Link Quality-Based Transmission Power Adaptation for Reduction of Energy Consumption and Interference
© Jinglong Zhou et al. 2010
Received: 28 May 2010
Accepted: 1 September 2010
Published: 26 September 2010
Today, many wireless devices are mobile and battery powered. Based on the fact that battery capacity is still limited, energy saving is an important issue in wireless communication. Meanwhile, the number of wireless devices continues to increase and this creates interference problems between wireless devices. In this paper, we look at transmission power control and propose a mechanism that tries to achieve minimum energy consumption or emission under any circumstance. Lower transmission power levels may result in more retransmissions, but in total, energy consumption or emission still can be reduced in many scenarios. To evaluate the performance of our mechanism, we used real wireless channels in an indoor environment to carry out measurements. The measurement results indicate that a significant amount of energy consumption or emission reduction can be achieved for the transmitter in most scenarios compared to using a fixed transmission power level for all packets.
Plenty of wireless devices use battery-based power, but the battery technology does not keep up. To increase device service duration, saving power is crucial. Power saving in communication can be achieved by different methods at different communication layers. Power-aware routing selects routes that together consume less energy or use devices that have more energy . In the MAC layer, the receiver can turn off the receiver function periodically to save energy . Another way of saving energy is to adapt the transmission power for the transmission of packets. Power transmission adaptation can achieve two benefits: save energy and reduce interference. Interference is becoming an increasing problem due to the enormously growing number of wireless devices. One way to alleviate this problem is to reduce the emitted transmission power.
The motivation for transmission power adaptation for energy saving and interference reduction stems from the fact that many of the current wireless communication systems (e.g., IEEE 802.11 and IEEE 802.15.4) usually use a fixed default transmission power level for all transmissions. However, when two nodes are very close to each other, the default power level is much higher than required to successfully deliver all packets. This both wastes energy and creates unnecessary interference. A lower transmission power level may require a larger number of retransmissions, but overall less energy will be emitted or consumed for each transmission and in total, there may be less waste. Therefore, a trade-off is possible between the number of retransmissions and energy consumption for each packet delivery. This trade-off requires the knowledge of the packet delivery ratio (PDR) for each transmission power level. We call this the PDR-table. The PDR-table differs between different links and different environments. To always select the transmission power level that consumes the least energy or have lowest energy emission, a self-adaptive transmission power adaptation mechanism is required that accurately observes the PDRs. In this work, we focus on IEEE 802.11 and IEEE 802.15.4 as our experiment technology. However, our methods can be used in other radio technologies as well.
Energy consumption for IEEE 802.11 is not so crucial as for IEEE 802.15.4, since IEEE 802.11 is normally used with larger devices, such as laptops, PDAs, and mobile phones, which can be recharged easily. However, minimizing energy emission is still important because of the interference. For IEEE 802.15.4, energy consumption is critical due to its use in wireless sensor networks. Therefore, we mainly discuss interference reduction for IEEE 802.11 and energy saving for IEEE 802.15.4.
In this paper, we propose a power transmission control mechanism that is based on gathering PDRs for every transmission power level (the PDR-table). It consists of two phases: initialization and updating. It can be used both as an interference reducing mechanism and an energy saving mechanism depending on the energy model. We propose five different methods for the initialization phase. In the updating phase, we use an exponential weighted moving average (EWMA) method to update the PDR for each transmission power level and use the result to select the optimal level. To the best of our knowledge, we are the first to select the transmission power that achieves the minimum energy consumption or emission for delivering a certain amount of information based on link PDR-tables. We explore the maximum potential reduction of energy emission and consumption by an investigation of all relevant parameter combinations in our mechanism. The proposed mechanism is evaluated based on measurement data and the results indicate that significant savings can be achieved in many scenarios compared to always using the default transmission power level. We also compare our PDR-based mechanism with one that uses signal strength. Also there, the results indicate a significant improvement.
