A sensor resource management system that employs fuzzy logic to provide the utility functions to a game theoretic approach is developed. The application looks at a virtual fence problem where several unattended ground sensors are placed in remote locations to act as virtual sentries. The goal of the approach is to maximize the battery life while tracking targets of interest. This research also considers the incorporation of uncertainty into the fuzzy membership functions. Both type-2 fuzzy logic and the use of conditional fuzzy membership function are employed. The type-2 fuzzy logic is employed in the case of acoustical sensor tracking accuracy degradation, while the condition-based membership functions are used to adapt to different conditions, such as environmental conditions and sensor performance degradation, over time. The resource management process uses fuzzy logic to determine which of the sensor systems on a sensor pod is used to provide initial classification of the target and which sensor or sensors are to be used in tracking and better classifying the target if it is determined to be of value to the mission. The three different approaches are compared to determine when the best times for the more complex approaches are warranted. 1. Introduction A resource allocation technique for extending the battery life of unattended ground sensors (UGS) while still providing tracking and classification capabilities can be applied to the increasingly remote monitoring activities of UGS systems. Fuzzy logic approaches to this resource allocation can incorporate fixed membership functions [1, 2] and be advanced by applying type-2 fuzzy logic [3], or conditional fuzzy membership functions can be used to model the sensors’ capabilities. The use of UGS has become increasingly necessary in environmental protection and other applications, as noted in [4, 5]. In remote locations, the use of on-site personnel to monitor illegal or dangerous activities is too costly economically and can be physically impractical. These activities, such as illegal mining [6], illegal cultivation [7], illegal logging [8], poaching, dumping of toxic chemicals, criminal activity [9], for example, smuggling, and recreational uses [10], such as shooting and off-highway vehicle use where such use is illegal and causes severe environmental damage, are becoming more common. Gates, fences, and signs have proven ineffective at discouraging much of this illegal use [11]. To provide a continual monitoring presence and be able to deploy sparse enforcement resources have required the employment
References
[1]
S. C. Stubberud and K. A. Kramer, “Sensor network activation with a fuzzy-based game theory,” in Proceedings of the IEEE International Instrumentation and Measurement Technology Conference, pp. 1133–1138, Graz, Austria, 2012.
[2]
S. C. Stubberud and K. A. Kramer, “Unattended ground sensor resource allocation using a fuzzy game,” in Proceedings of the International Conference on Systems Engineering (ICSE '12), pp. 1–6, Coventry, UK, 2012.
[3]
S. C. Stubberud and K. A. Kramer, “Resource allocation with uncertain sensor models,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1–7, Brisbane, Australia, 2012.
[4]
R. R. Brooks, P. Ramanathan, and A. M. Sayeed, “Distributed target classification and tracking in sensor networks,” Proceedings of the IEEE, vol. 91, no. 8, pp. 1163–1171, 2003.
[5]
H. K. Cordell, C. J. Betz, G. Green, and M. Owens, “Off-highway vehicle recreation in the United States, regions, and states: a national report from the national survey on recreation and the environment (NSRE),” US Forest Service, pp. 1–2, 2005.
[6]
M. El Gammal and M. Eltoweissy, “Towards aware, adaptive and autonomic sensor-actuator networks,” in Proceedings of the 5th IEEE international Conference on Self-Adaptive and Self-Organizing Systems, pp. 210–211, Ann Arbor, Mich, USA, 2011.
[7]
D. S. Hammond, V. Gond, B. De Thoisy, P. M. Forget, and B. P. E. DeDijn, “Causes and consequences of a tropical forest gold rush in the Guiana Shield, South America,” Ambio, vol. 36, no. 8, pp. 661–670, 2007.
[8]
M. Mallery, “Marijuana National Forest: Encroachment on Public Lands for Cannabis Cultivation,” Berkeley Undergraduate Journal, vol. 23, no. 2, pp. 1–50, 2010.
[9]
W. F. Laurance, “A crisis in the making: responses of Amazonian forests to land use and climate change,” Trends in Ecology and Evolution, vol. 13, no. 10, pp. 411–415, 1998.
[10]
J. F. Tynon and D. J. Chavez, “Crime in national forecasts: a call for research,” Journal of Forestry, vol. 104, no. 3, pp. 154–157, 2006.
[11]
K. J. Hammer, “Gate-crashing: road closure gates cannot effectively eliminate trespass,” Swan View Coalition Report, pp. 1–8, 2001.
J. M. Mendel, R. I. John, and F. Liu, “Interval type-2 fuzzy logic systems made simple,” IEEE Transactions on Fuzzy Systems, vol. 14, no. 6, pp. 808–821, 2006.
[14]
X. Hu, P. Bonissone, and R. Subbu, “Robust model selection decision-making using a fuzzy supervisory approach,” in Proceedings of the IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM '09), pp. 126–132, April 2009.
[15]
D. Zhai and J. Mendel, “Centroid of a general type-2 fuzzy set computed by means of the centroid flow algorithm,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 1–8, 2011.
[16]
A. Ezekiel, “Advancements in ground moving target identification in radar vision,” in Proceedings of the National Fire Control Symposium, pp. 1–5, 2011.
[17]
S. Blackman, Multiple-Target Tracking with Radar Applications, Artech House, Norwood, Mass, USA, 1986.
