# Physical layer study in a goal of robustness and energy efficiency for wireless sensor networks

Denis Dessales, Anne-Marie Poussard, Rodolphe Vauzelle, Noël Richard, François Gaudaire, Christophe Martinsons

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Denis Dessales, Anne-Marie Poussard, Rodolphe Vauzelle, Noël Richard, François Gaudaire, et al. Physical layer study in a goal of robustness and energy efficiency for wireless sensor networks. Conference on Design and Architectures for Signal and Image Processing (DASIP), 2010, Oct 2010, Edinburgh, United Kingdom. pp.214 - 221, 10.1109/DASIP.2010.5706267. hal-00867214

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PHYSICAL LAYER STUDY IN A GOAL OF ROBUSTNESS AND ENERGY EFFICIENCY FOR
WIRELESS SENSOR NETWORKS

D.Dessales, A-M.Poussard, R.Vauzelle, N.Richard *

XLIM-SIC Laboratory
University of Poitiers
Poitiers, France

dessales,poussard,vauzelle,richard@sic.univ-poitiers.fr

F.Gaudaire, C.Martinsons

Acoustic & Lighting
CSTB
Grenoble, France

francois.gaudaire,christophe.martinsons@cstb.fr

ABSTRACT

In this paper we propose a method to specify a robust and energy efficiency physical layer for wireless sensor networks. Thus, from an energy model we show the influence of the channel on energy consumption. Our channel model is a realistic propagation model that takes into account all environmental specificity (geometric, electric) according to a real environment. The Bit Error Rate (BER) is used to assess the radio link quality and calculate the energy per bit transmitted successfully. Simulation results show clearly that significant gains are obtained by adjusting the transmission power in function of the kind of link between the central node and individual nodes. This study show also the importance of a cross-layered approach to optimize effectively the robustness and the lifetime of a wireless sensor network.

**Index Terms—** Physical layer, WSN, energy consumption, MAC sub-layer

1. INTRODUCTION

In a context of sustainable ecological development, evaluation of building energy performances have become paramount. Methods to assess energy performances of buildings [1] are based mostly on theoretical models (by considering new building materials, ideal implementation, simplified physical phenomena, numerical approximation methods ...) or on experimental data obtained in real conditions but limited in time and for specific aspects (analysis of electricity consumption, energy and heat loss ...). In reality, performances of a building are highly variable and depend on several factors: effective use of space and office organization, occupants behaviour, equipment evolution and influence of the environment surrounding buildings, traffic, climate changes. In order to take into account these parameters in assessment and/or diagnostic methods of buildings performances, a solution consists in performing a long term study (more than 5 years). This

study must be conducted with a fine temporal resolution of the acquisition data adapted to the studied physical quantities (presence / absence, number of occupants), and with the actual use of installed equipment (heating, air conditioning, ventilation, lighting, shading and condition of opening: doors and windows). For this it is necessary to use non-intrusive methods, not to disturb the occupants' behavior, not use the power-grid and not to modify the operation of automatic control and regulation.

In this goal, building monitoring systems based on wireless sensor networks are needed. Nevertheless the main problem encountered in the design of this kind of network is the desired lifetime and autonomy. To do this, it is important to implement mechanisms to minimize energy consumption [2], including communication protocols in line with the application by using strategies of sleep and wake up for sensors [3]. Moreover, managing a large number of low information flows is also a challenge, use a central node is essential to manage and coordinate all data streams. Finally it is important that the reduction of energy consumption do not negate all the efforts of robustness of communication, in particular against the various possible sources of interference (continuous, intermittent, narrowband, broadband...).

Recent advances in wireless sensors network was organized essentially around the MAC protocols [3, 4]. It is the layer that optimizes the most the electrical consumption. Moreover, in addition to managing access to the channel, it manages the mechanisms of sleep of nodes. In the same time, the radio chips consumptions have decreased. So, it was expected that these technical gains could be enough for embedded application. Unfortunately, industrial experiments have shown the limits of such solutions in terms of autonomous sensors in energy. Because of the interdependence of various parameters affecting consumption, many studies [5, 6, 7] agree that an approach "cross layer" is required to optimize the consumption of wireless sensor networks. A cross-layered approach minimizes transfer overhead by having data shared among layers. The cross-layer interaction can be considered in two ways. On one hand, the traditional layered structure

<footnote>*Thanks to the CAIMAN project and the National Agency of Research in France, for funding.</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Network architecture</figcaption></figure>

is preserved. Each layer is informed about the conditions of other layers, and the mechanisms of each layer still stay intact. On the other hand, the protocol stack is treated as a system and not individual layers [8], in order to provide a single communication module. This last approach tends to be the more efficient. However it prevents the easy development and evolution of a network component, and removes all the service guarantees offered by each network layer. That's why, most research efforts focus on an traditional layered approach that endures more transfer overhead. Others use a cross-layer approach only between three layers at maximum. In [9], the authors provide analysis of energy consumption with three different MAC protocols, then conclude that single-hop communication can be more efficient. In [10], routing (Network layer) depends not only on a metric of distance or link quality, but also on the state of the node (in sleep or awake, ie MAC sub-layer).

