Available online at www.sciencedirect.com
Procedia Engineering 47 (2012) 908 – 911
Proc. Eurosensors XXVI, September 9-12, 2012, Kraków, Poland
Simulation and Implementation of an Attractiveness based On-Demand Routing Algorithm for Wireless Sensor Networks Martin Brandla, , Karlheinz Kellnera, Christian Fabianb b
a Danube University Krems, Dr. Karl Dorrek Str. 30, 3500 Krems, Austria University of Applied Sciences, Matthias Corvinus-Str. 15, 3100 St. Poelten, Austria
Abstract A sensor network with an on-demand data centric routing strategy is presented. The routing between the sensor nodes and the data sink(s) (base station/s) is based on attractiveness-metric (Pa) gradients for the data forwarding decisions within the network. The performance of Pa based routing was simulated by the multi-agent simulation tool NetLogo and compared with the popular and widely used Dynamic Source Routing (DSR) algorithm. Additionally, to test the long term stability and the performance of the Pa based routing algorithm, several sensor nodes for local temperature and humidity measurement were placed in wine yards to monitor the risk of downy mildew infection. © 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Symposium Cracoviense © 2012 by Elsevier Ltd.license. Sp. z.o.o. Published Open access under CC BY-NC-ND Keywords: Wireless sensor network; NetLogo; On-demand routing; Data centric routing
1. Introduction Sensor nodes of Wireless Sensor Networks (WSNs) are designed to be energy saving, cheap and robust devices. The sensor nodes can form ad-hoc networks to deliver the recorded sensor data sets to one ore more destinations called data sinks. Sensor nodes are typical not in direct contact with the data sinks, therefore multi-hop routing algorithms are necessary to direct the sensor data to addressed destinations. Multi-hop routing protocols for wireless sensor networks have been proposed in several works. Most common table driven reactive (on-demand) multi-hop protocols are AODV [1] and DSR [2-3]. In this
Martin Brandl. Tel.: +43 2732 893 2601; fax: +43 2732 893 4600. E-mail address:
[email protected].
1877-7058 © 2012 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Symposium Cracoviense Sp. z.o.o. Open access under CC BY-NC-ND license. doi:10.1016/j.proeng.2012.09.294
Martin Brandl et al. / Procedia Engineering 47 (2012) 908 – 911
protocols the path discovery procedure terminates either when a route has been found or no route is available after examination of all route permutations. Compared to the proactive routing protocols e.g. DSDV [4] and WRP [5], the routing information from each node to every other node in the network is maintained at all times and is found by flooding the network with routing discovery procedures. Therefore on-demand routing protocols need more maintenance affords as well as proactive routing protocols need more overhead in the rout discovery process. Our developed attractiveness-metric gradient based routing strategy (Pa), provides a new concept for a data-centric WSN routing protocol. The attractiveness-metric Pa consid-ers the actual energy states of neighboring nodes for the routing decision and there-fore ensures energy aware operation. 1.1. Principle of Pa based routing The Pa based routing is based on a listen/sleep scheme providing enhanced energy saving and requires therefore synchronization among all neighboring nodes. The initial synchronization of the network is triggered by the base station which floods the network with “Sync” messages which are spread by the sensor nodes. All unsynchronized nodes stay in a receive mode until they recognize a “Sync” message to initialize their sleep and wake-up timers. The initial attractiveness level Pa of the network nodes is simultaneous distributed with the “Sync” messages and weighted by the nodes individual characteristics (free buffer memory, energy status etc.). The attractiveness level is reduced with each hop by a predefined hop to hop decrease rate and ensures therefore a loop free data centric routing. If one sensor node wants to send data sets to the base station, a decision has to be made to determine which neighbor node should become the next hop for the collected data. This decision is called routing decision. Therefore, a node sends out a Route Discovery Query (RDQ) to its neighboring nodes. Before the neighbors give an answer, they make a preprocessing of their attractiveness-value Pa. The highest last received attractiveness-level from the neighbors is weighted with the current energy status and sent to the requesting node, called Route Discovery Reply (RDR), where it is also post-processed. The node which offers the highest remaining attractiveness-metric becomes the next hop for the data transmission. A detailed description of the routing algorithm is given in Brandl et.al. [6]. 2. Methods A NetLogo [7-8] based simulation environment was designed to proof the performance of the Pa based routing algorithm and to compare the results with common used routing strategies. For a first proof of concept, Pa based routing was compared with the wide used on-demand and table driven DSR algorithm. Pa based routing is an easy to implement source based on-demand routing algorithm without the need for any stored routing tables or routing maintenance, and under same preconditions yields a better network performance than DSR. A clear limitation for Pa based routing is given by the data packet forwarding along a dynamic attractiveness gradient which only has its source by the base station. Therefore data packets can only be routed from the data source to the base station and not to other sensor nodes which is generally possible in DSR. Both routing algorithms showed a good data transmission performance for low and medium node densities within the communication range. At high node densities, Pa based routing achieved a higher data throughput as well as a higher average data transmission rate and for all node densities a lower collision to transmission ratio. In general, we found that in most scenarios where the sensor nodes are randomly distributed within the simulation area, Pa based routing gained a better performance than DSR.
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3. Practical stability and performance test of the sensor network To test the long term stability and the performance of the Pa based routing algorithm, we placed 12 nodes for local temperature and humidity monitoring in wine yards. We have placed our sensor nodes in one of the most recognized wine growing regions of Austria called the Wachau. During the growth of grapes, the infection of grapevines with downy mildew and its outbreak is feared by the farmers. It is known, that after infection the downy mildew fungi plasmopara viticola start growing under warm humid conditions on the leave of grapevines. To stop the growth of downy mildew, the grapevines can be treated with fungicides but each application is costly and has a problematic impact on the environment. By monitoring the local humidity and temperature data in the vine yards, we can classify the monitored area into regions with a higher and a lower risk potential on fungi growth. This determined data can help farmers to apply fungicides only at high risk regions reducing therefore the overall amount of these agents. The growth of downy mildew is manly given at temperatures and humidity’s where the dew point (100% humidity) is reached. Our limit to classify an area as a high risk region was given
for local measured humidity’s grater than 90% and a minimum rainfall of 10 mm/m2 over the last 24 hours. In Figure 1 an aerial view of the monitored vineyards as well as the locations of the sensor nodes are shown. The black lines between the sensor nodes depict the routing paths from the nodes to the base station where the sensor data sets are collected.
Fig. 1. Sensor locations within the monitored vineyard area.
Fig. 2. Logged temperature, humidity, rainfall and barometric pressure on different sensor locations.
The data sets which are received at the base station (temperature, humidity, barometric pressure and the rainfall) are displayed on a user interface and shown in Figure 2. The recorded data of each sensor can be displayed individually over a selectable time span. The barometric pressure as well as the rainfall was only measured at the base station where the humidity and the temperature were measured at each sensor node. 4. Conclusions A wireless sensor network utilizing Pa based routing was presented. To proof the stability of the routing algorithm as well as the performance of the network nodes, an outdoor experiment for detecting the growth risk of downy mildew was done. Sensor nodes are place within different vine yards for logging
Martin Brandl et al. / Procedia Engineering 47 (2012) 908 – 911
the local temperature and humidity profiles. From these profiles a risk map for the growth of downy mildew can be calculated and can be used for a local treatment of the affected regions. Acknowledgements The authors would like to thank the government of Lower Austria and the European Commission (EFRE) for their support of the project (Project ID: WST3-T-91/004-2006).
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