**Energy Efficiency in Cooperative Wireless Sensor Networks**

Glauber Brante, Marcos Tomio Kakitani and Richard Demo Souza

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/47780

## **1. Introduction**

20 Will-be-set-by-IN-TECH

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**7. References**

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Wireless Sensor Networks (WSNs) are composed by a large number of sensor nodes, which are usually small in size and are deployed inside or close to some phenomenon of interest. Moreover, since usually there is no need for regular or predefined deployment, the sensors can be placed over irregular or inaccessible areas. Therefore, according to [2], it is expected that the sensors possess self-organizing capabilities. Such attributes provide to the WSNs a large number of applications such as medical, military, and commercial. For instance, in medical applications wireless sensor networks can be used in patient monitoring systems. In the military, the fast set-up and self-organizing characteristics of the sensors make WSNs interesting for communication applications, security, monitoring, and terrain recognition. Commercial applications can include inventory management, product quality control and monitoring disaster areas.

The nodes in a WSN are typically equipped with limited power sources such as batteries, whose recharge or replacement may not always be possible or of economical interest. Moreover, batteries capacity presented a modest increase in the last decades when compared to the gains obtained in computational capacity and wireless throughput, which motivates the study of the energy efficiency of these devices. The wireless throughput has grown by roughly one million times and the computational capacity has had an increase of 40 million times since 1957, while the average nominal battery capacity has increased only 3.5 percent per year over the last two decades, as shown in [11, 24]. Thus, according to [9], due to these power source limitations, the overall energy consumption and energy efficiency have great importance and are major concerns in the design and analysis of wireless sensor networks.

Another challenge faced by WSNs is the wireless environment itself. The wireless channel is a difficult and unpredictable communication medium. A signal transmitted through wireless is subjected to many factors, such as noise, random fluctuations in time (usually referred to as fading), attenuation due to moving objects, etc. Therefore, a reliable system design comes at the expense of a significant amount of power, required to transmit a block of data from

©2012 Brante et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 2 Will-be-set-by-IN-TECH 374 Energy Effi ciency – The Innovative Ways for Smart Energy, the Future Towards Modern Utilities Energy Efficiency in Cooperative Wireless Sensor Networks <sup>3</sup>

the sensors to the sink. According to [14], one of the most promising techniques to overcome such limitations of the wireless medium is to exploit diversity techniques. Time diversity, frequency diversity and spatial diversity are among the most common strategies used in wireless transmissions. For instance, the use of error correction codes is an example of time diversity, introducing a level of correlation among the symbols to be transmitted. Orthogonal Frequency-Division Multiplexing (OFDM) and spread spectrum techniques are examples of frequency diversity. Recently, spatial diversity, through the use of multiple antennas, has been on the focus of many works, as for instance in [3, 12, 30].

starts with simple examples considering only three nodes, as in the case of the classical relay channel in [31], and is further generalized to multiple nodes randomly distributed over a field.

Energy Effi ciency in Cooperative Wireless Sensor Networks 375

The modern cooperative communications concept is based on the classical relay channel model in [31], which allows different nodes to share resources in order to achieve a more reliable transmission. Relay channel is usually referred to systems where the relay is a dedicated device, without information of its own to transmit. On the other hand, the term cooperative communication is used when the relay is another user or node in the same network, which also has information to transmit. The main objective of this approach is to achieve spatial diversity gains, which usually are obtained by adding more antennas to the nodes. However, in cooperative communications spatial diversity is obtained through the shared use of the source and the relay antennas. Thus, even if each device has only one antenna, spatial diversity can be obtained, what in this case is usually referred to as

The spatial diversity exploits the use of multiple antennas at the transmitter and/or receiver in order to create independent paths for transmitting the same information, allowing the system

• improve the link quality, and therefore decrease the transmission error probability as in

When only the receiver is equipped with multiple antennas, as illustrated in Figure 1(a), diversity combining techniques such as Maximal Ratio Combining (MRC) can be applied. The case that only the transmitter has multiple antennas is illustrated in Figure 1(b). In this scenario, one of the most effective techniques is the Alamouti scheme, which establishes a space-time coding for the symbols to be transmitted. And when there is a combination of multiple antennas at both the transmitter and the receiver the system is known as MIMO

**Figure 1.** Spatial diversity through the use of multiple antennas: (a) at the receiver; (b) at the transmitter;

A comparison of different spatial diversity techniques is shown in Figure 2. The figure compares the bit error rate (BER) as a function of the signal-to-noise ratio (SNR), which is defined as *Eb*/*N*0, where *Eb* is the energy per information bit, and *N*<sup>0</sup> is the power spectral density of the noise. In this particular example, the wireless channels are independent and

• increase the transmission rate without increasing the bandwidth, as in [12, 33];

• combine the two previous alternatives in a hybrid option, as in [13, 39].

(Multiple-Input Multiple-Output), as shown in Figure 1(c).

Then, Section 4 presents the final comments of this chapter.

**2. Cooperative communications**

cooperative diversity.

**2.1. Spatial diversity**

(c) at both (MIMO system).

to:

[3, 30];

However, for the spatial diversity gains to be obtained in practice it is necessary that the antennas are sufficiently spaced at the transmitter and receiver. Small-sized devices such as sensor nodes do not dispose of sufficient area to place multiple antennas appropriately spaced. Another practical way to obtain spatial diversity is through the use of cooperative communications. Cooperative communications are based on the channel model introduced by [31], which was originally composed by three nodes: one source of information, the destination of the communication, and a relay node. The relay node is responsible for helping the communication between the source and the destination, so that it may be possible to establish a more reliable communication, or to reduce the transmission power. Thus, exploiting the broadcast nature of the wireless channel, the relay may be able to overhear the transmission from the source in a first time instant, and then retransmit this information to the destination in a second time instant.

At the time that it was proposed by Van der Meulen in 1971, the relay channel was of more theoretical interest. However, due to technological advances of wireless communications in the last decades, a renewed interest in cooperative communications appeared motivated by the recent works of [20, 26], showing that cooperation is a strong practical candidate to improve robustness and help in reducing the energy consumption of wireless networks.

Motivated by these recent advances in the cooperative communications field, and by the importance of reducing the energy consumption of wireless devices, the energy efficiency of some transmission schemes for wireless sensor networks is analyzed in this chapter. The goal is to outline the best strategy in terms of energy efficiency given the characteristics of a network. Such characteristics can include, for instance, the amount of error allowed at the receiver, or the maximum delay in the communication between two nodes. Moreover, in order to approximate the theoretical results to a practical sensors network scenario, the following analysis seeks to model the network in a realistic way. As the nodes in a WSN are often at close distances to each other, the severity of the wireless fading must be taken into account, since when nodes are closer, a better wireless channel is expected. Moreover, another factor that cannot be ignored in the energy efficiency analysis of WSNs is the consumption of the internal circuitry of the devices. As shown in [10, 25, 28], in networks where the nodes are distant, the transmit power dominates over the consumption of the RF circuits. However, when the nodes are closer, the circuitry consumption becomes relevant.

In the following, Section 2 reviews some important concepts of cooperative communications. This section starts by showing the gains that can be obtained with spatial diversity, followed by the introduction of the relay channel as a practical way to obtain spatial diversity. Moreover, some cooperative protocols are presented at the end of the section. In the sequence, Section 3 shows some applications of such concepts to wireless sensor networks. Various WSN scenarios are analyzed in terms of the energy consumption of the devices. The section starts with simple examples considering only three nodes, as in the case of the classical relay channel in [31], and is further generalized to multiple nodes randomly distributed over a field. Then, Section 4 presents the final comments of this chapter.
