The Impact of Transmission Power Control in Wireless Sensor Networks

2013 IEEE 12th International Symposium on Network Computing and Applications

The Impact of Transmission Power Control in Wireless Sensor Networks Lina Xu, Declan T. Delaney, Gregory M.P. O’Hare and Rem Collier CLARITY: Centre for Sensor Web Technologies University and College of Dublin, Ireland email: [email protected], {declan.delaney, gregory.ohare, rem.collier}@ucd.ie Abstract—It is a common belief that reducing transmission power on a sensor can reduce the energy cost on transmission and therefore reduce the overall power consumption in Wireless Sensor Networks (WSNs). This belief has motivated many studies on transmission power control (TPC). An examination of this belief is presented in this paper, questioning the conditions where TPC can benefit a network. On a single link, we discover there is a limited saving achievable on a sensor itself from reducing transmission power, contradicting previous research. In a star network, however, reducing transmission power can decrease the total energy consumption on other sensors. We conclude that TPC can save energy in a certain network not through reducing a sensor itself’s cost as the common belief leads us to expect, but through reducing other sensors’ physical and MAC layer receiving.

In this paper, we analyze existing research and the methodologies that they use in order to evaluate their approaches. Our new sensor platform provides a more accurate measurement of battery capability, which can be used to evaluate TPC directly and explicitly. From our experiments based on this sensor, we have discovered that for a single link, changing a sensor’s transmission power does not have a significant effect on the power consumption of itself, which contradicts common wisdom. However in a star network, reducing a sensor’s transmission power will reduce the total power consumption of the network. We argue that the energy that has been saved is not from reducing the sensor’s transmission consumption, but actually from reducing other sensors’ physical and MAC layer receiving.

Keywords—Transmission power control (TPC), PRR, RSSI, LQI, energy efficient.

The remainder of this paper is organized as follows. Section II reviews the related work about TPC. In section III, we present the motivation of our work. In section IV, we evaluate the impact of communication cost in the big picture of energy consumption. In section V, we exam the common belief on a single link. In section VI, we illuminate the performance of reducing transmission power in a network and deliberate the results. Finally conclusions are drawn in section VII.

I.

I NTRODUCTION

The sensors deployed in a wireless sensor network (WSN) have limited energy supply with which to power themselves. Reducing energy consumption with a view to extending their lifetime has become essential. One approach is to reduce the communication consumption through transmission power control (TPC) protocols. Existing studies of TPC are based on a common belief that specifying the minimum transmission power level can preserve the required communication reliability while saving sensors’ energy [1]. This belief is drawn from the fact that decreasing the transmission power leads to a reduction of current draw. For example, the CC2420 [2] data sheet states that if the transmission power changes from 0 to -25 dBm, the current draw will drop from 17.4 to 8.5 mA.

II.

A wide body of research on TPC in Media Access Control (MAC)[4][5] and routing [6] protocols is available. Due to the diverse requirements of deployment, researchers suggest implementing TPC on the network layer rather than the MAC layer. In LMN/LMA [7], every sensor starts with the same initial transmission power and periodically broadcasts a life message including its unique identity. It compares the number of received message with the threshold in order to adjust its transmission power. In this way, it sets the optimal transmission power based on the local node density. They test the algorithm in OMNeT++ simulation system. In the initial phase, PCBL [8] broadcasts a considerable number of packets to obtain an accurate PRR value as a LQE. If the measured PRR is larger than the threshold, it decreases transmission power and vise versa. PCBL refers to current consumption as total power consumption, which is inaccurate. To avoid an issue with excess broadcast, ATPC [9] uses RSSI and LQI instead of PRR as LQE. Each sensor only needs to broadcast a small number of packets and then establishes its own neighbours table. The table is formed with entries of sensor ID and proper transmission power level. They evaluate the approach with numeric calculation of power consumption based on the

In TPC, a link quality estimator (LQE) characterizes the real time communication qualities. A threshold is set to be the minimum LQE value that can guarantee the required communication reliability. It is captured from pre-experiment data. If LQE > Threshold, transmission power decreases. Otherwise, it increases for the sake of improving LQE to reach the minimum required quality. Packet Receive Rate (PRR), Radio Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) [3] are three common metrics use in estimating link quality in TPC. Compared to RSSI and LQI, PRR has a higher correlation with link quality [3]. A good RSSI estimation can be obtained over a small number of measurements and can converge quicker than LQI. Over a larger set of measurements LQI can serve as a good link estimator. 978-0-7695-5043-5/13 $26.00 © 2013 IEEE DOI 10.1109/NCA.2013.38

R ELATED W ORK

255

Fig. 1.

