Design, Implementation, and Experiments on Outdoor Deployment of Wireless Sensor Network for Environmental Monitoring

Transcription

1 Design, Implementation, and Experiments on Outdoor Deployment of Wireless Sensor Network for Environmental Monitoring Jukka Suhonen, Mikko Kohvakka, Marko Hännikäinen, and Timo D. Hämäläinen Tampere University of Technology / Institute of Computer and Digital Systems {jukka.suhonen, mikko.kohvakka, marko.hannikainen, timo.d.hamalainen}@tut.fi Abstract. This paper presents the design, implementation, and practical real world experiments of an energy optimized multi-hop wireless sensor network (WSN) targeted at environmental monitoring. The WSN is fully autonomous and consists of energy-efficient and scalable communication protocols and low-power hardware platform. Software tools are developed for configuring and analyzing large scale networks. The network has been deployed in outdoor environment consisting of 20 nodes covering over 2 km 2 area. The results show that the multihop network works autonomously, reacts to environmental changes, and is able to operate temperatures down to -30 C. The hardware nodes operating on 433 MHz frequency provide over 1 km communication distances, while still having sufficient throughput and low energy consumption. The deployed nodes had a lifetime of 6 months with a 1600 mah battery, while generating 4 packets per minute. 1 Introduction Wireless sensor network (WSN) is an emerging ad-hoc network technology that may consist of thousands of sensor nodes combining environment sensing, data processing, and wireless networking with extremely low energy and cost. Applications for WSN have been envisioned in home, outdoor, and industrial environments. An environmental sensor network can be deployed in hostile environments or over large geographical areas to provide accurate and localized data. The vast number of sensor nodes introduces several challenges. The network must be scalable and autonomous, as the reconfiguration of individual nodes is not feasible. Also, since recharging or changing power sources is not practical or possible, the network must be extremely energy-efficient to allow a lifetime of even several years [1]. Still, the network must have adequate throughput and delay for the target application. Few environmental monitoring applications utilizing WSN have been published. In NIMS [2], PC104 based devices use suspension cables to obtain low interference links to sensors deployed on the ground. Sensors perform complex data processing and aggregation, but the network itself is not suitable for large scale deployments. The implementation in [3] has wireless nodes that are inserted in glaciers. The network activity is low as sensors transmits readings to a base station once per day, which allows successful use of solar panels. [4] presents results with 25 multi-hop PicoNodes that utilize Bluetooth physical layer with a custom data link layer and energy aware routing scheme [5]. S. Vassiliadis et al. (Eds.): SAMOS 2006, LNCS 4017, pp , c Springer-Verlag Berlin Heidelberg 2006

2 110 J. Suhonen et al. While the results in office environment show less than 4% packet loss, nodes have only few months lifetime with two 1400 mah batteries. Implementations that are based on Mica II motes [6] have been published in [7], [8], and [9]. [7] introduces a multihop network that measures temperature in a vineyard consisting of 65 nodes. [8] presents a WSN for bird observation during four month deployment with 150 nodes. [9] describes a surveillance system for moving vehicles and consists of 70 nodes. Although the Mica platform provides adequate processing and sensor capabilities for most sensor applications, its energy consumption is too large to allow lifetime of years with low-capacity batteries. In general, the problem with the presented proposals is that the network has either too short lifetime, or the usage is limited to certain applications. This paper presents a measurement network that uses our WSN, referred to as Tampere University of Technology WSN (TUTWSN). The network has been developed to address the challenges on WSNs, gather experiences on applying a WSN for practical purposes, and to create tools and methods for analyzing a large scale network. Furthermore, the network addresses how to cover large area with long-range multi-hop communications, while still having low energy consumption. The measurement network is presented in Fig. 1. Our ultra-low power hardware nodes utilize a long-range 433 MHz radio. A node has an energy-efficient and scalable TUTWSN protocol stack containing embedded sensor applications, TUTWSN routing protocol (TUTWSNR), and a TDMA-based Medium Access Control (MAC). The developed sensor applications provide control access to node configuration, gather temperature readings, and collect WSN self-diagnostics for network analyzation purposes. A sensor node referred to as sink collects data from other nodes by injecting interests into the network. An interest defines gathered data and collection intervals. A configuration software run on a PC is used to set the interests without tedious reprogramming. A gateway computer receives data from the sink and forwards it to a specifically designed remote database over TCP/IP through a IPSec/VPN tunnel. An easy-to-use web software has been implemented for viewing measurements and network status. TUTWSN is the first WSN to provide extensive tool set for network analyzation and simulation. Cross-layer design has been used in network protocols to achieve energyefficiency and scalability. Although TUTWSN is used to receive temperature and selfdiagnostics packets in this paper, the network itself is bidirectional and symmetric, and can be used to transfer any data. Unlike other published environmental monitoring Fig. 1. Measurement network consisting of TUTWSN and network analyzation facilities. Protocol stack in sensor nodes contains communication protocols and sensor applications.

