlinking Smart Things in the Internet of Things

In the emerging Internet of Things (IoT) environment, things are interconnected but not interlinked. Interlinking relevant things offers great opportunities to discover implicit relationships and enable potential interactions among things. To achieve this goal, implicit correlations between things need to be discovered. However, little work has been done on this important direction and the lack of correlation discovery has inevitably limited the power of interlinking things in IoT. With the rapidly growing number of things that are connected to the Internet, there are increasing needs for correlations formation and discovery so as to support interlinking relevant things together effectively. In this paper, we propose a novel approach based on Multi-Agent Systems (MAS) architecture to extract correlations between smart things. Our MAS system is able to identify correlations on demand due to the autonomous behaviors of object agents. Specifically, we introduce a novel open-sourced framework, namely CEIoT, to extract correlations in the context of IoT. Based on the attributes of things our IoT dataset, we identify three types of correlations in our system and propose a new approach to extract and represent the correlations between things. We implement our architecture using Java Agent Development Framework (JADE) and conduct experimental studies on both synthetic and real-world datasets. The results demonstrate that our approach can extract the correlations at a much higher speed than the naive pairwise computation method

SECF: Improving SPARQL Querying Performance with Proactive Fetching and Caching

Querying on SPARQL endpoints may be unsatisfactory due to high latency of connections to the endpoints. Caching is an important way to accelerate the query response speed. In this paper, we propose SPARQL Endpoint Caching Framework (SECF), a client-side caching framework for this purpose. In particular, we prefetch and cache the results of similar queries to recently cached query aiming to improve the overall querying performance. The similarity between queries are calculated via an improved Graph Edit Distance (GED) function. We also adapt a smoothing method to implement the cache replacement. The empirical evaluations on real world queries show that our approach has great potential to enhance the cache hit rate and accelerate the querying speed on SPARQL endpoints

A Learning Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases

Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications as they provide facts and relationships that can be automatically understood by machines. Curated knowledge bases usually use Resource Description Framework (RDF) as the data representation model. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has problems like network instability and latency, which affect the query efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL queries to vector representation that are fed into machine-learning algorithms. A time-aware smoothing-based method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive experiments performed on real-world queries showcase the effectiveness of our approach, which outperforms the state-of-the-art work in terms of the overall querying speed

Olfactory function following open rhinoplasty: A 6-month follow-up study

<p>Abstract</p> <p>Background</p> <p>Patients undergoing any type of nasal surgery may experience degrees of postoperative olfactory dysfunction. We sought to investigate "when" the olfactory function recovers to its preoperative levels.</p> <p>Methods</p> <p>In this cohort design, 40 of 65 esthetic open rhinoplasty candidates with equal gender distribution, who met the inclusion criteria, were assessed for their olfactory function using the Smell Identification Test (SIT) with 40 familiar odors in sniffing bottles. All the patients were evaluated for the SIT scores preoperatively and postoperatively (at week 1, week 6, and month 6).</p> <p>Results</p> <p>At postoperative week one, 87.5% of the patients had anosmia, and the rest exhibited at least moderate levels of hyposmia. The anosmia, which was the dominant pattern at postoperative week 1, resolved and converted to various levels of hyposmia, so that no one at postoperative week 6 showed any such complain. At postoperative week six, 85% of the subjects experienced degrees of hyposmia, almost all being mild to moderate. At postoperative six month, the olfactory function had already reverted to the preoperative levels: no anosmia or moderate to severe hyposmia. A repeated ANOVA was indicative of significant differences in the olfactory function at the different time points. According to our post hoc Benfronney, the preoperative scores had a significant difference with those at postoperative week 1, week 6, but not with the ones at month 6.</p> <p>Conclusion</p> <p>Esthetic open rhinoplasty may be accompanied by some degrees of postoperative olfactory dysfunction. Patients need a time interval of 6 weeks to 6 months to fully recover their baseline olfactory function.</p

Correlation management and search for the Internet of Things

The Internet of Things (IoT) is a compelling paradigm, which aims to enable everyday physical things embedded with electronics, software, sensors, and network connectivity to collect and exchange data on the Internet. It is anticipated that by 2020, billions of things get connected to the Internet. Creating future IoT search engines is a key step towards unlocking answering the above question. Future search engines can potentially in revolutionise various applications in different domains. Existing approaches for searching the IoT use simple techniques to obtain a list of things for a query. The state of the art needs to be improved in different aspects. For instance, it is often disregarded that in the context of IoT, we have two types of users including machines and human users. In addition, many have complained about the absence of the real-world IoT data. Unsurprisingly, a common question that arises regularly nowadays is “Does the IoT already exist?”. So far, little has been known about the real-world situation on IoT, its attributes, the presentation of data and user interests. Moreover, existing approaches also disregard the attribute based correlations between things in the real-world. In this dissertation, we review the state of the art in IoT search domain and propose a novel framework to collect and analyse IoT data. Our system is also able to resolve IoT queries based on the knowledge that is acquired from the IoT data sources. Furthermore, we introduce a novel technique to extract the correlations between things. Our framework is capable of using the correlations to improve the quality of search results for both types of users. We investigate the scalability and the effectiveness of our approach using large scale and real-world datasets. Moreover, we investigate two case studies in transport systems in our research. The first case study, challenges the complex problem of taxi ridesharing in the context of smart cities. The second case study, involves a real-time prediction method for flight delays based on the IoT sourced data.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2016

ThingSeek: A Crawler and Search Engine for the Internet of Things

The rapidly growing paradigm of the Internet of Things (IoT) requires new search engines, which can crawl heterogeneous data sources and search in highly dynamic contexts. Existing search engines cannot meet these requirements as they are designed for traditional Web and human users only. This is contrary to the fact that things are emerging as major producers and consumers of information. Currently, there is very little work on searching IoT and a number of works claim the unavailability of public IoT data. However, it is dismissed that a majority of real-time web-based maps are sharing data that is generated by things, directly. To shed light on this line of research, in this paper, we firstly create a set of tools to capture IoT data from a set of given data sources. We then create two types of interfaces to provide real-time searching services on dynamic IoT data for both human and machine users