Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

A Semantic-enabled Framework For Future Internet Of Things Applications

While the challenge of connecting Internet of Things (IoT) devices at the lowest layer has been widely studied, integrating and interoperating huge amounts of sensed data of heterogeneous IoT devices is becoming increasingly important because of the possibility of consuming such data in supporting many potential novel IoT applications. A common approach to processing and consuming IoT data is a centralized paradigm: sensor data is sent over the network to a comparatively powerful central server or a cloud service, where all processing takes place. However, this approach has some limitations as it requires devices to interact directly with a cloud which is not cost effective. First, it has high demands on the device's storage and computational capabilities. Second, as devices grow rapidly in a deployment area, sending all the data to a centralized cloud server requires high network bandwidth. Moreover, this often creates data privacy concerns as all raw data will be sent to a centralized place. To address the above limitations for building future Internet of Things applications, we present an early design of a novel framework that combines Internet of Things, Semantic Web, and Big Data concepts. We not only present the core components to build an IoT system, but also list existing alternatives with their merits. This framework aims to incorporate open standards to address the potential challenges in building future IoT applications. Therefore, our discussion revolves around open standards to build the framework, rather than proprietary standards

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

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

Edge Influence Computation in Dynamic Graphs

Reachability queries are of great importance in many research and application areas, including general graph mining, social network analysis and so on. Many approaches have been proposed to compute whether there exists one path from one node to another node in a graph. Most of these approaches focus on static graphs, however in practice dynamic graphs are more common. In this paper, we focus on handling graph reachability queries in dynamic graphs. Specifically we investigate the influence of a given edge in the graph, aiming to study the overall reachability changes in the graph brought by the possible failure/deletion of the edge. To this end, we firstly develop an efficient update algorithm for handling edge deletions. We then define the edge influence concept and put forward a novel computation algorithm to accelerate the computation of edge influence. We evaluate our approach using several real world datasets. The experimental results show that our approach outperforms traditional approaches significantly

Automatic Text Summarization Using Fuzzy Inference

Due to the high volume of information and electronic documents on the Web, it is almost impossible for a human to study, research and analyze this volume of text. Summarizing the main idea and the major concept of the context enables the humans to read the summary of a large volume of text quickly and decide whether to further dig into details. Most of the existing summarization approaches have applied probability and statistics based techniques. But these approaches cannot achieve high accuracy. We observe that attention to the concept and the meaning of the context could greatly improve summarization accuracy, and due to the uncertainty that exists in the summarization methods, we simulate human like methods by integrating fuzzy logic with traditional statistical approaches in this study. The results of this study indicate that our approach can deal with uncertainty and achieve better results when compared with existing methods

Toward Unified Cloud Service Discovery for Enhanced Service Identification

Nowadays cloud services are being increasingly used by professionals. A wide variety of cloud services are being introduced every day, and each of which is designed to serve a set of specific purposes. Currently, there is no cloud service specific search engine or a comprehensive directory that is available online. Therefore, cloud service customers mainly select cloud services based on the word of mouth, which is of low accuracy and lacks expressiveness. In this paper, we propose a comprehensive cloud service search engine to enable users to perform personalized search based on certain criteria including their own intention of use, cost and the features provided. Specifically, our cloud service search engine focuses on: 1) extracting and identifying cloud services automatically from the Web; 2) building a unified model to represent the cloud service features; and 3) prototyping a search engine for online cloud services. To this end, we propose a novel Service Detection and Tracking (SDT) model for modeling Cloud services. Then based on the SDT model, a cloud service search engine (CSSE) is implemented for helping effectively discover cloud services, relevant service features and service costs that are provided by the cloud service providers

Fog computing for sustainable smart cities: a survey

The Internet of Things (IoT) aims to connect billions of smart objects to the Internet, which can bring a promising future to smart cities. These objects are expected to generate large amounts of data and send the data to the cloud for further processing, specially for knowledge discovery, in order that appropriate actions can be taken. However, in reality sensing all possible data items captured by a smart object and then sending the complete captured data to the cloud is less useful. Further, such an approach would also lead to resource wastage (e.g. network, storage, etc.). The Fog (Edge) computing paradigm has been proposed to counterpart the weakness by pushing processes of knowledge discovery using data analytics to the edges. However, edge devices have limited computational capabilities. Due to inherited strengths and weaknesses, neither Cloud computing nor Fog computing paradigm addresses these challenges alone. Therefore, both paradigms need to work together in order to build an sustainable IoT infrastructure for smart cities. In this paper, we review existing approaches that have been proposed to tackle the challenges in the Fog computing domain. Specifically, we describe several inspiring use case scenarios of Fog computing, identify ten key characteristics and common features of Fog computing, and compare more than 30 existing research efforts in this domain. Based on our review, we further identify several major functionalities that ideal Fog computing platforms should support and a number of open challenges towards implementing them, so as to shed light on future research directions on realizing Fog computing for building sustainable smart cities