3 Design, Implementation, and Experiments on Outdoor Deployment of WSN 111 WSNs, TUTWSN has a long lifetime, while not being limited to a specific application. Also, the network can be implemented on nodes having very low memory and processing capabilities. The network is verified by an extensive deployment in outdoor environment. In our measurements, we use interests that instruct each node to send both its temperature and diagnostics information twice per minute. It should be noted that TUTWSN is autonomous, and does not need any user software or a connection to backbone networks to operate. The rest of the paper is organized as follows. TUTWSN protocol stack is presented in Section 2. Section 3 presents data visualization and network analyzation software. The prototype hardware is presented in Section 4. Section 5 presents the deployment and discusses the obtained results. Section 6 concludes the paper. 2 TUTWSN Protocol Stack The TUTWSN protocol stack contains MAC, routing, and application layers. TUT- WSN uses clustered mesh topology. A cluster consists of a cluster head and subnodes as shown in Fig. 2. A cluster head can receive and transmit data to any node within communication range, while subnodes save energy by communicating only with the cluster head. A sensor node can change its role between cluster head and subnode. 2.1 MAC Layer TUTWSN MAC uses TDMA-based channel access, where each cluster operates on its own frequency (cluster channel). In addition, a common network channel is used to advertise and detect clusters. A cluster head maintains a periodic data exchange structure (access cycle) on its cluster channel as shown in Fig. 3. An access cycle consists of active and idle periods. Active period begins with a cluster beacon (CB) that is followed by a super frame. A super frame consists of two type of communication slots, reserved and ALOHA slots. Data is exchanged in reserved slots that provide collision free communication. Contention based ALOHA slots are used when joining a cluster and requesting a reservation. Cluster beacons signal cluster information, time schedules, and slot allocations within current the active period. The communication between cluster head and subnodes takes place in the active period. During the idle period cluster head sleeps, communicates with other clusters, and sends/receives periodically network beacons (NB) in the common network channel. A network beacon contains cluster timing and channel information that is required for other nodes to gain sync to the cluster. The access cycle length and the number of ALOHA and reserved slots are adjustable parameters. The optimal access cycle length depends on the amount of the network traffic, because it causes a trade-off between delay, throughput, and energy consumption. Current implementation has 4 ALOHA slots, 8 reserved slots, and 2 s access cycle length. The parameters are selected by the expected traffic on the network. 2.2 Network Layer In TUTWSNR, each node maintains a routing table to known sinks. A node selects the neighbor that minimizes cost to the sink as its next hop. The cost is calculated from

