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![]() Wireless Sensor Network, 2010, 2, 698-702 doi:10.4236/wsn.2010.29084 Published Online September 2010 (http://www.SciRP.org/journal/wsn) Copyright © 2010 SciRes. WSN Sensors Dynamic Energy Management in WSN Xianghui Fan, Shining Li, Zhigang Li, Jingyuan Li College of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, China E-mail: fxianghui@vip.qq.com Received July 2, 2010; revised August 16, 2010; accepted September 12, 2010 Abstract A wireless sensor node is typically battery operated and energy constrained. Therefore, it is apparent that optimal energy management is one of the most important challenges in WSN development. However, energy management requires in-depth knowledge and detailed insight concerning specific scenarios. After Carrying out a large number of experiments in precision agriculture, we find that it is the sensors that have never been concerned consuming the most energy of the node. In order to conserve energy and prolong the lifetime of WSN, we design and carry out a dynamic energy management strategy for sensors. The basic idea is to shut down all sensors’ power when not needed and wake them up when necessary. Valuable conclusions are extracted and analyzed. Keywords: WSN (Wireless Sensor Network), Precision Agriculture, Dynamic Energy Management, TinyOS 1. Introduction A large number of intelligent micro-sensor nodes with sensing, processing and wireless communicating capa- bilities form wireless sensor network (WSN), which completes complicated tasks in some specific field, such as precision agriculture. Compared with old methods, WSN has significantly drawn extensive attention. It does not rely on fixed infrastructure and has many characteris- tics such as fast setup, strong survivability and so on [1]. It has been considered as a good scheme to conduct pre- cision agriculture data collection and processing. In 2002, Intel has a project looking at how WSN can be used to improve grape production. They worked with agricul- tural scientists on a long-term deployment of WSN in a wine grape vineyard. By densely monitoring and analyz- ing they found the relationship between grape quality and climatic conditions. It has been proved that WSN could play a role in precision agriculture. Just the same as other applications, energy constraint of sensor nodes is the major problem for precision agri- culture. Data aggregation [2] and low power listening [3] algorithms are effective method to reduce energy con- sumption in normal wireless sensor networks. However, after a sufficient number of experiments we found that energy consumption in precision agriculture has some special issues. Generally speaking, in order to monitor the growth conditions of crops, one node has to connect with many sensors, such as Co2 sensor, air temperature sensor, air humidity sensor, light sensor, soil tempera- ture/moisture sensor and so on. Although the sensors consume a large portion of the energy, we never pay any attention to this issue in our previous research. It is necessary to reduce the energy consumption of sensors. We design and carry out a sensor dynamic energy man- agement (SDEM) to reduce energy consumption of sen- sors and extend network lifetime. The basic idea is to shut down sensors when not needed and wake them up when necessary [4,5]. The experimental results indicate that SDEM is an effective technique in reducing node energy consumption without significantly degrading performance. The remainder of this paper is organized as follows. Section 2 gives the energy consumption of all parts of the sensors in precision agriculture. And we get a con- clusion that the sensors consume most of the energy. The architecture of the SDEM is described in Section 3, and Section 4 reports the hardware and software design and some considerations about implementation. In Section 5, the actual deployment is described in detail, and finally Section 6 reports our conclusion and gives some direc- tions on the ongoing work. 2. Sensors Energy Consumption Analysis A sensor node has several major components: processor, memory, A/D converter, sensing unit and radio. Each node sleep mode corresponds to a particular combination ![]() X. H. FAN ET AL. Copyright © 2010 SciRes. WSN 699 of component power modes. In general, if there are N components labeled (1, 2, … , N) each with i K sleep modes, the total number of node sleep modes is 1 n i ik [6]. However, from a practical point of view not all sleep modes are useful. Optimizing the key issue could achieve a noticed effect. In our case, the NPUnode used in precision agriculture consists of a processor Atmel2561, which has rich resource such as 256K bytes in-system programmable flash, 4K bytes EEPROM, 8K bytes SRAM and so on, a RF230 chip acts as the radio unit, an AT45DB041B chip acts as an extern memory. And the NPUnode has been equipped with six sensors, just as showing in Figure 1. Each component is con- trolled by the micro-operating system (TinyOS). Tabl e 1 enumerates the characteristics of all devices of the NPUnode. Sensor transducers translate physical phenomena to electrical signals, and are classified as either analog or digital devices depending on the type of their output. There are several sources of power consumption in a sensor, including 1) signal sampling and conversion of physical signals to electrical ones, 2) signal conditioning, and 