Energy Consumption Model and Measurement Results for Network Coding-enabled IEEE Meshed Wireless Networks

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1 202 IEEE 7th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) Energy Consumption Model and Measurement Results for Network Coding-enabled IEEE 802. Meshed Wireless Networks Achuthan Paramanathan, Ulrik W. Rasmussen, Martin HundeblZlll, Stephan A. Rein, Frank H.P. Fitzek Aalborg University, Department of Electronic Systems Gerg6 Ertli acticom GmbH, Germany Abstract-This paper presents an energy model and energy measurements for network coding enabled wireless meshed networks based on IEEE 802. technology. The energy model and the energy measurement testbed is limited to a simple Alice and Bob scenario. For this toy scenario we compare the energy usages for a system with and without network coding support. While network coding reduces the number of radio transmissions, the operational activity on the devices due to coding will be increased. We derive an analytical model for the energy consumption and compare it to real measurements for which we build a flexible, low cost tool to be able to measure at any given node in a meshed network. We verify the precision of our tool by comparing it to a sophisticated device. Our main results in this paper are the derivation of an analytical energy model, the implementation of a distributed energy measurement testbed conducting a series of measurements, and finally a comparison of the analytical energy model and the data achieved by our testbed. I. INTRODUCTION Lately a large number of research projects and publications have aimed their focus on investigating the so-called green wireless communication [], [2], [3], [4]. Green communication deals with the tasks to reduce the overall energy consumption in order to reduce the C02 emission caused by information and communication technology (ICT). A more simple explanation for the interest in this research area are the huge electricity bills of network and service operators. Reducing the energy consumption, sometimes referred as power saving, is a fairly old research area that started after the first wireless devices came to the market e.g. [5]. With the new trends in the mobile handheld sector, the problem of energy saving becomes more evident: As the computational power is following Moore's law of doubling every two years, the users benefit from more and more applications and services. The battery capacity, however, is just doubling every ten years. Nevertheless, the overall goal is to reduce the energy consumption wherever possible. In order to design energy efficient protocols, we need models and measurement tools to validate our protocols. Models help us to quickly understand the behavior of simple network topologies, but they might be harder to derive for larger arbitrary networks. Measurement setups are always time consuming, but can be applied to any topology. The majonty of energy measurements were simply focused on single devices [6]. To support large arbitrary networks energy consumption has to be measured at any node in the network. First attempts to support distributed energy measurements are currently in preparation [7]. In this paper we compare an analytical model with a fullyfletched distributed energy measurement testbed. However, in order to model larger networks it is necessary to understand the elementary topologies. Therefore we compare the results from the analytical model and the measurements for the Alice and Bob scenario, see Figure. Furthermore we are interested to quantify the impact caused by the usage of network coding. Recently, there has been a lot of research effort and interest in applying network coding to wireless meshed networks [8], [9]. Katti et al. introduced a practical method named COPE [0] in which packets from different unicast sessions are XOR' ed together and forwarded in a single transmission. A similar technique named CATWOMAN (Coding Applied To Wireless On Mobile Ad-hoc Network) was introduced by Hundeb0 et al. [] focusing on commercial of-the-shelf WiFi products. This approach was applied on top of an existing routing scheme called BATMAN (Better Approach To Mobile Adhoc Networking). The source code for CATWOMAN implementation in BATMAN can be found at [2]. After our extensive investigation on the throughput of the CATWOMAN approach for Alice and Bob, we are interested in this paper in the energy consumption with and without network coding. Therefore we derive an analytical model and we build our own testbed. Finally the analytical model is compared and verified with the measured data. The paper is structured as follows. In Section II we present our activity model and methodology used throughout the paper. Our measurement tool and the testbed is presented in Section III. Analytical and the experimental results are reported and discussed in Section IV. Finally, in Section V we conclude our work on the behalf of our results. II. ACTIVITY MODEL FOR ALICE AND BOB In this section, we describe the simple Alice and Bob scenario to familiarize the reader with our setup. Then we will /2/$ IEEE 286