through reducing transmission power. [16] has indicated that radio does not have the largest weight on power consumption. Besides, [17] also illustrates that for surge, transmission only cost 8% of the total usage, while receiving and CPU cost 60% and 27% (including idle and active phrases) respectively. We show the percentage of transmission cost with different sending frequencies in Figure 2: 0.1s, 0.5s, 1s and 10s. The sensor only sends and sleeps. We can see the transmission cost is less than 1% if the sensor sends 1 packet in every 10 seconds. When

Possible effects of TPC.

current draw from [2]. ODTPC [10] provides a TPC algorithm based on user demand. The optimal transmission power level is only calculated when there is a communication need. It avoids the broadcasting overhead problem in the initialization phase. ODTPC does not provide clear and sufficient detail about the method of measuring power consumption. Some work [11] treats the sum of the transmission power as the total power consumption. [12][13] indicate that transmission power control is only effective in a single hop cellular/star network, not in multihop sensor networks. III.

Fig. 2.

a sensor keeps sending data without sleeping, it will reflect up to 80% of all energy consumption. TPC may perform well in such a case. In most real work applications, sensors spend the majority of their time on sleep states or listening, allowing insignificant saving on transmission. We propose that when evaluating a TPC protocol, measuring the power consumption of the entire sensor is necessary and important in order to discover the real impact.

P ROBLEM S TATEMENT

Figure 1 presents two major ways in which TPC can impact on a WSN. x expresses the common belief that all the current studies are based on: reducing a sensor’s transmission power can reduce the transmission consumption, and therefore reduce the total power use. Besides x, y (reducing MAC payer receiving to reduce total power use) is another reason why TPC can save energy for a network, whose contribution is usually ignored. The fact that a node listens to transmitted packets on the MAC or physical layer even if these packets are not addressed to it is called overhearing. It is energy costing [14] . When a sensor’s transmission power is reduced, the local node density is reduced by the reduction of transmission range. Some of the other sensors in the network may not hear this sensor any more. Hence overhearing will be mitigated.

V.

TPC F OR A S INGLE L INK

TPC is driven by a common belief that reducing a sensor’s transmission power to its minimum while maintaining LQE above a certain threshold will reduce overall energy consumption. In this section, we will test this hypotheses in a single link network.

In this paper, we propose that for some sensors, TPC has little impact on the whole network through x, but significant influence through y. To demonstrate our prediction, we perform experiments on transmission power on a single link and in a star network. Through a set of single link experiments, we provide results that show little benefit in power consumption when reducing transmission power, refuting the common belief. We also evaluate the impact of reducing transmission power in a star network. According to [12][13][15], TPC shows the optimal performance in a star network. We find that the total power usage is decreased when transmission power is reduced. If we conclude that energy can be saved in a star network when reducing a sensor’s transmission power but not on a single link between two nodes, we must address the reason for this. This contradiction motivates us to find the differences between a single pair node and a star network. Based on this knowledge, we propose a prediction that the cause of power saving in a network is not achieved by reducing transmission consumption, but rather by reducing MAC layer receiving through decreasing node density, shown in y. IV.

Transmission cost in the whole consumption.

A. Threshold A threshold is an important concept in TPC in preserving the link quality. Without it, TPC has no criterion as to what level the transmission power can be reduced to. According to former studies [9] [18] [3], PRR is the direct metric of link quality, but RSSI and LQI are often considered more convenient to use. In order to set the threshold using RSSI or LQI as the LQE instead of PRR, we investigate the relationship between them in an office area. Between RSSI and LQI, we choose RSSI because it converges more quickly. In addition, we have found that when RSSI is over a threshold, LQI is also qualified. Even though similar research has been undertaken, we still need to discover the threshold for our environment. All the data is collected in a pair node network to avoid MAC conflict. In our experiments, we use a Tyndall intelligent wireless sensor node (iWSN), which is a Java board based sensor akin to SunSPOT 1 . It is embedded with the ARM920T core, 4 MB pSRAM, 32 MB ROM, CC2420 radio chip and power management layer. Its communication range in an office area is 20∼30 metres. iWSN’s power management layer can report battery level with greater accuracy [19]. Through real time experiments, the threshold in our environment should be set to -38 to guarantee a qualified LQI and PRR. To further

T RANSMISSION COST— THE BIGGER PICTURE

The impact of reducing transmission cost through reducing transmission power (Figure 1 x) also depends highly on the proportion of transmission out of the total power usage. If the transmission is only a small percentage of the total power consumption, TPC has little affect on the total power consumption

1 http://www.SunSPOTworld.com/

256

protocol and CSMA/CA MAC protocol. All the sensors except Sensor 2 run the same code that sends 100 packets (32 bytes per packet) by unicast and then sleeps for 1 second in each round. The receiver is turned on. When no packets are received, it is in the idle state. They send packets at the same transmission power level (-3 dBm) for the first 20 rounds. Thereafter Sensor 6 changes its power level to -25 dBm. They all run another 20 rounds. During this whole experiment, every sensor’s RSSI value is over the threshold (RSSI = -30 dBm). Sensor 2 connected to a computer does not send any packets but only counts those received on the MAC layer and prints the results out.