4 112 J. Suhonen et al. Fig. 2. TUTWSN clustered mesh network topology Fig. 3. TDMA-based channel access in TUT- WSN MAC the number of hops to the sink, remaining energy, link reliability, and thetransmission power required to reach the next hop cluster. Sender decided unicast transmissions is used to communicate with the next hop nodes. A node joins its next hop cluster in MAC layer, thus becoming a member of that cluster. The routing begins with a setup phase. Initially, the sink sends routeadvertisement to its neighbors. When a cluster head receives new advertisement, it calculates a new cost to the sink based on the cost-field included in the packet and the cost that is required to reach the nexthopcluster.ifthecostdecreases,thenodechangesitsnexthopandsendsadvertisement with updated cost to its neighbors. Eventually, all nodes have a route to the sink. Sink asks data from nodes by declaring an interest that defines the type of data that the sink is interested in and reply generation interval (once, on change, or period). Furthermore, an interest can be limited to a certain group of sensor nodes by defining an area code or time-to-live field (hops from sink) into the interest. The interest is broadcast in the reverse direction of established gradients. When a node does not have a connection to the sink, it performs periodic network scans. After the node has detected its neighbors, the node request them for routes and interests. In this way, a node that is not part of the network establishes a route to the sink, when it is brought in the communication range of a connected cluster. After the routes have been established, extensive network scans are not needed. 2.3 Embedded Sensor Applications Sensor nodes have three embedded sensor applications, sensor control, temperature, and WSN self-diagnostics. A light-weight operating system (OS) provides timer services and message passing between application layer and network stack. A sensor control application handles received control messages, thus allowing remote configuration of a node. Temperature and self-diagnostics applications generate packets, if the node has received an interest requesting for that data. Temperature application performs sensing on a digital sensor or reads value from analog-to-digital converter (ADC). The sensing interval is set in the related interest. Also, the interest includes measurement range that defines the values that cause generation of a reply packet to the sink that defined the interest. WSN self-diagnostics application maintains statistics of sensor voltage, buffer state, performed network scans, a list of known neighbor nodes, and transmitted/received traffic counters.

5 Design, Implementation, and Experiments on Outdoor Deployment of WSN TUTWSN Prototype Hardware The hardware architecture of the prototype is presented in Fig. 4. Arctic operating conditions set high requirements for components and batteries. Thus, all the components have extended temperature range ( 40 C). The operation of the TUTWSN node is controlled by a Microchip PIC18LF4620 MCU. Available 64 kb program memory and 4 kb data memory are sufficient for TUTWSN protocol stack and application algorithms. 1 kb EEPROM is used for non-volatile configuration data, such as node address and node status log. The controller has high energy-efficiency and versatile power saving modes. Utilized clock frequency is 4 MHz resulting 1 MIPS performance. An internal 10-bit ADC is utilized for monitoring battery energy status. Nordic Semiconductor nrf905 operating at 433 MHz license-free frequency band is used as radio transceiver. Totally 9 non-overlapping frequency channels are available between MHz and MHz. Radio data rate is 50 kbps, which is adequate for low data rate WSN applications. Internal transmission and reception buffers and Cyclic Redundancy Check (CRC) error detection reduce MCU loading. The radio has 100 dbm sensitivity and adjustable transmission power from 10 dbm to +10 dbm enabling long transmission range with efficient antennae. A folded dipole antenna is implemented directly on a printed circuit board. The antenna is selected due to a small size and low directivity. Antenna impedance is also near to the transceiver output impedance requiring only a minimum impedance matching. In addition, the antenna fits well in a slim tube enclosure selected for the nodes. Temperature sensing is implemented by a Dallas Semiconductor DS620 sensor interfaced with a digital I2C bus. The sensor has ±0.5 C accuracy from 0 to +70 Cand an operating temperature range of 55 Cto+125 C. A CR123A primary lithium battery specified with 3 V / 1600 mah capacity and from 40 Cto+60 C operating temperature is selected as power source. Battery voltage is converted to 2.25 V supply voltage by a MAX1725 linear regulator. According to our measurements, linear regulators suit well for the WSN node current profile, which consists of very short and high current bursts, while around 99% of the time node is in low power sleep mode. The implemented long-range TUTWSN prototype is presented in Fig. 5. The prototype is 255 mm x 21 mm sized, and encapsulated in a waterproof plastic enclosure. Fig. 4. TUTWSN prototype hardware architecture Fig. 5. Long-range TUTWSN prototype

