Presentation on theme: "RET Summer Internship Program UNT, 2009 Funded by NSF Grants: NSF IIS-0844342 DLR 0431818, CI-TEAM 0636421, CRI 0709285 Dawn Chegwidden Sharon Wood."— Presentation transcript:
RET Summer Internship Program UNT, 2009 Funded by NSF Grants: NSF IIS-0844342 DLR 0431818, CI-TEAM 0636421, CRI 0709285 Dawn Chegwidden Sharon Wood
Introduction Wireless Sensor Network (WSN) technology has a broad range of present and future applications for monitoring environmental conditions in the field. Applications may include water quality, soil moisture, rain events, medical monitoring, sprinkler systems, agriculture, and volcanic activity ,, The advantages of these WSN systems are that they can be set up in the field and remotely monitored from a laptop or office computer. A working system is cost effective and should require only periodic maintenance. Some potential problems that need to be considered when deploying a WSN system include vegetation humidity, temperature, weather, and topography Environmental conditions affect the sensor networks thus affecting robustness of the network system overall Some of the sensor issues when deploying the WSN in the field are: receiver signal strength (RSS), packet receiver rate (PRR), battery power and hopping distance between nodes. 
Distance Effects on Received Signal Strength(RSS) and Packet Receiver Rate (PRR) Our research will take place at two sites: (1) Discovery Park and (2) Pecan Creek Wastewater Treatment Facility Due to the importance of WSN and PRR in the design and implementation of a WSN in the field, our research will focus on: How does distance between motes affect RSS and PRR? How does topology effect the RSS and PRR? Does temperature and humidity affect the RSS and/or PRR?
Receiver Signal Strength (RSS) Receiver signal strength is important in WSN deployment RSS is a measure of the received radio frequency (RF) signal RF sensitivity is represented by the lowest RSS signal that retrieves complete data from a neighboring node Field tests are necessary to determine the “best” range for sensor topology Environmental habitats vary in topography and vegetation This can present a problem when deploying nodes in the field Therefore, an evaluation of the relationship between RSS,PRR and distance can help determine the placement of nodes in the field.
Packet Receiver Rate (PRR) Packet receiver rate is an indicator of the % data received from a neighboring node. The % of data being received is related to the RSS and the distance between sensor nodes. Data retrieval is critical in maintaining a workable WSN system If transmission strength is weak then the PRR will decrease and data could be lost and energy consumption will be higher due to re-transmission The goal is to find a balance between RSS, PRR, and distance so that data retrieval is close to 100 %
Our Wireless Sensor Technology Wireless sensors are small and compact. They contain a data acquisition board (small computer) and sensor board The sensor board contains various sensors for measuring temperature, humidity, RSS, PRR, and battery voltage Panasonic Alkaline Plus AA batteries were used to power the sensors in the field Solar panels have been used to extend battery life though they were not used in this experiment With duty cycling, the sensor alternates between an awake and sleep period which can extend the life of the sensor’s batteries.
Wireless Measurement System This is a wireless communications system in which every node has router capacity  2.4 GHz RF power: 3 dBm Sensitivity: -101 dBm Outdoor Range – 300 m Indoor Range – 50 m Battery – 2X AA batteries Iris XM2110 Oxbow: www.xbow.com
WSN Sensor Mote: Data Acquisition Board Features: MDA 300 A multi-functional board with temperature and humidity sensor It is designed to be used as the primary interface between iris board and external sensors It can be used for environmental and habitat monitoring such as humidity, temperature, and soil moisture.  Crossbow: www.xbow.com
The Experimental Design: Discovery Park Discovery Park is located on a large open filed with little vegetation behind the university research center. Our deployment network consisted of a two row, 150 x 10 meter, network matrix Each node was approximately 10 meters from its nearest neighbor nodes, making the furthest node 150 meters away Data collection was every 6 minutes Data collection began at 2 pm on July 25, 2009 and ran for 45 hours. 10 meters 150 meters
Discovery Park WSN Deployment Pictures WSN nodes were deployed in two rows of 15 10 meters apart and 10 meters across. Nodes were set out July 24th Due to a “bug” in the program, nodes were brought back in on July 25 th after 12:00 noon They were re-programmed and then deployed on July 26, at 2 pm
Experimental Design- Discovery Park 1514131211 109 87654321 30*29*282726252423*22*212019181716* non-transparent lid, transparent lid, *Experimental data was retrieved and analyzed from nodes 16,22, 23, 29 and 30 Slide adapted with permission from Laura De Lemos
Discovery Park: Data Graphs Node 23 The received signal strength and distance of node 23 was compared to its surrounding neighbors There is a negative correlation between neighboring sensors which is indicated by the red line The packet receiver rate and distance of node 23 was also compared to its surrounding neighbors PRR showed 100% data collection for most of the nodes up until about 70 m
Discovery Park: Data Graphs Node 23 This graph demonstrates how temperature and humidity will affect RSS Higher temperature can lead to a slight decrease in RSS There was a 2-5 dBm RSS loss as temperature goes from 25 0 C to 45 0 C. As humidity increases, the RSS becomes stronger.