ensure the communication quality, we set the threshold of an office environment as RSSI = −30 dBm. Our experimental results generate different values of the threshold but confirm the observations from previous studies. B. Energy consumption for a single link In this section we reveal whether reducing transmission power will decrease the power consumption in a two node network. From real time measurements, we learn that when the transmitter is deployed at 1 metre from the receiver, no matter how the transmitter’s transmission power changes, the RSSI can stay above the threshold (RSSI = −30). In this experiment, we place a iWSN as the transmitter at 1 metre from the receiver. It sends 100 packets including the current battery readings to the receiver at each power level from 0 to -22 dBm. The receiver records the battery reading and calculates the average round draw of the sender at each power level. A sensor in a WSN will repeat the same piece of code unless it is interrupted. One duty cycle, including active and sleeping phases, is called a round. According to common belief and existing work, round cost should decrease when the transmission power decreases.

Figure 4 shows the measurements of round consumption and round delay of the first 20 rounds and the second 20 rounds respectively. We can see that round consumption of Sensor 6 does not decrease after its transmission power is changed from -3 dBm to -25 dBm. Its round delay almost remains at a constant level. Sensor 1, whose transmission power does not change during this experiment, actually saves some power and meanwhile decreases its round delay. Comparing Sensor 3, Sensor 4 and Sensor 5’s round consumption and round delay in the first 20 rounds and the second 20 rounds, we see similar results to that of Sensor 1. The experimental results from Sensor 6 confirm our single link experiment: decreasing a sensor’s transmission power has very limited impact on its own energy consumption. Sensor 1 sends the same number of packets and does not receive any packets at the network layer. This raises the question as to where Sensor 1 actually makes its energy savings. We argue that the energy is from MAC layer receiving. When Sensor 6 lowers its transmission power, it can no longer communicate with other free range sensors directly. Therefore it lessens its impact on other sensors in the network. We will discuss this in the following subsection.

We repeat the same experiment 10 times to obtain statistical confidence. Figure 3 shows the average energy cost of the 10 experiments and the STD (standard deviation) at each power level. The average cost varies from 0.0846 to 0.0868 mAh with the ST D < 0.37% at each power level. Based on the average values from 10 repeated experiments, one can see the cost has no significant reduction as the transmission power decreases. The impact of changing transmission power is not as former studies expected. Through this experiment, we discover that TPC has a small effect on power consumption with minimal savings expected. This fact exposes a question: is TPC always valuable in a sensor network? In the next section, we will illustrate the relationship between transmission power and energy consumption in a star network.

Fig. 3.

B. MAC layer receiving During a sensor’s operation it normally has to perform three tasks: sending, receiving and sensing. Sensing cost is normally not considered for the reason that it is irrelevant in the context of a transmission power control problem, but the effect of receiving in overall energy consumption should not be overlooked. By receiving we do not only mean at application or network level, but also at physical and MAC layer, which is also called overhearing. Even when a sensor is not receiving any data from other sensors at the network level, but on the physical layer and sometimes even MAC layer, it still can hear the packets that are being sent around in the network. Depending on the network protocols and implementations, the data may not be passed to the higher level communication stacks. While this form of overhearing does not consume as much energy as application level receiving, physical and MAC layer receiving still costs energy.

Total power consumption at at each power level.

VI.

TPC I N A N ETWORK

We already reveal that TPC has little impact on a sensor itself from the single link experiment. In this section, we will analyze the effect of changing a sensor’s transmission power on other sensors in a star network.

In the experiment, Sensor 2 is placed just beside Sensor 1 to detect MAC layer receiving. After Sensor 6 changes its transmission power to -25 dBm, it receives ≈ 20% less data packets. Since Sensor 1 is deployed adjacent to Sensor 1, we can assume that Sensor 2’s MAC layer receiving and Sensor 1’s are the same. Sensor 1 receives less packets at the MAC layer after Sensor 6 reduces its transmission power, so it costs less energy per round. Since the CC2420 radio chip can not perform transmission and receiving at the same time, receiving

A. Energy consumption of sensors in a network 6 free range sensors and one base station (BS) are deployed as a start network. Sensor 6 is 0.5 metre from BS and all other sensors are 5 metres from BS. They all can communication with the BS in a single hop, based on the LQRP routing

257

Fig. 4.

Results of changing transmission power of sensor 6 (from -3 to -25 dBm).

data, even at MAC layer, will delay sending. If the receiver does not hear any packets, it will go to in an idle state, and then the radio chip will switch from receiving mode to sending mode. When Sensor 6 lowers its transmission power, Sensor 1 can no longer receive from Sensor 6. It will then use less time to complete a round. The time that has been saved is also because of a reduction in MAC layer receiving.

