Minimum Energy coding in CDMA Wireless Sensor Networks

Fischione, Carlo; Johansson, Karl Henrik; Sangiovanni-Vincentelli, A.; Ares, Brais

doi:10.1109/twc.2009.080239

Cited by 18 publications

(6 citation statements)

References 17 publications

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“…Extra power is therefore consumed to transmit data to neighbors and to receive data from neighbors. Further, since multiple nodes may want to communicate to each other at the same time, a channel access method for shared medium has to be adopted (TDMA, FDMA, and CDMA scheduling schemes or some combinations of them [2,11,20]). Distributed Source Coding (DSC) [28,44], also known as Slepian-Wolf coding (in the lossless case) [36] and Wyner-Ziv coding (in the lossy case) [42], overcomes the problem of inter-node communication: sensors do not communicate with each other, but send their compressed outputs to a central point, e.g., the base station, which jointly decodes the encoded streams.…”

Section: Introductionmentioning

confidence: 99%

A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

Marcelloni

Vecchio

2010

Evol. Intel.

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Since tiny nodes of a wireless sensor network (WSN) are typically powered by batteries with, due to miniaturization and costs, a limited capacity, with the aim of extending the lifetime of WSNs and making the exploitation of WSNs appealing, a lot of research has been devoted to save energy. Although a number of factors contribute to power consumption, radio communication has been generally considered its main cause and thus most of the techniques proposed for energy saving have mainly focused on limiting transmission/reception of data, for instance, through data compression. As sensor nodes are equipped with limited computational and storage resources, enabling compression requires to develop purposelydesigned algorithms. To this aim, we propose an approach to generate lossy compressors to be deployed on single nodes based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. The quantization levels and thresholds, which allow achieving different trade-offs between compression performance and information loss, are determined by a two-objective evolutionary algorithm. We tested our approach on four datasets collected by real WSN deployments. We show that the lossy compressors generated by our approach can achieve significant compression ratios despite negligible reconstruction errors and outperform LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes.

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Section: Introductionmentioning

confidence: 99%

A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

Marcelloni

Vecchio

2010

Evol. Intel.

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“…This research theme has already been discussed in the literature, but some research challenges persist. The first is reducing energy consumption through spread spectrum transmission [9]. The process of spread spectrum transmission requires more computation process in general.…”

Section: Introductionmentioning

confidence: 99%

A Method to Assign Spread Codes Based on Passive RFID Communication for Energy Harvesting Wireless Sensors Using Spread Spectrum Transmission

Takahashi¹,

Inoue²,

Kawakita³

et al. 2015

Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Considerable research has been conducted on systems that collect real-world information by using numerous energy harvesting wireless sensors. The sensors need to be tiny, cheap, and consume ultra-low energy. However, such sensors have some functional limits, including being restricted to wireless communication transmission. Therefore, when more than one sensor simultaneously transmits information in these systems, the receiver may not be able to demodulate if the sensors cannot accommodate multiple access. To solve this problem, a number of proposals have been made based on spread spectrum technologies for resistance to interference. In this paper, we point out some problems regarding the application of such sensors, and explain the assumption of spread codes assignment based on passive radio frequency identification (RFID) communication. During the spread codes assignment, the system cannot work. Hence, efficient assignment method is more appropriate. We consider two assignment methods and assessed them in terms of total assignment time through an experiment. The results show the total assignment time in case of Electronic Product Code (EPC) Global Class-1 Generation-2 which is an international standard for wireless protocols and the relationship between the ratio of the time taken by the read/write command and the ratio of total assignment time by the two methods. This implies that more efficient methods are obtained by considering the time ratio of read/write command.

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“…Wireless sensor networks interconnected by these low-cost sensor nodes can be used for industrial applications, security surveillance, medical, environment and weather monitoring etc [1], [2]. Usually the sensors are powered by the energy constraint batteries, for which replacement, when possible, is very difficult and expensive.…”

Section: Introductionmentioning

confidence: 99%

Realistic Energy Consumption Model for On-Off Keying Based Minimum Energy Coding

Yue

Andrieux

Diouris

2014

2014 IEEE 79th Vehicular Technology Conference (VTC Spring)

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The main objective of this paper is to present realistic energy consumption models to evaluate the energy consumption including both the transmission energy and the circuit energy at the transmitter when using Minimum Energy coding (ME-coding) On-Off Keying (OOK) scheme, we consider a simple communication link between sensor nodes. We present two energy models with a specific circuit (MAX1472) and we consider also the limitations, introduced by a practical circuit, in terms of symbol bit rate and output power. The results with the first model demonstrate that the total energy consumption of ME-Coding OOK is lower than that of the uncoded OOK, but the optimal choice of the code must take into account the limitations of the circuit. The second model with MAX1472 shutdown mode brings a reduction of the total energy consumption compared with the first model but is limited in the symbol rate. Due to this limitation, the optimal choice is finally the first model with a larger bit rate.Index Terms-Wireless sensor networks (WSNS), Energy efficiency, Minimum energy coding.

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Minimum Energy coding in CDMA Wireless Sensor Networks

Cited by 18 publications

References 17 publications

A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

A two-objective evolutionary approach to design lossy compression algorithms for tiny nodes of wireless sensor networks

A Method to Assign Spread Codes Based on Passive RFID Communication for Energy Harvesting Wireless Sensors Using Spread Spectrum Transmission

Realistic Energy Consumption Model for On-Off Keying Based Minimum Energy Coding

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