Modeling Energy Consumption of Wireless Sensor Networks by SystemC

2010 Fifth International Conference on Systems and Networks Communications Modeling Energy Consumption of Wireless Sensor Networks by SystemC Wan Du, Fabien Mieyeville, and David Navarro Lyon Institute of Nanotechnology (INL), University of Lyon, France {wan.du, fabien.mieyeville, david.navarro}@ec-lyon.fr Abstract Energy consumption is one of the most important metrics in wireless sensor networks because of the limited power supply in sensor nodes. Many efforts have been taken to reduce the energy consumption of the hardware, software, communication protocols and applications. Simulation is widely used to evaluate the performance of these new designs. Thus, it is necessary to accurately model the energy consumption during the simulation of WSN. In this paper, an energy model for WSN is proposed. It has been implemented in IDEA1, a simulator for WSN developed by SystemC and C++. It enables the energy estimation of both the hardware components of an individual node and the whole sensor network. It can be easily calibrated to different types of node if the electrical characteristics of the hardware components are available. An application based on IEEE 802.15.4 and MICAz motes is studied to demonstrate the capability of the energy model. Keywords-wireless sensor networks; energy model; systemlevel; simulation; SystemC I. INTRODUCTION The requirements of small size and low cost result in limited energy supply on sensor nodes, so energy consumption is one of the most important metrics in Wireless Sensor Networks (WSNs) [1]. In order to extend the lifetime of sensor networks, many efforts have been taken to reduce the energy consumptions of the hardware, software, communication protocols and applications. Thus, it is necessary to accurately estimate the energy consumption behavior of the WSN system when the new techniques and algorithms are proposed. Three techniques are generally accepted to evaluate the performances of WSN systems: analytical method, physical testbeds and simulation. Petri nets are used in [2] to model the energy consumption of the processors in WSN. However, many constraints imposed on sensor networks, such as limited resources, decentralized collaboration and fault tolerance, necessitate the use of complex algorithms that usually defy analytical methods [3]. Testbed is a direct method, but it is costly and time-consuming to establish, and in some applications, the statistical results obtained by the repetition of tests becomes impossible due to the long testing time. As a result, simulation is widely used by the WSN designers. It allows rapid evaluation and easy configuration of the system, and it provides the possibility to predict the behavior of the systems under development that the hardware is not available. In order to enable the accurate energy consumption estimation for WSN, we propose an energy model implemented in IDEA1 [4], a simulator for WSN developed in SystemC and C++, which can model not only the network, but also the hardware and software layer of an individual node in detail. This energy model enables the energy estimation of both the hardware components of an individual node and the whole sensor network. It can be easily calibrated to different type of nodes based on the datasheets of the hardware components or the measurements of testbed. The transitions between states are considered in this energy model. The rest of this paper is organized as follows. Section II describes a brief overview of the existing energy-aware WSN simulators. Section III describes the proposed energy model and its implementation in IDEA1. Section IV presents the simulation results of an application, in which the energy consumption and packet delivery rate of an IEEE 802.15.4 network is analyzed. This case study demonstrates the performance of the energy model. Section V concludes this paper and describes the future work. II. RELATED WORKS Simulators for WSN can be broadly classified into 3 categories: general network simulator, node emulator and node simulator. A more detailed analysis of the existing WSN simulators can be found in [5]. Here, we mainly focus on the typical energy-aware simulators. SensorSim [6] is a WSN extension for NS-2 [7] that is the most used general network simulator in Mobile Ad hoc NETwork (MANET) research [8]. SensorSim models the sensor node in two parts: software model (sensor function model) and hardware model (micro sensor model). The network protocol stack includes the IEEE 802.11 MAC layer and Dynamic Source Routing (DSR) routing protocol. The hardware