EM: Energy Management Tool for Wireless Sensor Networks

922 Anais EM: Energy Management Tool for Wireless Sensor Networks André Hahn Pereira 1, Cíntia Borges Margi 1 1 Escola Politécnica Universidade de São Paulo (USP) Departamento de Engenharia da Computação e Sistemas Digitais Av. Prof. Luciano Gualberto, travessa 3, 158. São Paulo - 05508-010 {apereira,cbmargi}@larc.usp.br Abstract. Wireless Sensor Networks (WSNs) are typically energy constrained, hence, self-organization and energy management are two fundamental factors determining their performance and lifetime. While communication protocols are usually power aware, there is little support to make the WSN node itself power aware. We present EM, an energy management tool for WSNs, which main idea is to extend the node lifetime, by adapting its duty cycle and the amount of tasks executed. 1. Introduction Wireless Sensor Networks (WSNs) are typically energy constrained and hard to access once deployed, hence, self-organization and energy management are two fundamental factors determining their performance and lifetime. Considerable effort was put to develop mechanisms for network discovery and information flow management early on [Heinzelman et al. 2000, Intanagonwiwat et al. 2000, Solis and Obraczka 2005]. Since the vast majority of networks studied employed very simple sensors (providing few bits per measurement and consuming little power) and consequently, the bulk of energy consumption is due to communication-related tasks, most of early research in WSN power conservation has addressed solely communication issues. Examples include power-aware protocols at the MAC layer, data aggregation mechanisms, and strategies for predictive activation/transmission, topology control, power-aware routing, etc. Another common power conservation approach in WSN deployments is the use of duty cycles [Mainwaring et al. 2002, Tolle et al. 2005], which alternate nodes between active and idle, low-power periods. Next, power management node based approaches were proposed. Lachenmann et al. [Lachenmann et al. 2007] presented a programming abstraction to implement WSN energy aware applications, in environments where there was no redundancy and nodes were not supposed to fail. Using code blocks energy consumption information, obtained from simulators, the authors propose the use of energy consumption levels, which should be selected according to the expected node lifetime. Weddell et al. [Weddell et al. 2009] developed a modular plug-and-play system to control several energy sources attached to a single WSN node, including energy harvesting systems such as solar panels. The presented system is also capable of managing the charge from rechargeable batteries and providing information to the node operating system. In order to analyze and model power consumption over long periods of time, and therefore predict the lifetime of a node and change the control policy accordingly, it is useful to consider a number of elementary tasks whose scheduling and execution

XXX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos 923 are controlled by a resource manager [Benini et al. 2000]. Each task has an associated power consumption cost and execution time (where both of these could be random variables). Stochastic models can then be employed for predicting the statistics of the time series of tasks, and therefore the expected energy consumed over a period of time [Mini et al. 2003]. Recently, approaches where the application itself has power management mechanisms have been presented. Panuncio et al. [Panuccio et al. 2011] propose a distributed action recognition framework that, given a lower bound on the classification accuracy, will minimize the power consumption of the system. PowerSense [Matthews et al. 2011] follows the same trend. In this paper, we present EM, an energy management tool for WSNs. The main idea is to extend the node lifetime, by adapting its duty cycle and the amount of tasks executed. However, EM does not depend on the application being executed, being able to be used in several different scenarios, devices and applications. Furthermore, the tool is configurable and uses the information provided by the user to determine the threshold to optimize the node energy consumption. The remainder of this document is organized as follows. Section 2 addresses the motivation behind this work. A complete description of tool architecture is presented in Section 3, and is followed by the demo proposal in Section 4. Section 5 provides the information regarding to the source code and related documents, as well as the website that hosts them. Finally, we conclude our paper and discuss future work in Section 6. 2. Motivation Power management is still a key issue in WSNs, as well as in mobile computing. Although there has been significant contributions to the field, the constant change of applications, devices and constraints require flexible, portable and configurable tools. Margi et al. [Margi et al. 2006b] present a thorough energy consumption characterization for wireless camera networks, given it is critical to develop resource management policies. Following this work, authors provide a quantitative power consumption and temporal analysis of a set of basic tasks as well as duty cycles representative of activities carried out by wireless camera networks targeting surveillance applications [Margi et al. 2006a]. These works showed that an approach based on tasks could be effective. However, it was still necessary to develop a flexible tool that implemented these ideas. We have ongoing projects on wireless sensor networks applied to security mechanisms [Santos and Margi 2011] as well as to mobile health [Polizel et al. 2011]. Furthermore, typical current devices do not provide mechanisms to achieve power management. Therefore, in order to extend node and network lifetime, it is critical to use flexible power management tools, and EM was developed to fill this gap. 3. The EM tool The developed tool intends to control energy consumption in a sensor node with limited energy available. The idea is that the node should be able to remain working for a reasonable amount of time, even if that means reducing the utility of the node, limiting its

