Energy-Model-Based Optimal Communication Systems Design for Wireless Sensor Networks

Li, Ye; Qiao, Daji; Xu, Zhikun; Xu, Da; Miao, Fen; Zhang, Yuwei

doi:10.1155/2012/861704

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…The energy-model-based optimal communication systems design for wireless sensor networks are proposed in (Li et al, 2012). While most of the literature focuses either on data compression algorithms or performance analysis of compression schemes for energy efficiency, still analysis is required about performance of compression algorithms for different type of input data.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Chakraborty¹,

Tharini²

2017

American Journal of Applied Sciences

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Wireless Sensor Networks (WSN) are comprised of spatially distributed sensor nodes, where each node contains sensors, processors and transceivers for communicating data. Regardless of the application in which the sensor network is serving, the data generated in the network eventually must be delivered to the sink. However the limited network bandwidth, frequent node/link failure along with the unreliable communication medium poses great challenges for node to node communication in WSN. Hence, energy efficient data compression algorithms are necessary for sensor nodes as they enhance the transmission efficiency in WSN. Compressive sensing is a new compression algorithm in which the input signal is converted into sparse signal and the sparse signal is further converted into a signal of reduded dimension than original signal. The dimensionality reduction improves the transmission efficiency. This new concept is recently applied in WSN, however suitable threshold selection to sparsify the one dimensional sensor reading and suitable sparifying basis for image input data are not considered in literature. Hence, in this paper analysis of compressive sensing algorithm with a suitable threshold selection is performed in order to increase the level of sparsity for one dimensional data and a suitable sparsifying basis selection is performed for image data. Results indicate that compressive sensing with suitable threshold selection improves transmission and bandwidth efficiency in case of low correlated one dimensional sensor data and a suitable basis improves the quality of transmission for image sensor data and hence the overall lifetime of sensor network can be increased.

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Chakraborty¹,

Tharini²

2017

American Journal of Applied Sciences

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“…where P ac , P sl are the power consumed in the ACTIVE- [7], [9], [14], [15], [22] f c = 951.10M Hz T = 100ms a loss = 15.5 L = 2Kb b loss = 5.38 P c = 12.5mW δ N = 5.35…”

Section: System Level Energy Consumption Modelmentioning

confidence: 99%

Energy Efficient Transmission Approach for WBAN Based on Threshold Distance

Chen

Wang

2015

IEEE Sensors J.

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Energy efficiency is a key concern for wireless sensor nodes, especially for wireless body area network (WBAN) in which sensors operate in close vicinity to, on or even inside a human body. In this paper, we firstly present a system level energy consumption model associated with transmission distance d and transmission data rate over on-body wireless communication link. Then, based on the analysis of tradeoff between circuit energy and transmission energy on distance, a threshold distance d th which is responsible for the proportion of transmission energy and circuit energy is derived for energy saving in WBAN. With the case of d ≤ d th , since circuit energy is comparable to transmission energy consumption, the total energy consumption can be saved by optimizing transmission data rate R. Simulation results show that a 59.77% or even more energy saving is achievable by using the optimized scheme, compared with baseline scheme. With d > d th , since the total energy consumption is monotonically decreasing with respect to time t, an offline algorithm is applied to energy saving by prolonging transmission time within the deadline time. Additionally, on the basis of the offline algorithm, a battery-aware transmission approach is presented for WBAN by using battery electrochemical property. Experimental results show that, by using the presented batteryaware approach, 71.05% and 60.81% energy saving can be obtained, in comparison with the baseline and offline schemes, respectively.Index Terms-Energy efficiency, wireless body area network (WBAN), threshold distance, battery, recovery effect.

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“…Because radio sections in sensor nodes contribute to the overall energy dissipation to a large extent [6,7], research has focused on radio design to achieve low-power transceivers by following different approaches. One of them is to investigate the power consumption of different RF blocks and try to come up with energy consumption models (as in [8][9][10]) subject to a number of conditions. Using these models, existing design tradeoffs can be combined together to find the optimum values for some design and operating parameters from the energy efficiency perspective.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], both previously-mentioned tradeoffs were combined in one analysis to minimize the total average power consumption in a duty-cycled transceiver. Li et al in [10] studied the conventional RF architectures for four different modulation techniques (on-off keying, phase-shift keying, quadrature amplitude modulation, and frequency-shift keying). For each modulation, they presented a model for the consumed energy per bit, and optimized for different bandwidth (narrow, medium, or wide) and data rate (low, middle, or high) requirements.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design Parameters for Ultra-Low-Power RF Transceivers in Wireless Sensor Networks

Mahfouz

Meijerink

Bentum

2016

2016 IEEE Global Communications Conference (GLOBECOM)

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In wireless sensor networks, the need for ultra-low power consuming nodes is one of the main motivations for research in such field. Because radio sections in sensor nodes contribute to a large extent to the overall power consumption, the focus of this study is on the RF transceiver. The aim is to reduce the average power consumption which depends significantly on the circuit architecture design, operating data rate, and duty cycle. In a symmetric communicating system, due to the tradeoff between transmitting power and receiver sensitivity on one hand, as well as between phase noise tolerance and power dissipation in local oscillators on the other hand, the design and operating parameters of the transceiver need to be determined from the perspective of the average power consumption. Therefore, in our study, as an initial step in system design, the optimum for instantaneous data rate, noise figure, and oscillator power budget are analytically determined. The analysis is carried out, taking into consideration an existing in-channel wideband interference, on two transceiver architectures: RF envelope detection and conventional heterodyne. The transceiver in both architectures employs on-off-keying modulation and duty cycling. The optimums are then calculated numerically based on design constants obtained from a frequently-cited RF envelope transceiver, indicating that an energy efficiency improvement of up to 5 dB can still be achieved.

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Energy-Model-Based Optimal Communication Systems Design for Wireless Sensor Networks

Cited by 7 publications

References 19 publications

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Performance Analysis of Threshold Based Compressive Sensing Algorithm in Wireless Sensor Network

Energy Efficient Transmission Approach for WBAN Based on Threshold Distance

Optimum Design Parameters for Ultra-Low-Power RF Transceivers in Wireless Sensor Networks

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