model includes many components, such as CPU, radio transceiver and sensors. The state of these hardware components is changed based on the function that is carried out by the software model. The trace of the state transition is recorded, and the power model uses this information to calculate the energy consumption of different hardware components. By doing this, the energy consumption of the whole network can be simulated. However, the CPU and sensor device models have not been implemented and the simulator is no longer in active development. Furthermore, IEEE 802.11 is designed for high speed connectivity and is not widely used in the field of WSN. We use SystemC [9] to model the sensor node. SystemC is an embedded system description tool that enables the hardware/software (HW/SW) co-simulation at system-level, so we can develop a more 978-0-7695-4145-7/10 $26.00 2010 IEEE DOI 10.1109/ICSNC.2010.20 94

elaborate sensor node model. In addition, our protocol stack is based on IEEE 802.15.4 standard [10]. PowerTOSSIM [11] is an energy-aware WSN emulator based on TOSSIM [12]. They can emulate the execution of TinyOS [13] code on MICA motes, and they permits developing algorithms, studying system behaviors and observing interactions among the nodes in a controlled environment. PowerTOSSIM is an extension to TOSSIM in evaluation of the power consumption. However, these emulators are constrained to specific platform (typically MICA motes) or programming language (typically TinyOS/NesC). In addition, TOSSIM loses the fine-grained timing and interrupt properties of the code that can be important when the application runs on the hardware and interacts with other nodes [14]. In contrast, the hardware and software concurrences within the sensor node can be modeled by SystemC. SystemC Network Simulation Library (SCNSL) [15] is a networked embedded system simulation library. It integrates the embedded system design and network-level simulation together. The embedded systems are modeled by SystemC. All the embedded system modules are connected to a network module that transfers the packet from one node to another. Based on SCNSL, we developed IDEA1. It enables the design space exploration at an early stage for WSN system designers. The IEEE 802.15.4 standard has been modeled and many commercial off-the-shell microcontroller and transceiver prototypes have been implemented. IDEA1 provides the node platform designers the possibilities to evaluate the network performance of their new designs, and it also allows the protocol designers to evaluate the proposed algorithms on new hardware even if it has not been available commercially. The models developed for the simulation can also be synthesized into lower-level designs. A more detailed description of IDEA1 can be found in [4]. In this paper, we mainly focus on the energy model implemented in IDEA1 and present how it can be used to help the designers to capture the energy consumption behavior of their new design. The energy model implemented in IDEA1 can distinguish the operations of microcontroller and transceiver, consider the transition of states and be calibrated to different hardware platforms. III. THE ENERGY MODEL AND ITS IMPLEMENTATION A sensor node generally consists of a microcontroller, a transceiver, one or several sensors and one or several power supplies. In the current version of our energy model, we mainly focus on the consumptions of microcontroller and transceiver. The sensor model may be added in the future version. Both the microcontroller and transceiver are modeled as finite state machines. Each state and the transitions between two states are associated with a constant current load. The energy model tracks the transition of states and the timing information as the sensor network system is under operation and calculates the energy consumption of every hardware components at runtime. We first give a brief description of IDEA1, and then present the implementation of the energy model in detail. As an example, the energy model is calibrated to MICAz motes. SystemC simulation kernel Node 1 Software Model Graphical User Interface Sensors Microcontroller Transceiver NodeProxy Battery Node 2 Wireless Network Model Figure 1. Architecture of IDEA1 [4] NodeProxy The architecture of IDEA1 is illustrated in Fig. 1. The sensor node consists of hardware and software. The hardware mainly includes microcontroller, transceiver, sensor and battery. The software model includes operating system, middleware, protocol stack, and application implementation. The node is connected to the same network instance by a proxy