LUIZ H. A. CORREIA DANIEL FERNANDES MACEDO ALDRI LUIZ DOS SANTOS ANTÔNIO ALFREDO FERREIRA LOUREIRO JOSÉ MARCOS SILVA NOGUEIRA

. UFMG - ICEX DEPARTAMENTO DE CIÊNCIA DA C O M P U T A Ç Ã O UNIVERSIDADE FEDERAL DE MINAS GERAIS LUIZ H. A. CORREIA DANIEL FERNANDES MACEDO ALDRI LUIZ DOS SANTOS ANTÔNIO ALFREDO FERREIRA LOUREIRO JOSÉ MARCOS SILVA NOGUEIRA

1 Challenges and Experiences in the Design of Transmission Power Control Protocols for Wireless Sensor Networks Luiz H. A. Correia 1,2 Daniel F. Macedo 1 Aldri L. dos Santos 1,3 Antonio A. F. Loureiro 1 José Marcos S. Nogueira 1 Dept. of Computer Science, Federal University of Minas Gerais Belo Horizonte-MG, Brazil Dept. of Computer Science, Federal University of Lavras Lavras-MG, Brazil Dept. of Computer Science, Federal University of Ceará Fortaleza-CE, Brazil E-mails: {lcorreia,damacedo,aldri,loureiro,jmarcos}@dcc.ufmg.br Abstract Communication is usually the most energyconsuming event on Wireless Sensor Networks (WSNs). Hence, energy-aware medium access control (MAC) protocols are fundamental to extend the lifetime of those networks. One important technique to mitigate energy consumption in the MAC layer is the adjustment of the transmission power. This article discusses the benefits of transmission power control (TPC) protocols, examines the issues in the implementation of such protocols, and summarizes the results of their first evaluation in existing hardware. Experimental results show that TPC is an effective method to reduce energy consumption in sensor nodes, and should be integrated in the design of new MAC protocols. TPC protocols allowed a reduction of up to 57.5% in the energy required to transmit packets, at the cost of a negligible reduction in the delivery rate. We also provide a glimpse of future challenges in WSNs using TPC-aware MAC protocols. Index Terms Transmission power control, wireless sensor networks, medium access control, energy-saving. I. INTRODUCTION Wireless Sensor Networks (WSNs) are a subclass of traditional mobile ad hoc networks (MANETs), and consist of a large number of sensor nodes, composed of processor, memory, battery, sensor devices, and transceiver. These nodes send monitoring data to an access point (AP), which is responsible for forwarding data to the users. Unlike traditional ad hoc networks, in general it is not possible to replace or recharge node batteries due to the large number of nodes deployed or to inhospitable environmental conditions. Hence, energy conservation is a critical factor in WSNs [1]. The strong limitations on energy consumption are present in the design of every aspect of WSNs. Among the operations performed by sensor nodes, communication is usually the most energy consuming. Since the medium access control (MAC) layer coordinates the transmission of every packet, this layer must be fine-tuned to improve its energy-awareness. There are three main techniques that can be employed in the MAC layer to decrease energy consumption. In the first technique, nodes coordinate transmissions and receptions among themselves. Since each node knows when to receive and send data, collisions are diminished, and it is possible to completely turn the radio off when it becomes idle [2]. The second technique consists of turning off nodes which are not essential to the network operation, thus reducing the amount of packets transmitted. This task is commonly performed by topology control protocols, which periodically rotate the dormant nodes, while still maintaining a connected network and ensuring sensor and actuator coverage over the sensed area [3]. By applying the two techniques above mentioned, the amount of unnecessary traffic is reduced to the minimum, and the radio is active only when strictly necessary. Hence, the last energy reduction strategy is to minimize the amount of energy required to send packets. Whenever a node has to transmit data, it does so at the lowest transmission power necessary to reach the destination, thus less energy is consumed. There are several benefits on reducing the transmission power. The first one is the reduced number of collisions, as shown in figure 1, where the polygons represent the transmission range of nodes A and D. The dashed lines represent the regular transmission range, while the solid lines represent the reduced transmission range. In this example, if nodes A and D, at the same time, transmit data at the regular transmission power to nodes B and C, respectively, a collision will occur at node B. If the transmission power is reduced to the minimum necessary to reach the destination of the packet, no collisions occur. Since A can send data to B without disrupting any transmissions from D to C, the network will be able to maintain a larger amount of data in transit, increasing its capacity and throughput [4]. There are several transmission power control protocols for mobile ad hoc networks, however few of those were designed with sensor networks in mind [5], [6], [7]. Those protocols do not consider various effects found in wireless communication, such as non-isotropic propagation, interference, noise, antenna directionality, varying input voltage, which significantly affect