2 analyze and present an activity model which later allows us to derive the system power and the system energy consumption. A. The Alice and Bob scenario Figure shows the Alice and Bob scenario. It is assumed that Alice and Bob cannot directly exchange data hence need the help of a relay. Each packet received by the relay needs to be forwarded to the other node. As given in the figure (left side of Figure ), four time slots are needed to exchange two packets between Alice and Bob. Using network coding, two incoming packets are coded together and broadcasted in one time slot to both nodes resulting in a usage of only three time slots [3], [0]. In the most simple case the coding is a bit-wise XOR operation. More sophisticated approaches use random linear network coding [4]. The main difference between the state-of-the-art approach in network coding is the change in paradigm from store-and-forward to compute-andforward. We assume symmetric traffic in this paper, resulting in the fact that Alice and Bob send with the same data rate. For this we induce a range of different loads into Alice and Bob. The induced load is expressed as L(l) = l. C[Mbit/ s], where l is the unitless load factor ranging from 0 to and C the link capacity. Fig. I. Basic scenario which consists of three nodes. Alice (A). Bob (B). and the relay (R), where two packets are exchanged among Alice and Bob with help of the relay. The labels on the packets S, R, and B stand for send, receive, and broadcast, respectively. On the left side we see the store-andforward approach and on the right side the use of network coding leading to compute-and-forward. that the sending activity of Alice or Bob increasing linearly with the offered load. The activity of the relay increases twice as fast until a given load of 0.5. At this given point, Alice as well as Bob are contributing with a load of 0.5 resulting in an additional load contribution for the relay of 0.5. Therefore, for the approach without network coding, the system capacity is fully used for an offered load of 0.5. If we increase the load further, Alice and Bob will keep increasing their sending activity at the expense of the sending activity of the relay. This behaviour results from the MAC fairness introduced by the IEEE All three nodes have the same sending activity for a given load of 2/3. Even if we increase the load further, the activity of each load is limited to /3. In 2(c) we see the receive activity without network coding. Until a load of /2 for Alice and Bob and 2/3 for relay, the nodes increase their receiving activity linearly, whereas the receiving activity of the relay is twice as high as for Alice or Bob. After a load of 2/3 the receiving activity of the relay stabilize around 2/3. However, after the congestion relay only gets /3 for sending, this leads to a receiving activity of /6 for Alice and Bob. As given by Equation, Figure 2(e) gives the activity for being idle for the approach without network coding. In Figure 2(b), 2( d), and 2(f), we are showing the activity levels for the network coding approach. In 2(b), the send activity for all three nodes are given. Due to the coding of two packets with the resulting output of a single packet, the sending activity of Alice, Bob, and the relay are the same. The sending activity increases linearly until a load of2/3 where the capacity of the channel is fully used. From that point onwards the sending activity is limited to /3. Figure 2(d) gives the receive activity for the network coding approach which is similar to the plot 2(c) for the approach without network coding with an exception of that now both Alice and Bob have /3 receiving activity in phase III. Figures 2(f) is giving the idle activity for the network coding approach which once again is derived by Equation taking Figure 2(b) and 2( d) into account. B. Activity Model The goal of this subsection is to derive the activities of each node in the given scenario. We distinguish three different states, namely, send, receive, and idle. We introduce the parameter a as the proportion of time in which a node stays in a given state, whereas as specifically represents the proportion of any time period a node is actively sending. Similarly, ar and ai represent the proportion for receiving or being idle. For any given load l we assume: In Figure 2 we show the individual activity levels for sending, receiving, and idle for the two approaches using network coding in 2(b), 2(d) and 2(f) and without network coding in 2(a), 2(c) and 2(e). In 2(a) we see the activity level for the relay as well as for Alice or Bob (throughout this paper the activity of Alice and Bob can be used interchangeably). Here we see () Phase I II III Load [l] A&B l I WoNC Send [as] R I - NC WoNC A&B l I R l I A&B l -[ - 2-6' Receive [ar R I I 2 NC A&B l l R I I 2 TABLE I SENDING AND RECEIVING ACTIVITY LEVEL FOR ALL THREE NODES IN DIFFERENT PHASES FOR BOTH WITH(NC) AND WITHOUT(WoNC) NETWORK CODING. In Table I we give a detailed description of the activity 287

