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message, power 1.y when the radio is transmitting a message at full power, and power 0.z when the radio is in sleep mode (the actual values of x, y, and z depending on the specific card).
Before ending this section, we want to remark that the ratio 1.y used in our model refers to the relative power consumption of the radio when it transmits at maximum power. On the other hand, we shall see that topology control protocols are based on the ability of the wireless node to dynamically adjust its transmitting range. This feature is actually available on some commercial IEEE 802.11 cards, such as those produced by CISCO. For instance, the CISCO Aironet IEEE 802.11 a/b/g card can use transmit powers ranging from 1 mW to 100 mW. However, this value refers to the power consumption of the RF amplifier, which is only a part of the total power consumed by the wireless card. In fact, the card consumes significant energy also to power up the other analog and digital circuitry.
How to model power consumption when the radio is not transmitting at maximum power is not clear. Most of the approaches presented in the literature are concerned with the transmit power only, which is typically modeled using one of the formulas reported in Section 2.1. Unless otherwise specified, this book will also follow this simplistic energy model. In particular, we will use the following definition of energy cost:
Definition 2.3.1 (Energy cost) Given a range assignment RA for a certain network Md = (N, L), the energy cost ofRA is defined as
c(RA) = J2 RA(u)a,
where a is the distance-power gradient.
Note that the above definition of energy cost is coherent with our working assumption that the radio signal propagates according to the log-distance path model.
2.3.2 Sensor networks
In case of sensor networks, the task of providing a simple yet realistic energy model is relatively simpler, as compared to the case of ad hoc networks. In fact, sensor networks are
22 MODELING AD HOC NETWORKS
Table 2.3 Measured power consumption of a Rockwellĺs WINS sensor node
MCU Mode Sensor Mode Radio Mode Total Power (mW)
On On Tx (power 36.3 mW) 1080.5
On On Tx (power 0.12 mW) 771.1
On On Rx 751.6
On On Idle 727.5
On On Sleep 416.3
On On Removed 383.3
Sleep On Removed 64.0
typically composed of homogeneous devices, which are usually very simple. Furthermore, since many sensor nodes have been designed in the research community, their features are very well known. As a result, several sets of energy consumption measurements of wireless sensor nodes have been reported in the literature (Raghunathan et al. 2002).
Table 2.3 reports the power dissipation of a Rockwellĺs WINS sensor node (Rock-wellScienceCenter 2004). The node is composed of three main components: the microcontroller unit (MCU), the sensing apparatus (sensor), and the wireless radio. If we consider the power consumption of the wireless radio only, we have the following sleep : idle : rx : tx ratios: 0.09:1:1.07:2.02. Note that these ratios are quite similar to the case of 802.11 wireless cards, except for a somewhat higher power consumption when the radio is transmitting at maximum power. When the transmit power is minimum (0.12 mW), the idle : tx ratio in the WINS sensor is 1.12. So, there is an almost twofold power consumption increase when varying the transmit power from the minimum to the maximum value. This means that varying the transmit power level has a considerable effect on the nodeĺs energy consumption.
2.4 Mobility Models
Node mobility is a prominent feature of ad hoc networks and, in some cases, also of WSNs. As a consequence, studying the performance of ad hoc/sensor networking protocols in the presence of mobility is a fundamental stage of the design process. Since real implementations of ad hoc/sensor networks are scarce, real-life movement patterns are very difficult to obtain, and the common approach is to use synthetic mobility models and simulation.
Mobility models for ad hoc/sensor networks should
- resemble real-life movements: Given the wide range of ad hoc and sensor network applications, the movement patterns to consider are numerous: they range from cam-puswide movement of students to vehicular motion in highways, from movement of groups of tourists in a urban scenario to rescue squads motion in disaster areas, and from sensors carried around by ocean flows to animal movement in animal tracking WSN applications. Providing a unique mobility model that resembles all these types of mobility is virtually impossible. However, a mobility model should be representative of at least one application scenario.
- be simple enough for simulation/analysis: Since mobility models are used in the simulation of ad hoc networks, the model should be simple enough to be integrated in the
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simulator and to keep the simulation running time reasonable. Furthermore, using relatively simple mobility models eases the task of deriving meaningful analytical results concerning fundamental network parameters in presence of mobility. In turn, these results can be used to optimize the performance of ad hoc/sensor networking protocols.