2 Fig. 1. Collision A B C D Transmission range with power control Transmission range without power control Adjusting transmission power to avoid collisions. wireless communications [8]. To better understand and to propose improved solutions to this problem, we are evaluating transmission power control (TPC) protocols on real environments. In particular, we are defining and implementing TPC protocols for the commercial Mica2 wireless sensor nodes, from Crossbow Inc., a technology originated in Berkeley University, and extensively used in WSN research. To our knowledge, this is the first experimental evaluation of TPC protocols. Preliminary results showed a reduction of 27% in energy consumption, when compared to fixed transmission range solutions [9]. In this article we show the enhancements made to the protocols proposed in work mentioned above, which lead to ever more improved results, achieving energy savings of up to 57.5% per transmitted packet in some scenarios. Due to these promising results, we advocate that TPC must be considered in the design of future WSNs. II. BENEFITS OF TRANSMISSION POWER CONTROL Transmission Power Control (TPC) allows several improvements in the operation of WSNs, such as the establishment of links with higher reliability, communication with minimum energy cost, and better reuse of the medium. In the following we briefly explain each one of them. Links with higher reliability: when used in conjunction with link reliability assessment algorithms, power control techniques can improve the reliability of a link. Upon detecting that link reliability is below a certain threshold, the MAC protocol increases the transmission power, improving the probability of successful data transmissions. Communication at minimum energy cost: using a fixed transmission power in the communication, nodes waste energy since some links already have a high probability of a successful delivery. Hence, TPC protocols could decrease the transmission power to a level where link reliability is high, but energy consumption is lower. Better reuse of the medium: by transmitting at the exact power needed to ensure a successful communication, signal range is nothing broader than it was supposed to. Thus, only nodes which really must share the same space will contend to access the medium, decreasing the amount of collisions in the network. This reduced number of collisions will enhance network utilization, lower latency times, and reduces the probability of hidden and exposed terminals [5]. These situations arise when a transmission is blocked because the transmitter or the receiver believe that, by transmitting at that moment, there might be a collision, when in fact this would never occur. A smaller transmission power mitigates their likelihood, since less nodes overhear transmissions from others. III. CHALLENGES IN TPC Reception power is highly correlated to signal propagation. Reijers et al. [8] and Lal et al. [10] demonstrated that signal propagation does not follow the concentric circle model. Also, it varies with humidity, temperature, node and antenna positioning, and the existence of obstacles. Finally, the signal is influenced by the existence of obstacles and moving sources of interference (animals, devices transmitting at the same frequency, buildings, cars, etc). The first technique that comes to mind to determine the minimum transmission power is the usage of signal propagation models commonly used in simulations (e.g. Friis, Two ray ground, Gilbert-Elliot, etc). However, this approach does not overcome the existence of obstacles. Consider the network topology in figure 2, where node A broadcasts a message to nodes B and C. Although node B is the closest to A, C will probably receive the signal from A with a higher signal strength than B (the strength is indicated by the thickness of the line), since there is an obstacle blocking the signal. Without providing nodes with a priori knowledge of existing obstacles and how the signal will degrade on each obstacle, which is impossible in most sensor nodes deployments, propagation models might yield unreliable results. Furthermore, propagation methods which address the dynamics of the medium are too complex to be employed in restricted hardware. Hence, TPC methods avoid the use of propagation models, thus they periodically probe the environment to identify the current situation of the medium, adjusting the transmission power accordingly. Fig. 2. A Barrier Signal fading when facing an obstacle. When adjusting the transmission power, it is necessary to determine whether the transmission power will be equal for every node in the neighborhood, or if it varies for each neighbor. Gomez and Campbell evaluated this trade-off in the context of wireless multi-hop networks [4]. The authors C B

