A Delay-Aware Network Structure for Wireless Sensor Networks With In-Network Data Fusion

Cheng, Chi‐Tsun; Leung, Henry; Maupin, Patrick

doi:10.1109/jsen.2013.2240617

Cited by 74 publications

(70 citation statements)

References 23 publications

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“…The top-down network formation approaches used in [1] and [7] try to avoid long connections by means of finding the heaviest ksubgraph in the network. These approaches perform well for small networks.…”

Section: Discussionmentioning

confidence: 99%

“…In-network data fusion may not be applicable in all scenarios. In [7], Cheng et al proposed another delay-aware network structure for applications with partially fusible or even non-fusible packets. Shen et al tried to create a minimum spanning tree that can minimize energy consumption of DCPs while satisfy some given delay conditions [8].…”

Section: Related Workmentioning

confidence: 99%

“…In the simulations, performance of the original DADCNS will be used as references. The DADCNS will be constructed as a single cluster and multiple clusters using the topdown network formation approaches proposed in [1] and [7], respectively. In order to evaluate the effect from network size (N ) to the performance of networks with different network structures, N is varying from 3 to 99 with a step-size of 3.…”

Section: A Simulation Settingsmentioning

confidence: 99%

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To Split or Not to Split? From the Perspective of a Delay-Aware Data Collection Network Structure

Cheng

Ganganath

2013

2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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Abstract-Collecting data from massive numbers of individual nodes is always a challenging task in wireless sensor networks. The duration of a data collection process, which can greatly affect the detection capabilities of a network, should be reduced whenever possible. For scenarios where only a single cluster is allowed, the delayaware data collection network structure can minimize the duration of a data collection process. The aim of this paper is to explore the possibilities of improving the original delay-aware network structure by splitting the single tree structure into multiple clusters. Analyses on the conditions and effects of splitting the aforementioned structure are presented. Based on the analyses, two novel network splitting algorithms using k-means clustering algorithms are proposed. Simulation results show that the proposed network splitting algorithms may further reduce the duration of a data collection process. With the help of the k-means algorithms, communication distance among sensor nodes can be further reduced especially for networks with large numbers of wireless sensor nodes.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Simulation Settingsmentioning

confidence: 99%

See 1 more Smart Citation

To Split or Not to Split? From the Perspective of a Delay-Aware Data Collection Network Structure

Cheng

Ganganath

2013

2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

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“…They also showed that a data collection process can be significantly shortened by employing devices with multiple transceivers. The above works provided the foundations for the development of delay-aware data collection network structures in [6], [10], [11]. In [6], Cheng et al introduced a delay minimized network structure for fusible data and its corresponding formation algorithms for centralized and distributed systems.…”

Section: Related Workmentioning

confidence: 99%

“…In [6], Cheng et al introduced a delay minimized network structure for fusible data and its corresponding formation algorithms for centralized and distributed systems. In [10], Cheng et al introduced another network structure to facilitate opportunistic in-network data fusion, which the upper bound will never exceed that of a star network. For sensory systems that require consecutive data collection processes, a delay-aware network structure and its formation algorithm have been proposed in [11] to resolve conflicts among transmission schedules.…”

Section: Related Workmentioning

confidence: 99%

Concurrent Data Collection Trees for IoT Applications

Cheng

Ganganath

Fok

2017

IEEE Trans. Ind. Inf.

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Abstract-Internet of Things (IoT) systems comprise massive volumes of smart devices. Through exchanges of information, smart objects are capable of reasoning and generate higher level of intelligence. The effectiveness of data collection processes is a key factor to the success of IoT systems as it can seriously affect the freshness of the captured data. Efficient data collection processes have been well-studied on sensory systems with static topologies and single data extraction point. Smart devices in IoT systems are often shared by different parties, therefore concurrent data collection processes are always expected. Such a unique characteristic of IoT systems has imposed new challenges to the designs of efficient data collection processes. In this paper, concurrent data collection trees specifically designed for IoT applications are proposed. It is shown that, comparing with an existing single-user data collection structure, systems with the proposed tree structures can significantly shorten their concurrent data collection processes.

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Sparse random compressive sensing based data aggregation in wireless sensor networks

Yin

Liu

Guo

et al. 2018

Concurrency and Computation

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SummaryIn wireless sensor networks (WSNs), the volume of data is increasing at an unpredictable rate, which inevitably leads to high spatial‐temporal correlation. To eliminate data redundancy, some researchers have proposed many data aggregation methods. However, a few of aggregation approaches can handle energy consumption and latency simultaneously. Therefore, in this paper, we propose an efficient algorithm, called Delay‐Minimum Energy‐Balanced (DMEB) data aggregation, which benefits from the superiority of the sparse random measurement matrix and minimum delay algorithm. Owing to the sparsity characteristics of the measurement matrix, only the nodes whose corresponding elements in the matrix are non‐zero take part in the measurement. Each measurement can form an aggregation tree with minimum delay. After a sink node receives all the measurements, original readings can be recovered precisely. In addition, we adopt a novel scheduling method to avoid information interference. Experiment results demonstrate that, under recovering the original data accurately, the proposed data aggregation algorithm can not only shorten delay in data collection process but also reduce communication cost and prolong network lifetime during data transmission process.

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A Delay-Aware Network Structure for Wireless Sensor Networks With In-Network Data Fusion

Cited by 74 publications

References 23 publications

To Split or Not to Split? From the Perspective of a Delay-Aware Data Collection Network Structure

To Split or Not to Split? From the Perspective of a Delay-Aware Data Collection Network Structure

Concurrent Data Collection Trees for IoT Applications

Sparse random compressive sensing based data aggregation in wireless sensor networks

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