The rest of this paper is organized as follow: Section 2 introduces related work and Section 3 presents our measurement results and shows the potential reduction of energy consumption and emission. Our PDR-based transmission power adaptation mechanism is introduced in Section 4. In Section 5, our experimental system is described and in Section 6, the measurement results are presented. The paper is concluded in Section 7.
2. Related Work
Transmission power control requires good knowledge of the correlation between link quality and transmission power levels. This correlation has been studied before via measurement activities. In [3, 4], the correlation of transmit power level and packet delivery probability was analyzed in different indoor scenarios. Based on their observations, small adaptations in the power level do not change the packet delivery ratio in any measurable way. Some work also discussed combinations of power and rate adaptation to achieve good performance. In , it was proposed to select data rate and transmission power based on link quality. The method was applied in an indoor environment and achieved higher throughput than the traditional mechanism. However, energy consumption was not calculated.
Most previous work on applying transmission power adaptation schemes was more focused on reducing interference, maintain connectivity, and topology control, such as [6–9]. Paper  discusses the use of transmission power control to select reliable links and disable unreliable links via a blacklisting method in order to improve the system performance. Paper  discusses the use of transmission power control to reduce interference and simulation results reveal that throughput can be increased by adapting the transmission power in an ad hoc network. This shows the benefit of reducing energy emission. However, the aim of these papers were to maintain the link quality at a certain level, control the topology, and increase throughput by using transmission power adaptation. Energy was not their main focus and the selected transmission power level does not always result in the minimum energy consumption or emission level.
A few papers address energy saving explicitly. The authors of  proposed to use a RTS-CTS handshake in the highest power level to discover the channel quality and then use the lowest possible power level for the data packet. Simulation results show that the proposed power mechanisms can achieve energy savings without degrading the throughput. However, in their proposal, a separate channel is used for controlling, which means that adaptations to the IEEE 802.11 standard are necessary. Meanwhile, a theoretical model does not reflect the real channel situation accurately. In , a loop-based mechanism is used to adapt the transmission power level to achieve the minimum required power level for message delivering. Simulation results show that energy can be saved and throughput can be increased. However, this work also assumes that a RTS-CTS handshake is used. Moreover, a mechanism that adapts the transmission power level one level at the time will be too slow for fast channel variances. It may take several periods for the system to choose the appropriate power level.
In , the authors propose a power saving algorithm that adjusts the transmission power and extends the network lifetime. Again, only simulations are used to validate the proposed protocol. Paper  is the most similar work to ours; transmission power adaptation was used for power saving in different scenarios. However, the optimal transmission power level is set by the received signal strength. We use PDR information for two reasons. First of all, the mapping between PDR and received signal strength is not straight forward and noise and interference have a large impact on the mapping. Second, different receivers have different sensitivity levels and using received signal strength may require different thresholds for different devices. A PDR-table method is affected by different devices. We compare this mechanism with our mechanism in Section 6.
3. Energy Emission and Consumption Measurements
where is the energy emission created by the transmission power level. For IEEE 802.11, the transmission power range is from 0 to 15 dBm and for IEEE 802.15.4, it is from −25 to 0 dBm . Our 802.15.4 device has 31 different power levels, but we used only 15 of them, which we calculate in this simplified way: level 3 corresponds to −23 dBm and level 31 corresponds to 0 dBm and then we assume a linear correlation to map the transmission power levels in between to the different energy emission levels in dBm.
If we only calculate the energy emission to the environment, (1) and (2) are used. If we calculate the total energy consumption of the whole transmitter, (1) and either (3) or (4) are used.
At the receiver side, we recorded the PDR for each transmission power level. When doing this for our scenarios, we obtained the results in Figures 2(c) and 2(e). We can see that a certain transmit power level achieves the minimum energy emission or consumption and they are different for different links. The minimum energy emission level for each link in Figure 2(c) is 3, 6 and 9 for each link, respectively. For the energy consumption, we use log scale to show the results due to the large differences. We can still see that there is a level which results in the lowest energy consumption for the transmitter, and this level is not the highest power level.
