Energy Performance of High Data Rate and Low Power Transceiver

based Wireless Body Area Networks

Nanhao Zhu and Ian O’Connor

Université de Lyon, Institut des Nanotechnologies de Lyon (INL) – UMR5270 – CNRS,

Ecole Centrale de Lyon, 36 av. Guy de Collongue, 69134 Ecully, France

Keywords: Energy Consumption, WBANs, Energy Performance, SystemC, Enhanced ShockBurst (ESB), ShockBurst

(SB), nRF24L01+, Energy-efficient Strategies.

Abstract: Emerging Wireless Body Area Networks (WBANs) are receiving increasing interest from researchers and

designers. Specific requirements for small-scale dimensions, low-latency, lightweight and limited power

capacity mean that the key challenge in WBANs design is in the adoption of energy-efficient strategies for

better system performance, and in the efficient use of high data-rate and ultra-low-power transceivers. This

paper presents a high-level energy-aware SystemC-based model and simulation of Nordic’s Enhanced

ShockBurst (ESB) and ShockBurst (SB) baseband protocol engine. The model includes data from Energy

consumption experiments using nRF24L01+ transceiver, enabling detailed exploration of energy

conversation strategies. With this model, we show that a high data-rate ESB and SB transmission at 2Mbps

can save more than 60% and 80% energy respectively, and it has 3x higher lifetime expectancy than the

250Kbps low data-rate communication with a payload collecting strategy.

1 INTRODUCTION

In recent years, a new type of network known as

wireless body area networks (Chen et al., 2011);

(Latré et al., 2011) has attracted increasing interest

from academia and industry alike. Generally, a

WBAN node consists of ultra-low-power, small-

size, lightweight sensors coupled to RF transceiver

for wireless communication as well as

microcontroller for processing. WBANs are usually

used in patient monitoring, sports and fitness

applications. With the wearable and implantable

features, the design of a WBAN sensor node is

strictly constrained to small dimensions and low

weight. Batteries which account for a significant part

of these metrics in the overall system, are

consequently limited in size and weight, which in

turn results in reduced energy capacity. For

implanted devices which must operate over a

lifetime of months or more typically years, the

battery replacement or recharging is inconvenient

and costly. Hence, energy-efficient devices and

sensing/communication strategies are crucial

considerations for the design of a WBAN sensor

node.

The focus on energy consumption optimization is

mainly on the radio transceiver, since wireless

communication is typically the most power

consuming part (Latré et al., 2011); (Weder, 2010).

With high energy consumption, existing Bluetooth-

based device is not suitable for WBANs while

ZigBee-based device cannot satisfy latency

requirements of some WBAN scenarios because of

its low data-rate. High data rate and ultra-low-power

transceiver can be a good solution.

The Nordic nRF24L transceiver series (Nordic

homepage) can support up to 1Mbps/2Mbps high

data rate and they are ultra-low-power compared

with other frequently used devices (Zhu et al., 2011),

and its built-in MultiCeiver function (nRF24L01+

datasheet) supports a simple star network topology,

which is typically preferred by most WBAN

applications.

In order to predict in an accurate way of the

system behavior and network performance of the

designed WBAN in an application context, and to

further explore energy-efficient communication

strategies, it is necessary to build a high-level and

energy-aware model of such a transceiver. We first

present a SystemC-based simulation models for both

Enhanced ShockBurst (ESB) and ShockBurst (SB)

mode (nRF24L01+ datasheet), which are the Nordic

141

Zhu N. and O’Connor I..

Energy Performance of High Data Rate and Low Power Transceiver based Wireless Body Area Networks.

DOI: 10.5220/0004310901410144

In Proceedings of the 2nd International Conference on Sensor Networks (SENSORNETS-2013), pages 141-144

ISBN: 978-989-8565-45-7

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

embedded protocol engines for its 2.4GHz

transceivers. Then we use this SystemC based model

to simulate the energy consumption of various

WBAN scenarios and extract a detailed analysis of

energy performance.

The organization of the rest of the paper is as

follows. In section 2, some related works are

described. In section 3, we first briefly present the

modeling concept and then show some experimental

simulation results with detailed analysis. Finally, we

conclude in section 4.

2 RELATED WORKS

Nordic’s ESB- and SB-based nRF24L transceiver

series are widely used in WBAN application

scenarios by many research communities.

By using nRF24L01, (Sonavane et al., 2009)

propose an adaptive power control algorithm by

simply configuring the programmable output power

on transceiver.

A CSMA/CA based MAC protocol (Zhurong et

al., 2008) is proposed and tested in a nRF24L01-

based pulse oximetry sensor with some experimental

results, however no energy consumption data.

(Weder, 2010) presents a system level simulation

model of nRF24L01 in MiXiM, and simulation

results on energy consumption are given only at

1Mbps data rate and 0dBm power scenario.