3 showed that per-link range adjustments outperform global range transmission adjustments by 50%. Thus, instead of globally defining a transmission range that keeps the network connected, wireless networks should adjust transmission ranges on each link. Gomez and Campbell also demonstrated that the average traffic capacity per node is constant even if more nodes are added to a fixed area network, when transmission power control is employed. This is not true, however, if the transmission range is kept fixed. For such networks, the capacity decreases when more nodes are added, since more transmissions interfere with each other. Existing TPC protocols have been evaluated by simulation, where it is assumed that the transmission power can be set to any arbitrary value. This is not possible on real radios, which provide a few dozen possible transmission power settings. The CC1000 transceiver, for example, used in the Mica2 WSN platform, provides 22 transmission settings, while the CC2420 transceiver, used in the MicaZ and Telos platforms, provides only 10. Hence, if a given radio can transmit only at 2, 4, or 5 dbm, and the ideal transmission power is calculated as 2.542 dbm, the effective transmission power will be increased to 4 dbm. Finally, the calculations employ floating-point operations, which are available in MANETs, but usually not in most sensor nodes. Furthermore, the existing methods needed to be adapted in order to operate in current WSNs. IV. ASSESSING THE IDEAL TRANSMISSION POWER In order to assess the ideal transmission power, it is important to highlight a few concepts, extensively defined in [5]. First, data are transmitted at a given transmission power (P T X ), and received at a reception power (P RX ). The reception power is a value that lies within the physical limits of the radio. If the reception power is lower than the minimum reception limit of the radio (radio sensitivity), the data will not be properly received. On the other hand, if the reception power is higher than the maximum reception limit of the radio, the data will not be received. The reception and transmission powers usually differ in value, since the signal looses energy when traveling in the medium from transmitter to receiver. Such a signal variation is called gain (G A B ), and is usually defined as the fraction of the transmission power that reaches the destination (formally, G A B = P RX P T X ). The gain is directional, meaning that a link is asymmetric concerning the transmission power. The consequence of this, for instance, is that a node A might be able to hear a node B, but node B might not be able to hear A when both transmit with the same power. Another factor that influences the communication is the background noise (also referred as noise), caused by signals naturally present in the environment that are picked by the radio. Hence, the received data will be the sum of the transmitted data and the noise. The ideal transmission power is the minimum transmission power necessary to send data, and must conform to the following restrictions: The radio must be capable of transmitting at the ideal transmission power, that is, it must be lower than the maximum power supported by the radio, and higher than the minimum power supported by the radio. Since existing hardware do not allow setting the transmission power to an arbitrary value, the radio transmitter must select the value from a discrete set of pre-defined power values. In order to ensure the correct reception of data, the reception power must lie within the physical limits of the radio, and the received signal must be discernible from the noise. This is attained by ensuring that the reception power is higher than the noise by a certain amount, called signal to noise ratio threshold (SNR threshold ) [5], [9]. Figure 3 shows signal and noise measured at Mica2 nodes in an indoor environment. Signals received below the SNR threshold are less likely to be decoded correctly, since the probability of occurrence of signal spikes in the noise interfering in the reception is high. For reception strengths above SNR threshold, however, interferences are much less likely to happen. The transmitted power must provide a reliable channel, where messages arrive at the receiver without transmission errors. These errors occur due to incorrect reception, caused by weak signals at the receiver, or by subtle increases in the noise. To avoid such situations, the transmission power must be periodically refreshed to accommodate changes in the environment. In Figure 3, we see that, in a time interval of 10s (e.g, in the 80-90s range) the noise ncrease by 3 dbm. In such situations, the sender should detect the harsher conditions in the receiver, and elevate the transmission power. Signal strength (dbm) Fig. 3. -78-80 -82-84 -86-88 -90-92 -94-96 -98 Empirical data Average noise SNR threshold -100 0 50 100 150 200 Time (s) Noise variation in time. Existing transmission control methods employ two approaches to fulfill the requirements above. The first one uses link quality estimators to determine when the transmission power is lowered or raised. The second approach uses calculations based on the attenuation of the received signal to determine the action to be taken on the transmission power. The aim of those methods is to provide communication just above the minimum transmission power, yet yielding an acceptable link quality. These methods ensure that the abovementioned restrictions are met using empirical measurements, which work as follows.