Discovery Park: Data Graphs Node 23 This graph looks at the RSS from node 23 to its three neighbors (29,22, 16) It appears that on 8 PM on Sunday, July 27, there was 2-4 dBm jump Node 23 did not receive a signal from node 16 until 8 PM It is not clear why signal from node was not received until after 8:00 PM. One possibility might be an increase humidity and decreased temperature due to rain
Discovery Park: Data Graphs Node 23 This graph compares the trend between temperature, humidity, and battery voltage As temperature decreased humidity increased Battery voltage dropped from 3.0 V to 2.6 V Conclusion: Voltage drops due to usage but does not appear to be significantly affected by temperature or humidity
Discovery Park: Data Graphs Node 30 This graph is similar to Node 23 The RSS drops to -90 after 120 meters The PRR is not stable after 90 meters
Result Discussions: Evaluating the data, we conclude that a compromise between distance, RSS and PRR would result in the following : Experimental data seems to show that the RSS like cool, wet weather and as opposed to warm, dry weather This being the case, recommended distance between nodes would be 50 meters in areas with low vegetation RSS sensitivity -85 dBm or less for better PRR Using the recommended distance would allow for variable environmental factors associated with Texas summers
Part 2: Experimental Goals: Pecan Creek Wastewater Treatment Facility To measure the signal transmission strength and packet receiver rate and determine how it is affected by distance. To determine if temperature and humidity affect the signal transmission strength and packet receiver rate To compare the results between our initial tests at Discovery Park with data collected at Pecan Creek Recent data collected from Greenbelt Corridor was compared as well
Experimental Design: Pecan Creek We deployed 8 nodes and one gateway at Pecan Creek in an area with moderate vegetation The nodes were set up in a clearing beyond already existing nodes that were currently being used in another experiment. Nodes were set up randomly Sensor # 1 was used as our gateway sensor. The nodes were activated at Thursday, 9 AM, July 30, 2009 Nodes were collected on Monday, 9 AM, August 3, 2009
Pecan Creek Nodes Distance and Elevation Diagram Gateway 540 ft 543 ft 539 ft 541 ft 536 ft540 ft 543 ft 544 ft Transparent lid cover Non- transparent lid cover 15 m 16 m 20 m 15 m 20 m 17 m 16 m 21 m 16 m 11 m 18 m 20 m 16 m Elevation (ft) # 26 # 25* #1 # 23 # 27* # 5* # 21 # 24 # 22 Node Identification # *Data evaluated included notes 5,25, and 27
ID # 1 N33.19587- W097.07283 5 N33.19585- W097.07265 21 N33.19581- W097.07244 24 N33.19595- W097.07250 22 N33.19609- W097.07257 27 N33.19618- W097.07266 26 N33.19615- W097.07284 25 N33.19601- W097.07284 23 N33.19603- W097.07267 11636323437311523 51620172736372520 213620163345534332 243217162129383218 22342733211326 11 273736452913172516 263137533826171520 251525433226251516 23 20321811162016 Distance between nodes at Pecan Creek using GPS Coordinates Data evaluated from nodes 5, 21, 25, 26,and 27
Data Graph Comparison: RSS/PRR and Distance Discovery Par k- little vegetation Pecan Creek - mixed vegetation and clearings Greenbelt Corridor - dense vegetation D.P.* Node 23 P.C*. Node 27 * D. P (Discovery Park, P.C (Pecan Creek), GBC. (Green Belt Corridor) GBC*, Node 4 Dense Vegetation
Data Graph Comparison: RSS, Temperature and Humidity P.C. Node 27D.P. Node 23 Discovery Park : 2- 4 temperature dBm drop 4 – 6 % humidity increase Temperature range: 30 0 C to 45 0 C Pecan Creek 1 -2 dBm temperature drop 1 – 2 % humidity increase Temperature range: 20 0 C to 40 0 C GBC. Node 4 Greenbelt Corridor (Dense Vegetation) There was not a significant decrease in RSS due to temperature No significant increase in RSS in increased humidity
Data Graph Comparisons: RSS Over Time P.C. Node 27D.P. Node 23 GBC. Node 4 Data graphs indicate that the RSS varies over time.. Temperature, humidity, weather, and vegetation can affect the signal strength The graphs below are an interesting comparison between low, moderate, and dense vegetation It indicates the importance of distance and vegetation with respect to both RSS and PRR
Related Work Outdoor research in the Sonoran Desert showed a reduction in RSS during the hottest times of the day. There was also a noticeable daily variation in the signal strength. A linear decrease of about a 8 dBm was noted in RSS for the transition from 25 C to 65 C. While Texas temperature ranges were similar to the Sonoran Desert, there was significantly less humidity and different vegetation. Research on potato fields also concludes that radio waves propagate better with high humidity (i.e. night and rain).  UNT research at the Greenbelt Corridor ( area of dense vegetation) indicated that nodes can transmit 30 m with 95% PRR and 50 m with 80% PRR.Seasonal variations can also affect the RSS and PRR. Greenbelt Corridor – Jue (Jerry) Yang set out 15 nodes in the Greenbelt after examining our Discovery Park data. It was interesting to note that the graph for dense vegetation showed that RSS and PRR became unstable at 30 meters or less. 