interface. By using nodeproxy, nodes can be designed as pure SystemC modules, which enable the use of all the advantages of SystemC in HW/SW co-design, verification and synthesis. The network module manages the network topology, handles the concurrencies among different nodes and transfers packets among them. The wireless channel models are implemented in the network module too. SystemC simulation kernel acts as the simulation engine. It schedules the concurrences within the sensor node and updates the state of all the modules at every simulation cycle. A graphical user interface (GUI) has been developed to facilitate the system configuration, visualization of network topology, control of simulation and analysis of results. Each hardware component of the sensor node is modeled as an individual SystemC module. They are connected to each other by ports and signals. The state machine of microcontroller is controlled by the software execution. The state transition in transceiver is caused either by commands from microcontroller or by network events. While the simulation is running, the states are updated according to the software execution and network events. The software is divided into different tasks, such as data processing, SPI communication, Cyclic Redundancy Check (CRC) calculation, etc. The execution time of each task is estimated by their assembly codes. The operation trace of the every node and their components can be simulated. The battery module tracks the current variations in the trace file and computes the power consumption of every component. This energy model can be easily calibrated to different types of node. As an example, we calibrate it to the MICAz [16] motes. The current consumptions of the main operation modes of ATMega128 [17] and TI CC2420 [18] are summarized according to their datasheets, as presented in Table I, and the finite state machine model of TI CC2420 is illustrated in Fig. 2. Both the current consumption of each state and the timing information of the transitions between two states are considered. 95

TABLE I. CURRENT CONSUMPTIONS OF ATMEGA128 AND TI CC2420 WITH A 3.3 V POWER SUPPLY [17] [18] Microcontroller ATMega128 Transceiver TI CC2420 Mode Consumption Mode Consumption Active 9mA Sleep 20µA Idle 4mA Idle 426µA Power Save 8.9 µa Rx 18.8mA Power down 0.3µA Tx (0dBm) 17.4mA Tx (-1dBm) 16.5mA Tx (-3dBm) 15.2mA Tx (-5dBm) 13.9mA Tx (-7dBm) 12.5mA Tx (-10dBm) 11.2mA Tx (-15dBm) 9.9mA Tx (-25dBm) 8.5mA All states SRXON, 12 symbol periods Rx Figure 2. SXOSCOFF SRFOFF Sleep Idle SXOSCON, 1ms SRFOFF Transmission finish, 12 symbol periods ACK required or SRXON, 12 symbol periods STXON, 12 symbol periods Model of CC2420 transceiver with 3.3 V power supply IV. SIMULATION AND RESULTS In this section, an application is studied to demonstrate the capability of the energy model. In this application, 8 nodes and 1 coordinator are deployed to compose a WSN network with a star topology. All the nodes can directly communicate with the coordinator. They use IEEE 802.15.4 slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) algorithm to access the channel. They read the sensor data periodically. The reading frequency is presented as sample rate. The node transmits the sensor data to the coordinator as soon as the data arrives. The size of the sensor data is one byte, so the payload field of every packet is 1 byte. The calibrated energy model used here is base on MICAz motes, as presented in Table I. IEEE 802.15.4 supports the slotted CSMA-CA algorithm in beacon mode by the conception of superframe, as shown in Fig. 3. The beacon packets are transmitted periodically by the coordinator to describe the superframe structure. Beacon Interval (BI) defines the superframe length, which includes an active period and an inactive period. BI and the length of active portion are determined by two parameters, Beacon Order (BO) and Superframe Order (SO). The minimum length of a superframe is fixed to 960 symbols corresponding to 15.36 ms, assuming 250 kbps in the 2.4 GHz frequency band. The active portion may consist of two periods, contention access period (CAP) and contention free period (CFP). During CAP, nodes use the slotted CSMA-CA algorithm to access the channel. Tx Figure 3. The typical structure of a superframe [10] Many cases with various sample rates have been studied in order to find the energy consumptions of IEEE 802.15.4 under different traffic loads and with different configurations. BO is set to 3 values (0, 1 and 2) respectively to set the system in different duty cycles. SO is set to 0 for all the simulations. The other parameters of the