EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things

Zhou, Zhangbing; Tang, Jine; Zhang, Liang-Jie; Ning, Ke; Wang, Qun

doi:10.1007/s00779-013-0710-y

Cited by 38 publications

(18 citation statements)

References 33 publications

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“…They explained that the sensors can be put in sleep mode when the coverage area can be compromised for energy saving. In 2014, Zhou et al [20] designed an "energy-efficient index tree" (EGF-tree) to save the energy utilized in collecting, querying, and aggregating data from sensors located in multiple regions in IoT. They proposed a technique to construct an EGF-tree that organizes the sensor nodes into a tree structure.…”

Section: Related Workmentioning

confidence: 99%

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An Energy-Efficient Architecture for the Internet of Things (IoT)

Kaur

Sood

2017

IEEE Systems Journal

238

105

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Internet of things (IoT) is a smart technology that connects anything anywhere at any time. Such ubiquitous nature of IoT is responsible for draining out energy from its resources. Therefore, the energy efficiency of IoT resources has emerged as a major research issue. In this paper, an energy-efficient architecture for IoT has been proposed, which consists of three layers, namely, sensing and control, information processing, and presentation. The architectural design allows the system to predict the sleep interval of sensors based upon their remaining battery level, their previous usage history, and quality of information required for a particular application. The predicted value can be used to boost the utilization of cloud resources by reprovisioning the allocated resources when the corresponding sensory nodes are in sleep mode. This mechanism allows the energy-efficient utilization of all the IoT resources. The experimental results show a significant amount of energy saving in the case of sensor nodes and improved resource utilization of cloud resources.

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Section: Related Workmentioning

confidence: 99%

“…The first two cases compare the PA with some other energy efficiency methods discussed in Section II. The methods used for comparison are SoT [19], EGF-tree [20], ECH-tree [21], and object group localization (OGL) [22]. The third case highlights the suitability of the PA in various scenarios.…”

Section: Comparative Analysismentioning

confidence: 99%

An Energy-Efficient Architecture for the Internet of Things (IoT)

Kaur

Sood

2017

IEEE Systems Journal

238

105

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“…In [21], Golab discussed dynamic scheduling in streaming data warehouses and proposed scheduling framework which combined the features of data stream systems and data warehouses. ZhangBing proposed an energy-efficient index tree which carried multi-region aggregation queries [22]. He also proposed a subregion query processing mechanism for a subregion interest in underwater wireless sensor network [23].…”

Section: Introductionmentioning

confidence: 99%

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

Yan

Wang²,

et al. 2016

Pers Ubiquit Comput

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As the huge number of mobile devices (e.g., smart phones, tablets and netbooks) increases, more and more people choose to use the Internet services financed by mobile Internet service providers (MISPs). To provide better services, it is quite necessary for MISPs to analyze the information hidden in the big data stream generated by users. Therefore, processing the real-time big data stream efficiently has become increasingly important. However, traditional static data storage technology fails to meet the demands of real-time data processing. To improve processing capacity, many parallel processing structures are proposed, which brings up the problem about how the parallel devices can be scheduled to maximize their efficiency. Accordingly, a dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services is proposed, and a stream query graph is built to calculate the weight of every edge. The edge with the minimum weight is selected to send tuples. Simulation results show that the proper number of the logic devices can dramatically reduce system response time. Furthermore, system context switching is reduced by increasing the number of tuples sent each time.

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“…As a key enabling technique of IoT, wireless sensor networks (WSNs) [29,12,28] is a hot research topic nowadays and has been used widely for supporting the applications in many domains [16,20], including habitat monitoring, pollution monitoring [18,5,6], and object tracking [17,10]. Generally, sensors are deployed in a region of interest [1,7], and they collaborate to form a sensor network which is capable of collecting sensory data and reporting the phenomenon to a data collection node called sink or base station [23,9,15].…”

Section: Introductionmentioning

confidence: 99%

Top-k Queries in Wireless Sensor Networks Leveraging Hierarchical Grid Index

Tang

Wang

Sun

et al. 2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

Self Cite

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In the Internet of Things, smart things communicate with each other, and sensed data are aggregated and queried for satisfying certain requests of end-users. Wireless sensor networks (WSN) is a key enabling technique for achieving the vision of the Internet of Things. In real applications of WSN, such as environmental monitoring and vehicle tracking, they may require to launch spatial queries for collecting sensory data to achieve certain goals. Among various queries, top-k query is one of the fundamental operators in many applications of wireless sensor networks for phenomenon monitoring. In this paper we propose a novel top-k query algorithm based on hierarchical grid index and range filter of head nodes, which is able to filter out as many unlikely top-k results as early as possible within the network from transmission.

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EGF-tree: an energy-efficient index tree for facilitating multi-region query aggregation in the internet of things

Cited by 38 publications

References 33 publications

An Energy-Efficient Architecture for the Internet of Things (IoT)

An Energy-Efficient Architecture for the Internet of Things (IoT)

A dynamic assignment scheduling algorithm for big data stream processing in mobile Internet services

Top-k Queries in Wireless Sensor Networks Leveraging Hierarchical Grid Index

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