Abstract
Airborne sensor platforms are becoming increasingly significant for both civilian and military operations; yet, at present, their sensors are typically idle for much of their flight time, e.g., while the sensor-equipped platform is in transit to and from the locations of sensing tasks. The sensing needs of many other potential information consumers might thus be served by sharing such sensors, thereby allowing other information consumers to opportunistically task them during their otherwise unscheduled time, as well as enabling other improvements, such as decreasing the number of platforms needed to achieve a goal and increasing the resilience of sensor tasks through duplication. We have implemented a prototype system realizing these goals in Mission-Driven Tasking of Information Producers (MTIP), which leverages an agent-based representation of tasks and sensors to enable fast, effective, and adaptive opportunistic sharing of airborne sensors. Using a simulated large-scale disaster-response scenario populated with publicly available Geographic Information System (GIS) datasets, we demonstrate that correlations in task location are likely to lead to a high degree of potential for sensor-sharing. We then validate that our implementation of MTIP can successfully carry out such sharing, showing that it increases the number of sensor tasks served, reduces the number of platforms required to serve a given set of sensor tasks, and adapts well to radical changes in flight path.
- Jude Allred, Ahmad Bilal Hasan, Saroch Panichsakul, William Pisano, Peter Gray, Jyh Huang, Richard Han, Dale Lawrence, and Kamran Mohseni. 2007. SensorFlock: An airborne wireless sensor network of micro-air vehicles. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys’07). ACM, New York, NY, 117--129. Google ScholarDigital Library
- Jacob Beal, Danilo Pianini, and Mirko Viroli. 2015. Aggregate programming for the internet of things. IEEE Computer 48, 9 (2015), 22--30. 1364-503XGoogle ScholarDigital Library
- Jacob Beal, Kyle Usbeck, Joseph Loyall, and James Metzler. 2016a. Opportunistic sharing of airborne sensors. In Proceedings of the 12th International Conference on Distributed Computing in Sensor Systems. 25--32.Google ScholarCross Ref
- Jacob Beal, Kyle Usbeck, Joseph Loyall, Mason Rowe, and James Metzler. 2016b. Adaptive task reallocation for airborne sensor sharing. In Proceedings of the Workshop on Engineering Collective Adaptive Systems. 168--173.Google ScholarCross Ref
- David G. Bel, Frank Kuehne, Chris Maxwel, Randy Kim, Kushyar Kasraie, Tom Gaskins, Patrick Hogan, and Joe Coughlan. 2007. NASA world wind: Opensource GIS for mission operations. In Proceedings of the IEEE Aerospace Conference. 1--9.Google ScholarCross Ref
- Richard A. Burne, Anna L. Buczak, Vikram R. Jamalabad, Ivan Kadar, and Eitan R. Eadan. 1999. Self-organizing cooperative sensor network for remote surveillance. In Enabling Technologies for Law Enforcement and Security. International Society for Optics and Photonics, 124--134.Google Scholar
- Marco Carvalho, Adrian Granados, Kyle Usbeck, Joseph Loyall, Matthew Gillen, Asher Sinclair, and James Hanna. 2011. Integrated information and network management for end-to-end quality of service. In Proceedings of MILCOM. 1604--1609.Google ScholarCross Ref
- Chee-Yee Chong and Srikanta P. Kumar. 2003. Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE 91, 8 (2003), 1247--1256.Google ScholarCross Ref
- Kevin A. Delin. 2002. The sensor web: A macro-instrument for coordinated sensing. Sensors 2, 7 (2002), 270--285.Google ScholarCross Ref
- Shlomi Dolev. 2000. Self-Stabilization. MIT Press. Google ScholarDigital Library
- Shane B. Eisenman, Nicholas D. Lane, and Andrew T. Campbell. 2008. Techniques for improving opportunistic sensor networking performance. In Distributed Computing in Sensor Systems. Springer, 157--175. Google ScholarDigital Library
- Barbara Essendorfer and Wilmuth Mueller. 2009. Interoperable sharing of data with the coalition shared data (CSD) server. In North Atlantic Treaty Organization (NATO)/Research and Technology Organization (RTO): C3I in Crisis, Emergency and Consequence Management. 7--1--7--12.Google Scholar
- Lukas Esterle, Peter R. Lewis, Xin Yao, and Bernhard Rinner. 2014. Socio-economic vision graph generation and handover in distributed smart camera networks. ACM Transactions on Sensor Networks (TOSN) 10, 2 (2014), 20. Google ScholarDigital Library
- Maria Fox, Alfonso Gerevini, Derek Long, and Ivan Serina. 2006. Plan stability: Replanning versus plan repair. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS). 212--221. Google ScholarDigital Library
