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Cooperative Control of Distributed Autonomous Vehicles in Adversarial Environments 2002 MURI Minisymposium Ameesh Pandya Prof. Greg Pottie 2002 MURI Minisymposium Overview • Fault tolerant communication network supporting hierarchical distributed communication network. • Robust network algorithm for highly dynamic mobile nodes (say, UAVs). • Providing minimum communications between mobile nodes to minimize the probability of jamming. • Working closely with Prof. Speyer’s group to develop the communication model according to control traffic. 2002 MURI Minisymposium Wireless Communication Model Application Layer Transport layer IP Network Link Layer MAC Layer Radio Channel 2002 MURI Minisymposium Our Concentration Application Layer Transport layer IP Network Link Layer MAC Layer Radio Channel 2002 MURI Minisymposium Area of Concentration QoS Constraints for Control Traffic • Data Rate for the control traffic : 2 Mbps – This could be considered as the upper bound. – Achieved by using 2 Mbps modem. • Latency for control traffic: 0 – 100 ms – Worst latency is 100ms for control traffic. 2002 MURI Minisymposium Channel Capacity • Capacity constraint for the control traffic. • Channel capacity in terms of received and transmitted power, jamming power, spread factor, bit rate. • Goal is to know the reliable transmitting distance between the nodes at 2Mbps for the given parameters. 2002 MURI Minisymposium Channel Capacity • Assumptions: – Isotropic antenna – Spread spectrum modulation. – For Low probability of intercept (LPI), Pr/WsN0 = 0.1, where Pr is the received power and Ws is the band width of spread spectrum signal. 2002 MURI Minisymposium Channel Capacity • Shannon’s Equation: Pr C W log 2 (1 ) WN 0 where, Pr is the received power, W is the channel bandwidth. • For isotropic antenna, Pt Pr 4d 2 where Pt is the transmitted power • Spread factor, f = Ws/R, where Ws is the band width of the spread spectrum signal and R is the information rate in bps. 2002 MURI Minisymposium Channel Capacity • If we do not consider broadband jammer, then C W log 2 (1 1 Pt ) WN 0 4d 2 • In presence of broadband jammer capacity becomes: C W log 2 [1 Pt 4d 2 1 ] 5 Jav' Eb W ( N0 2 2 ) r f R N 0 ' where, J av is the average jamming power at distance r from the receiver • If we use CDMA, then in presence of jammer for Nu simultaneous users, channel capacity is given by (assuming identical signal power): C W log 2 [1 Pt 1 ] 4d 2 W ( N 2 N u 1 E ) 0 b f 2002 MURI Minisymposium Simulation Result • • • • 2002 MURI Minisymposium Achievable transmitting distance at 2 Mbps for different values of transmitting power. Here, the transmitting power is assumed to be 1 Watt and 2 Watts. Assuming available channel bandwidth to be 100Mbps. Simulation carried out with the assumption of ideality i.e. no jammer and propagation loss. MAC Layer Clustering • Considering n nodes (UAVs). • Selecting clusters (cluster heads). • Each cluster having back bone node. • Using optimal cluster algorithm. 2002 MURI Minisymposium Future Objectives • Developing clustering algorithms for mobile nodes in dynamic environment. • Clustering algorithms: – UAV - UAV – UAV - UGV • Obtaining simulation results on the performance and robustness of the algorithm. 2002 MURI Minisymposium Insight • The solution to the communication network model for this particular problem “may” be very close to IPv6. • Looking into this possibility. 2002 MURI Minisymposium