4 In the first method, nodes periodically send HELLO messages, which must be acknowledged by the receiver. This is used to estimate the channel reliability. Whenever the percentage of acknowledged packets drops below a predetermined limit, the transmission power is increased. When the percentage of acknowledged packets gets higher than a certain threshold, the transmission power can be decreased. Such methods are called iteratives, as the transmission power is refined after each packet transmission (or iteration), and depends on the past behavior of the link. In the second method, when receiving an incoming packet, a node calculates the ideal transmission power based on the received signal power, the power originally transmitted, and the average noise. The new value of the ideal transmission power is then sent to the transmitter, usually piggy-backed in acknowledgement messages. Subsequent transmissions from that transmitter employ the value of the least received ideal transmission power. This class of methods is called attenuation methods. V. EXPERIMENTAL RESULTS The experiments were performed in an outdoor environment without obstacles. We used two Mica2 nodes, one receiver and one transmitter, separated from 5 to 20m of each other, and placed 71 cm above the ground. We did not place the nodes directly on the ground because, in such situations, the communication range drops to a few meters. The sender transmitted a total of 1000 packets in a rate of 4 packets per second. We compared the transmission power control (TPC) protocols to B-MAC [11], the standard MAC protocol of the Mica2 platform, in two situations: transmission at the maximum power allowed by the radio, 5 dbm, and at the standard transmission power, 0 dbm. The protocol B-MAC, when transmitting at 5 dbm, gives the minimum packet error rate. This situation is taken as the best-case scenario for evaluation of the TPC methods. During our experiments, we faced various challenges associated with energy sources in the nodes. Since nodes operate on batteries, our results depend heavily on the power source, varying according to the capacity of the battery, the manufacturer, and the decay of the voltage. This decay affects the signal strength readings provided by the radio. In order to avoid all those sources of error, our experiments were made with nodes plugged in UPSs to provide a predictable energy flow. The first implementations and studies of the attenuation and iterative TPC methods are presented in another paper [9]. Our initial evaluation showed that both methods experienced shortcomings. The iterative method, for example, tended to use very low transmission powers, causing frequent packet reception errors. Hence, the reception power would be too close to the average noise. The attenuation method experienced frequent fluctuations in the transmission power due to variations in the signal strength and voltage. This variation incurred in packet losses, thus the protocol should increase and decrease the transmission power smoothly. Using the knowledge acquired in the first experiment, we addressed the deficiencies of the two methods and made the modifications presented in the following. The iterative method was changed, at the transmitter side, to never decrease the transmission power below a certain SNR threshold. We have avoided constant variations in the transmission power by using a weighted average over the last transmission values. Older values contribute with an exponentially decreasing weight in the calculation of the current transmission value. The graphic in Figure 4 shows average delivery rates in function of distance among nodes. For simplicity, we have shown only the results of the improved methods and B-MAC protocol. When compared to the results presented in [9], the modified versions have attained increases of nearly 10% in packet delivery. The most impressive outcome of this experiment, however, is that the TPC methods have achieved delivery rates up to only 3% smaller than the ones achieved by the B-MAC protocol at 5 dbm, although sending messages at a much lower transmission power, as shown in Figure 5. We also found that TPC protocols extend the connectivity of the network, when compared to B-MAC at 0 dbm, as nodes are able to communicate with more distant nodes at an acceptable delivery rate for distances of 15 and 20m. Delivery rate (%) Fig. 4. 100 90 80 70 60 50 40 30 B-MAC (5 dbm) 20 B-MAC (0 dbm) 10 Iterative method Attenuation method 0 5 10 15 20 Distance (m) Average delivery rate when varying the distance among nodes. Converting the achieved transmission power values to the energy consumed by the radio, we found that the attenuation method has consumed 57.5% and 43% less energy than B- MAC in 5m and 10m, respectively, for a transmission power of 5 dbm. Compared to B-MAC at 0 dbm, the attenuation method has consumed 35% and 13.5% less energy for the same distances. The iterative method has consumed up to 13% more energy than the attenuation method, since it is more conservative when decreasing the transmission power. These promising results show TPC is an attractive energysaving technique, and its use barely affects the delivery rate. The next experiment aimed at evaluating the delay imposed in the network when two concurrent communications are in place. Before transmitting data, nodes check if the medium is busy, in order to avoid interference with ongoing transmissions. When the medium is busy, nodes backoff for a random period of time before making a new attempt to transmit the packet. Thus, a lower probability of a busy medium enhances the throughput and the number of simultaneous transmissions in the network. For this experiment we positioned four nodes