[4]

[5]

[6]

In a word, reducing transmission power will shorten the transmission range, and therefore decrease the possibilities that its neighbours hear the packets it sends out to the network. We believe that this is the reason why when we reduced a sensor’s transmission power in a network, the total power consumption decreased, as shown in Figure 1 y. In a network if the transmission cost will not be reduced by TPC, TPC can still be used as a density control protocol to reduce overhearing. VII.

[7]

[8]

[9]

[10]

C ONCLUSION

In this paper we demonstrate through experimental results that reducing a Tyndall iWSN sensor’s transmission power does not inherently reduce its transmission cost, but can lessen the impact of overhearing on other sensors in a network reducing overall energy expenditure in the network. A TPC mechanism introduces an overhead in terms of energy cost and average network delay. Furthermore, their performance highly relies on the hardware and the MAC layer’s implementations. In conclusion, to decide whether a TPC protocol is suitable for a given system, a real experiment must be conducted. In addition, TPC’s focus should move from reducing transmission cost to density control. Whether TPC can benefit a network depends on: 1) the hardware and environment, 2) the effect of receiving, 3) percentage of transmission consumption in the whole picture and 4) communication type.

[11]

[12]

[13]

[14] [15]

[16] [17]

R EFERENCES [18]

[1]

I. Khemapech, A. Miller, and I. Duncan, “A survey of transmission power control in wireless sensor networks,” in PGNet, 2007. [2] “Texas instrument: Cc2420,” 2007. [Online]. Available: http://www.ti.com/product/cc2420/ [3] N. Baccour, A. Koubˆaa, L. Mottola, M. A. Z´un˜ iga, H. Youssef, C. A. Boano, and M. Alves, “Radio link quality estimation in wireless sensor networks: A survey,” Trans. Sen. Netw., 2012.

[19]

258

L. Correia, D. Macedo, A. dos Santos, A. Loureiro, and J. Nogueira, “Transmission power control techniques for wireless sensor networks,” Computer Networks, vol. 51, no. 17, 2007. C. J. Colbourn, M. Cui, E. L. Lloyd, and V. R. Syrotiuk, “A carrier sense multiple access protocol with power backoff (csma/pb),” Ad Hoc Netw., 2007. O. Chipara, Z. He, G. Xing, Q. Chen, X. Wang, C. Lu, J. Stankovic, and T. Abdelzaher, “Real-time power-aware routing in sensor networks,” in IWQoS, 2006. M. Kubisch, H. Karl, A. Wolisz, L. Zhong, and J. Rabaey, “Distributed algorithms for transmission power control in wireless sensor networks,” in Wireless Communications and Networking, 2003. D. Son, B. Krishnamachari, and J. Heidemann, “Experimental study of the effects of transmission power control and blacklisting in wireless sensor networks,” in Sensor and Ad Hoc Comm and Networks, 2004. S. Lin, J. Zhang, G. Zhou, L. Gu, J. A. Stankovic, and T. He, “Atpc: adaptive transmission power control for wireless sensor networks,” in Embedded networked sensor systems. ACM, 2006. J. Kim, S. Chang, and Y. Kwon, “Odtpc: On-demand transmission power control for wireless sensor networks,” in ICOIN, 2008. G. Lu and B. Krishnamachari, “Energy efficient joint scheduling and power control for wireless sensor networks,” in Sensor and Ad Hoc Comm and Networks, 2005. M. Meghji and D. Habibi, “Transmission power control in multihop wireless sensor networks,” in Third International Conference on Ubiquitous and Future Networks, 2011. M. Mahir and H. Daryoush, “Transmission power control in single-hop and multi-hop wireless sensor networks,” in Multiple access communications. Springer-Verlag, 2011. A. Woo, T. Tong, and D. Culler, “Taming the underlying challenges of reliable multihop routing in sensor networks,” in SenSys, 2003. J. Jeong, D. Culler, and J.-H. Oh, “Empirical analysis of transmission power control algorithms for wireless sensor networks,” in Networked Sensing Systems, 2007. H. A. Nguyen, A. Forster, D. Puccinelli, and S. Giordano, “Sensor node lifetime: An experimental study,” in PERCOM Workshops, 2011. O. Landsiedel, K. Wehrle, and S. Gotz, “Accurate prediction of power consumption in sensor networks,” in Embedded Networked Sensors. IEEE Computer Society, 2005. K. Srinivasan and P. Levis, “Rssi is under appreciated,” in EmNets, 2006. M. J. O’Grady, P. Angove, W. Magnin, G. M. P. O’Hare, B. O’Flynn, J. Barton, and S. C. O’Mathuna, “Enabling intelligence on a wireless sensor network platform,” in PAAMS, 2012.