Evaluation of coordination strategies for heterogeneous sensor networks aiming at surveillance applications

A new challenge in the sensor network area is the coordination of heterogeneous sensors (with different sensing, mobility and computing capabilities) in an integrated network. This kind of sensor networks have clearly high relevance in surveillance systems, in which both low-end static ground sensor nodes and more sophisticated sensors carried by mobile platforms, such as unmanned aerial vehicles (UAVs), cooperate. This paper provides an analysis of two different strategies to guide the collaboration among the sensor nodes mentioned above, applied to area surveillance systems. The first analyzed problem is related to the choice of the UAV instance that will respond to a given alarm issued by a ground sensor node. The second issue is the estimation of the response time until any UAV can be engaged in handling an alarm and effectively handles it. Two strategies are introduced and compared: one based on a pheromone inspired approach and another based on utility functions inspired on risk profiles that models decisions of investors in the stock market.

[1]  Paul J. M. Havinga,et al.  Enabling mobility in heterogeneous wireless sensor networks cooperating with UAVs for mission-critical management , 2008, IEEE Wireless Communications.

[2]  Eric Bonabeau,et al.  Swarm Intelligence: A New C2 Paradigm with an Application to Control Swarms of UAVs , 2003 .

[3]  Marios M. Polycarpou,et al.  Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[5]  Tales Heimfarth,et al.  Experiments with Biologically-Inspired Methods for Service Assignment in Wireless Sensor Networks , 2008, BICC.

[6]  H. Van Dyke Parunak,et al.  Performance of digital pheromones for swarming vehicle control , 2005, AAMAS '05.

[7]  Christian Bettstetter,et al.  On the minimum node degree and connectivity of a wireless multihop network , 2002, MobiHoc '02.

[8]  Pini Gurfil,et al.  Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication , 2008, IEEE Transactions on Control Systems Technology.

[9]  Tales Heimfarth,et al.  ShoX: An Easy to Use Simulation Platform for Wireless Networks , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[10]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.