5 Transmission power (dbm) Fig. 5. 6 4 2 0-2 -4 B-MAC (5 dbm) -6 B-MAC (0 dbm) Iterative method -8 Attenuation method 5 10 15 20 Distance (m) Average transmission power when varying the distance among nodes. directly on the ground in an empty corridor, as portrayed in figure 6 (arrows denote data transmission), transmitting 1000 packets at a rate of 12.5 packets per second. As the transmission range is significantly lowered when nodes are placed on the ground, nodes were very closely deployed. Fig. 6. 50cm 310cm Setup of the medium reuse experiment. 90cm Table I shows the transmission power and the probability of the node finding a busy medium for each protocol. For this given node configuration, B-MAC at 5 dbm has a 29.6% chance of rescheduling the transmission for a later time due to an ongoing transmission from other node. When B-MAC transmits at 0 dbm, this probability is lowered to 19.9%. The TPC protocols showed even better results, decreasing the probability of collisions to 7.2% using the iterative approach, and to 4% using the calculated approach. TABLE I RESULTS FOR THE MEDIUM REUSE EXPERIMENT. Protocol Prob. of Avg. tx. power busy medium (dbm) B-MAC 29.6% 5 B-MAC 19.9% 0 Iterative 7.2% ( 7.25 ± 0.29) Calculated 4% ( 14.75 ± 0.48) VI. FUTURE CHALLENGES IN TPC The introduction of transmission power control in wireless ad hoc and sensor networks provides new opportunities to increase the network performance. However, the behavior of those protocols is still unclear in some situations. We identified several challenges in the adoption of TPC protocols and we discuss some of them in the following. Transmission power aware routing and topology control protocols: with transmission power control, routing protocols are able to reach more nodes, as node neighborhood can be extended towards the maximum range of the radio. However, transmitting to a distant node over a multi-hop path might be more energy-efficient than using single-hop paths. Existing routing protocols traditionally are unaware of this fact, since the amount of energy required to transmit data to their neighbors is always the same. Hence, transmission power control requires routing decisions to consider the transmission power. Topology control protocols can also benefit from transmission power control. These protocols turn off redundant nodes while keeping sensing coverage of the area. To keep the network connected, topology control protocols sometimes have to let active some non-productive nodes just to maintain connectivity. Thus, by increasing the transmission power of productive nodes, the number of active but redundant nodes can be reduced. Node mobility: although WSNs are mostly static, some networks might employ mobile nodes. In this scenario, the network suffers from partitions when nodes move out of the network, requiring adjustments in transmission power to reach the mobile nodes that leave the covered zone. On the other hand, when nodes move closer to the network, their transmission power can be lowered. The challenge in node mobility is how to cope with motion and different speeds, which dictate the frequency of transmission power updates. Multicast and broadcast messages: the existing transmission power control techniques assume that the receiver acknowledges the reception of a packet. In multicast and broadcast transmissions, however, acknowledgements are not advisable because of the complexity that they bring to the protocols and of the potentially high number of messages sent by the receivers. There are a large number of publications focusing on calculating the ideal transmission power in order to provide a connected topology. Broadcast and multicast messages could be sent at this power, and multi-hop paths would be used to route the message to the remaining nodes not reachable within the transmission range. However, it is not proved yet that this is the most energy-efficient solution. That is, the transmission power could be higher, achieving a large percentage of the destination nodes with a single hop, and diminishing the number of message forwards needed to reach the more distant nodes. Reconfigurable radios: also known as software radios, this new kind of transceivers allows a fine adjustment of physical layer parameters such as operating frequency, bandwidth, number of channels and modulation techniques [12]. By dynamically changing these parameters, WSNs will be more resilient to changes in environmental conditions. Such resilience can be provided by MAC protocols, if they are capable of negotiating radio parameters among nodes. Software radios can positively affect how TPC protocols operate, since nodes will be able to decrease the transmission power even further by employing modulation and encoding techniques more resilient to bit errors. The transmission power will encompass a much larger optimization space, since MAC protocols could tradeoff energy consumption and bandwidth. Underwater sensor networks: communication in underwater sensor networks pose even more challenges than terrestrial WSNs, since data are transmitted using acoustic waves [13].

