A Node Sleeping Algorithm for WSNs Based on the Minimum Hop Routing Protocol

Ren, Zhi; Sai-nan, Shi; Wang, Qing-Ming; Yao, Yukun

doi:10.1109/caman.2011.5778776

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…It can reduce the energy consumption of the network by minimizing idle listening time and reducing the number of state switching of sensor nodes. In [3], based on the characteristics of the minimum hop routing, the authors designed two different strategies for terminal nodes and intermediate nodes to make sensor nodes periodically sleep and wake up. This method can reduce idle listening time to prolong the network lifetime.…”

Section: Energy-efficient Mechanism For Data Gatheringmentioning

confidence: 99%

“…Current techniques have been developed to support sensory data gathering in WSN and IoT. Specifically, some researchers propose to adopt the efficient distributed wake-up scheduling scheme for gathering sensory data [2,3]. These methods can save energy by making some sensor nodes enter the sleep state, while it is hard to design an efficient routing tree and nodes' activity schedule algorithm, which can simultaneously minimize transmitting time delays and energy consumption, and a hot spot problem isn't the focus of their attention.…”

Section: Introductionmentioning

confidence: 99%

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Energy-efficient sensory data gathering based on compressed sensing in IoT networks

Zhou

Zhang

et al. 2020

J Cloud Comp

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The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may reflect a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented in terms of the category of sensory data. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted one in the cloud. Experiments are conducted and evaluation results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption.

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Section: Energy-efficient Mechanism For Data Gatheringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-efficient sensory data gathering based on compressed sensing in IoT networks

Zhou

Zhang

et al. 2020

J Cloud Comp

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show abstract

“…As sensor nodes are both data originator and data router [17], hence they collaborate to deliver data from source to sink. [15] Distinguishes between terminal nodes which only collect data and the in addition to this data forwarding intermediate nodes. Furthermore the sensor nodes can be consolidated in distinct clusters with a certain cluster head where the sensed data of direct neighbours, which is most likely to have some consonances, is brought together prior to the transmission.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sensor Networks Communication: Energy Efficient Paradigm

Roopashree¹

2014

IJRET

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As the number of application in wireless sensor networks are increasing because of ongoing miniaturization of electro-mechanical parts, its subsidiary elements and drastic decrease in the cost. The communication among sensor nodes consumes more energy than the actual computation of information. The consumption not only takes place in transmission state, but also relatively high in idle state. The focus of this paper is to identify the main source of energy dissipation, as well as counter measure to ensure prolonged network lifetime. In addition to these, different types of protocols for routing, medium access control and transport protocols are presented and clarified by the means of examples.

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A Node Sleeping Algorithm for WSNs Based on the Minimum Hop Routing Protocol

Cited by 2 publications

References 8 publications

Energy-efficient sensory data gathering based on compressed sensing in IoT networks

Energy-efficient sensory data gathering based on compressed sensing in IoT networks

Wireless Sensor Networks Communication: Energy Efficient Paradigm

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