To show that this phenomenon not only exists for IEEE 802.11 with 2 Mbps data rate, we carried out measurements for many data rates. The power trade-off for IEEE 802.11 with different rates is presented in Figures 2(d) and 2(f). It is interesting to see that for higher data rates, for example, 54 Mbps, the level that results in minimum energy consumption and emission is 15. This is caused by the fact that the link quality is so poor and struggles even with full power.
Based on the four groups of results shown in Figures 2 and 3, we can see that almost all the links have a PDR from 0 to 1 within a 10 dBm transmission power difference. In almost all situations, the PDR is higher for larger transmission power levels. From Figures 2(c) and 3(c), we can see that given a data rate and packet-size, links with better PDR always requires less energy emission and consumption to deliver the same number of packets. However, if we are also able to change the data rate and packet-size, it is possible to further lower the energy emission and consumption.
4. PDR-Based Transmission Power Control
For a certain channel, if the correlation between , , and is known and constant, the best combination can be selected easily. However, the actual channel PDR-table can be quite different from link to link as shown in Figures 2 and 3 and this is also indicated in . Therefore, to have an efficient transmission power control, we need a good mechanism of learning this PDR-table in real time. Meanwhile, the PDR-table may change due to several reasons, such as mobility, environmental changes, and interference. Hence, a self-adapting mechanism is required.
For each link, we need to keep a PDR-table that contains all the values for the different transmission power levels. The PDR-table may contain values for all possible transmission levels or only a subset of them. The values are not dynamic and can be calculated beforehand for each of the transmission power level based on the chosen energy model. Since (1) will be used for both the energy emission and consumption calculation, we can use the same transmission power control mechanism for both.
We divided the mechanism into two phases; the initialization phase and the updating phase. The initialization phase tries to quickly learn or "guess" the correlation between the transmit power level and the PDR once a new communication link is established. The updating phase keeps on updating this PDR-table and adapts the transmission power during the whole communication period. The initialization phase should be very short compared to the updating phase. Hence, the initialization phase is more useful for small amounts of traffic and the updating phase is more useful for large amounts of traffic. We describe the two phases in detail in the following two sections.
For neither phase, we do not generate any extra packets to probe the PDR-table. Instead, we use the normal data packets to "learn" the channel and select the appropriate transmission power level. If acknowledgments are being used, which is the case for most wireless links, including 802.11 and 802.15.4, the sender can use them to find out about the packet losses. Otherwise, this information needs to be passed back to the sender in another way. The energy emission or consumption calculation for all methods have the same prerequisite, the same amount of information need to be delivered.
4.1. Initialization Phase
Default start. Start using the default power level (15 dBm in 802.11 or 0 dBm in 802.15.4) and then immediately move on to the updating phase. This means only one packet is transmitted and depending on whether it was received or not or for the default power level. The remaining s in the PDR-table are set to .
Sampling. Send 10 packets in all transmission levels to probe the channel and then use the obtained measurements to build the initial PDR-table and then move on to the updating phase.
Historical. Use the last recoded PDR-table (recorded based on the latest communication record between two nodes). The sender sends 10 small packets (40 Bytes) with full transmission power and the receiver reads and sends back the received signal strength. The sender then compares this with the received signal strength recorded last time. The original table is shifted left or right with the difference value based on the signal strength difference and forms the new PDR-table.
Combined. First collect the received signal strength as in the Historical method. If the signal strength between now and the previous communication are similar (within 2 dBm difference), the Historical method is used. Otherwise, the Sampling method is used.
A better initialization method starts closer and converges faster to the optimal transmit power. In Section 6.1.1, we will compare all these methods with the Fixed method, which sends all packets with default power level during both the initialization and updating phases and hence makes no use of the PDR knowledge.
