Zhangbing Zhou

An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things

In the Internet of Things, smart things communicate with each other, and sensed data are aggregated and queried to satisfy certain requests of end-users. When a region of interest requires to be monitored continuously, the strategy that each query is to be executed independently through gathering sensed data of target sub-regions may not be energy efficient, since the values reported by sensors may have no significant difference in proximate sensing time-slots in some applications. To mitigate the energy consumption in this context, in this paper we firstly divide the region with sensor nodes evenly into grid cells, and propose an energy-efficiency hierarchical clustering index tree to organize these grid cells. Then, we develop a time-correlated region query technique for answering continuous queries. Generally, sensor nodes report their values to the base station at the beginning, and report their values only when these values are changed significantly with respect to those reported previously. Queries are answered through assembling the values of interested sensors saved in the base station. Theoretical analysis and experimental results show that our proposal is energy efficient compared with traditional techniques.

Skewness-aware clustering tree for unevenly distributed spatial sensor nodes in smart city

SUMMARY n nEfficient spatial index is essential for querying spatial sensor nodes in the context of smart city. Sensor nodes are usually unevenly distributed in real situations. In this setting, R-tree and its variants may cause large overlap and coverage among branch nodes, which impact the query efficiency greatly. To address this challenge, this paper proposes a novel skewness-aware clustering tree (SWC-tree) by clustering sensor nodes. Sensor nodes in a dense region will be put into the same node. Thus, overlap and coverage among node regions are less than that of R-tree and its variants. As dense regions contain more sensor nodes, we assign a higher priority to these region nodes for facilitating the query operation. Experimental results show that in the context of skewed distribution, SWC-tree is efficient in performance for conducting insertion, deletion, and query operations of sensor nodes. Copyright

Data Collection Middleware for Crowdsourcing-based Industrial Sensing Intelligence

In this paper, crowdsourcing-based industrial sensing intelligence (CISI) is proposed as a collaborative approach for large-scale monitoring in modern industrial plants, targeting at improved productivity and increased workplace safety. The proposed approach focuses on middleware, which considers both application and industry-grade requirements. Through embedding crowdsourcing knowledge at different levels and supporting QoS services, systems based on CISI can perform effective work assignment and flexible configuration of wireless sensor networks (WSNs). This paper presents a middleware that addresses these characteristics, which is an extension of GSN, our earlier work on middleware for rapid deployment and integration of heterogeneous sensor networks. Wireless sensor devices and wearable equipment are employed as modeling tools for the middleware implementation.

Energy utilization concerned sleep scheduling in Wireless Powered Communication Networks

Wireless Powered Communication Networks (WPCN) is one of the promising approaches to extend the lifetime of the energy-constrained wireless networks such as Wireless Sensor Networks (WSNs). With the advancement in energy harvesting in terms of Wireless Energy Transmission (WET), WPCN overcomes the problem of replacing fixed energy sources such as batteries in difficult access areas of industrial networks. However, residual energy along with harvested energy is not always enough to support the data transmission in IWSNs. This article introduces a energy utilization concerned sleep scheduling in WPCNs with an aim to balance network demands and residual energy with harvested energy. In the proposed scheme, when the harvested energy combined with residual energy is less than the energy consumption due to data transmission, then the sensor node goes to sleep-state in order to prevent death acceleration. As the sleep node obtains enough energy, this node goes to active-state in next epoch if the network demand increases. Finally, a trade-off between the time to harvesting energy and data transmission is obtained through extensive simulation.

Sleep scheduling in wireless powered industrial wireless sensor networks: poster abstract

With the advancement in energy harvesting, wireless powered communication networks overcome the problem of replacing fixed energy sources, e.g., batteries in difficult access areas of industrial networks. However, the harvested energy is not always enough to support reliable and low end-to-end data transmission in industrial wireless sensor networks (IWSNs). This poster introduces an energy utilization-concerned sleep scheduling in wireless-powered IWSNs with an aim to balance network demand and residual energy.

Using wearable equipment to construct monitoring maps in large-scale petrochemical plants

This paper focuses on using wearable equipment to make workers participate in constructing monitoring maps in large-scale petrochemical plants, which collaborates with static sensor nodes in given areas. Furthermore, this study provides a sensing pattern with less cost and higher flexibility, which effectively contribute to collective effort with static sensor nodes. Several open research issues are discussed.

Novel DR-tree index based on the diagonal line of MBR

The application of spatial database is increasingly widespread. How to effectively store and organize multidimensional space data and improve the processing efficiency of multidimensional data has become a central issue. R-tree is one of the most widely used spatial indexes. Due to the existing large overlap and coverage among the nodes of R-tree, the search path of a data object is not unique, and the search efficiency declines sharply when the amount of data increases. Based on the analysis and research of previous index trees, a novel DR-tree index based on the diagonal lines of MBR is proposed in this paper. DR-tree uses the diagonal line of MBR to indicate spatial data objects and construct R-tree, still adopt the endpoint coordinates of MBR diagonal to signify the location of leaf nodes or non-leaf nodes. Since MBR is simplified, the coverage and overlap among regions are also reduced. Experimental results show that the new index tree is superior to R-tree in performance. The query paths of data objects are single, the insertion, deletion, and query efficiency of data objects are significantly improved, and the performance of the new index tree becomes more apparent when the amount of data objects increases.

Cloud-based Data-intensive Framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE.

MR ✳ -tree: novel indexing and retrieving mechanism for spatial objects in mobile PowerPoint pages

Different from the traditional office software, due to the limited storage and computational capability of mobile devices, how to support the page editing in a fast and energy-efficient manner is fundamental. To address this challenge, we propose an MR*-tree based indexing mechanism for supporting the fast and energy-efficient retrieval of spatial objects in mobile PowerPoint pages. Based on this, techniques are developed to support the operations when editing mobile PowerPoint pages. Prototype has been implemented for facilitating the operations of spatial objects when editing mobile PowerPoint pages. Experimental evaluations on computers, pads, and smart phones show the effectiveness and efficiency of this technique.

Spatial Objects Indexing and Retrieval for Mobile PowerPoint

Different from the traditional office software, due to the limited storage and computational capability of mobile devices, how to support the page editing in a fast and energy efficient manner is fundamental. To address this challenge, we propose an MR*-tree based indexing mechanism for supporting the fast and energy-efficient retrieval of spatial objects in mobile Power Point pages. Based on which, techniques are developed to support the operations when editing mobile Power Point pages. Prototype has been implemented for supporting the operations of spatial objects in editing mobile Power Point pages.