slotted CSMA-CA algorithm are set to the values defined by default in IEEE 802.15.4 standard [10]. Each simulation includes 1000 sample data, for example, the application runs 1000 seconds if the sample rate is 1. Each simulation stops at the time when the 1001st sample data arrives. Each case is simulated 10 times with random seeds. The simulation results, both the packet delivery rate (PDR) and energy consumption per packet, are presented in Fig. 4. PDR is the ratio of the number of packets successfully received by the coordinator to the number of packets needed to be sent by the nodes. Energy consumption per packet is the average energy consumed for successfully transmitting one packet. In the light traffic load area on the left side, the PDR remains constant (97%) and the energy consumption per packet decreases as the sample rate increases. In these cases, the sample interval is so long that every node can accomplish its transmission before the new sensor data arrives. The peak value of PDR is about 97% for the slotted CSMA-CA algorithm in this application. PDR is a constant, which means the average number of packets transmitted during one sample interval is the same for different sample rates, so the energy consumption per packet is less if the sample interval is shorter. In addition, for the same sample rate, the smallest BO consumes the most energy, because one sample interval includes more superframes (also more active portions). The tracking synchronization is used, so the nodes wake up to track the beacon packet at the beginning of every superframe even if they have no data to send. The smallest energy consumption occurs and the PDRs begin to decline when the sample rate reaches one value (e.g. 30 when BO is 0.), where the channel is utilized the most effectively. In this case, every node can accomplish its transmission before new sensor data arrives, but the interval between the last node turns to sleep and the new sensor data arrives is very short, so the nodes spend the least energy during the idle state. The PDR of the case with bigger BO begins to decline earlier, because SO is the same and the active portion per sample interval is shorter for the bigger BI. As the sample rate continues increasing, the PDRs decrease and the energy consumptions augment due to the increase of collisions. Some nodes can not accomplish their transmissions before new sensor data arrives. The nodes have a buffer to store the new sensor data temporarily. They must keep transmitting if the buffer is not empty. 96

Figure 4. Energy consumption per packet of IEEE 802.15.4 slotted CSMA-CA algorithm. Figure 5. Average power consumptions of microcontroller and transceiver with BO =0 Figure 6. Average power consumptions of microcontroller and transceiver with BO =2 After a short increase, the energy consumption per packet remains constant during a section, and the smallest BO consumes the least energy. The reason is that the system becomes saturated due to the heavy traffic load. In these cases, the node buffer always has new senor data and there are always 8 nodes for the contention of the channel access. The energy consumed in the active portion of superframe accounts for the major part of the total energy consumption, which can be observed in Fig. 5 and Fig. 6. Because SO is the same, the number of packets transmitted per superframe is almost the same for the various BI. The application with bigger sample rate lasts less time and needs less number of superframes, so the number of transmitted packets during the whole application is smaller and its PDR is less than the one with a smaller sample rate. Because the number of transmitted packets per superframe is almost the same, the energy consumption per packet keeps as a constant. Because of the same energy consumption in active portion for different BO, so the smallest BO has shortest inactive portion and consumes the least energy if the sample rate is the same. As the sample rate continues increasing, the energy consumption per packet augments, because the number of transmitted packets per superframe begins to diminish. In these cases, the nodes may read the sensor data more than once during one superframe and the sensing operation (analog to digital conversion) disturbs the communication operation. Except the energy consumption of the whole network, our energy model also can track the power consumption of the hardware components. For the same application, the average power consumptions of microcontroller and transceiver in the active and idle states are presented in Fig. 5 and Fig. 6. From these two figures, we can understand