4.2. Updating Phase
In the updating phase, most packets are transmitted with the transmission power level that minimizes (1). If two levels have the same power consumption, then the higher transmission power level will be used.
where the means the current estimation of PDR for a certain transmission power level in interval , is the calculation of PDR for this power level in interval , and the smoothing factor is used to tune the speed of updating. This is only done for values that had a transmission in the PDR-table during the interval. We used an interval of 10 packets.
We defined another parameter which controls the probability that a packet will use another level than the selected optimal level. This probability is defined as . The level to probe is selected uniformly among the other levels in the PDR-table. The performance of the updating phase with different and is investigated in Section 6.1.2.
5. Experimental Setup
All experiments were carried out in a typical indoor office environment. They were done at night when there were very few people walking around. For each scenario, we collected a packet trace and used a post processing approach to compare every method and parameter. In this way, every parameter combination could be compared based on the same actual link in a fair way.
5.1. IEEE 802.11 Test-Bed
For all our IEEE 802.11 experiments, we used two HP laptops (HP7400) equipped with 3Com 108 Mbps 11g XJACK PC wireless cards. Linux 2.6 and the Madwifi driver version 0.9.4 were used. We specially wrote a one-hop communication program, which had a sender and a receiver part. The node running the sender program controlled the transmission power level for each packet transmission. A fixed packet-size (1500 Bytes) was used during all experiments. We used broadcast packets to avoid MAC level retransmissions and the receiver side recorded the number of received packets. In a real system, feedback from the retransmission mechanism can be used instead.
We used channel 7 during the experiments. Long duration observations were done of the noise level for this channel and the value was around −96 dBm with a maximum variance of 2 dBm. Different distances (8, 16, and 20 meters, resp.) were used in the experiments to generate different channel conditions, but always nonline of sight (NLOS). We name these scenarios as S1, S2, and S3. For the experiments with different data rates, we used a distance of 20 m with another NLOS channel. Therefore, we call it S4.
5.2. IEEE 802.15.4 Test-Bed
We used an IEEE 802.15.4 compliant device in the 2.4 GHz ISM band from Moteiv, called Tmote sky that uses the CC2420 wireless chip . During the experiment, the USB was used as power supply. As in IEEE 802.11, we also wrote a one-hop communication program for these devices. We used three different payload sizes. They were 20, 50 and 100 Bytes. IEEE 802.15.4 has a packet header, which consists of 11 Bytes of PHY header and 6 Bytes MAC header. The standard data rate (250 kbps) was used during all experiments. We used only 15 different transmission power levels for the Tmote to be more comparable with our 802.11 experiments. Since there are 31 possible levels, we only used the odd levels between 3 and 31. Based on , they correspond to dBm as follows: Level 3 corresponds to −23 dBm, level 31 to 0 dBm and the levels in between are mapped in an almost linear fashion.
All the experiments were done in a channel that did not interfere with any IEEE 802.11 radio. We also did experiments in a channel that was impacted by IEEE 802.11 radio interference and found that the result was not much influenced. We used broadcast packets in the same way as in IEEE 802.11. We recorded the number of received packets and the used transmission power levels.
The IEEE 802.15.4 experiments were done in the same location as for IEEE 802.11, however, different distances were used. All channel were NLOS and the distances were 12, 14, 16, 18 m, respectively. We call these experiment scenarios T1 to T4. The experiments with different packet-sizes were done with 17 m between the sender and receiver with a NLOS channel.
5.3. Experiment Methodology
For each scenario, we collected a data trace by sending 30000 packets with different power levels during a period of 20 minutes. To be able to compare fairly between different methods and parameters, we used a post processing approach. In this approach, we took the trace and divided it into 200 batches. Each batch contained 150 packets, 10 packets of each power level. For each method and parameter combination, we emulated the process. This was done by assuming that only 10 packets were sent from each batch and it was up to the method to decide which power levels to pick. That is, for each emulation, only a fraction of the trace was used.
