Daqiang Zhang

Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions

The advances in wireless communication techniques, mobile cloud computing, and context- aware technologies boost a growing interest in the design, development, and deployment of vehicular networks for emerging applications. This leads to an increasing evolutionary tendency to change from vehicular networks toward cloud-assisted context-aware vehicular cyberphysical systems. In this article, we first propose a multi-layered context-aware architecture and introduce two crucial service components, vehicular social networks and context-aware vehicular security. Then we propose an application scenario regarding the context-aware dynamic parking services by illuminating the cloud-assisted architecture and logic flow. Finally, we investigate the challenges and possible solutions, including context-aware safety hazard prediction, context-aware dynamic vehicle routing, and context-aware vehicular clouds.

Data mining for the Internet of Things: literature review and challenges

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing

The advances in wireless communication technologies, vehicular networks and cloud computing boost a growing interest in the design, development and deployment of Vehicular Cyber-Physical Systems (VCPS) for some emerging applications, which leads to an increasing demand on connecting Mobile Cloud Computing (MCC) users to VCPS for accessing the richer applications and services. In this paper, we first identify the key requirements of designing an efficient and flexible architecture for integrating MCC and VCPS. Based on the requirements, we design a VCPS and MCC Integration Architecture (VCMIA), which provides mobile services for potential users such as drivers and passengers to access mobile traffic cloud. Then, we ana- lyze two crucial cloud-supported components: GIS with traffic-aware capability and cloud-supported dynamic vehicle routing. Finally, we select Vehicle Maintenance Services (VMS) as an application scenario to carry out the validation. The proposed VCMIA can provide the flexibility for enabling diverse applications.

TASA: Tag-Free Activity Sensing Using RFID Tag Arrays

Radio Frequency IDentification (RFID) has attracted considerable attention in recent years for its low cost, general availability, and location sensing functionality. Most existing schemes require the tracked persons to be labeled with RFID tags. This requirement may not be satisfied for some activity sensing applications due to privacy and security concerns and uncertainty of objects to be monitored, e.g., group behavior monitoring in warehouses with privacy limitations, and abnormal customers in banks. In this paper, we propose TASA-Tag-free Activity Sensing using RFID tag Arrays for location sensing and frequent route detection. TASA relaxes the monitored objects from attaching RFID tags, online recovers and checks frequent trajectories by capturing the Received Signal Strength Indicator (RSSI) series for passive RFID tag arrays where objects traverse. In order to improve the accuracy for estimated trajectories and accelerate location sensing, TASA introduces reference tags with known positions. With the readings from reference tags, TASA can locate objects more accurately. Extensive experiment shows that TASA is an effective approach for certain activity sensing applications.

Middleware for pervasive computing: A survey

The rapidly emerging area of pervasive computing faces many challenging research issues critical to application developers. Wide heterogeneity of hardware, software, and network resources pose veritable coordination problems and demand thorough knowledge of individual elements and technologies. In order to ease this problem and to aid application developers, different middleware platforms have been proposed by researchers. Though the existing middleware solutions are useful, they themselves have varied features and contribute partially, for context, data, or service management related application developments. There is no single middleware solution that can address a majority of pervasive computing application development issues, due to the diverse underlying challenges. In this survey paper, we identify different design dimensions of pervasive computing middleware and investigate their use in providing various system services. In-depth analysis of the system services have been carried out and middleware systems have been carefully studied. With a view to aid future middleware developers, we also identify some challenging open research issues that have received little or no attention in existing middleware solutions.

Real-Time Locating Systems Using Active RFID for Internet of Things

The proliferation of the Internet of Things (IoT) has fostered growing attention to real-time locating systems (RTLSs) using radio frequency identification (RFID) for asset management, which can automatically identify and track physical objects within indoor or confined environments. Various RFID indoor locating systems have been proposed. However, most of them are inappropriate for large-scale IoT applications owing to severe radio multipath, diffraction, and reflection. In this paper, we propose a newly fashioned RTLS using active RFID for the IoT, i.e., iLocate, which locates objects at high levels of accuracy up to 30 cm with ultralong distance transmission. To achieve fine-grained localization accuracy, iLocate presents the concept of virtual reference tags. To overcome signal multipath, iLocate employs a frequency-hopping technique to schedule RFID communication. To support large-scale RFID networks, iLocate leverages the ZigBee. We implement all hardware using 2.45-GHz RFID chips so that each active tag can communicate with readers that are around 1000 m away in a free space. Our empirical study and real project deployment show the superiority of the proposed system with respect to the localization accuracy and the data transmission rate for large-scale active RFID networks.

Survey on context-awareness in ubiquitous media

Context-awareness assists ubiquitous media applications in discovering the changeable contextual information and adapting their behaviors accordingly. A wide spectrum of context-aware schemes have been proposed over the last decade. However, most of them provide partial functionalities of context-awareness in ubiquitous media applications. They are specified to a certain task and lack of systematic research on context-awareness. To this end, this survey aims at answering how close we are to developing context-aware applications in ubiquitous media in a systematic manner. This survey proposes a reference framework to identify key functionalities of context-awareness. Then, it investigates the state-of-the-art advances in every functionality of context-awareness. Finally, it points out potential directions in context-awareness research and tools for building and measuring context-aware ubiquitous media systems.

CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies

With the development of e-commerce, a growing number of people prefer to purchase medicine online for the sake of convenience. However, it is a serious issue to purchase medicine blindly without necessary medication guidance. In this paper, we propose a novel cloud-assisted drug recommendation (CADRE), which can recommend users with top-N related medicines according to symptoms. In CADRE, we first cluster the drugs into several groups according to the functional description information, and design a basic personalized drug recommendation based on user collaborative filtering. Then, considering the shortcomings of collaborative filtering algorithm, such as computing expensive, cold start, and data sparsity, we propose a cloud-assisted approach for enriching end-user Quality of Experience (QoE) of drug recommendation, by modeling and representing the relationship of the user, symptom and medicine via tensor decomposition. Finally, the proposed approach is evaluated with experimental study based on a real dataset crawled from Internet.

Cloud-Integrated Cyber-Physical Systems for Complex Industrial Applications

Recently, with many advances in wireless sensor networks, big data, mobile and cloud computing, Cyber-Physical Systems (CPS) can tightly couple cyber space with the physical world better than ever before. Also, the cloud-based systems can provide massive storage resources and low-cost computing as well as the flexibility of customizing the operating environment to Complex Industrial Applications (CIA). In our view, Cloud-integrated CPS (CCPS) will open the door to allow previously unachievable application scenarios to be built, deployed, managed and controlled effectively. In this paper, we propose a novel architecture of CCPS (termed CCPSA) and outline the enabling technologies for CIA. Then, we dissect three potential challenges and provide solutions from the perspective of CIA, including virtualized resource management techniques, the scheduling of cloud resources, and life cycle management. We hope this paper can provide insight and a roadmap for future research efforts in the emerging field of CCPS.

Context reasoning using extended evidence theory in pervasive computing environments

Most existing context reasoning approaches implicitly assume that contexts are precise and complete. This assumption cannot be held in pervasive computing environments, where contexts are often imprecise and incomplete due to unreliable connectivity, user mobility and resource constraints. To this end, we propose an approach called CRET: Context Reasoning using extended Evidence Theory. CRET applies the evidence theory to context reasoning in pervasive computing environments. Because evidence theory is limited by two fundamental problems-computation-intensiveness and Zadeh paradox, CRET presents evidence selection and conflict resolution strategies. Empirical study shows that CRET is desirable for pervasive applications.