3 energy consumption at any given distributed node, we develop our low-cost energy measurement tool. f O (a) Send activity without network coding vs. load. f O Load Factor [II (c) Receive activity without network coding vs. load. 0.6 > « (b) Send activity with network coding vs. load. 0 8."'0-'0"'.2.-,.'-.-'-,:'7"'"0.8.'.0 0"'.0-'0"'.2.-,.' ,O"'.6"',c- A. Measurement tool The measurement tool can be divided in two parts: our own energy measurement circuit and the data acquisition board, see Figure 3. In the following we will give a short overview of these two parts. The first part, the energy measurement circuit, is based on the Texas Instruments INA39 current shunt monitor Ie. This IC is specially designed for measuring current by using a shunt resistor in serial with the power supply and the node. The voltage drop over the shunt resistor is then used to calculate the current used by the node. Because the voltage drop is very low it needs to be amplified which is also done by this Ie. In addition to the current measurement the voltage of the power supply is also measured, this is done by dividing the supply voltage with some resistors, which also makes the voltage level fit inside our measuring range. The supply voltage measuring is done to ensure that a high current drain by the node, (d) Receive activity with network codin hich can cause a voltage drop of the power supply, will not vs. load.. cause an mcorrect energy measurement b y t h e too I. F or more information regarding this circuit we refer the reader to the INA 39-datasheet. The second part, the data acquisition board, is a low-cost chipkit UN032 development board from Digilent with a 0 bit AID converter. The two above mentioned measuring points (current and supply voltage) from the monitoring circuit are connected to two separate ADC input-ports on the UN032 board. These inputs are sampled with 87 KHz sample rate Load Factor [II and the sampled data is stored in the memory ' (e) Idle activity without network coding (f) Idle activity with network coding vs. vs. load. load..0 Fig. 2. Expected activities for offered load in Alice and Bob topology with and without network coding. level for the two approaches distinguishing between the three activity states. We differentiate between three load situations. Phase I is describing the load between 0 and 0.5, phase II the load between 0.5 and 2/3, and phase III between 2/3 and. These phases reflect the characteristic load points reported in [5]. After deriving the different possible activity levels in a given state, we are able to calculate the power used at any given load situation: The only missing information now are the power levels for the three different states, which we will measure in one of the following sections. III. BUILDING A MEASUREMENT TOOL AND A TESTBED In this section we describe the implementation of our measurement tool and the testbed. In order to measure the Fig. 3. Measurement tool, consisting of our own measurement circuit and a data acquisition board. In order to validate the measurement tool, it was compared with an Agilent 6639D power supply from Agilent Technologies. Where we measured an average percent of error of 0.76 %, where the Agilent device is considered to give the actual measurement value. 288

4 Alice /8 Relay /8 Test Server Bob /8 Fig. 4. Testbed consisting of three nodes: Alice, Bob and the relay. These nodes are all connected to a test server from where we control our testruns and couect the measured data. end of this section we compare these two results with each other. Using our measurement tools we carried out preliminary power measurements in order to estimate the values Ps, Pr and Pi for Equation 2. This is achieved by connecting our measurement tool to a single node and then forces this node to enter one of the three states: send, receive or idle for a period of 60 seconds. The mean values from this test are given in Table II. I 3.:S s w I 3.:: W I 2.::i w I TABLE II MEASUREMENT OF THE ENERGY CONSUMPTION THAT A NODE HAS IN THE DIFFERENT STATES. B. Testbed Our testbed used throughout this paper consists of three OMIP routers from Open-Mesh (Alice, Bob and relay). Each node is installed with CATWOMAN on top of a standard installation of the B.A.T.M.A.N-adv routing protocol. This standard installation of BATMAN-adv. allows non-batmanenabled nodes to communicate through the mesh, by bridging the mesh network with a virtual access point. To do this B.A.T.M.A.N-adv. uses the so-called promiscuous mode. The main reason for selecting the standard installation is to use an of-the-shelf routing protocol without modifications. For more information regarding the setup and installation of the B.A.T.M.A.N-adv. routing protocol we refer the reader to [6]. The installed nodes Alice and Bob are both placed 90 meters apart from each other with the relay in the middle, see Figure 4. Both Alice and Bob are configured such that they are not able to communicate directly with each other, therefore they are forced to use the relay. Each node is connected to a laptop for traffic generation and data logging, as given in Figure 4. The test execution is handled from the test server which monitors and creates a test report. This test repor combines all the measured data during a test into a single report. A selection from such report is e.g. the node CPU usage, amount of data packets sent, received and lost, power usage etc. In order to generate the UDP data stream between Alice and Bob the Iperf tool was used. Furthermore, each node is configured to use a fixed rate of Mbitls for data transmission (rate adaptation is disabled). Similarly to [7] we measure a link capacity C = 4.9 Mbitls for our configuration. The transmission rate is varied from 00 Kbitls to 3000 Kbitls in steps of 00 Kbitls. Each transmission