For the updating phase, % of the 10 packets were assumed to be transmitted with the currently selected best power level and % were assumed to be sent for probing the other power levels. These assumed packets were randomly selected from the trace, based on the power level and the batch it belonged to. From the trace, we checked whether the selected packets were received or not and used this information in the method. An important issue is that, due to the limited number of packets on each nonbest transmission power level (e.g., % for each interval is only 1 packet), the PDR for each transmission power level is only updated when there is a packet transmission in this interval. Since this random selection introduces variance, we repeated this process 300 times and calculated the mean and 95% confidence interval.
Parts of the packets are sent in the initialization phase and parts are in the updating phase. Each transmission was done with a certain transmission power level and took a certain duration. Therefore, the total energy emission or consumption was the sum of all energy emitted or consumed for all the transmissions. We processed the data using this method several times and due to some random factors in the processing, the total energy emission from each processing are hardly exactly the same. However, they are quite similar and the confidence intervals are very small, so we did not plot them and only plotted the average expected energy emission for a certain method and parameter combination. We did the same processing for the updating phase as well.
Unfortunately our IEEE 802.11 card did not support fast power variation. Based on measurements, we could conclude that it took our card about 1 second to change from the highest to the lowest transmission power level. Hence, we divided the time into intervals, each of 8 seconds long. In each interval, we first transmitted 200 packets with one transmission power level and then paused for two seconds. Right after the pause, we modified the power level to the next level and waited two seconds. The power level was changed in a round robin fashion between all 15 levels. For IEEE 802.15.4, we changed the power level per packet, which caused no problems.
6. Performance Evaluation
In this section, we evaluate the performance of our PDR-based mechanism. The energy emission and energy consumption are discussed in the following two sections, starting with the energy emission. In Section 6.3, we look at strategies to optimize both.
6.1. Energy Emission Reduction
First, we present the emission reduction results for both the initialization and updating phases.
6.1.1. Initialization Phase
6.1.2. Updating Phase
Another parameter to investigate is . Figure 6(b) shows the results of using and different amounts of probing packets. We can see that for each scenario, the optimal values for each link are all between 5 to 10%, which suggests that we should not send too many packets to probe other transmission power levels. However, the optimal is different for each link. The general rule is that, when the link is worse (PDR is lower for most power levels), the optimal is larger, which suggest that for lossy links, more probing should be done. However, a value of 10% performs well enough for all scenarios.
We further processed the measurement results with the assumption that is equal to 0.5 and we compared the expected energy emission with different values, from 1 to 50. The results are shown in Figure 7(b). The optimal value for is around 5% and more probes will result in more energy emission.
6.1.3. Impacting Factors for the Updating Phase
We can see that for the three experiments with lower data rates (2, 5.5, 6 Mbps), the effect is the same as in Figure 6(a). However, for the high rates (11, 54 Mbps), due to the lower PDR at almost all the transmission power levels, the updating phase cannot do much to reduce energy emission and 15 dBm is optimal. Therefore, does not have much impact on the performance. Since these experiments were done in the same location, we can see that 5.5 Mbps is the most power saving data rate. However, if we change to another location with another distance, another data rate may be optimal, which suggests that not only the transmission power level, but also the data rate can be selected to save power.
It is also important to know the performance with different packet-sizes. In order to experiment with different packet-sizes over a similar link, we used 802.15.4 and changed the packet sending method. For each packet-size, we sent 30000 packets in 200 batches. We splitted each of the three different packet-size experiments into 10 equalled sized parts and we interleaved the parts of the experiments in a round robin fashion. In this way, the three packet-sizes experiment are experiencing very similar channel conditions. By interleaving, we mitigate unfairness caused by slowly changing link conditions.
Since all the scenarios are stationary, we still need to investigate the performance in a mobile environment. Instead of moving the devices around, which is time consuming and difficult to replicate between experiments, we emulated a mobile channel by stitching together the traces from scenario T1, T2, and T3. The way we generate mobility is not ideal, but it still gives us some insights. It shows how channel variance can affect the selection of and , which is our interest.