the system more clearly. Firstly, the total power consumption is smaller if BO is 2, because the inactive portion is longer. Secondly, the microcontroller consumes more energy than transceiver when the sample rate is small, because the current consumption at the idle state of microcontroller is very bigger than transceiver. As the sample rate increases, both the microcontroller and transceiver spend less time at the idle state during the active portion of superframe, so their power consumptions in the active state augments. The transceiver consumes more energy when the sample rate is big, because the current consumption at the active state of transceiver is bigger than microcontroller. Finally, the power consumptions are the same when the sample rate is 100 and 1000, because the nodes are always busy in the active portion of superframe in these two cases. All the above explanations of the simulation results have been proved by the simulation log, which records all the important events occurred during the simulated application is under operation. V. CONCLUSION In this paper, we proposed an energy model for WSN. It enables the energy estimation of both the whole sensor network and the components in an individual node. It can be easily calibrated to different type of nodes based on the electrical characteristics on the datasheet of hardware components or testbed measurements. Currently, we are calibrating and validating the energy model by some testbed measurements. REFERENCES [1] K. Römer and F. Mattern, The design space of wireless sensor networks, IEEE Wireless Communications. v11 i6. pp. 54-61, Dec. 2004. 97

[2] A. Shareef andf Y. F. Zhu, "Energy Modeling of Processors in Wireless Sensor Networks Based on Petri Nets," Proc. International Conference on Parallel Processing - Workshops, pp.129-134, Sep. 2008 [3] G. Chen, J. Branch, M. J. Pflug, L. Zhu, and B. Szymanski, SENSE: A Wireless Sensor Network Simulator, Advances in Pervasive Computing & Networking, pp 249-267, 2004. [4] W. Du, F. Mieyeville, and D. Navarro, IDEA1: A SystemC-based System-level Simulator for Wireless Sensor Networks, Proc. 2010 IEEE International Conferece on Wireless Communications, Networking and Information Security (WCNIS2010), In Press. [5] W. Du, D. Navarro, F. Mieyeville, and F. Gaffiot, Towards a Taxonomy of Simulation Tools for Wireless Sensor Networks, Proc. International Conference on Simulation Tools and Techniques (SIMUTools 2010), Mar. 2010. [6] S. Park, A. Savvides, and M. B. Srivastava, SensorSim: A Simulation Framework for Sensor Networks, Proc. ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems, pp. 104-111, Aug. 2000. [7] Network simulator ns-2, http://www.isi.edu/nsnam/ns/, Feb. 2010. [8] S. Kurkowski, T. Camp, and M. Colagrosso, MANET simulation studies: the incredibles, Mobile Computing and Communications Review, vol. 9, no. 4, pp. 50 61, 2005 [9] IEEE Std 1666-2005 IEEE Standard SystemC Language Reference Manual. IEEE Std 1666-2005, pages 1 423, 2006. [10] IEEE. Wireless medium access control (MAC) and physical layer (PHY) specifications for low rate wireless personal area networks (WPANs). IEEE Sandard 802.15.4-2006 [11] V. Shnayder, M. Hempstead, B. Chen, G. W. Allen, and M. Welsh, Simulating the power consumption of large scale sensor network applications, Proc. the 2nd International Conference on Embedded Networked Sensor Systems, pp. 188 200, 2004 [12] P. Levis, N. Lee, M. Welsh, and D. Culler, Tossim: Accurate and scalable simulation of entire tinyos applications, Proc. International Conference on Embedded Networked Sensor Systems, Nov. 2003, pp. 126-137, doi: 10.1145/958491.958506 [13] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. E. Culler, and K. S. J. Pister, System architecture directions for networked sensors, ACM SIGPLAN Notices, v.35 n.11, Nov. 2000, p.93-104, doi:10.1145/356989.356998 [14] B. Titzer, D. Lee, and J. Palsberg, Avrora: Scalable sensor network simulation with precise timing, Proc. International Symposium on Information Processing in Sensor Networks, April 2005, pp. 477-482, doi: 10.1109/IPSN.2005.1440978 [15] F. Fummi, D. Quaglia, and F. Stefanni, A SystemC-based Framework for Modeling and Simulation of Networked Embedded Systems, Proc. Forum on Specification, Verification and Design Languages (FDL2008), Sep. 2008, pp. 49-54, doi:10.1109/fdl.2008.4641420. [16] MICAz datasheet, http://www.xbow.com/products/product_pdf_files/wireless_pdf/mic AZ_Datasheet.pdf, Feb. 2010. [17] ATMega128 datasheet, www.atmel.com/atmel/acrobat/doc2467.pdf, Feb.2010 [18] CC2420 datasheet, focus.ti.com/lit/ds/symlink/cc2420.pdf, Feb.2010 98