In this work, our experimental results provide a

comprehensive energy consumption analysis in both

ESB- and SB-modes.

3 MODELING AND CASE STUDY

To the best of our knowledge, this paper is the first

to propose a SystemC-based simulation model for

Nordic high data-rate and ultra-low-power nRF

transceivers as used in WBANs. We extend Nordic

nRF24L transceiver series module to a SystemC-

based simulator (Wan Du et al., 2011), our

transceiver module supports both ESB and SB-

modes and as the ESB-supported transceiver (e.g.

nRF24L01) model is backward-compatible with SB-

based transceiver (e.g. nRF24E1) models, so

heterogeneous simulations can be handled.

In SystemC simulation, SC_THREAD process

(Black and Donovan, 2009) is used to model ESB-

and SB-modes in the finite state machine. Figure 1

shows a brief state diagram.

Figure 1: Brief State Diagram ESB and SB Mode.

Another feature of the transceiver module is that

the register configuration method is used, so

different nodes objects could maintain individual

settings during simulation.

In this section, an ECG monitoring application is

studied to analyze the energy consumption, and

some optimization strategies are proposed. The

energy performance is focused on the use of the

Nordic hardware embedded ESB- and SB- modes.

The sensor node of the ECG monitoring application

is equipped with an ECG lead sampling at 200 Hz,

and the size of each data is set to 2 bytes. While the

nRF24L01-based platform is widely used in

WBANs for ECG applications (Weder, 2010);

(Zhurong et al., 2008), we base our experiments on

the more recent nRF24L01+ due to its drop-in

device compatibility and improved RF performance.

3.1 Energy Consumption of ESB and

SB Mode

This experiment analyzes energy consumption with

ESB- and SB- modes for varying output power and

data rate values. Based on our ECG application

scenario, a packet containing 2 bytes of payload data

is transmitted every 5ms by PTX (Primary

Transmitter) and the simulation runs for a

transmission of 500 packets. The PRX (Primary

Receiver) is configured in continuous RX mode, and

default values are used for all other packet

parameters (1 byte preamble, 5 bytes address, 1 byte

CRC, power supply of 3.3 V). Figure 2 shows the

simulation results and the energy consumptions are

compared in Table 1.

SENSORNETS2013-2ndInternationalConferenceonSensorNetworks

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Figure 2: Energy Consumption of ESB and SB Mode.

Table 1: Energy Consumption of ESB and SB.

Data Rate

ESB (PTX)

0dBm VS -18dBm

SB (PTX)

0dBm VS -18dBm

2Mbps 5.35% higher 9.84% higher

1Mbps 8.45% higher 15.65% higher

250Kbps 15.02% higher 28.06% higher

Data Rate

ESB (PTX) SB (PTX)

2Mbps@PTX

(Energy efficient)

65% total

energy is saved

in 2Mbps

83% total

energy is saved

in 2Mbps

250Kbps@PTX

(Energy Consumption)

3.2 Detailed Consumption and Radio

Task Profiling

By using the same application as in part A, Table 2

shows the detailed energy consumption information

and radio task time profiling.

Table 2: Energy Consumption and Task Time.

Detailed Energy Consumption (mJ) @2Mbps/0dBm

ESB-PTX ESB-PRX SB-PTX SB-PRX

Standy1 0.20 0 0.21 0

RX_settling 1.91 1.91 0 1.91

TX_settling 1.72 1.73 1.72 0

TX 0.75 0.60 0.67 0

RX 0.74 105.14 0 108.32

Task Profiling (ms) @2Mbps/0dBm

Standy1 2339.8 0 2423.2 0

RX_settling 65 65 0 65

TX_settling 65 65 65 0

TX 20 16 18 0

RX 16 2360 0 2431.3

For PTX devices in ESB- and SB- modes, large

amount of energy can be saved in low-power

Standby1 mode (less than 4% total energy, over 90%

task time). For PRX devices in ESB- and SB-

modes, the RX mode takes over 90% task time and

also consumes over 95% total energy. The total

energy consumption of PRX in ESB-mode is about

20x that of PTX, while in SB- mode, it rises to about

42x.

3.3 Energy Consumption, Packet

Payload and Lifetime

The first part of Figure 3 shows the relationship

between energy consumption and packet payload.

When compared with (Weder, 2010), our simulation

results provide more information on different

transceiver data rate scenarios (all in ESB-mode):

transmission of packet with 2 bytes payload every

5ms, then 4 bytes every 10ms until 30 bytes every

75ms. The total payload during transmission is the

same (0.4 bytes/ms), and all the simulations are

launched for the same duration. For implanted

devices, WBANs are likely to have higher packet

error rate (PER) due to the impact of cutaneous and

subcutaneous tissue, as well as body movement

(Chen et al., 2011); (Latré et al., 2011). Hence, PER

is necessary for energy profiling. In the second part

of Figure 3, 5% PER and 10% PER are used

respectively in the channel module.

