A lightweight sensor scheduling algorithm for clustered wireless sensor networks

Liberati, Francesco; Oddi, Guido; Lanna, Andrea; Pietrabissa, Antonio

doi:10.1109/med.2015.7158881

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…This strengthens the rationale for aggregating the data as much as possible in the data source-sink(s) path. A key to the WSN energy saving is also the scheduling algorithm [25], which selects an optimal subset of sensors that are allowed to measure/transmit data at a certain time based on the current health status of the nodes to be scheduled. In addition, the WSN energy performance is dependent on both the selected hardware platform and the data processing algorithms.…”

Section: Wsn Data Processing: Overview and Objectivesmentioning

confidence: 99%

“…A lightweight sensor scheduling algorithm is used [25], which introduces dynamically variable schedules with the aim of fairly distributing the sensing tasks among the nodes based on a feedback on the current health status of the nodes to be scheduled. In addition, data fusion algorithms could be applied to the sensor data to further reduce the data size.…”

Section: P1(t) P6(t) P5(t) P4(t) P3(t) P2(t)mentioning

confidence: 99%

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Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

Zhai

Vladimirova

2016

Journal of Aerospace Information Systems

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Abstract:The SWIPE project (Space Wireless Sensor Networks for Planetary Exploration) aims to design a wireless sensor network (WSN) , which consists of small wireless sensor nodes dropped onto the Moon surface to collect scientific measurements. Data gathered from the sensors will be processed and aggregated for uploading to a lunar orbiter and subsequent transmission to Earth. In this paper efficient data processing/fusion algorithms are proposed, the purpose of which is to integrate the scientific sensor data collected by the WSN, reducing the data volume without sacrificing the data quality to satisfy energy constraints of WSN nodes operating in the extreme Moon environment. The results of an extensive simulation experiment targeted at the SWIPE lunar exploration mission is reported, which quantifies the performance efficiency of the data processing scheme. It is shown that the proposed data processing algorithms can reduce the WSN node energy consumption significantly, decreasing the data transmission energy up to 91%. In addition, it is shown that up to 99% of the accuracy of the original data can be preserved in the final reconstructed data.

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Section: Wsn Data Processing: Overview and Objectivesmentioning

confidence: 99%

Section: P1(t) P6(t) P5(t) P4(t) P3(t) P2(t)mentioning

confidence: 99%

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

Zhai

Vladimirova

2016

Journal of Aerospace Information Systems

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A Two-Layer Controller Scheme for Efficient Signal Reconstruction and Lifetime Elongation in Wireless Sensor Networks

Frezzetti

Manfredi

2016

IEEE Sensors J.

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Abstract:\ud Random sampling compressive sensing (RSCS) is a prominent compression algorithm suitable for wireless sensor network as it can significantly reduce the number of samples required for signal reconstruction. This improves the network lifetime, but can introduce uncertainty in the signal reconstruction. In this paper, we propose a two-layer controller that ensures efficient signal reconstruction (i.e., low level of reconstruction error) and high network lifetime. The proposed controller is composed of a global controller, developed at the fusion center layer, and two local controllers, implemented at each node layer. The former steers the RSCS reconstruction error to a desired value, while the latter are implemented to reduce the energy consumption of each node. A performance evaluation of the proposed scheme is carried out in terms of reconstruction error regulation and network lifetime. Simulation results show the effectiveness of the proposed scheme compared with two main strategies existing in the literature

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A lightweight sensor scheduling algorithm for clustered wireless sensor networks

Cited by 2 publications

References 17 publications

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

Efficient Data-Processing Algorithms for Wireless-Sensor-Networks-Based Planetary Exploration

A Two-Layer Controller Scheme for Efficient Signal Reconstruction and Lifetime Elongation in Wireless Sensor Networks

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