The Energy-per-Useful-Bit Metric for Evaluating and Optimizing Sensor Network Physical Layers

Ammer, J.; Rabaey, Jan M.

doi:10.1109/sahcn.2006.288533

Cited by 55 publications

(25 citation statements)

References 8 publications

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“…To analyze (13), it is more convenient to rewrite it in the following format: (24) Similar to the blind setup we can prove that if the feasible set of (24) is not empty, then the optimal is attained for . Using this result, we can relax one of the arguments of the problem.…”

Section: B Knowledge-aided Setupmentioning

confidence: 99%

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Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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Abstract-Reliability and energy consumption in detection are key objectives for distributed spectrum sensing in cognitive sensor networks. In conventional distributed sensing approaches, although the detection performance improves with the number of radios, so does the network energy consumption. We consider a combined sleeping and censoring scheme as an energy efficient spectrum sensing technique for cognitive sensor networks. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on the detection performance, by optimally choosing the sleeping and censoring design parameters. The constraint on the detection performance is given by a minimum target probability of detection and a maximum permissible probability of false alarm. Depending on the availability of prior knowledge about the probability of primary user presence, two cases are considered. The case where a priori knowledge is not available defines the blind setup; otherwise the setup is called knowledge-aided. By considering a sensor network based on IEEE 802.15.4/ZigBee radios, we show that significant energy savings can be achieved by the proposed scheme.Index Terms-Cognitive sensor networks, detection and fusion performance, distributed spectrum sensing.

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Section: B Knowledge-aided Setupmentioning

confidence: 99%

“…The transmitter dissipates energy to run the radio electronics and the power amplifier. Following the model in [21] and [24], to transmit one bit over a distance , the radio spends:…”

Section: B Case Study For Ieee 802154/zigbeementioning

confidence: 99%

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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“…An awake cognitive sensor computes the received signal energy and locally decides on the presence or absence of the licensed system based on the decision rule in (1). If it comes up with a decision, then it sends its decision result to the FC.…”

Section: Distributed Detection Performance Analysismentioning

confidence: 99%

Energy-efficient spectrum sensing for cognitive sensor networks

Maleki

Pandharipande

Leus

2009

2009 35th Annual Conference of IEEE Industrial Electronics

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Abstract-We consider a combined sleeping and censoring scheme for energy-efficient spectrum sensing in cognitive sensor networks. We analyze the detection performance of this scheme by theoretically deriving the global probabilities of detection and false-alarm. Our goal is to minimize the energy consumption incurred in distributed sensing, given constraints on the global probabilities of detection and false-alarm, by optimally designing the sleeping rate and the censoring thresholds. Using specific transceiver models for sensors based on IEEE 802.15.4/ZigBee, we show the energy savings achieved under an optimum choice of the design parameters. I. INTRODUCTIONThe family of wireless networks -sensor networks, personal area networks, local area networks, cellular networks etc has seen tremendous growth recently, resulting in demand for radio spectrum. Traditionally, radio spectrum allocation has been based on exclusive, licensed use of portions of spectrum to wireless systems. This has resulted in a perceived dearth of spectrum available for use for newer wireless networks and applications. Radio spectrum measurements [13] however indicate that large portions of spectrum licensed to wireless systems remain under-utilized. Consequently there is a growing interest in unlicensed use of empty portions in order to improve spectrum utilization [3], [5], [17]. A promising approach for such secondary spectrum access is the use of cognitive radios. A cognitive radio can alter its radio transmission parameters autonomously based on active monitoring of spectrum in order to access spectrum on a secondary basis while coexisting with licensed systems or other unlicensed systems.In this paper, we consider a cognitive sensor network that performs spectrum sensing in order to determine empty radio channels and limits its transmissions on channels that are found vacant in order to reduce harmful interference to licensed systems. Our study is motivated by recent developments in regulatory and standardization bodies aimed at permitting the use of portable devices and low-power sensors to operate on a secondary basis in VHF-UHF bands licensed to television broadcasting systems. In this context, reliable spectrum sensing that is energy efficient is critical.The cognitive sensor network comprises of a fusion center (FC) and a number of cognitive sensors that carry out sensing

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“…The metric of network lifetime is also used (e.g., see Bhardwaj [8]), where energy efficiency is represented by the total operational time of a network. Ammer and Rabaey [9] propose a metric of energy-per-useful-bit (EPUB) to evaluate and compare sensor network physical layers.…”

Section: Figure Of Meritmentioning

confidence: 99%

Energy-Aware Data Compression for Wireless Sensor Networks

Puthenpurayil

Bhattacharyya

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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Data compression techniques have extensive applications in power-constrained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit D/E, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit.Index Terms-DSP software, lossless data compression, linear predictive coding, low power design, wireless sensor networks.

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The Energy-per-Useful-Bit Metric for Evaluating and Optimizing Sensor Network Physical Layers

Cited by 55 publications

References 8 publications

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Energy-efficient spectrum sensing for cognitive sensor networks

Energy-Aware Data Compression for Wireless Sensor Networks

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