6 114 J. Suhonen et al. The prototype consists of two separate boards, one for MCU, radio, voltage regulation and temperature sensor, and other extension board for battery, push button, LED and I/O connector. Also, other types of sensors and energy scavenging circuits can be easily implemented in the extension board increasing flexibility for various applications. 3.1 Measured Static Power Consumption and Radio Range The measured minimum power consumption of a prototype node at 3.0 V supply voltage is 31 µw, when all components are in sleep mode. The static power consumptions of individual components in active mode are presented in Table 1. According to the measurements, transceiver consumes significantly more power than the rest of prototype components. Transceiver in reception mode consumes 11.8 times the power of MCU. Data transmission at 10 dbm transmission power consumes 29.4 times the power of MCU. Thus, the transmission of 1 bit of data at 50 kbps data rate consumes energy equivalent to the execution of 647 instructions on MCU. For energy efficiency, both the transmission and reception time should be minimized. Radio transmission ranges with four power levels are measured outdoors in an open space and line-of-sight conditions. One prototype is placed 1.5 m above a snowy ground and configured to periodically transmit beacons with the four possible power levels, while another node is moved away from the transmitter around 2 m above the ground, and is receiving beacons. The measured power levels with minimum and maximum Table 1. The power consumption of the TUTWSN prototype components at 3.0 V supply voltage Component Power (mw) Energy MCU nj / instruction ADC nj / sample Temperature sensor µj/sample Radio RX nj / bit ( Radio TX@-10 dbm nj / bit ( Radio TX@+10 dbm µj/bit ( ) 256 bit packet, includes start-up trancient, MCU in sleep mode Out of range Required TX power (dbm) Maximum antenna gain Minimum antenna gain Distance (m) Fig. 6. Measured radio range versus transmission power

7 Design, Implementation, and Experiments on Outdoor Deployment of WSN 115 antenna gains are shown in Fig. 6. In an open space, a path loss increases quiteproportionally to the distance. At maximum gain, beacons transmitted at 10 dbm, 2 dbm, 6 dbm, and 10 dbm power levels are received until 58 m, 150 m, 250 mand375 mdistances, respectively. At minimum gain, the distances are 42 m, 83 m, 117 mand240 m, respectively. A measured antenna directivity is around 2 dbi. 4 User Software for Network Analyzation The configuration software communicates with a TUTWSN sink via a serial port interface. The software defines interests and writes them to the sink, which propagates Fig. 7. Configuration software showing the real-time status and active routes in the network. A dialog for setting interests is presented on right. Fig. 8. Web software for local residents showing measurement history

8 116 J. Suhonen et al. interests to the network. Also, the software can configure a node by sending/receiving packets to the node through the sink. The adjustable node parameters contain description, area code used to select nodes in interests, sensor role (subnode/cluster head), and network wide configuration parameters that have a trade-off between performance and energy-usage (e.g. access cycle length). The configuration software connects to a database for storing received data, and analyzing earlier measurements and WSN self-diagnostics history. The self-diagnostics history allows to detect bottlenecks on network, find erroneous nodes, and predict the lifetime of a node based on a battery voltage usage history. Figure 7 shows the capture of the software on GNU/Linux desktop environment. The web software is targeted at end-users and can be used with any device having a Web browser. The data is processed completely on server side with Java Servlets, which eases the requirements of the device using the service. The shown diagnostics information contain variables that affect the reliability of measured values, such as packet reception interval and the time of the last received measurement. The service starts with a selection of deployment area. Next, the map of the area containing sensors and last measured values is presented. A user can examine the measurement history of an individual sensor or a group of sensors, as shown in Fig Outdoor Deployment and Measurement Results The outdoor deployment consists of 19 nodes covering 2 km 2 area. The nodes do not generally have line of sight and are located over 1 m above the ground, typically bound in a tree as shown in Fig. 9. So far, the nodes have been deployedover 4 months from November 2005 to March Because the TUTWSN protocol stack is under development, few different revisions of the protocols have been used. Therefore, the presented results measured with current version are obtained since January Two of the nodes are subnodes, while the rest of the nodes act as cluster heads. (a) (b) Fig. 9. The typical deployment of nodes in trees

