characteristics of the IEEE 802.15.4 radio signal in indoor environments such as âpower decay factorâ due to near/far effect and âfading distribution.â Then, the ...
Propagation Characteristics of IEEE 802.15.4 Radio Signal and Their Application for Location Estimation Shinsuke Hara†, Dapeng Zhao†, Kentaro Yanagihara†, Jumpei Taketsugu†, Kiyoshi Fukui‡, Shigeru Fukunaga‡and Ken-ichi Kitayama† † Graduate School of Engineering, Osaka University ‡ Corporate R&D Group, Oki Electric Industry Co., LTD.
Abstract— IEEE Std 802.15.4 defines the physical (PHY) and medium access control (MAC) layers for low-cost and very low-power short-range wireless communications. One of the promising applications offered by the standard is wireless senor networks. To begin with, this paper shows the propagation characteristics of the IEEE 802.15.4 radio signal in indoor environments such as “power decay factor” due to near/far effect and “fading distribution.” Then, the paper presents a wireless sensor network system for location estimation which utilizes only received signal strength indicator (RSSI) and discusses the location estimation performance.
I. I NTRODUCTION Recently, wireless sensor network, which is composed of a lot of network nodes, has drawn much attention. In wireless sensor network, each network node has a wireless communication capability to communicate with other network nodes and an electronic interface to connect sensors. In this paper, a network node with sensor is called “a sensor node.” In wireless sensor network applications, location information on sensor nodes and targets is often important. For instance, in supermarkets, customers’ location information with time stamp can be used to analyze the trend of their favorite food items. Visitor’s interest analysis in exhibition also requires the location information of visitors. A wireless sensor network where each node has an ultrasonic sensor can give accurate estimates of distances between the sensor node and unknown targets. However, it cannot identify the targets, so to this end, it needs to give each target a marker or an ID number in other method. Therefore, when a wireless sensor network gives each target the same communication capability as the sensor nodes, if it can estimate the location for the target only with a parameter which is estimated through wireless communication, it does not have to purposely give each target an individual ID number. This is because communication protocol implicitly includes a marker, for instance, such as a medium access control (MAC) address. Furthermore, the limited interface of sensor node can be used for other sensing purposes. IEEE Std 802.15.4 is a standard to provide ultra-low complexity, low-cost, and extremely low-power wireless connectivity among inexpensive devices such as sensor nodes[1]. It defines the physical (PHY) and MAC layers, and furthermore
0-7803-8887-9/05/$20.00 (c)2005 IEEE
it has a function of measuring the received signal power as a received signal strength indicator (RSSI)[2]. Therefore, if wireless communication among all sensor nodes and targets is based on IEEE Std 802.15.4, a sink node can estimate the locations of the targets, only by gathering their RSSI information from the sensor nodes. In this case, the estimation performance depends much on the propagation characteristics of the IEEE Std 802.15.4 radio signal. To begin with, this paper presents the propagation characteristics of the IEEE Std 802.15.4 radio signal which are obtained by measurements. Based on the measured data, the paper then constructs a statistical model on the propagation characteristic, and proposes a wireless sensor network system for location estimation. Finally, the paper demonstrates the performance on location estimation for a target. II. IEEE S TD 802.15.4 IEEE Std 802.15.4 has three frequency bands; 868 MHz (868-868.6 MHz), 915 MHz (902-928 MHz) and 2.4 GHz (2.4-2.4835 GHz) bands, however, only the 2.4 GHz band is allowed in Japan. The bit rate, symbol rate and modulation are 250 kbits/sec, 62.5 ksymbols/sec and offset-quadrature phase shift keying (O-QPSK), respectively. The 2.4 GHz band is included in industrial, scientific and medical (ISM) band, so it is rich in interference. To mitigate the interference, therefore, direct sequence spread spectrum (DS-SS) with processing gain of 8 is adopted in the standard. The chip rate is 2 Mchips/sec. III. P ROPAGATION C HARACTERISTICS M EASUREMENTS Propagation Measurements were conducted in typical lecture rooms, laboratory rooms and corridors in a building of graduate school of engineering at Osaka University. Figure 1 shows a measurement set-up. In practical wireless sensor networks, a sensor node will install at a light unit of ceiling, at a human body or on a floor. Our measuring equipment can change the position of the transmitter/receiver from floor to ceiling. In the following, we show the measurement results when setting the height of transmitter and receiver to 60 cm. The transmitter has a quarter wavelength (1/4λ) mono-pole antenna and emits a signal with power of 0 dBm. whereas the receiver also has a 1/4λ mono-pole antenna.
