Yunchuan Sun

Internet of Things and Big Data Analytics for Smart and Connected Communities

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.

The schema theory for semantic link network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.

Cost-Efficient Strategies for Restraining Rumor Spreading in Mobile Social Networks

With the popularity of mobile devices, mobile social networks (MSNs) have become an important platform for information dissemination. However, the spread of rumors in MSNs present a massive social threat. Currently, there are two kinds of methods to address this: blocking rumors at influential users and spreading truth to clarify rumors. However, most existing works either overlook the cost of various methods or only consider different methods individually. This paper proposes a heterogeneous-network-based epidemic model that incorporates the two kinds of methods to describe rumor spreading in MSNs. Moreover, two cost-efficient strategies are designed to restrain rumors. The first strategy is the real-time optimization strategy that minimizes the rumor-restraining cost by optimally combining various rumor-restraining methods such that a rumor can be extinct within an expected time period. The second strategy is the pulse spreading truth and continuous blocking rumor strategy that restrains rumor spreading through spreading truth periodically. The two strategies can restrain rumors in a continuous or periodical manner and guarantee cost efficiency. The experiments toward the Digg2009 data set demonstrate the effectiveness of the proposed model and the efficiency of the two strategies.

Data Security and Privacy in Cloud Computing

Data security has consistently been a major issue in information technology. In the cloud computing environment, it becomes particularly serious because the data is located in different places even in all the globe. Data security and privacy protection are the two main factors of users concerns about the cloud technology. Though many techniques on the topics in cloud computing have been investigated in both academics and industries, data security and privacy protection are becoming more important for the future development of cloud computing technology in government, industry, and business. Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review different security techniques and challenges from both software and hardware aspects for protecting data in the cloud and aims at enhancing the data security and privacy protection for the trustworthy cloud environment. In this paper, we make a comparative research analysis of the existing research work regarding the data security and privacy protection techniques used in the cloud computing.

Constructing the Web of Events from raw data in the Web of Things

An exciting paradise of data is emerging into our daily life along with the development of the Web of Things. Nowadays, volumes of heterogeneous raw data are continuously generated and captured by trillions of smart devices like sensors, smart controls, readers and other monitoring devices, while various events occur in the physical world. It is hard for users including people and smart things to master valuable information hidden in the massive data, which is more useful and understandable than raw data for users to get the crucial points for problems-solving. Thus, how to automatically and actively extract the knowledge of events and their internal links from the big data is one key challenge for the future Web of Things. This paper proposes an effective approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things, which starts with grasping the critical data for useful events by filtering data with well-defined event types in the schema. A case study in the context of smart campus is presented to show the application of proposed approach for the extraction of events and their internal semantic links.

Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things

Massive sensing data are generated continuously in the Internet of Things. How to organize and how to query the big sensing data are big challenges for intelligent applications. This paper studies the organization of big sensing data with event-linked network (ELN) model, where events are regarded as primary units for organizing data and links are used to represent the semantic associations among events. Several different types of queries on the event-linked network are also explored, which are different from queries on traditional relational database. We use an instance of smart home to show the effectiveness and efficiency of organization and query approaches based on the event-linked network.

Square-root unscented Kalman filtering-based localization and tracking in the Internet of Things

The Internet of Things (IoT), which is usually established over architectures of wireless sensor networks, provides an actual platform for various applications of personal and ubiquitous computing. Recently, moving target localization and tracking in an IoT environment have been paid more and more attention. This paper proposes a square-root unscented Kalman filtering (SR-UKF)-based algorithm to discover real-time location of a moving target in an IoT environment where there exist quantities of sensors. The data generated from wireless sensor nodes of the IoT make contributions to localization and tracking of the moving target. First, a least-square (LS) criterion-based mathematical model is proposed for localization initialization in an IoT scenario. Next, we employ an SR-UKF idea for the further localization and tracking. By using the data coming from sensor nodes near the target, real-time location of the moving target can be estimated by implementation of SR-UKF in an iterative fashion so as to achieve target status tracking. Simulation results show that the proposed algorithm achieves good performance in estimation of both position and velocity of the target with either uniform linear motion or variable-speed curve motion. Compared with some existing conventional extended Kalman filtering (EKF) or UKF-based methods, the proposed algorithm shows lower location/velocity estimation error under the same computational complexity, which demonstrates its potential significance in ubiquitous computing applications for an IoT environment.

A holistic approach to visualizing business models for the internet of things

The Internet of Things (IoT) promises huge potential economic benefits. However, current IoT applications are in their infancy and the full potential of possible business opportunities is yet to be discovered. To help realize these economic benefits, workable business models are required that show where opportunities exist. In this article we describe the Business DNA Model - a representation of a business model in terms of Design, Needs, and Aspirations, which greatly simplifies presentation, analysis, and design of business models. This model can be used by IoT stakeholders to generate and analyse stories, models, and projects for strategic management, business strategy, and innovation. We present one scenario - smart logistics - to illustrate how the Business DNA Model might be applied.

Schema Theory for Semantic Link Network

Semantic link network (SLN) is a loosely coupled semantic data model for managing Web resources. Its nodes can be any types of resources. Its edges can be any semantic relations. Potential semantic links can be derived out according to reasoning rules on semantic relations. This paper proposes the schema theory for SLN including the concepts, rule-constraint normal forms and relevant algorithms. The theory provides the basis for normalized management of SLN and its applications. A case study demonstrates the proposed theory.

Discovering time-dependent shortest path on traffic graph for drivers towards green driving

Green transportation technologies that reduce fuel consumption, idling, and vehicle miles traveled while reducing acute congestion could play a significant role in reducing greenhouse gas emissions, particularly in major cities, around ports and freight hubs, and on major roads and corridors. The key to support green transportation is to discover real-time shortest path for drivers. A time-dependent traffic graph, generated from history traffic data can be used to predict the traveling time and to find the shortest path for drivers dynamically. There are two different types of queries on shortest paths: Query_FiST for starting at a fixed time and reaching the destination as early as possible and Query_BeST for choosing the best starting time to avoid the rush hours. Taking advantage of traffic graphs characteristics of sparsity and hierarchy, we propose two algorithms: Heap-based BellmanFord algorithm for Query_FiST and Extended BellmanFord algorithm for Query_BeST respectively. We prove the correctness of the algorithms and discuss their time complexity. A series of experiments are implemented on an open data set of real traffic collected from taxis and the results show that our algorithms outperform all existing algorithms practically. For the Query_FiST, we propose Heap-based BellmanFord algorithm to find the shortest path in a dynamically changing traffic graph and it works efficiently in practical implementations. Although in the worst case, the time complexity of our algorithm is worse than that of algorithms based on Dijkstras algorithm, our algorithm works better in practical performance by taking advantages of the two characteristics of traffic graphs to avoid the worst case.For the Query_BeST, we propose Extended BellmanFord algorithm to discover a shortest path with the best starting time in the traffic graph. The time complexity of our algorithm is O(kE*a(T)), where a(T) is the time cost to do several operations between two functions and is inevitable in every algorithm focusing on this problem. The proposed algorithm for Query_BeST performs better than other previous ones because the mentioned characteristics in traffic graphs guarantee a smaller constant k. The experiment results confirm this assertion.A series of experiments are implemented and the results show that our algorithms outperform existing solutions in terms of efficiency respectively.