924 Anais capabilities. The strategy is increasingly aggressive as the available energy decreases. In order to illustrate this idea, we use a state diagram depicted in Figure 1. The node starts with its energy budget at maximum and, as it decreases during the node s operation, once it reaches a certain threshold, it changes to state econ k, and so on, until it reaches state NOP, which halts the node s operation. Figure 1. EM state diagram. The tool controls the energy consumption through the blocking of the execution of a function of the node and through a reduced duty cycle. It is highly customizable, since the user is capable of defining as many states as necessary and can configure the task block rate, the duty cycle and the threshold for state changes for each state. Notice that this threshold is given by the the percentage of battery available to the device, as shown in Figure 2. Figure 2. EM energy threshold. Each state has a different characteristic of percentage of tasks executed and duty cycle, which allows the node lifetime to be extended. Note that EM begins in state OK, automatically added by the tool, with full duty cycle and no block rate and ends in state NOP, also added by the tool, with no duty cycle. The EM tool architecture is presented in Figure 3. The tool wraps the OS (operating system), so that every system call is now performed through the EM. Therefore, the tool is able to decide which task will or will not be executed, and to compute the energy consumption.

XXX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos 925 Figure 3. EM architecture. 3.1. Used resources The tool was developed using.net Micro Framework version 4.1. The programming language used was C# and the programming environment was Microsoft Visual Studio 2010. The FEZ Spider Mainboard [Electronicsl 2012] was used as the platform of deployment, this mainboard is a.net Gadgeteer-compatible mainboard. It has a 72MHz, 32-bit ARM7 processor, 4.5MB of flash memory and 16MB of RAM memory. The mainboard has 14 sockets which are compatible with.net Gadgeteer s, they comprise the pins of the processor, which have different capabilities, such as 10-bit analog inputs, 10- bit analog outputs, PWM and Serial Peripheral Interface. Microsoft.NET Gadgeteer is an open source toolkit for electronic devices prototyping using.net Micro Framework, its goal is speeding up the development of electronic devices with its solderless assembly which makes this environment ideal for fast prototyping. There are several different types of extension modules already available for the toolkit. So, with this toolkit no effort is necessary on specific hardware implementations, and it is possible to embed the EM tool in any.net Gadgeteer compatible board without any difficulty. 3.2. Configuration file To use the EM tool it is needed to create a configuration file, with the following specifications: Available charge: The first line contains the total charge available to the sensor node, in millicoulombs. Current values: The next two lines of the configuration file contain the current consumed by the sensor when idle and when hibernating, respectively, in milliamperes. The states configurations: The first part of the configuration file contains informations about each of the states the node can reach, namely: the block rate, the duty cycle, the cycle length and the battery percentage threshold for it to start operating. Function s charge cost: The second part of the configuration file contains informations about each of the functions available, the name of the method called when the function is to be executed and its charge consumption, in millicoulombs. 3.3. Method calls After writing the configuration file the tool is ready to use, it just needs to be initialized with a method call to InitManager having the string of the configuration file as a param-