Conclusions and Recommendations Pecan Creek and the Greenbelt are the areas being monitored by the TEO site. Both sites hope to expand their sensors to over 100 for monitoring temperature, humidity, and soil moisture. Discovery Park helped establish base line data in an area with very little vegetation and interference The Pecan site has some trees closer to the gateway and a wide clearing further away. The Greenbelt has many trees and few real clearings. Experimental data showed a distance between nodes of 70 meters at the Discovery Park and 45 meters at Pecan Creek. We suggest that due to the variable temperatures and humidity during Texas summers that the distance between motes should be 30 meters or less in forested areas and 50 meters in clearings. Data indicates that the next step should be to deploy a network using the above distances and evaluate the relationship between RSS and PRR while collecting real-time environmental data such as soil moisture
References:  Gupta, Sandeep, Gianni Giorgetti and Kenneth Bannister. "Wireless Sensor Networking for "Hot" Applications: Effects of Temperature on Signal Strength, Data collection and Localization." HotEmNets (2008).  Holland, Matthew M., Ryan G. Aures and Wendi B. Heinzelman. Experimental Investigation of Radio Performance in Wireless Sensor Networks. Rochester, New York: University of Rochester.  Srinivasan, Kannan and Philip Levis. RSSI is Under Appreciated. Stanford, CA: Stanford University.  Thelen, John, Dann Goense and Koen Langendoen. Radio Wave Propagation in Potato Fields. July 2009.  Yang, Jue, et al. Integration of Wireless Sensor Networks in Environmental Monitoring Cyber Infrastructure. Denton, Texas.  Yang, Jue. " “Greenbelt Corridor Data” Denton, TX, August 2009. Fernandez-Martinez, Roberto, J, Ordieres and A Gonzales-Marcos. "Low Power Wireless Sensor Networks in Industrial Environment." 12th WSEAS International Conference on SYSTEMS (2008): 643- 648.  Iris XM 2110/MDA 300 Data Sheets, http://www. xbow.com  Hussain, Sajid, and Md Shafayat Rahman. Received Signal Strength Indicator to Detect Node Replacement and Replication Attacks in Wireless Sensor Networks. Wolfville, NS, Canada, Acadia University.
Acknowledgements: We started out as two non-technical science teachers. However, through the course of this internship we learned a lot. Not just about the technical aspects of wireless sensors, but about doing a research experiment, evaluating data, and presenting our conclusions, The hands-on approach was extremely valuable not only for us, but for our students. We truly want to thank the Electrical Engineering Department for the very unique experience. We plan to carry this back to our individual campuses and hopefully encourage high school students to consider a careers in electrical engineering.
Heartfelt thanks for making this experience possible: Dr. Miguel Acevedo, Interim Chair, Mechanical and Energy, Engineering Dr. Shenli Fu, Professor, Electrical Engineering Dr. Oscar Garcia, Professor and Founding Dean of Electrical Engineering Dr. Rubio Garcia, Associate Dean of Outreach and Public Relations Dr. Xinrong Li, Professor, Electrical Engineering Dr. Yan Huang, Associate Professor, Computer Science and Engineering Dr. Murali Varanasi, Professor and Department Chair, Electrical Engineering Mitchell Horton, Graduate Student Ning (Martin) Xu, Graduate Student Nitya Kmdukuri, Technical Support
Special Thanks to Jue (Jerry) Yang Without your help this learning experience would not have been possible. Jerry – You’re the BEST!
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