Adaptive sleep discipline for energy conservation and robustness in dense sensor networks

Greunen, J. van; Petrović, Dragan; Bonivento, A.; Rabaey, Jan M.; Ramchandran, Kannan; Vincentelli, Alberto Sangiovanni

doi:10.1109/icc.2004.1313225

Cited by 37 publications

(31 citation statements)

References 6 publications

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“…In many scenarios of relevant interest, wireless multi-hop networks do not have fixed communication paths [11], but the end-to-end path followed by packets happens according to a dynamic selection of hops [12,13]. Indeed, it is often impossible to build fixed routing tables for these networks due to the time-varying communication channel and network topology.…”

Section: Wireless Multi-hop Networkmentioning

confidence: 99%

Time-Delay Estimation and Finite-Spectrum Assignment for Control Over Multi-Hop WSN

Witrant

Park²,

Johansson³

2010

Wireless Networking Based Control

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Section: Wireless Multi-hop Networkmentioning

confidence: 99%

Time-Delay Estimation and Finite-Spectrum Assignment for Control Over Multi-Hop WSN

Witrant

Park²,

Johansson³

2010

Wireless Networking Based Control

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“…In the same WSN, communication links are usually subject to different channel conditions [19]. Let's consider a n-link relay network model, where the source, relay and destination nodes form two wireless channels.…”

Section: Figure 4: Example Of a Multi-hop Relay Networkmentioning

confidence: 99%

“…Adaptive sleep (AS) technique, therefore, has been proposed so that node sleep times can be adjusted based on current fading conditions [16]. Most of the time when there is no communication, nodes are powered down and operated at the minimum power level.…”

Section: Energy Optimizationmentioning

confidence: 99%

Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks

Zhao¹,

Bdira²,

Ibnkahla³

2014

IJCA

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This paper introduces cognitive MAC-layer techniques to wireless sensor networks (WSN) to optimize Network survivability. We compare Adaptive Modulation (AM) over flat-fading channels, with data rate and transmit power being varied according to channel conditions with two variants: Adaptive Modulation with Idle mode (AMI) and a new Adaptive Sleep with Adaptive Modulation (ASAM) which dynamically adjusts the transmission and sleep modes based shared global information on channel conditions. These introduced cognitive methods assume power allocation schemes that improve energy efficiency and this node life assuming multi-hop relay networks.Simulation results indicate that a notable reduction in energy consumption can be achieved by jointly adapting the data rate and the transmit power in WSNs. The proposed ASAM algorithm can considerably improve node lifetime compared to AM and AMI. The optimal power control values and optimal power allocation factors are further considered for multi-hop relay networks, respectively, thus reducing the need for higher layer network protocols in local switching.

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“…To overcome this problem, we follow the same approach proposed in [31], where an Additive Increase and Multiplicative Decrease (AIMD) algorithm leads to a fair distribution of the wake-up duties within a single cluster. Specifically, each node that is waiting to forward a data packet observes the time before the first wake-up in the forwarding region.…”

Section: Wake-up Rate and Radio Power Adaptationmentioning

confidence: 99%

“…Some cross-layer design challenges of the physical, MAC, and network layers to minimize the energy consumption of WSNs have been surveyed in [29][30][31]. However, many of the cross-layer solutions proposed in the literature are hardly useful for the application domain we are targeting, because they require sophisticated processing resources, or instantaneous global network knowledge, which are out of the capabilities of real nodes.…”

Section: Introductionmentioning

confidence: 99%

Design Principles of Wireless Sensor Networks Protocols for Control Applications

Fischione

Park

Marco

et al. 2010

Wireless Networking Based Control

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Control applications over wireless sensor networks (WSNs) require timely, reliable, and energy efficient communications. This is challenging because reliability and latency of delivered packets and energy are at odds, and resource constrained nodes support only simple algorithms. In this chapter, a new system-level design approach for protocols supporting control applications over WSNs is proposed. The approach suggests a joint optimization, or co-design, of the control specifications, networking layer, the medium access control layer, and physical layer. The protocol parameters are adapted by an optimization problem whose objective function is the network energy consumption, and the constraints are the reliability and latency of the packets as requested by the control application. The design method aims at the definition of simple algorithms that are easily implemented on resource constrained sensor nodes. These algorithms allow the network to meet the reliability and latency required by the control application while minimizing for energy consumption. The design method is illustrated by two protocols: Breath and TREnD, which are implemented on a test-bed and compared to some existing solutions. Experimental results show good performance of the protocols based on this design methodology in terms of reliability, latency, low duty cycle, and load balancing for both static and time-varying scenarios. It is concluded that a system-level design is the essential paradigm to exploit the complex interaction among the layers of the protocol stack and reach a maximum WSN efficiency.

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Adaptive sleep discipline for energy conservation and robustness in dense sensor networks

Cited by 37 publications

References 6 publications

Time-Delay Estimation and Finite-Spectrum Assignment for Control Over Multi-Hop WSN

Time-Delay Estimation and Finite-Spectrum Assignment for Control Over Multi-Hop WSN

Joint Adaptive Modulation and Adaptive MAC Protocols for Wireless Sensor Networks

Design Principles of Wireless Sensor Networks Protocols for Control Applications

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