926 Anais eter and each method should invoke StartMethod, which is of the type boolean, with the method s name as a parameter and check whether it returns true or false to see if the method should run or not. The rest of the management is done automatically by EM, it controls when the device should sleep, when to change states and how much energy has been used. Internally the tool has a variable to keep track of the spent energy and also to keep track of how many times in a row a method has been executed. Every end of cycle, with length defined by the user, EM recalculates its state to check if the transition to the next state should happen. 3.4. Energy consumption calculation To evaluate the amount of charge spent and available for the device the EM tool relies on information given by the user through the configuration file. This information comprises the average current when the device is idle and hibernating, and information about energy consumed to execute each method. These values must be obtained through measurements of the device in each of these conditions. To obtain the data used for the evaluation of the program a Agilent E3631A DC Power Supply providing 7V of input was used and the measurement was done with a Agilent 34401A Digital Multimeter connected to the computer. The data of the current consumed by the device was captured by the computer through the LabView program. The values of idle and sleep current were measured with different sets of attached modules. These values are available together with the tool and can be used if the device is also a FEZ Spider. Table 1 shows average current in milli-amperes drawn from the Microsoft.NET Gadgeteer node when running the different benchmarks configurations. Standard deviations are also presented. Table 2 shows the average charge cost in millicoulombs to execute different tasks, standard deviations are also presented. State Mainboard Ethernet Camera Ethernet + Camera idle 139.7 ± 0.2 188.6 ± 0.5 176.6 ± 0.8 232.6 ± 1.5 hibernate 46.40 ± 0.01 95.28 ± 0.04 48.19 ± 0.01 97.4 ± 0.2 Table 1. Average current in milli-amperes and standard deviation drawn by the Microsoft.NET Gadgeteer s node. Task Ethernet Camera Ethernet + Camera take picture 53 ± 12 54 ± 3 receive photo through network 53 ± 5 61 ± 1 Table 2. Average charge cost in millicoulombs and standard deviation to execute the tasks. Assume the node has both the camera and Ethernet modules connected, and it runs on the OK state. If it is on for 600 seconds and it takes and transmits 60 pictures, the charge consumption is about 146.46 C. On the other hand, if it is on econ state, with bocking rate of 2 tasks out of 3 and duty cycle of 75%, its charge consumption will be

XXX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos 927 123.88 C for the same 600 seconds. Therefore, if the application can handle the loss of events, the overall lifetime will be higher with EM usage given a relative increase of 15%. 4. Demo Proposal The demonstration of the EM tool will be done with two FEZ Spider devices, one with a network and camera modules and the other with network and display modules. Figure 4 illustrates the demo scenario. Figure 4. EM demonstration proposal. The device with the camera will be the one with EM tool installed while the other device will communicate with it to request the photos taken and will display the received photos on its display. As there is currently no way of powering the device with batteries, the power supply will be through a DC power supply. Also the network connection between the devices will be done with Ethernet modules. It will be possible to notice the reduced functionality and consequent saving of energy through the periodicity on which the photo displayed updates, as illustrated by the message exchange sequence in Figure 5. 5. Source Code and Documentation Availability The EM tool and its related documentation is available at: http://www.larc.usp. br/ cbmargi/em It is important to notice that EM, as of now, was developed for a specific environment, the.net Micro Framework, using some particularities of the.net Gadgeteer toolkit. Thus the tool currently works only in devices compatible with.net Gadgeteer, though only a small modification is needed for it to be compatible with.net Micro Framework compatible devices in general. 6. Concluding Remarks In this paper, we introduced EM, an energy management tool for WSNs. To extend the node lifetime, the tool changes the node s duty cycle and the amount of tasks it executes. The tool is configurable and uses the information provided by the user to determine the threshold to optimize the node energy consumption.