- Matthew Gillen, Joseph Loyall, Kyle Usbeck, Kelly Hanlon, Andrew Scally, Joshua Sterling, Richard Newkirk, and Ralph Kohler. 2012. Beyond line-of-sight information dissemination for force protection. In Proceedings of the Military Communications Conference (MILCOM).Google ScholarCross Ref
- Matthew Gillen, Joseph P. Loyall, and Joshua Sterling. 2011. Dynamic quality of service management for multicast tactical communications. In Proceedings of the 14th IEEE Computer Society Symposium on Object/Component/Service-oriented Real-time Distributed Computing (ISORC). 11--18. Google ScholarDigital Library
- Prem Prakash Jayaraman, Charith Perera, Dimitrios Georgakopoulos, and Arkady Zaslavsky. 2013. Efficient opportunistic sensing using mobile collaborative platform mosden. In Proceedings of the Inernational Conference on Collaborative Computing: Networking, Applications and Worksharing. IEEE, 77--86.Google ScholarCross Ref
- Andreas Krause, Eric Horvitz, Aman Kansal, and Feng Zhao. 2008. Toward community sensing. In Proceedings of the 7th International Conference on Information Processing in Sensor Networks. IEEE Computer Society, 481--492. Google ScholarDigital Library
- Michael J. Kristan, Jeffrey T. Hamalainen, Douglas P. Robbins, and Patrick Newell. 2009. Cursor-on-Target Message Router User’s Guide. Technical Report MITRE Product--MP090284. MITRE.Google Scholar
- Nicholas D. Lane, Shane B. Eisenman, Mirco Musolesi, Emiliano Miluzzo, and Andrew T. Campbell. 2008. Urban sensing systems: Opportunistic or participatory? In 9th Workshop on Mobile Computing Sys. and App. 11--16. Google ScholarDigital Library
- Qilian Liang, Xiuzhen Cheng, S. C. Huang, and Dechang Chen. 2014. Opportunistic sensing in wireless sensor networks: Theory and application. IEEE Transactions on Computers 63, 8 (2014), 2002--2010. Google ScholarDigital Library
- Joseph Loyall, Matthew Gillen, Jeffrey Cleveland, Kyle Usbeck, Joshua Sterling, Richard Newkirk, and Ralph Kohler. 2012. Information ubiquity in austere locations. Procedia Computer Science 10 (2012), 170--178. ANT 2012.Google ScholarCross Ref
- Chaoying Ma and Jean Bacon. 1998. COBEA: A CORBA-based event architecture. In Proceedings of the 4th USENIX Conference on Object-Oriented Technologies and Systems. Volume 4. 9--9. Google ScholarDigital Library
- Huadong Ma, Dong Zhao, and Peiyan Yuan. 2014. Opportunities in mobile crowd sensing. IEEE Communications Magazine 52, 8 (2014), 29--35.Google ScholarCross Ref
- William Miller. 2004. Cursor-on-target. Military Information Technology Online 8, 7 (2004).Google Scholar
- B. Nebel and J. Koehler. 1995. Plan reuse versus plan generation: A theoretical and empirical analysis. Artificial Intelligence 76, 1--2 (1995), 427--454. Google ScholarDigital Library
- OMG Data Distribution Service Portal. Retrieved March 6, 2012 from http://portals.omg.org/dds/.Google Scholar
- H. Van Dyke Parunak and Sven Brueckner. 2007. Concurrent modeling of alternative worlds with polyagents. In Multi-Agent-Based Simulation VII. Springer, 128--141. Google ScholarDigital Library
- Danilo Pianini, Mirko Viroli, and Jacob Beal. 2015. Protelis: Practical aggregate programming. In Proceedings of the 2015 ACM Symposium on Applied Computing. 1846--1853. Google ScholarDigital Library
- Doug Robbins. 2007. Unmanned aircraft operational integration using MITRE’s cursor on target. The Edge 10, 2 (2007).Google Scholar
- David S. Rosenblum and Alexander L. Wolf. 1997. A design framework for internet-scale event observation and notification. SIGSOFT Software Engineering Notes 22, 6 (Nov. 1997), 344--360. Google ScholarDigital Library
- M. Schneider. 1993. Self-stabilization. Computing Surveys 25 (1993), 45--67. Google ScholarDigital Library
- Xiang Sheng, Xuejie Xiao, Jian Tang, and Guoliang Xue. 2012. Sensing as a service: A cloud computing system for mobile phone sensing. In Sensors, 2012 IEEE. IEEE, 1--4.Google Scholar
- Minho Shin, Cory Cornelius, Dan Peebles, Apu Kapadia, David Kotz, and Nikos Triandopoulos. 2011. AnonySense: A system for anonymous opportunistic sensing. Pervasive and Mobile Computing 7, 1 (2011), 16--30. Google ScholarDigital Library
- Kyle Usbeck, Matthew Gillen, Joseph Loyall, Andrew Gronosky, Joshua Sterling, Ralph Kohler, Kelly Hanlon, Andrew Scally, Richard Newkirk, and David Canestrare. 2015. Improving situation awareness with the android team awareness kit (ATAK). In SPIE Defense+ Security. International Society for Optics and Photonics, 1--22.Google Scholar
- Ming Xiong, Jeff Parsons, James Edmondson, Hieu Nguyen, and Douglas Schmidt. 2007. Evaluating technologies for tactical information management in net-centric systems. In Proceedings of the Defense and Security Symposium. International Society for Optics and Photonics, 1--11.Google ScholarCross Ref
Index Terms
- Adaptive Opportunistic Airborne Sensor Sharing
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