By opting for a tree architecture (figure 1) the central nodes that collects data from sensors forward them via ethernet link. So we deported the issues related to routing on layers below (MAC and physical). Despite the gains possible with routing (optimal path choice), this protocol layer is required to fully distributed systems but still significant in terms of consumption. We have seen above, that the optimization of the MAC layer was essential to minimize the consumption of the network. However, this optimization requires an optimal definition of the physical layer. Indeed, the choice of the transmission system (robustness, duration of radio transmissions ...) goes directly affect the structure and frame length (synchronization, error detection, retransmissions ...), but also the mechanisms of sleep and wake up of sensors. It is therefore essential to have a parameter to assess and quantify the quality of the MAC layer, both in terms of robustness, and consumption. This parameter is the energy per bit required to transmit one bit successfully. That's why, we have chosen to focus our research efforts on the joint optimization between the physical MAC layers, by considering the impact of the physical layer on the MAC sub-layer parameters in terms of

electrical consumption and robustness of communications.

Note this paper concerns essentially this physical layer optimization and his impact on the MAC sub-layer. It is organized as follow: a realistic energy consumption evaluation based on an energy consumption model per correct received bit [11] is presented in section 2. Simulations and Results are presented in section 3. Then we discuss about the consequences on the MAC sub-layer.

## 2. PHYSICAL LAYER OPTIMIZATION

### 2.1. Principle

In order to minimize the overall consumption of our wireless sensor networks, we focus our efforts on the link between the central node and a sensor. The specification of physical layer adapted to each type of peer-to-peer link enables to optimize the system and thus achieve significant energy gains. In practice, we propose to improve the physical layer for each type of sensor by taking into account the realistic wave propagation conditions. Then we evaluate this optimization and the possible consequences on the MAC protocols. The approach is based on an evolution of an energy consumption model per correct received bit, and is valid for any kind of wireless sensor network.

### 2.2. Energy consumption model

Many studies propose models to estimate energy and optimize the consumption of wireless sensor networks. For example in [12], the model used is a simple description of the use of the radio that take into account the duration of transmissions/receptions, the duration of startup, and transmission/reception powers. In [13], the authors incorporates startup energy and transmit energy in the goal to compare m-ary modulations and binary modulations. They conclude that binary modulations are more effective for applications of wireless sensor networks. These energy models do not take into account the quality of transmissions in the calculation of the energy, while the consumption of a wireless sensor network is necessarily dependent of the robustness of communications. In a goal of cross-layer optimization, it is important to chose an energy model that take into account significant parameters from the physical and MAC layers. That's why, we chose the energy consumption model proposed in [11], also we can express the energy per bit transmitted successfully (equation 1).

$$E_b = \frac{P}{\text{rate}(1 - \text{BER})^{fl}} \quad (1)$$

where P is the total power consumed and depends on the transmission power (Fig.2), rate the bit rate transmission, fl is the length of the frame output, and BER the bit error rate.
<figure><img src="image_3.png" /><figcaption>Fig. 2. Energy per bit successfully transmit in function of transmission power at 433 MHz</figcaption></figure>

By expressing the energy per bit transmitted successfully according to BER, this model can takes into account the transmission chain (modulation, coding ) and the channel characteristics (path loss, signal to noise ratio ...), but also consumptions induced by retransmissions of frames. If we trace the energy per bit transmitted versus the transmission power (Fig.2), it is possible to determine the transmitted power in order to obtain the minimum energy per bit without neglecting the robustness of communications. For low transmission power, the energy per bit increases with the number of re-transmissions; for transmission power more elevated, energy per bit increases as the power amplifier must deliver more power. Thus, the optimal transmission power is located on the minimum of the curve (red line on Fig.2).

In [11], BER is basically evaluated: the channel model used is a white Gaussian noise channel with a BPSK modulation. To specify a robust and low consumption physical layer, we propose to improve the BER estimation of the model with a realistic indoor radio channel which considers the propagation environment characteristics. Moreover different transmission chains are introduced according to the choice of the radio chip considered in our application [14].