Finally, in Figure 4, we analyze the radio lifetime

expectancy by using a Li-ion battery with a capacity

of 160mAh and coin cell size, which is used by

Generic Wireless Node of Human++ BANs platform

(Huang et al., 2009). The lifetime estimation is

calculated as follows:

( *160) / _ /1000 / 3600 / 24LT SimT Total mAh

(1)

Where LT is lifetime, SimT represents the simulation

time and Total_mAh denotes the total current

consumption during the simulation period.

To sum up, results in Figure 4 show that 2Mbps

scenario is the most energy-efficient way of using

payload collecting strategy for PTX, which can save

from 64.9% (2 bytes) to 70.3% (30 bytes) energy

when compared to 250Kbps, and for 1Mbps scenario

the ratio are from 21.0% to 25.3%. Even with the

introduced PER, high data-rate (2Mbps) scenario

can still provide better energy performance than the

other two data-rate scenarios, when considering 5%

PER, 6.6% (30 bytes) to 12.1% (2 bytes) more

energy is consumed compared with the ideal 2Mbps

scenario, and with 10% PER, the ratio rises to 17.7%

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to 23.9%.

Figure 3: Energy Consumption, Payload Size and

PER@ESB mode.

Figure 4: Lifetime Estimation of Payload Collecting

Strategy.

On the other hand, lifetime estimation shows that

with low data rate (250kbps), the PTX radio cannot

last for one month, while with the high data rate

(2Mbps), the radio can run for nearly 3 three

months. Therefore high data rate based transmission

is suggested for lower energy consumption, besides

it can also provide much better latency and less

over-the-air collisions.

4 CONCLUSIONS

In this paper, a SystemC structured ESB/SB based

nRF transceiver simulation model is presented, with

high data rate and low power features. Then

nRF24L01+ transceiver is selected for performance

analysis of energy consumption in a WBANs

scenario. Results demonstrate that more than 60%

and 80% energy are consumed respectively in ESB-

and SB- mode in low data rate (250Kbps)

transmission compared to 2Mbps high data rate

transmission, besides high data rate will be more

energy-efficient in payload collecting strategy, and

can last for about 3 months with a coin cell size

battery. These results can provide valuable

information for WBANs application designers.

REFERENCES

M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, V. C. Leung,

Body Area Networks: A Survey, Mobile Networks and

Applications, Volume 16, Issue 2, pp. 171-191, Apr

2011.

Benoît Latré, Bart Braem, Ingrid Moerman, Chris Blondia,

Piet Demeester, A Survey on Wireless Body Area

Networks, Wireless Networks, Volume 17, Issue 1, pp.

1-18, Jan 2011

Andreas Weder, An Energy Model of the Ultra-Low-

Power Transceiver nRF24L01 for Wireless Body

Sensor Networks, Computational Intelligence,

Communication Systems and Networks),2010 Second

International Conference, pp. 118-123.

Nordic 2.4GHz RF homepage, Avaiable:

http://www.nordicsemi.com/eng/Products/2.4GHz-RF.

nRF24L01+ datasheet, Available: http://www.nordicsemi.

com/eng/content/download/2726/34069/file/nRF24L01

P_Product_Specification_1_0.pdf.

S. S. Sonavane, V. Kumar, B. P. Patil, MSP430 and

nRF24L01 based Wireless Sensor Network Design

with Adaptive Power Control, ICGST-CNIR Journal,

Volume 8, Issue 2, pp. 11-15, Jan 2009.

C. Zhurong, H. Chao, L. Jingsheng, and L. Shoubin,

Protocol architecture for wireless body area network

based on nrf24l01, Automation and Logistics,2008,

IEEE International Conference on, pp. 3050–3054.

D. C. Black, J. Donovan, SystemC: From the Ground Up,

2nd ed., Springer 2009. ISBN 0-387-69957-0.

Li Huang, Maryam Ashouei, Firat Yazicioglu, Julien

Penders, Ultra-Low Power Sensor Design for Wireless

Body Area Networks-Challenges, Potential Solutions,

and Applications, International Journal of Digital

Content Technology and its Applications, Volume 3,

Issue 3, pp. 136-148, Sep 2009.

N. Zhu, F. Mieyeville, D. Navarro, W. Du, I.

O’Connor, Research on high data rate wireless sensor

networks, 14eme Journées Nationales du Réseau

Doctoral de Micro et Nanoélectrionique, May 2011.

W. Du, F. Mieyeville, D. Navarro and I. O’Connor,

"IDEA1: A validated SystemC-based system-level

design and simulation environment for wireless sensor

networks," EURASIP Journal on Wireless

Communications and Networking, 2011:143, Oct 2011.

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transmission

[

]

0dBm

Radio Lifetime [days]

@2Mbps

@2Mbps-5%PER

@2Mbps-10%PER

@1Mbps

@250kbps

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