Received Signal Power [dBm]
Figure 2 shows a measured data on the received signal power in Room 414. Here, vertical polarization is used for transmitter and receiver antennas. There were many desks, lockers and partitions in the room, so a large fluctuation in the received signal power was observed. Figure 3 shows a power decay characteristic, namely, the received signal power (P) against the distance (r) between the transmitter and receiver in the room. Here, vertical polarization is also used for transmitter and receiver antennas. The received signal power is proportional to r−2.35 and the correlation coefficient is 0.85. Figure 4 shows a fading characteristic, namely, the probability density against the received signal power normalized by the average. The probability distribution function (pdf) of the received signal power is well approximated by an exponential function with correlation coefficient of 0.92. This means that the envelope variation due to fading is Rayleigh-distributed. From the measurement data obtained in different rooms and corridors, it was found that the power decay factor (β) ranges in [1.90, 4.75] and the pdf of the received signal power is well approximated with an exponential function in any places. Therefore, the conditional pdf of the received signal power when r is given is written as p(P |r) Λ(r)
P 1 − Λ(r) e Λ(r) = αr−β (1.90 ≤ β ≤ 4.75)
=
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9 ] [m 7 8 e c tan 6 Dis 4 5 Signal Emission Point 3 2 1
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Fig. 2.
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A measured data in a room.
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Measurement set-up.
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Measured Data
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r -2.35
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1
Fig. 3.
10 Distance (r) [m]
100
A power decay characteristic.
(1) (2)
where α is a constant. Finally, Table I summarizes the results on the propagation characteristics in Room 414. We tested three different combinations of polarizations for transmitter/receiver antennas. Different power decay factors are obtained for the different combinations, but the pdfs of the RSSIs are well approximated by the exponential distribution for all the combinations, with high correlation coefficients. IV. A N IEEE S TD 802.15.4-BASED W IRELESS S ENSOR N ETWORK S YSTEM FOR L OCATION E STIMATION Figure 5 shows a wireless sensor network system for location estimation, where there are N sensor nodes and a target and communication among all the sensor nodes and the target is based on IEEE Std 802.15.4. The sensor nodes are installed on a plane and the target is put on the same
0.8 0.7 Probability Density (y)
Fig. 1.
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0.4
y=exp(-x)
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0.5 1.5 2.5 3.5 0 1 2 3 Received Signal Power normalized by Average (x=P/Λ(r)) Fig. 4.
A fading characteristic.
TABLE I P ROPAGATION CHARACTERISTICS . Power Decay Factor (Correlation Coefficient)
Exponential Distribution Correlation Coefficient
2.35 (0.85) 3.50 (0.50) 1.90 (0.27)
0.92 0.86 0.82
p(P1 , · · · , PN |r1 , · · · , rN ) = ri
N
p(Pi |ri )
...
...
y
(xi, yi)
...
Sensor Node
...
plane. Each sensor node measures the RSSI for the target and sends the information to a sink node. The sink node gathers all the information on the RSSI values measured at all the sensor nodes, and estimates the location of the target (X, Y ) using an estimation algorithm. We assume that the distance (d) between the neighboring sensor nodes is the same and their locations (xi , yi ) are all known (i = 1, · · · , N ). In addition, we assume that the target does not change its location during the measurement. Define the conditional pdf of the received signal power; P1 , · · · , PN when the distance; r1 , · · · , rN is given as (i means the sensor node index)
...
Polarization (Receiver Antenna) Vertical Vertical Horizontal
known location
1
Room 414
Polarization (Transmitter Antenna) Vertical Horizontal Horizontal
(x5, y5)
(x6, y6) (x7, y7) Target (X,Y)
(x2, y2)
(x3, y3)
(x8, y8)
d (x1, y1)
(x4, y4)
(x9, y9)
(3)
i=1 = (X − xi )2 + (Y − yi )2 (4)
where p(Pi |ri ) is given by (1) replacing P and r by Pi and ri , respectively. The maximum likelihood estimate of (X, Y ) maximizes the logarithm of (3), so we have N ∂ log p(Pi |ri ) =0 (5) ∂X i=1 X=X N ∂ log p(Pi |ri ) = 0. (6) ∂Y i=1 Y =Y The equations (5) and (6) mean a two-dimensional nonlinear equations, so many methods are applicable to obtain a solution. In the paper, we employed the Newton-Raphson method[3]. The statistical model given by equation (1) contains a parameter to estimate in advance, that is, the value of power decay factor. We define the true and estimated values as β and β, respectively, and set β = 2.35. V. S IMULATION R ESULTS AND D ISCUSSIONS Figure 6 shows the root mean square (RMS) estimation error of the target location against the power decay factor. Here, the RMS estimation error is normalized by the distance between the neighboring sensor nodes (d). We assume that the power decay factor is perfectly estimated, namely, d = d = 2.35 and the number of RSSI measurement is one. There are three curves in the figure, corresponding to the performance with the number of sensor nodes=32 , 42 and 62 , respectively. As the power decay factor increases, the estimation performance is improved. Therefore, the combination of horizontal