928 Anais Figure 5. EM demonstration message exchange sequence. As future work, we intend to port EM to other environments, such as TinyOS. We also would like to develop an energy monitoring hardware, which will monitor the current flowing to the system and obtain an accurate measurement of the energy consumed by the sensor node. 7. Acknowledgments This work was partially supported by CNPq/Brazil (Conselho Nacional de Desenvolvimento Cientfico e Tecnolgico) under research grant number 482342/2011-0, and by Microsoft Research. References Benini, L., Bogliolo, A., and Micheli, G. D. (2000). A survey of design techniques for system-level dynamic power management. IEEE Transactions on VLSI Systems, 8(3):299 316. Electronicsl, G. (2012). Fez spider mainboard - ghi electronics. http://www. ghielectronics.com/catalog/product/269. Heinzelman, W., Chandrakasan, A., and Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on, volume 2, pages 3005 14, New York, NY, USA. IEEE. Intanagonwiwat, C., Govindan, R., and Estrin, D. (2000). Directed Diffusion: a scalable and robust communication paradigm for sensor networks. In MobiCom 00: Proceedings of the 6th annual international conference on Mobile computing and networking, pages 56 67, New York, NY, USA. ACM.

XXX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos 929 Lachenmann, A., Marrón, P. J., Minder, D., and Rothermel, K. (2007). Meeting lifetime goals with energy levels. In SenSys 07: Proceedings of the 5th international conference on Embedded networked sensor systems, pages 131 144, New York, NY, USA. ACM. Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., and Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In WSNA 02: Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pages 88 97, New York, NY, USA. ACM. Margi, C. B., Manduchi, R., and Obraczka, K. (2006a). Energy consumption tradeoffs in visual sensor networks. In 24th Brazilian Symposium on Computer Networks (SBRC 2006), Porto Alegre, RS. Sociedade Brasileira de Computao. Margi, C. B., Petkov, V., Obraczka, K., and Manduchi, R. (2006b). Characterizing energy consumption in a visual sensor network testbed. In 2nd International IEEE/Create-Net Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom 2006), New York, NY, USA. IEEE. Matthews, J., Chang, M., Feng, Z., Srinivas, R., and Gerla, M. (2011). Powersense: power aware dengue diagnosis on mobile phones. In Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, mhealthsys 11, pages 6:1 6:6, New York, NY, USA. ACM. Mini, R. A., Loureiro, A. A., and Nath, B. (2003). Prediction-based energy map for wireless sensor networks. In Proceedings of IFIP-TC6 8th International on Conference Personal Wireless Communications (PWC 2003), pages 12 26. Panuccio, P., Ghasemzadeh, H., Fortino, G., and Jafari, R. (2011). Power-aware action recognition with optimal sensor selection: an adaboost driven distributed template matching approach. In Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, mhealthsys 11, pages 5:1 5:6, New York, NY, USA. ACM. Polizel, A. S., Wada, E. D., and Alves, R. C. A. (2011). Redes de Sensores sem fio Aplicadas Fisioterapia, Trabalho de Concluso de Curso, Escola Politcnica da Universidade de So Paulo. Santos, M. A. S. and Margi, C. B. (2011). TinySharing: Uma ferramenta para compartilhamento de segredos em redes de sensores sem fio. In Anais do Simpsio Brasileiro de Redes de Computadores (SBRC). Salo de Ferramentas., Campo Grande, MS, Brasil. Solis, I. and Obraczka, K. (2005). Efficient continuous mapping in sensor networks using isolines. In Mobiquitous 2005. Tolle, G., Polastre, J., Szewczyk, R., Turner, N., Tu, K., Buonadonna, P., Burgess, S., Gay, D., Hong, W., Dawson, T., and Culler, D. (2005). A macroscope in the redwoods. In SenSys 05: Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 51 63, New York, NY, USA. ACM. Weddell, A., Grabham, N., Harris, N., and White, N. (2009). Modular plug-and-play power resources for energy-aware wireless sensor nodes. In Proceedings 6th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2009), pages 1 9, New York, NY, USA. IEEE.