is measured for a duration of 60 seconds and is defined as one testrun. Our test consists of five testruns per transmission rate and from these the average current, voltage and throughput are collected and used for calculating the power and the energy per bit ratio for each node. IV. ENERGY MODEL AND MEASUREMENT S In this section we present the results for the analytical model as well as the results for the measurement campaign. At the A. Results for the Analytical Model We note that we are now multiplying the load factor l with the capacity C = 4.9 Mbitls in order to compare the analytical model with the real measurements that we will present later on. By using the power values given in Table II and Equation 2 we derive the power vs. load plot given in 6(c). We see that for both approaches the power value increases with the load in phase I and II, whereas the power stabilizes in phase III. We also see that for the case without network coding the power level is slightly higher in comparison to with network coding in phase I and phase II. The increase in the power level in phase III, for the case with network coding, is a result from the network coding receive-activity in compare to the case without network coding (Figure 2(c) and 2(d)). Recall that network coding doubles the throughput, while the sending activity stays the same. With the doubled throughput, however, the receiving activity has to be twice as well. In Figure 6(a) we depict the throughput for the two approaches vs. the offered load as given in [5]. By dividing the power in 6(c) with the throughput given in 6(a) we derive the energy per bit as presented in 6(e). We see that the energy per bit in phase I is nearly the same with and without network coding and decreasing with an increasing offered load. In phase II the energy per bit for the approach without network coding is increasing and for the approach with network coding it is decreasing. In phase III the energy per bit has stabilized to a value of 6 j. J /bit and 3 j. J /bit for the approach without and with network coding, respectively. The interested reader is referred to the work in [8] for the explanation of the throughput model in 6(a). B. Energy Measurements For the energy measurements in our testbed we are running multiple tests with different load situations as explained beforehand. These measurements were conducted at nighttime where the WiFi interference caused by other wireless devices is expected to be minimal. The first result is the throughput vs. the load given in 6(b) showing the difference between the approaches with and without network coding. Figure 6(d) shows the power vs. the offered load. We see that the power 289

5 values increase linearly for low load situations, whereas the approach without network coding yields to larger values. Later the power level for the approach without network coding is stabilizing at 9.77 Watts, whereas the approach with network coding is still increasing and stabilizes later at 9.82 Watts. In Figure 6(d) we see that in low load scenarios network coding results in lower power values as the reduction of radio transmission activities is reduced due to coding. For high load scenarios more power is used by the nodes for the case with network coding in receiving in compared to the case without network coding. By combining 6(b) and 6(d) we derive 6(f) reporting the energy per bit vs. offered load. We see that for low load scenarios the two approaches yield the same energy per bit values whereas an increased offered load results in larger energy per bit values for the approach without network coding (6 ILJ /bit) compared to the approach with network coding (3.8 ILJ /bit). C. Comparison of Model and Measurements In order to compare the results achieved by the model and the measurements, we start to compare the intermediate results. Figure 6(a) and 6(b) represent the throughput vs. load. In general, these two plots fit very well. The expected throughput for network coding is slightly smaller than expected. This is due to the fact that we could not assure fully symmetric traffic. Nevertheless network coding achieved significant larger throughput than without network coding. Comparing Figure 6(c) and 6(d) the power vs. load for the model and the measurements also match. For load situations lower than 3000 Kbitls both (model and measurement) report a larger power value. After that, the measurements report larger power values than the model. Furthermore the model expects the power values to fully stabilize after 3000 Kbitls, whereas the measurements show steady increase for an increasing load. In order to explain the increase in the power level seen in Figure 6(d) for both with and without network coding, we present Figure 5(a) and 5(b). 70',-,, -, than the approach without network coding. Nevertheless the CPU is never fully loaded. More interestingly is the difference between the relay and the other two nodes. Whereas the relay is stabilizing the CPU cycles after 3000 Kbitls, Alice as well as Bob are still increasing the CPU cycles as the offered load increases. The reason for this behavior is based on the fact that the laptops generating the traffic are overloading Alice and Bob, this means more packets are entering the node than can be sent out over the wireless medium. Packets that arrive at Alice and Bob have to be received and stored and some of them have to be discarded afterwards. Those activities leads to an increase in the power values beyond the offered load of 3000 Kbitls. Such an effect is not covered by our analytical model === ===,-, 3000 E 2500 :c '" :;'2000 " c. -, Without Network Coding With Network Coding (a) Model: Throughput. 