... x
1 Fig. 5. An RSSI measurement-based sensor network for location estimation of a target.
polarization for transmitter antenna and vertical polarization for receiver antenna will give a more accurate estimate of target location, because the combination has a larger power decay factor as shown in TABLE I. In addition, as the number of sensor nodes increases, the estimation performance is improved for the same power decay factor. Figure 7 shows the normalized RMS estimation error against the number of sensor nodes. Here, we also assume that the power decay factor is perfectly estimated, namely, β = β = 2.35. There are three curves in the figure, corresponding the performance with the number of RSSI measurements=1, 2 and 3, respectively. Increasing the number of RSSI measurements has a larger impact on the estimation performance than increasing the number of sensor nodes. Especially, increasing the number of RSSI measurements from 1 to 2, we can much improve the location estimation performance. When we employ 32 sensor nodes with the number of RSSI measurements=2, we can make the normalized RMS estimation error be less than 0.3. This means that, if we set the distance (d) between the neighboring sensor nodes to 3 m, the achievable location estimation accuracy is less than 1 m. However, even when we increase the number of sensor nodes from 32 , we cannot much improve the location estimation performance. On the other hand, as compared with the case with the number of
0.8
Normalized RMS Estimation Error
RSSI measurements=2, the performance gain is small for the case with the number of RSSI measurements=3. We have so far assumed that the power decay factor is perfectly estimated. Figure 8 shows the normalized RMS estimation error against the mismatch of power decay factor, namely, β − β. Here, we assume that the number of RSSI measurement is one. There are three curves in the figure, corresponding to the performance with the number of sensor nodes=32 , 42 and 62 , respectively. If the power decay factor is accurately estimated, a wireless sensor network with more sensor nodes gives better estimation performance. However, if the power decay factor is not accurately estimated, it cannot always give an accurate estimate of target location. Therefore, accurate pre-estimation of power decay factor is essential for a given space where we want to deploy a wireless sensor network for location estimation with the proposed RSSI measurement. Finally, Figure 9 shows an example of location estimation result. Here, we assume that the power decay factor is perfectly estimated, namely, β = β = 2.35 and the number of RSSI measurement is two. The estimation accuracy depends on the location of the target, but accurate estimation is obtained for 52 sensor nodes.
Number of RSSI Measurements=1
0.7 0.6 0.5 0.4 0.3 0.2
Number of Sensor Nodes=3 2 Number of Sensor Nodes=4 2 Number of Sensor Nodes=6 2
0.1
0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 Power Decay Factor Fig. 6.
Power decay characteristics.
VI. C ONCLUSIONS
R EFERENCES [1] J.A.Gutierrez, et al.,“Low-Rate Wireless Personal Area Networks Enabling Wireless Sensors with IEEE 802.15.4TM,” Standards Information Network IEEE Press 2004. [2] IEEE Std 802.15.4TM , IEEE Standard for Information technology– Telecommunications and information exchange between systems–Local and metropolitan area networks–Specification requirements–Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), 2003. [3] W.H.Press, et al., NUMERICAL RECIPES in C, Cambridge University Press, 1988.
0.6
Normalized RMS Estimation Error
This paper has shown the propagation characteristics of the IEEE 802.15.4 radio signal in an indoor environment. The propagation measurement of the signal has revealed that the variation of the received signal power strictly follows an exponential distribution with a power decay factor. Furthermore, the paper has proposed a wireless sensor network system which can estimate the location of a target only with RSSI measurements and has discussed the estimation performance by computer simulations. The numerical results have shown that, a larger power decay factor gives a more accurate estimate of target location, the number of RSSI measurements has a larger impact on improving the location estimation performance than the number of sensor nodes, and accurate pre-estimation of power decay factor is essential for a given space where we want to estimate the location of a target with the proposed wireless sensor network system.
0.5 0.4 0.3 0.2 Number of RSSI Measurements=1 Number of RSSI Measurements=2 Number of RSSI Measurements=3
0.1 0
22
32 Fig. 7.
42 52 62 7 2 82 Number of Sensor Nodes Location estimation performance.
92 10 2
2.0
Number of RSSI Measurements=1
Normalized RMS Estimation Error
1.8 Number of Sensor Nodes=3 2 Number of Sensor Nodes=4 2 Number of Sensor Nodes=6 2
1.6 1.4 1.2 1.0 0.8 0.6 0.4
True Power Decay Factor=2.35
0.2
0-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1.0 1.2 Mismatch of Power Decay Factor Fig. 8.
Mismatch of power decay factor.
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Number of RSSI Measurements=2 Location of Sensor Node True Location of Target Estimated Location of Target Fig. 9.
Example of location estimation.