## 2.3. Realistic BER estimation

To perform a realistic estimation of the BER, we propose to use a propagation model based on a realistic model with ray-tracing. Then we recreate a typical transmission chain in wireless sensor networks, based on elements already implemented in the radio chip selected for prototyping [14]. The complete transmission chain used is presented in figure 3. It consists of three main blocks:

* A transmission block containing himself various elements: The information to be transmitted using different modulations (ASK, FSK, MSK); An optionnally interleaver (rows to columns), that transforms an error on bits clustered, in an error over the entire binary sequence, therefore easier to correct; And an optionnally convolutional coder, adding redundancy to detect and

<figure><img src="image_4.png" /><figcaption>Fig. 3. Transmission chain with FEC</figcaption></figure>

correct errors introduced by the propagation channel. The convolutional coder implemented on the chip radio selected is a coder with a code rate equal to 1/2, ie the binary sequence to transmit is then doubled.

* A block corresponding to the transmission channel. The proposed solution is a semi-deterministic model relied on a ray-tracing model associated with a spatio-temporal variability model.

* And finally a reception block which contains the same elements as the transmission block to decode the binary message.

### 2.3.1. Ray tracing channel model

CRT, a 3D radio wave propagation simulator based on an optimized ray-tracing and developed in the XLIM-SIC laboratory [15] is used to determine all the paths between two nodes of a sensor network. Moreover, it takes into account all the characteristics of the environment (electric, geometric) and provides information of each radio path (magnitude, phase, delay, polarization). So the complex impulse response of each radio link can be estimated by:

$$ \tilde{r}(t) = \sum_{k=1}^{N} \tilde{a}_{k} \tilde{s}(t - \tau_{k}) \quad (2) $$

where $\tilde{r}(t)$ is the received signal, $\tilde{s}(t)$ the transmitted signal, $\tau_k$ the delay for the $k^{th}$ path and $\tilde{a}_k$ the complex coefficient of the path k. In this case, the channel is modeled by a Tapped-Delay-Line filter.

### 2.3.2. Channel variability

Classically, a radio channel presents spatio-temporal [16] fluctuations. In our case, the nodes of the network don't move. So, the channel will be only affected by temporal variation. This kind of variation is caused by people moving or the obstruction of the predominant path by a person. To take into account these temporal variations in the channel modeling, we consider a statistical law describing the small-scale fading of the channel. The complex coefficient $\tilde{a}_k$ for the
path k is correlated with a Rayleigh distribution for NLOS radio link (Non Line Of Sight), or with a Rice distribution for LOS link (Line Of Sight). Also, $\tilde{r}(t)$ can be modified by:

$$ \tilde{r}(t) = \sum_{k=1}^{N} (\tilde{a}_k c_k) \times \tilde{s}(t - \tau_k) + n(t) \quad (3) $$

where $c_k$ is the complex Rayleigh or Rice coefficient, and $n(t)$ the Gaussian noise.

### 3. SIMULATION AND RESULTS

In this section, we evaluate the performances of the proposed solution in a representative set of rooms of a building for a large number of radio links. Then we express the energy per bit transmitted successfully to adjust the power transmission to levels that ensure less consumption for equivalent robustness.

#### 3.1. Studied Configurations

The presented results correspond to a representative indoor environment. The network architecture chosen for the network is a classic hierarchical architecture composed of two elements, gateways and sensors. A gateway will serve as a central node to export data collected to a server (database). It will serve as manager of communications and will collect the data of 2 or 3 pieces, ie a transmission range about 25/30 meters. In our environment, the gateway is located in one of the four rooms of the environment (the black point in figures 4 to 7). The parameters are defined according to the constraints of our application and by the physical layer of the radio chip. So, the rate is equal to 250 Kbps and the frame length to 80 bits. Moreover the noise power is fixed around the threshold of chip sensitivity, ie -105 dBm.

#### 3.2. Analysis results

In this section, the results are presented in three forms for three levels of analysis. Firstly, maps of transmit power (figures 4 to 7) show the influence of the environment on quality of service (BER) and energy efficiency, according to the position of receivers. Secondly, tables enable a quantitative analysis. And finally cumulative distribution functions are used to analyse the distribution of transmit power.

Figures 4 to 7 shows the transmission power evolution allowing to guaranty BER of $10^{-3}$, for two transmission chains (with and without forward error correction), three modulations (ASK, FSK, MSK) and two different frequency band (433 MHz, 868 MHz). The color scale used corresponds to variations in transmission power, blue for -30 dBm and red for 10 dBm. The table 1 and 2 show a quantitative analysis of the previous figures for NLOS receivers (6180 positions) and for all the receivers (6340 positions).