9 Design, Implementation, and Experiments on Outdoor Deployment of WSN Distribution of Traffic Geographic locations of deployed sensor nodes (identified with numbers 1-18) and the distribution of transmitted traffic on selected nodes is shown in Fig. 10. Average successfully transmitted traffic per node is presented in Fig. 11. The bandwidth usage between temperature and self-diagnostics data was equal. Control traffic (route advertisements and interests) used less than 1% of bandwidth. Since nodes originate the same amount of traffic, the difference in traffic volumes is caused by forwarded data. The nodes located in the edge of the network transmit less data, since routing algorithm tries to minimize required energy and hops, thus preferring routes through centrally located nodes. The nodes 17 and 18 do not forward data, because they were configured as subnodes. Node 6 experienced high link error rates due to bad location, which resulted into low traffic. A significant portion of the traffic to the sink is forwarded via node 2. Node 2 sent 91% of its traffic (8.7 bit/s) to the sink, which corresponds to over 1/3 of the traffic received by the sink (17.8 bit/s). Although node 2 is located relatively close to the sink, other nodes have a to the sink through it because the sink is inside a building while the node 2 is outside. Thus, nodes have better connection to the node 2. Sink / gateway Node 4 1% 72% 8 9% 18% 5 6% 4% 14% 15% 17% 44% 12 34% 66% m % 15 91% % 1% m Lake % 1% 11% 63% 20% 3 Fig. 10. Geographic locations of deployed sensor nodes and the distribution of transmitted traffic in selected nodes (node 16 is outside the picture, 250 m north of node 4) A node had only one active next hop route at a time. Route changes are caused by a broken next hop link due to communication errors, or changes in network conditions that caused routing to change next hop node. An average time between route changes

10 118 J. Suhonen et al. 10 Temperature (C) Nov 11-Dec Min Average Max 06-Jan Date 01-Feb 27-Feb Fig. 11. Average transmitted traffic per node Fig. 12. Measured minimum, maximum, and average day temperatures was 30 minutes, caused typically by routing algorithm balancing the network load. Typical hop count from a node to the sink was 4, while the maximum count was 8. The longest link is 1.1 km from node 3 to node 15. This is notably more than the measured communication ranges presented in Fig. 6. The difference is caused by reflections from the ground and buildings. The measured values were obtained in an opens space, while the deployment environment contains cliffs, icy surface of the lake, and other elements of terrain that can enhance the radio wave propagation. 5.2 Temperatures Day temperatures during the measurement period are shown in Fig. 12. The temperatures are averaged over readings from all sensor nodes. The temperature changes significantly and often radiply. For example, on January 23, 2006 the lowest temperature was 21.8 C, while the highest temperature on the next day was 5.2 C. The rapid changes can be seen in Fig. 13 that shows temperature per hour on a selected sensor. Temperature changes introduce challenges to the equipment and protocols. As the temperature alternates between below zero and above zero, the casing must be compact to prevent water damage. The MAC protocol must compensate clock drift, since the Temperature (C) Feb 24-Feb 25-Feb Date 26-Feb 27-Feb Temperature ( o C) Jan-27 Feb Temperature Voltage 2.7 Feb-12 Feb-20 Feb-28 Time Voltage (V) Fig. 13. Rapid environmental temperature changes on a selected sensor (node 8) Fig. 14. The effect of temperature to voltage (node 8)

11 Design, Implementation, and Experiments on Outdoor Deployment of WSN 119 oscillating frequency of crystals depends slightly on temperature. On the deployment region, temperature does not change evenly and some nodes might be inside buildings. 5.3 Energy Consumption A node measured its battery voltage with ADC. The voltage information was send to the sink in diagnostics packets. The energy consumption is calculated with drop in battery voltage. However, a short term development on voltage level cannot be used, as temperature affects the level. The effect is seen on Fig. 14 that shows temperatures and voltages measured on a selected node. Figure 15 presents voltages of two sensor nodes (2 and 8) and average voltage drop. The average voltage drop is calculated with linear regression, because the battery discharge rate is near linear between voltages 2.9 V and 2.6 V with presented temperatures and light load. The steeper voltage drop on node 2 is caused by heavy traffic. Figure 16 presents the voltages and incremental sum of transmitted and received packets on both nodes. The result indicates that it is beneficial to add a new node near a highly loaded node. In this way, the traffic between them averages and the network lifetime increases. 5.4 Discussion The experiments are providing vast amount of information about the real operation of WSN nodes and radio links in forested, low temperature outdoor environment. According to the experiments, a forest attenuates radio wave propagation significantly. Achieved radio range in a forest has been below 100 meters, while the longest measured range has been near 1.5 km. An edge of the forest seems to operate as a reflector causing notable gain in antenna radiation pattern. Also, radio wave propagation has been notably affected by snowfall, rain, humidity, temperature, and the frost and snow in trees, ground, and around the nodes. Hence, the quality of radio links and the network topology changes dynamically although nodes are stationary. Dynamic network topology affects significantly on the routing protocol operation. The experiments depict that the entire route to a sink must be considered in the route selection. As the difference between link qualities is very high, examining only next hop Voltage (V) Jan-27 Measured voltage (node 8) Measured voltage (node 2) Average voltage (node 8) Average voltage (node 2) Feb-2 Feb-8 Feb-15 Feb-21 Feb-28 Time Voltage (V) Jan-27 Feb-2 Packets (node 8) Packets (node 2) Voltage (node 8) Voltage (node 2) Feb-8 Feb-15Feb-21Feb-28 Time Rx/tx packets (10 6 ) Fig. 15. Decrease in the battery voltage of two selected sensors Fig. 16. Voltage decrease and transmitted/ received packets in sensors

