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The main idea is that the sending node computes an appropriate transmit power based on its traf c distance d and an estimate of the interference level it experiences, instead of collision avoidance information (CAI) advertised by neighboring nodes used in references 37 and 38 The power function, -PCS, is de ned as P (t) = Pmax (d/dmax ) , where dmax is the transmission range of Pmax , is a constant between 0 and , and is the power attenuation factor Each value corresponds to a different power control scheme, and in one scheme the value is uniform to all the nodes in the network By choosing an appropriate value of , this scheme can improve energy ef ciency and channel spatial reuse; furthermore, it yields a fair throughput distribution among different destination ranges.

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Experiments show that this method has an inaccuracy of a few degrees (about 5 in reference 36 The need of extra hardware and the need of a minimal distance among the receivers result in some disadvantages in terms of cost and size of the sensor nodes 1124 Communication Range In some cases, the only information available to estimate a distance is the communication range of the sensor nodes If a node receives a data packet from another node, then the distance between these nodes is between zero and the maximum communication range Usually, techniques that use this method of distance estimation do not need an accurate distance, but only an interval To get only one distance (and not an interval), we can choose one point from the interval, like the middle point, for example.

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Simulation results show that -PCS can achieve improvement of over 40% in throughput, energy ef ciency by a factor of 3, and better fairness with respect to channel utilization Interference-Based MAC Schemes The power control algorithms discussed above can achieve good reduction in energy consumption However, it cannot improve the throughput This is because the exchange of RTS and CTS messages, which are used to silence neighboring nodes, prohibits potential concurrent transmissions in the neighborhood during the DATA/ACK packet transmission To increase spatial reuse, and thus increase the throughput, references 8 and 37 39 have proposed various interference-limited media access control algorithms In these algorithms, new transmission is admitted as long as it does not corrupt ongoing neighboring transmissions.

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In this last case, the maximum error of this estimation will be one-half the communication range This method of distance estimation has the advantage of being the most simple and with the lowest cost No extra hardware is required, nor is extra computation needed to estimate a distance On the other hand, an error of half the communication range for each distance estimation is not viable for the most localization systems Consider, for example, a communication range of 100 m In this case, the error of this method can be about 50 m for each distance estimation 1125 Comments About the Distance/Angle Estimation The choice of what method to use to estimate distance between nodes in a localization system is an important factor that in uences the nal performance of the system.

The basic idea is that by exchanging collision avoidance information (CAI) in the control packet, nodes compute the maximum allowable power levels that will not corrupt any ongoing transmissions in the neighborhood, and then they use this power level as the upper bound of the transit power level for subsequent packet transmissions including the control packets This is completely different from the idea of collision avoidance in current carrier sensing medium access control algorithms, where nodes in the carrier sensing range of an ongoing transmission should defer their potential transmissions PCMA The Power Controlled Multiple Access Protocol (PCMA) [8] proposes a exible variable bounded-power collision suppression model with two separate channels (data channel and busy tone channel), which is generalized from the transmit-.

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Usually, as will be shown in the next section, to estimate a position, a node uses at least three distance estimations, each of them with an associated error On the other hand, if only the accuracy of such methods were important, we could just use a TDoA that has lower errors But factors such as the size and cost (in terms of hardware, processor, and energy) of the nodes must also be taken into consideration Thus, the chosen method used to estimate distances will depend on the application requirements and also on the available resources Table 112 compares each one of the methods described in this section..

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