Reducing Average Power in Wireless Sensor Networks through Data Rate Adaptation

Lanzisera, Steven; Mehta, Ankur; Pister, Kristofer S. J.

doi:10.1109/icc.2009.5199403

Cited by 47 publications

(36 citation statements)

References 11 publications

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“…The authors of [10] propose DRACER (Dynamic Rate Adaptation and Control for Energy Reduction), which is an addition to the IEEE 802.15.4 specification along with a media access layer extension to select the appropriate data rate. In [11], the authors present an adaptive-CSMA/CA protocol, which accounts for varying channel and load conditions at a node by influencing the selection of either low energy or low delay transmission option.…”

Section: A Rate Adaptation In Wsnsmentioning

confidence: 99%

“…As mentioned in [10], they add 500kbps, 1000kbps and 2000kbps data rates to the existing data rate 250kbps in IEEE 802.15.4 specification [34] with a minimum of hardware changes. That makes variable data rate adaptation feasible for this work.…”

Section: ) Bsn Snr-pdr Mappingmentioning

confidence: 99%

“…Therefore, we define that the data rate r m in BSN is valued in the set r BSN : {250kbps, 500kbps, 1000kbps, 2000kbps}, namely, r m ∈ r BSN . Based on the analysis in [10], we obtain the map of SNR to PDR under the data rate r m in BSN:…”

Section: ) Bsn Snr-pdr Mappingmentioning

confidence: 99%

“…For WSNs, the authors in [10] present an addition to the 802.15.4 specification adding 500kbps, 1000kbps and 2000kbps data rates to the existing 250kbps with a minimum of hardware changes. This approach provides us variable data rates in BSN.…”

Section: Introductionmentioning

confidence: 99%

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Towards Energy Optimization Using Joint Data Rate Adaptation for BSN and WiFi Networks

et al. 2012

2012 IEEE Seventh International Conference on Networking, Architecture, and Storage

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Abstract-Body sensor networks (BSNs) and WiFi networks have been widely investigated due to the availability of sensor motes and WiFi devices, but they are commonly deployed separately. In this paper we propose to optimize the total communication energy consumption of BSN and WiFi (BSN-WiFi) networks using joint data rate adaptation. More specifically, we first elaborate the BSN-WiFi network system in four consecutive phases. Then based on the system, we analyze the communication energy consumption, throughput and time delay, and provide a signal-to-noise ratio and packet delivery ratio (SNR-PDR) mappings of BSN and WiFi networks. Next, we build an energy optimization model with constraints of SNR-PDR mappings, throughput, and time delay to minimize the total communication energy consumption in BSN-WiFi networks. With the input of SNR values, we solve this model by cvx to obtain the output of optimal data rates associated with SNR values, which are then tabulated for online data rate adaptation. Finally, we collect 20-minute traces from a specific BSN-WiFi network system for performance evaluation, and the results demonstrate that our optimal data rate solution achieves up to 86% energy savings comparing with the solutions using fixed data rates.

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Section: A Rate Adaptation In Wsnsmentioning

confidence: 99%

Section: ) Bsn Snr-pdr Mappingmentioning

confidence: 99%

Section: ) Bsn Snr-pdr Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Energy Optimization Using Joint Data Rate Adaptation for BSN and WiFi Networks

et al. 2012

2012 IEEE Seventh International Conference on Networking, Architecture, and Storage

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“…The condition of the signal in sensor networks can be characterized by signal SNR. To guarantee the integrity of received sensor signals, sensor networks often pose SNR requirements for the received sensor data [14,15].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Sensor Network Data Quality via Collaborated Circuit and Network Operations

Gao

Wang

Weng

et al. 2013

JSAN

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Abstract:In many applications, the quality of data gathered by sensor networks is directly related to the signal-to-noise ratio (SNR) of the sensor data being transmitted in the networks. Different from the SNR that is often used in measuring the quality of communication links, the SNR used in this work measures how accurately the data in the network packets represent the physical parameters being sensed. Hence, the signal here refers to the physical parameters that are being monitored by sensor networks; the noise is due to environmental interference and circuit noises at sensor nodes, and packet loss during network transmission. While issues affecting SNR at sensor nodes have been intensively investigated, the impact of network packet loss on data SNR has not attracted significant attention in sensor network design. This paper investigates the impact of packet loss on sensor network data SNR and shows that data SNR is dramatically affected by network packet loss. A data quality metric, based on data SNR, is developed and a cross-layer adaptive scheme is presented to minimize data quality degradation in congested sensor networks. The proposed scheme consists of adaptive downsampling and bit truncation at sensor nodes and intelligent traffic management techniques at the network level. Simulation results are presented to demonstrate the validity and effectiveness of the proposed techniques.

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Optimizing the throughput‐lifetime tradeoff in wireless sensor networks with link scheduling, rate adaptation, and power control

Kaddour¹

2015

Wireless Communications

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Throughput and lifetime are usually conflicting objectives in designing wireless sensor networks; hence, the right balance needs to be found. With this aim in view, we address in this paper the problem of minimizing the frame length defined within a time division multiple access scheme and the problem of maximizing network lifetime subject to a maximum frame length. The pursued solution in either case leverages a wide range of parameters related to coverage, routing, transmission power, and data rate. Furthermore, it is consistent with the physical interference model. To this end, we rely on column generation technique to derive near-optimal solutions even when the integrality constraints on coverage and flow variables are enforced. Moreover, we propose a polynomial-time heuristic algorithm to solve efficiently the underlying NP-hard problem of concurrent link selection with discrete power control and rate adaptation. Simulation results show that our heuristic algorithm leads to solutions within 3% of optimality while saving around 99% of computation time. Besides, the results illustrate the significant impact of power control and rate adaptation on throughput and lifetime improvement. Interestingly, we found that network lifetime can be significantly prolonged when traffic demands are sufficiently low at the affordable cost of small decrease in throughput.

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Reducing Average Power in Wireless Sensor Networks through Data Rate Adaptation

Cited by 47 publications

References 11 publications

Towards Energy Optimization Using Joint Data Rate Adaptation for BSN and WiFi Networks

Towards Energy Optimization Using Joint Data Rate Adaptation for BSN and WiFi Networks

Enhancing Sensor Network Data Quality via Collaborated Circuit and Network Operations

Optimizing the throughput‐lifetime tradeoff in wireless sensor networks with link scheduling, rate adaptation, and power control

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