3) analog to digital conversion. Given the diversity of sensors there is no typical power consumption number. In general, however, passive sensors such as temperature sensors etc., consume negligible power relative to other components of sensors, array sensors such as Co2 sen- sors can be large consumers of power. As shown in Ta- ble 1, the current value of Co2 sensor is 30mA, some- times even reaches the max value 150mA. And other sensors also have power dissipation. In accordance with Table 1, we draw a figure of en- ergy consumption of major components. Just as shown in Figure 2, the sensors energy consumption is far more than other components. According to electric power formula WPtUIt , we know that reducing the run- ning time is the only way to decrease energy consump- tion while not change the sensors characters. T is the data collection period, while woking Tis the working time of sensors and idle T means the period that sensors is in the idle state. And the relationship between them is working idle TT T and idle working TT . Data collection period is a long time in agriculture application. For ex- ample, the nodes we have deployed in greenhouse in YangLing only need to report the sensor data once an hour. This means that the sensors only need to work for several minutes per hour, so they should be turned off when they are idle to save energy. 3. Sensors Dynamic Energy Management Nodes energy consumption is application specific. In precision agriculture the sensors consume the greatest portion of the node energy. The reason is that one node is always connected with several sensors to monitor plant growth environment. However, the most time of sensors is in idle state and waste a lot of energy. For the purpose of saving energy of nodes and extending the life of WSN, we design the Sensors Dynamic Energy Management (SDEM) strategy which turns on the sensors when the node receives an acquisition command from root and turns it off when the sensors are in the idle state. The principle of SDEM is showed as Figure 3. 3.1. Hardware Design SDEM needs independent hardware design supporting. The NPUnode supports power with DC 5V or 3.6V Nickel-Hydrogen Battery. And the sensors used to monitor the plant growth environment need different voltage. A power supply of DC3.0V is designed for light sensor, soil temperature sensor, temperature and humid- ity sensor. Co2 sensor and soil Moisture sensor power are supplied with DC5.0V. The hardware design is de- scribed as in Figure 4. XC6221B and TPS61202 chips which are connected with ATmega2561 I/O pins trans- late battery power to DC3.0V and DC5.0V to meet above Figure 1. The NPUnode and equipped sensors. Table 1.The devices of the node and their characteristics. Device Type Characteristics Processor ATmega 2561V 256K Bytes Flash, 4K Bytes EEPROM, 8K Bytes Internal SRAM, Power :2.7~5.5V,10mA Radio RF230 Power: Voltage: 1.8V~3.6V, SLEEP: 0.1 μA, RX: 16 mA, TX: 17 mA; Fast Power-Up Time < 1 ms Flash AT45DB0 41 Power Supply: 2.7V~3.6V, 4 mA Active Read Temp and Humi Sensor SHT11 Temperature range:-40℃ to +123.8 ℃;Humidity range: 0 to 100% RH; Power : 3V, 0.5mA Light Sensor ISL29002 Range: 10,000lux~100,000lux;Power : 2.5V to 3.3V, <10mA Soil Temp Sensor PT1000 Range: -70°C to + 500°C;Power:3VDC, 3mA;Response time < 10s Soil Mois Sensor TDR-3 Range: 0~100%(m3/m3);Power Supply: 4.5~5.5V DC, 50~70mA; Response time: < 1s Co2 SensorGE/Telaire 6004 Range:0-2000ppm;Power: 4.3 VDC ~7.0 VDC, 30~150mA;Response time: < 2 min ![]() X. H. FAN ET AL. Copyright © 2010 SciRes. WSN 700 Figure 2. Energy consumption of node in precision agricul- ture. Figure 3. Principle of sensor dynamic energy management. demand. Inputting L level signal for I/O pins we can provide power for sensors. On the other hand, we can turn off the sensors power supply by inputting H level signal for I/O pins. It is easy to implement in TinyOS. 3.2. Software Design The software system considered here is based on TinyOS 2.1 [7] which is an embedded operating system espe- cially for WSN applications. The extreme power limita- tion of nodes forces the operating system to take very different approaches than traditional computing classes. TinyOS is implemented using NesC programming lan- guage—a dialect of C programming language. It inte- grates with model of components/module and events driven model. Over the past few years, TinyOS has grown from a small research project to dominant operat- ing system for low power wireless sensor networks. At a high level, TinyOS provides three things to make writing systems and applications easier [8]. 1) a compo- nent model, which defines how you write small, reusable pieces of code and compose them into larger abstractions; 2) a concurrent execution model, which defines how components interleave their computations as well as how interrupt and non-interrupt code interact; 3) application programming interfaces (APIs), services, components libraries and an overall component structure that simplify writing new applications and services. The HplAtm256- GenerallIOC is the most important components to design the SDEM. It exposes the ATmega256’s 53 digital I/O pins as 53 GeneralIO interfaces, hiding the slightly dif- ferent instruction sequences needed to perform some operations on some I/O pins. Some I/O pins can be set atomically in a single assembly instruction. On the pur- pose of turn on/off the sensors power, we need