Limits...
A data acquisition protocol for a reactive wireless sensor network monitoring application.

Aderohunmu FA, Brunelli D, Deng JD, Purvis MK - Sensors (Basel) (2015)

Bottom Line: It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes.The results from our in-house deployment testbed of 15 nodes have proven to be favorable.On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy.

View Article: PubMed Central - PubMed

Affiliation: Information Science Department, University of Otago, Dunedin 9016, New Zealand. femi.aderohunmu@otago.ac.nz.

ABSTRACT
Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain.

No MeSH data available.


Related in: MedlinePlus

A front view of a WISPES W24TH node prototype.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4482014&req=5

f6-sensors-15-10221: A front view of a WISPES W24TH node prototype.

Mentions: The SWIFTNET algorithm has been implemented on WISPES W24TH nodes, shown in Figure 6. It is based on the Jennic microprocessor [60], one of the few 32-bit architectures available in the market with a power consumption comparable to a TelosB [61]. It is equipped with 128 KB RAM, 512 KB FLASH with 2.4 GHz radio transceiver (IEEE802.15.4 compliant) and Zigbee Pro compliant [62]. It utilizes an aggressive power management method using 8 μA in sleep mode, 17 mA in transmit mode, and 23 mA in receive mode; this guarantees a longer battery lifetime. The details of the hardware design and the stack are presented in [63]. A dual client control platform is used that consists of MATLAB for the high-end reconstruction algorithm and the CACTI [64] a powerful network graphic tool for visualization, online data management, and storage.


A data acquisition protocol for a reactive wireless sensor network monitoring application.

Aderohunmu FA, Brunelli D, Deng JD, Purvis MK - Sensors (Basel) (2015)

A front view of a WISPES W24TH node prototype.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482014&req=5

f6-sensors-15-10221: A front view of a WISPES W24TH node prototype.
Mentions: The SWIFTNET algorithm has been implemented on WISPES W24TH nodes, shown in Figure 6. It is based on the Jennic microprocessor [60], one of the few 32-bit architectures available in the market with a power consumption comparable to a TelosB [61]. It is equipped with 128 KB RAM, 512 KB FLASH with 2.4 GHz radio transceiver (IEEE802.15.4 compliant) and Zigbee Pro compliant [62]. It utilizes an aggressive power management method using 8 μA in sleep mode, 17 mA in transmit mode, and 23 mA in receive mode; this guarantees a longer battery lifetime. The details of the hardware design and the stack are presented in [63]. A dual client control platform is used that consists of MATLAB for the high-end reconstruction algorithm and the CACTI [64] a powerful network graphic tool for visualization, online data management, and storage.

Bottom Line: It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes.The results from our in-house deployment testbed of 15 nodes have proven to be favorable.On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy.

View Article: PubMed Central - PubMed

Affiliation: Information Science Department, University of Otago, Dunedin 9016, New Zealand. femi.aderohunmu@otago.ac.nz.

ABSTRACT
Limiting energy consumption is one of the primary aims for most real-world deployments of wireless sensor networks. Unfortunately, attempts to optimize energy efficiency are often in conflict with the demand for network reactiveness to transmit urgent messages. In this article, we propose SWIFTNET: a reactive data acquisition scheme. It is built on the synergies arising from a combination of the data reduction methods and energy-efficient data compression schemes. Particularly, it combines compressed sensing, data prediction and adaptive sampling strategies. We show how this approach dramatically reduces the amount of unnecessary data transmission in the deployment for environmental monitoring and surveillance networks. SWIFTNET targets any monitoring applications that require high reactiveness with aggressive data collection and transmission. To test the performance of this method, we present a real-world testbed for a wildfire monitoring as a use-case. The results from our in-house deployment testbed of 15 nodes have proven to be favorable. On average, over 50% communication reduction when compared with a default adaptive prediction method is achieved without any loss in accuracy. In addition, SWIFTNET is able to guarantee reactiveness by adjusting the sampling interval from 5 min up to 15 s in our application domain.

No MeSH data available.


Related in: MedlinePlus