Number : WUCSE-2004-73 2004-1201 Mobile Agent Middleware for Sensor Networks : An Application Case Study

Agilla is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in wireless sensor networks (WSNs). Agilla allows users to create and inject special programs called mobile agents that coordinate through local tuple spaces, and migrate across the WSN performing application-specific tasks. This fluidity of code and state has the potential to transform a WSN into a shared, general-purpose computing platform capable of running several autonomous applications at a time, allowing us to harness its full potential. We have implemented and evaluated a fire tracking application to determine how well Agilla achieves its goals. Fire is modeled by agents that gradually spread throughout the network, engulfing nodes by inserting fire tuples into their local tuple spaces. Fire tracker agents are then used to form a perimeter around the fire. Using Agilla, we were able to rapidly create and deploy 47 byte fire agents, and 100 byte tracker agents on a WSN consisting of 26 MICA2 motes. Our experiments show that the tracker agents can form an 8-node Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Mobile Agent Middleware for Sensor Networks: An Application Case Study Chien-Liang Fok, Gruia-Catalin Roman, and Chenyang Lu Washington University in Saint Louis Saint Louis, Missouri 63130 E-mail: liang, roman, lu@cse.wustl. edu Abstract— Agilla is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in wireless sensor networks (WSNs). Agilla allows users to create and inject special programs called mobile agents that coordinate through local tuple spaces, and migrate across the WSN performing application-specific tasks. This fluidity of code and state has the potential to transform a WSN into a shared, general-purpose computing platform capable of running several autonomous applications at a time, allowing us to harness its full potential. We have implemented and evaluated a fire tracking application to determine how well Agilla achieves its goals. Fire is modeled by agents that gradually spread throughout the network, engulfing nodes by inserting fire tuples into their local tuple spaces. Fire tracker agents are then used to form a perimeter around the fire. Using Agilla, we were able to rapidly create and deploy 47 byte fire agents, and 100 byte tracker agents on a WSN consisting of 26 MICA2 motes. Our experiments show that the tracker agents can form an 8-node perimeter around a burning node within 6.5 seconds and that it can adapt to a fire spreading at a rate of 7 seconds per hop. We also present the lessons learned about the adequacy of Agilla’s primitives, and regarding the efficiency, reliability, and adaptivity of mobile agents in a WSN. Agilla is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in wireless sensor networks (WSNs). Agilla allows users to create and inject special programs called mobile agents that coordinate through local tuple spaces, and migrate across the WSN performing application-specific tasks. This fluidity of code and state has the potential to transform a WSN into a shared, general-purpose computing platform capable of running several autonomous applications at a time, allowing us to harness its full potential. We have implemented and evaluated a fire tracking application to determine how well Agilla achieves its goals. Fire is modeled by agents that gradually spread throughout the network, engulfing nodes by inserting fire tuples into their local tuple spaces. Fire tracker agents are then used to form a perimeter around the fire. Using Agilla, we were able to rapidly create and deploy 47 byte fire agents, and 100 byte tracker agents on a WSN consisting of 26 MICA2 motes. Our experiments show that the tracker agents can form an 8-node perimeter around a burning node within 6.5 seconds and that it can adapt to a fire spreading at a rate of 7 seconds per hop. We also present the lessons learned about the adequacy of Agilla’s primitives, and regarding the efficiency, reliability, and adaptivity of mobile agents in a WSN.