Several important points can be noted. Firstly, for a simple transmission chain without forward error correction (FEC), and for all the NLOS receivers, the maximum transmitted power necessary to guaranty a BER equal to $10^{-3}$ is 0 dBm. For LOS receivers, this power is always equal to -30 dBm. Concerning the radio frequency band, it appears that the coverage at 433 MHz is better than 868 MHz for the same guaranteed BER. Indeed, we can observe a coverage of 70% of the indoor environment at 433 MHz against 20% at 868 MHz. In the case of a transmission chain with FEC, the transmitted power varies over the range of adjustment (-30 to 10 dBm). Also we note a coverage of 97% at 433 MHz against 60% at 868 MHz. The use of FEC greatly improves coverage of the indoor environment, but the variation of the transmission power is broader.

<table><caption>Table 1. Guaranteed positions with BER < $10^{-3}$</caption><thead><tr><th rowspan="2">NLOS positions</th><th colspan="2">433MHz</th><th colspan="2">868MHz</th></tr><tr><th>ASK</th><th>FSK</th><th>ASK</th><th>FSK</th></tr></thead><tbody><tr><td>[0, -10] dBm</td><td>38,86%</td><td>46,39%</td><td>74,15%</td><td>73,52%</td></tr><tr><td>[-11, -20] dBm</td><td>49,24%</td><td>44,78%</td><td>19,33%</td><td>19,94%</td></tr><tr><td>[-21, -30] dBm</td><td>11,9%</td><td>8,83%</td><td>6,52%</td><td>6,54%</td></tr><tr><td>NLOS + LOS</td><td>72,65%</td><td>67,36%</td><td>24,3%</td><td>17,71%</td></tr></tbody></table>

<table><caption>Table 2. Guaranteed positions with BER < $10^{-3}$ (FEC)</caption><thead><tr><th rowspan="2">NLOS positions</th><th colspan="2">433MHz</th><th colspan="2">868MHz</th></tr><tr><th>ASK</th><th>FSK</th><th>ASK</th><th>FSK</th></tr></thead><tbody><tr><td>[10, 0] dBm</td><td>3,64%</td><td>5,58%</td><td>14,14%</td><td>16,82%</td></tr><tr><td>[0, -10] dBm</td><td>22,43%</td><td>23,18%</td><td>35,66%</td><td>41,24%</td></tr><tr><td>[-11, -20] dBm</td><td>25,91%</td><td>30,66%</td><td>33,15%</td><td>29,34%</td></tr><tr><td>[-21, -30] dBm</td><td>48%</td><td>40,58%</td><td>17,05%</td><td>12,6%</td></tr><tr><td>NLOS + LOS</td><td>97,95%</td><td>96,97%</td><td>64,53%</td><td>61,12%</td></tr></tbody></table>

To underline the modulation influence, it is interesting to observe the cumulative distribution functions (figures 8 and 9) for the two transmission chains. The first observation that we can note, whatever the transmission chain, is the better efficiency of the 433 MHz frequency band than the 868 MHz frequency band. Moreover, at 433 MHz, the difference between modulation ASK et FSK is more pronounced. Indeed, for the transmission chain without FEC nearly 50% of positions can be reduced at -15 dBm for modulation ASK at 433 MHz, against 50% of positions at -12 dBm for modulation FSK at 433 MHz. At 868 MHz the difference between the modulations is insignificant in terms of distribution. In other terms, at 868 MHz the modulation has less influence than the frequency band. With a transmission chain complete with FEC, we can see that we can reduce the transmission power of many more positions. Comparatively for the same
<figure><img src="image_5.png" /><figcaption>Fig. 4. ASK modulation at 433 MHz and 868 MHz</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. FSK modulation at 433 MHz and 868 MHz</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. ASK modulation at 433 MHz and 868 MHz with FEC</figcaption></figure>

modulation and the same frequency band, nearly 50% of positions can be adjusted to a transmission power of -20 dBm. Also we can note a difference around 7 dB between a transmission with and without FEC at 433 MHz, against 4 dB at 868 MHz.

Note that the results associated to the MSK modulation are not presented because they are similar to the ASK modulation. Nevertheless it is important to underline that the MSK

modulation provides a better spectrally efficiency. So, it is an interesting characteristic for the band sharing and the choice of the MAC protocol.