to use the interface GeneralIO to enable/disenable the pins of At- mel2561 which connect with XC6221B and TPS61202 by inputting Low/High level signal. The GeneralIO in- terface offers commands to configure, read and write a typical microcontroller digital I/O pin. Firstly, after the OS initializes all needed components and booted suc- cessfully, we call GeneralIO.makeoutput() to make the pins as a controller. Then the NPUnode enters a Figure 4. The hardware design of SDEM. ![]() X. H. FAN ET AL. Copyright © 2010 SciRes. WSN 701 ready state waiting for the command from root. Once the node receives the sampling command, it then calls the command GeneralIO.set() to turn on the sensors power and waits for 2 minutes for warming up the sensors. After reading all sensors data and sending it back suc- cessfully, nodes calls GeneralIO.clr() to turn off the sen- sors power. The Figure 5 and Figure 6 show the imple- mentation of SDEM. Figure 5.The module implementation of SDEM. configuration AgriMonitorAppC{} implementation{ Components AgriMonitorC as App; Components MainC; App.Boot -> MainC; Components HplAtm256GeneralIOC; App.PowerControl -> HplAtm256GeneralIOC.PortC0; ... } Figure 6.The configuration wired the needed components. 4. Experiment Results We deployed NPUnodes in field crop production in YangLing, LuoChuan, AnSai in ShaanXi province, just as showing in Figure 7. The NPUnodes are equipped with Co2 sensors, temperature and humidity sensors, light sensors, soil temperature sensors and soil moisture sensors to collect important data for plant growth envi- ronment. The collected data is gathered at the edge of the field by a field gateway and further transferred via GPRS to a PC server for data analysis. Once something odd happened the server will send message via MMS to farmers. In addition to the agronomic experiment, we expect to gather data and statistics on the behavior of NPUnodes in real-world experiment. For energy- efficiency considerations, we use SDEM strategy in NPUnodes and they reported data only once per hour. To get the exact value of sensors, we need to wait sensors warm-up for two minutes. The rest of the time we turn off the sensor power to save energy. From collected voltage data of NPUnodes, we make a comparison between the SDEM strategy and before methods. Just showing in Figure 8, we can give the con- clusion that the SDEM strategy has significantly saved energy. After making in-depth research we find that the Figure 7. NPUnode deployed in greenhouses with six sen- sors. 2468 10 12 14 16 18 20 3.4 3.45 3.5 3.55 3.6 3.65 3.7 3.75 3.8 3.85 Time /h Voltage/V Us ing SDMP Old m ethod Figure 8. Lift time of sensors between SDEM and before method. ![]() X. H. FAN ET AL. Copyright © 2010 SciRes. WSN 702 SDEM strategy almost has extended the NPUnodes life time from 1 month to 3 months. However, SDEM should save sensors energy 30 times than not using SDEM nodes, theoretically on the condition that NPUnode re- ports sensors data once per hour (all sensors only turn on for 2 minutes). The reason is that not only the sensors consume the energy, other components such as radio, processor etc. also consume the energy. 5. Conclusions and On-Going Work In precision agriculture, nodes are always equipped with several sensors which consume a large portion of nodes energy, especially active sensors. In order to save energy and extend the lifetime of nodes, we design and imple- ment the SDEM strategy based on our NPUnodes. The basic idea is to shut down devices when not needed and wake them up when necessary. And then we deployed our nodes in greenhouse. From the voltage data collected we give the conclusion that the SDEM strategy can sig- nificantly save energy of nodes, and extend the life time of nodes from one month to three month. The nodes need continuous work for one year or more in precision agriculture. So the nodes need an unfailing supply of energy. Next time, we will make a serial re- search and supply solar power for NPUnodes. Maybe it is a way to solve the energy problem finally. 6. References [1] S. M. Xiong, L. M. Wang, X. Q. Qu and Y. Z. Zhan, “Application Research of WSN in Precise Agriculture Ir- rigation,” International Conference on Environmental Science and Information Application Technology, Wuhan, July 2009, pp.297-330. [2] B. Krishnamachari, D. Estrin and S. Wicker, “Impact of Data Aggregation in Wireless Sensor Networks,” DEBS’02. [3] J. Polastre, J. Hill and D. Culler, “Versatile Low Power Media Access for Wireless Sensor Networks,” Proceed- ings of the 2nd International Conference on Embedded Networked Sensor Systems, Maryland, November 2004. [4] A. C. Sinha, “Dynamic Power Management in Wireless Sensor Networks, Design & Test of Computers,” Pro- ceedings of IEEE, Vol. 18, No. 2, 2001, pp. 62-74. [5] R. C. Luo, L. C. Tu and O. Chen, “An Efficient Dynamic Power Management Policy on Sensor Network,” Pro- ceedings of the 19th International Conference on Ad- vanced Information Networking and Applications, 2005, pp. 341-344. [6] C. Lin, N. Xiong, J. H. Park and T.-H. Kim, “Dynamic Power Management in New Architecture of Wireless Sensor Networks,” International Journal of Communica- tion Systems, Vol. 22, No. 6, 2008, pp. 671-693. [7] TinyOS Tutorials, Internet Available: http://docs.tinyos.net/index.php/TinyOS_Tutorials [8] P. Levis and D. Gay, “TinyOS Programming,” Cam- bridge University Press, England, 2009, pp. 6-7. |