0.Or-, 9.8 _ 9.6! 9.4 tr. 9.2 (c) Model: Power With Network Coding (b) Measurement: Throughput. With Network Coding Without Network Coding (d) Measurement: Power u Offered Load [Kbit/s[ (e) Model: Energy per bit. (f) Measurement: Energy per bit. (a) CPU Utilization for Alice or Bob. (b) CPU Utilization for the Relay. Fig. 5. CPU Utilization. Fig. 6. Energy model vs. measured data for the Alice and Bob topology. Figure 5(a) and Figure 5(b) show the CPU utilization on the wireless node for Alice or Bob as well as the relay for both modes (with and without network coding). With an increased offered load all nodes start to use more of the CPU cycles. If the offered load increases above 3000 Kbitls the network coding approach need approximately 6 % more CPU cycles In Figure 6(e) and 6(f) we show the energy per bit for the model and the measurement. We see that the results delivered by the model as well as measurement for the approach without network coding fits nearly perfectly. Both, the model and the measurement, predict that the energy per bit for high load scenarios will be exactly 6 ILJ /bit. The match between the 290

6 model and the measurements is slightly worse for the network coding approach, whereas the model foresees 3 J,J /bit for high load scenarios. The measurements report values around 3.8 J,J /bit. The mismatch is a result of the false prediction of the throughput and the power values for the high load scenarios. Where the error in the power values is very small, the prediction error in the throughput is the dominant one. Nevertheless, the presented results for the model as well as the measurements are clearly in line. V. CONCLUSION In this paper we presented an analytical model and its validation by means of real measurements for the energy consumption for a wireless meshed network based on IEEE 802. technology. This was achieved by comparing the impact of network coding with state of the art approaches. In this paper we show how we carry out energy measurement for distributed wireless nodes. In order to do so we introduced our own low cost measurement tool. The paper shows that our low cost tool is capable of giving quite accurate results in comparison to an Agilent 6639D. The new hardware allows system wide energy measurement at each node in a wireless meshed network. In addition we also present a simple energy consumption model for network coding enabled and network coding agnostic setup. Finally we compare the results of the analytical model and the real life measurement for a simplified Alice and Bob mesh network scenario. The measured results are in line with our analytical model even though the model does not include the operational cost due to coding (which is neglected in our analytical model, due to the simple XOR operations). It is shown that applying network coding saves energy for low and high load scenarios. For low load we could show that the energy consumption is almost.5% less for network coding compared to an approach that does not support network coding. For high load scenarios we see that the energy consumption for with network coding is slightly higher due to the computational power it requires, but as the throughput increases with network coding, the energy per bit ratio is improving so that the gain per Joule is higher. In the past, network coding has been featured for saving bandwidth in case of high load, as for low load there is no throughput gain. Our model and measurements, however, demonstrate that network coding gives even benefits for the case of low load - in that case saving energy at the nodes. In our future work we will extend the analytical work by including the error prone wireless transmission resulting in more radio transmission. In this paper we provide an elementary example to prove the analytical model and to introduce the distributed energy measurement. In the future work we will investigate arbitrary networks with large number of nodes. While for the simple Alice and Bob topology we could use the measurements to validate our analytical model, we expect that for the more complex topologies the measurements will help to derive suitable models. We will also consider combining sets of elementary models to build analytical models of larger networks. VI. ACKNOWLEDGEMENT This work was partially financed by the Green Mobile Cloud project granted by the Danish Council for Independent Research (Grant No ). The work of Gergo Ertli has been funded by the Research Project GREENET (PITN-GA ). REFERENCES [] L. M. Correia, D. Zeller, O. Blume, D. Ferling, Y. Jading, I. Godor, y. Jading, I. Godor, Y. Jading, and I. Godor, "Challenges and enabling technologies for energy aware mobile radio networks," Communications Magazine, IEEE, vol. 48, no., pp , 20. [2] G. GUr, F. Alagoz, and B. University, "Green wireless communications via cognitive dimension: An overview," Network, IEEE, vol. 25, no. 2, pp [3] w. Guo and T. 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