In conclusion, this analysis allows on one hand to specify a physical layer robust and energy efficient in defining the best transmitted power according to the configuration of the environment. But also on the other hand to identify parameters influencing the adjustment of transmission power in a
<figure><img src="image_8.png" /><figcaption>Fig. 7. FSK modulation at 433 MHz and 868 MHz with FEC</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 8. Cumulative distribution functions, without FEC</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 9. Cumulative distribution functions, with FEC</figcaption></figure>

qualitative way. Indeed we can say that the element that most affects the adjustment of the transmission power is firstly the carrier frequency band. Then, the position of the receiver, in particular with the number and kind of walls crossed, and finally the modulation used. Moreover, the better configuration

seems to be a transmission system including a MSK modulation with a 433 MHz frequency band and the use of FEC. It is still necessary to quantify the impact of the use of FEC, on the MAC layer. Indeed, FEC doubles the length of useful frame. It is more effective than the frame retransmissions, if his cost (number of gates, duration of a transmission) is less important than the cost induced by a retransmission.

### 3.3. Discussion

We now focus on consumptions and lifetimes brought into play after adjusting transmission power to minimum. By considering a simple MAC sub-layer where the collection of data is performed at regular time interval (figure 10), and initiated by a central node, we can roughly estimate the lifetime of a sensor. For this estimate, we do not care about reception phases of the sensor. The calculation of the averaged consumed current $I_{avg}$ is given by the equation 4:

$$I_{avg} = \frac{I_{ON} \times T_{Sensor} + I_{Stdby} \times T_{Sleep} + I_{Radio} \times T_{ON}}{T} \quad (4)$$

with $I_{ON}$ the current consumed by the radio part in standby mode and the current consumed by the sensor ($I_{ON} = I_{stdbyradio} + I_{Sensor}$). $I_{Stdby}$ corresponding to the total standby current ($I_{Stdby} = I_{stdbyradio} + I_{Stdbysensor}$). And finally $I_{Radio}$ related to the current consumed by the radio part during transmission.

For example, if we consider a detector of presence with $T_{Sleep} = 8s$, $T_{Sensor} = 2s$, a frame length of 80 bits and two transmission power, we are able to calculate the averaged consumed current by this sensor. Results of the calculation are shown in table 3. Also we can observe a reduction of the radio consumption by a factor 2 between the two transmission power, but surprisingly, very little difference in the averaged current. We can explain this by the short duration of radio emission $T_{ON}$, due to the length of the frame. Reducing energy consumption does not just pass by reducing the averaged consumed current but is also dependent of the duration of radio emission.
<figure><img src="image_11.png" /><figcaption>Fig. 10. Average consumption of a wireless sensor</figcaption></figure>

<table><caption>Table 3. Consumptions of a detector of presence with a frame length of 80 bits</caption><thead><tr><th>Transmission power</th><th>-30 dBm</th><th>10 dBm</th></tr></thead><tbody><tr><td><i>I</i><sub>Radio</sub></td><td>16,1 mA</td><td>36,36 mA</td></tr><tr><td><i>I</i><sub>avg</sub></td><td>96,61 μA</td><td>97,56 μA</td></tr></tbody></table>

If we trace the averaged consumed current in function of the detector lifetime as you can see in figure 11. We can estimate a threshold not to exceed. Beyond this threshold, the lifetime of the sensor decrease sharply. In the case of a detector of presence, the averaged consumed current must not exceed 2 mA, linked to a duration of radio emission $T_{ONMAX} = 400ms$. Evidently, the emission length depends on construction choices for MAC sub-layer, in particular the mechanisms of sleep and wake up of sensors. If we assume a periodic wakeup sheme like in [17], the modalities of sharing bandwidth time will directly affect the radio emission length and therefore the total energy consumption. Moreover, an other aspect, are the consequences on the structure and the length of frames. To limit the length of frames to not achieve a time $T_{ONMAX} \ge 400ms$ it is necessary to minimize the fields of synchronization (preambles, start of frame ...), control, and error detection (CRC, cyclic redundancy code).

## 4. CONCLUSION

In this paper, we propose an extension of the energy model proposed by [11] in a goal of cross-layer optimization, and show the influence of the channel on energy consumption. Simulation results show that significant gains are obtained by adjusting the transmission power in function of the kind of link between the central node and individual nodes. However the gains obtained are only valid if a joint optimization with MAC sub-layer is continued. Indeed, we have demonstrated that the physical layer optimization alone, is not suitable to achieve the desired lifetime for the wireless sensor network. Future works are dedicated to develop the cross-layer approach discussed before from the presented works. Indeed,

<figure><img src="image_12.png" /><figcaption>Fig. 11. Average consumption vs. lifetime</figcaption></figure>

once the transmission power is adjusted, the parameters (in particular the duration of radio transmission) that can optimize the overall energy system are in relation to the strategies of sleep and wake up of nodes (MAC sub-layer), as well as the frame length and the overhead incurred by the fields of synchronization and error detection of frames.

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