6 In such conditions, signal propagation and attenuation differs according to the nature of the environment (shallow or deep ocean, salinity, temperature, depth, surface winds) and spatial orientation of the link (vertical or horizontal). As sound waves propagate five orders of magnitude slower than radio waves, protocols must also account for the delay of each data transmission. In such situations, TPC protocols must be completely redesigned to address those restrictions. VII. CONCLUSIONS Wireless Sensor Networks (WSNs) are a specialized type of ad-hoc networks, where hundreds or thousands of lowcost nodes are networked to monitor a given region. Energy consumption is one of the fundamental constraints that must be minimized in WSNs, and communication is usually the most energy-intensive operation in such networks. Hence, the design of energy-aware protocols is a challenging task. The adjustment of the transmission power, performed by transmission power control (TPC) protocols, is a technique to diminish energy consumption. This article has examined some important topics related to energy savings in the design and implementation of such protocols. The requirements to the establishment of reliable links have been described, as well as two novel approaches to adjust the transmission power. Experimental results showed that, in certain scenarios, TPCaware MAC protocols are able to maintain their original packet delivery rates, while consuming up to 57.5% less energy per packet sent. Another benefit of TPC protocols is a higher utilization of the medium, since concurrent transmissions interfere less with each other. The use of TPC techniques brings new challenges to the design of WSNs, such as the awareness of the transmission power in routing and topology control protocols. [9] L. H. A. Correia, D. F. Macedo, D. A. C. Silva, A. L. dos Santos, A. A. F. Loureiro, and J. M. S. Nogueira, Transmission Power Control in MAC Protocols for Wireless Sensor Networks, in ACM/IEEE International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM), October 2005. [10] D. Lal, A. Manjeshwar, F. Herrmann, E. Uysal-Biyikoglu, and A. Keshavarzian, Measurement and characterization of link quality metrics in energy constrained wireless sensor networks, in IEEE GLOBECOM, December 2003, pp. 172 187. [11] J. Polastre, J. Hill, and D. Culler, Versatile low power media access for wireless sensor networks, in Proceedings of the 2nd international conference on Embedded networked sensor systems. ACM Press, 2004, pp. 95 107. [12] W. H. W. Tuttlebee, Software-defined radio: Facets of a developing technology, IEEE Personal Communications, vol. 6, no. 2, pp. 38 44, 1999. [13] I. F. Akyildiz, D. Pompili, and T. Melodia, Underwater acoustic sensor networks: research challenges. Ad Hoc Networks, vol. 3, no. 3, pp. 257 279, 2005. REFERENCES [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A Survey on Sensor Networks, IEEE Communications, vol. 40, no. 8, pp. 102 114, 2002. [2] L. H. A. Correia, D. F. Macedo, A. L. dos Santos, J. M. S. Nogueira, and A. A. F. Loureiro, A taxonomy for medium access control protocols in wireless sensor networks, Annals of telecommunications, vol. 60, no. 7-8, July/August 2005. [3] M. Cardei and J. Wu, Energy-Efficient Coverage Problems in Wireless Ad Hoc Sensor Networks, Journal of Computer Communications on Sensor Networks, 2004. [4] J. Gomez and A. T. Campbell, A case for variable-range transmission power control in wireless multihop networks, in Proceedings of the IEEE Infocom, vol. 2, March 2004, pp. 1425 1436. [5] J. P. Monks, Transmission power control for enhancing the performance of wireless packet data networks. Phd. thesis, University of Illinois at Urbana-Champaign, 2001. [6] E.-S. Jung and N. H. Vaidya, A power control MAC protocol for ad hoc networks, in MobiCom 02: Proceedings of the 8th annual international conference on Mobile computing and networking. New York, NY, USA: ACM Press, 2002, pp. 36 47. [7] M. Kubisch, H. Karl, A. Wolisz, L. C. Zhong, and J. Rabaey, Distributed algorithms for transmission power control in wireless sensor networks, in Proc. IEEE Wireless Communications and Networking Conference (WCNC 03), vol. 1, March 2003, pp. 558 563. [8] N. Reijers, G. Halkes, and K. Langendoen, Link layer measurements in sensor networks, in 1st IEEE Int. Conference on Mobile Ad hoc and Sensor Systems (MASS 04), October 2004, pp. 224 234.