The measurement results were segmented into different numbers of batches based on different mobility level assumptions. The mobile channel went from good (T1), to medium (T2), to bad quality (T3). To emulate different amount of mobility, we stayed with the same scenario data for different amount of time before changing to the next scenario trace. We used four different PDR change speeds, which we measured in number of batches. A mobility scenario which changes trace every 10 batches, that is, Mobility-10, is more mobile than one which changes only every 100 batches, that is, Mobility-100.
Variance over Time
Although all experiments were done with only changing the transmission power levels, our PDR-based method can also be applied to multi-factor environments. For example, we can keep records of three different data rates (or packet-sizes) but only five different power levels instead of all 15. The selection would then involve finding the most optimal rate (packet-size) and power level combination. It is also obvious to see that if proper and values are selected, the energy emission can be reduced in most scenarios.
6.2. Energy Consumption Reduction
where dBm and dBm for our wireless chip CC2420 according to .
During the implementation, we found that there is a logical error in the mechanism. If a packet is lost, no new signal strength value will be read and will not be updated. If, at the same time, , the transmission power will not be increased and this can lead to a deadlock. This situation could be quite possible, especially in mobile scenarios. Therefore, we adapted their method by letting a lost packet have an assumed received signal strength of −95 dBm, the lowest receivable signal strength for CC2420.
Furthermore, the authors do not specify the constant used when decreasing the transmission power level. In our implementation, we decrease the power by one level (corresponding to a decrease between 1 dBm and 3 dBm depending on the levels) when .
Quantitative comparison for the energy consumption of IEEE 802.15.4.
Signal strength-based (mJ)
The reason why the PDR-based method performs better is its direct use of the PDR to power level correlation. The signal strength-based method uses fixed thresholds for all the scenarios, which may work in some cases, but not in others, such as T1 and T2. The correlation between received signal strength and packet loss is simply too weak.
6.3. Trade-Off between Energy Emission and Consumption
where for IEEE 802.11 or for IEEE 802.15.4. If we set , we get (2) and will optimize for energy emission. If we set for IEEE 802.11 (or for IEEE 802.15.4), we optimize for energy consumption. However, it is also possible to set to a value in between and that would mean that we get a trade-off between energy emission and energy consumption.
We can also use to control the expected delay. A larger will penalize a lost packet more and this causes a higher power level to be chosen. The effect is fewer packet losses, fewer retransmissions, and thereby less delay.
Energy emission and consumption reduction are key challenges for wireless networks. In this paper, we proposed to select the appropriate transmission power level using PDR information in order to reduce the energy emission and/or consumption. We proposed to use the EWMA method to update the PDR and transmission power level correlation and use this to adapt the transmission power. We proposed four initialization phase methods to get a good transmission power level to start with. Furthermore, we investigated the optimal parameters for this correlation in order to achieve minimum energy emission or consumption. Different impacting factors were also analyzed. We carried out measurements in two types of test-beds and showed that a significant amount of energy can be saved for the transmitter in typical scenarios. We also compared our mechanism with a signal strength-based mechanism and showed improved energy savings. Finally, we demonstrated that our mechanism can be tuned to achieve a balance between the minimum energy consumption and emission, which enables the user to adaptively set the desired target.