12 120 J. Suhonen et al. quality when determining a route leads to unsatisfactory performance. Since the environment affects radio wave propagation significantly, cost-effective routing paths do not typically follow geometrically reasonable routes. The utilized cost-gradient based routing seems to work well in outdoor multi-hop networks without line-of-sight. The outdoor temperatures until March 2006 ranged from 31.5 Cto12.0 C. The high temperature variation reduced significantly the accuracy of crystals and thus, the accuracy of time synchronization. In the worst case scenario some nodes were inside buildings and other in outdoors resulting nearly 50 degrees difference in the operation temperature. The implemented hardware prototypes performed well during the whole test period. In some locations, nodes were not able to associate with the network for long periods of time. This was caused by poor radio link quality, not the hardware prototype itself. According to the reduction of battery voltages during the test period, an expected network lifetime is around 6 months. It should be noted that the network traffic consisted not only of temperature measurements but also diagnostics information, which increased load and decreased lifetime. Although the expected lifetime is satisfactory, some improvements for the energy efficiency will be made. The high variation in temperature decreased significantly the accuracy of TDMA synchronization, therefore increasing idle listening time prior to beacon receptions. A significant energy save is achieved by an algorithm that dynamically compensates the crystal drift. A further energy save is achieved by an algorithm, which adjusts the access cycle lengths for each cluster head according to traffic conditions. According to our energy analysis, these improvements will decrease network energy consumption to a quarter, thus increasing expected network lifetime to around 2 years. For comparison, this equals to 3.5 years with 2xAA batteries. 6 Conclusions This paper presents a complete measurement network based on a fully featured autonomous wireless sensor network. The network combines small energy consumption with adjustable network performance. The network protocols and hardware platform are energy-efficient, giving a node the lifetime of 6 months with a 1600 mah battery during deployment in harsh outdoor environment. The future work will focus on implementing different services, such as positioning into sensor networks. The presented network allows fast implementation and testing of new ideas in practice. References 1. Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: A survey on sensor networks. IEEE Communications Magazine 40 (2002) M. A. Batalin et al: Call and response: Experiments in sampling the environment. In: Proceedings of the 2nd international conference on Embedded Networked Sensor Systems. (2004) Martinez, K., Hart, J.K., Ong, R.: Environmental sensor networks. Computer 37 (2004) Reason, J.M., Rabaey, J.M.: A study of energy consumption and reliability in a multi-hop sensor network. Mobile Computing and Communications Review 8 (2004) 84 97

13 Design, Implementation, and Experiments on Outdoor Deployment of WSN Shah, R.C., Rabaey, J.M.: Energy aware routing for low energy ad hoc sensor networks. In: Wireless Communications and Networking Conference. (2002) Hill, J.L., Culler, D.E.: Mica: a wireless platform for deeply embedded networks. IEEE Micro 22 (2002) Beckwith, R., Teibel, D., Bowen, P.: Report from the field: Results from an agricultural wireless sensor network. In: Local Computer Networks. (2004) Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., Culler, D.: An analysis of a large scale habitat monitorin application. In: Proceedings of the 2nd international conference on Embedded Networked Sensor Systems. (2004) T. He et al: Energy-efficient surveillance system using wireless sensor networks. In: Proceedings of the 2nd international conference on Mobile system. (2004)