- Li Q, Aslam J, Rus D: Online power-aware routing in wireless ad-hoc networks. Proceedings of the 7th Annual International Conference on Mobile Computing and Networking (Mobicom '01), July 2001 97-107.View ArticleGoogle Scholar
- Choi J-M, Ko Y-B, Kim J-H: Enhanced power saving scheme for IEEE 802.11 DCF based wireless networks. Personal Wireless Communications, 2003, Lecture Notes in Computer Science 2775: 835-840.View ArticleGoogle Scholar
- Shrivastava V, Agrawal D, Mishra A, Banerjee S, Nadeem T: Understanding the limitations of transmit power control for indoor WLANs. Proceedings of the 7th ACM SIGCOMM Internet Measurement Conference (IMC '07), October 2007 351-364.View ArticleGoogle Scholar
- Lal D, Manjeshwar A, Herrmann F, Uysal-Biyikoglu E, Keshavarzian A: Measurement and characterization of link quality metrics in energy constrained wireless sensor networks. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM '03), December 2003, San Francisco, Calif, USA 1: 446-452.View ArticleGoogle Scholar
- Kim J, Huh J: Link adaptation strategy on transmission rate and power control in IEEE 802.11 WLANs. Proceedings of the 64th IEEE Vehicular Technology Conference (VTC '06), September 2006 2053-2057.Google Scholar
- Wattenhofer R, Li L, Bahl P, Wang Y: Distributed topology control for power efficient operation in multihop wireless ad hoc networks. Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM '01), 2001, Anchorage, Alaska, USA 3: 1388-1397.Google Scholar
- Lin S, Zhang J, Zhou G, Gu L, Stankovic JA, He T: ATPC: adaptive transmission power control for wireless sensor networks. Proceedings of the 4th International Conference on Embedded Networked Sensor Systems (SenSys '06), 2006, Boulder, Colo, USA 223-236.View ArticleGoogle Scholar
- Hackmann G, Chipara O, Lu C: Robust topology control for indoor wireless sensor networks. Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys '08), 2008, Raleigh, NC, USAGoogle Scholar
- Correia LHA, Macedo DF, Silva DAC, Dos Santos AL, Loureiro AAF, Nogueira JMS: Transmission power control in MAC protocols for wireless sensor networks. Proceedings of the 8th ACM Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, October 2005, Montreal, Canada 282-289.Google Scholar
- Son D, Krishnamachari B, Heidemann J: Experimental study of the effects of transmission power control and blacklisting in wireless sensor networks. Proceedings of the 1st Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON '04), 2004 289-298.Google Scholar
- Monks JP, Bharghavan V, Hwu WW: A power controlled multiple access protocol for wireless packet networks. Proceedings of the 20th Annual Joint Conference on the IEEE Computer and Communications Societies (INFOCOM '01), 2001, Anchorage, Alaska, USA 1: 219-228.Google Scholar
- Jung E-S, Vaidya NH: A power control MAC protocol for ad hoc networks. Wireless Networks 2005, 11(1-2):55-66. 10.1007/s11276-004-4746-9View ArticleGoogle Scholar
- Agarwal S, Katz RH, Krishnamurthy SV, Dao SK: Distributed power control in ad-hoc wireless networks. Proceedings of the 12th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '01), October 2001, San Diego, Calif, USA F59-F66.Google Scholar
- Kubisch M, Karl H, Wolisz A, Zhong L, Rabaey J: Distributed algorithms for transmission power control in wireless sensor networks. Proceedings of IEEE Wireless Communications and Networking (WCNC '03), 2003, New Orleans, La, USA 1:Google Scholar
- Xiao S, Dhamdhere A, Sivaraman V, Burdett A: Transmission power control in body area sensor networks for healthcare monitoring. IEEE Journal on Selected Areas in Communications 2009, 27(1):37-48.View ArticleGoogle Scholar
- Chipcon : CC2240: 2.4GHz IEEE 802.15.4 /ZigBee-ready RF Transceiver. http://www.chipcon.com
- Liaskovitis P, Schurgers C: Energy consumption of multi-hop wireless networks under throughput constraints and range scaling. ACM SIGMOBILE Mobile Computing and Communications Review 2009, 13: 1-13.View ArticleGoogle Scholar
- Williamson C: Internet traffic measurement. IEEE Internet Computing 2001, 5(6):70-74. 10.1109/4236.968834View ArticleGoogle Scholar
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