Professor Izhak Rubin Electrical Engineering Department UCLA August 2005 rubin@ee.ucla

1. Unmanned-Vehicle Aided Multi-Tier Autonomous Intelligent Wireless Networks:Mobile Backbone Networks Professor Izhak Rubin Electrical Engineering Department UCLA August 2005 rubin@ee.ucla.edu

2. FORCEnet Architecture using AINS Technologies Development of AINS system architecture for realizing FORCEnet using intelligent autonomous collaborating agents embedded in entities that perform communications networking, sensing, maneuvering and striking functions.

3. AINS Innovative Networking Technologies enable a Network-Centric C4ISR Operation Development of survivable and autonomously adaptable mobile communications network systems that support high quality transport of critical messaging flows and real-time streams in an adverse environment to enable network centric combat operations and warfare.

4. Our Approach • Breakthrough methods to guide intelligent platforms to rapidly mitigate network system gaps, substantially re-constitute degraded configurations and enhance performance, at the right place at the right time. • Such methods include the autonomous layout and control of unmanned networked platform formations and UAV swarms in a multi-tier hierarchical mobile backbone networked infrastructure, and the formation of internets-in-the-sky.

5. Our Innovative Networking Technologies: I • UV aided Mobile Backbone Networks (MBNs): Multi-tier adaptive autonomous networking • Robust survivable QoS Routing for mobile ad hoc wireless networks employing multi tier UV swarms • Architecture, infrastructure and approaches for the configuration of UAV platforms and swarms to jointly best support • Communications networking • Sensing tasks • Area search and surveillance

6. Our Innovative Networking Technologies: II • Power-control spatial-reuse Medium Access Control (MAC) protocols and algorithms • Integrated MAC scheduling, power control and routing leading to significant enhancements in the throughput efficiency of shared radio channels • Integrated System Management (ISM) • New paradigm in the design of system management architecture that combines monitoring, control and resource allocations for C4ISR systems

7. Robust Wireless Networking – Architecture and topology Synthesis • Synthesis of a multi-tier (land, air and sea based) mobile backbone network (MBN) • New distributed algorithms to configure the multi tier backbone network • Dynamical adaptivity to failures, application mixes and capacity requirements

8. Hierarchical Configuration of UV-aided Mobile Backbone Network (UV-MBN) ANet 3 ASPN 1 ASPN 2 ANet 1 Backbone Node Gateway ANet 2

9. Fig. 4. Sample of Flow Blocking rates for flows of different classes using the IRI QoS based admission control mechanism AINS based UV-aided Dynamically Reconfigurable Network mbns.exe mbns.exe UV aided Mobile Backbone Network Protocol (MBNP) Quality of Service (QoS) UV-aided operation MBN based On Demand Routing with Flow Control (MBNR-FC) Swarm Networking

10. Illustration of our heterogeneous Mobile Backbone Network (MBN)

11. UV aided Autonomous Mobile Backbone Network

12. (b) (a) (c) (d) Backbone Construction

13. The MBN Topology Synthesis Algorithm (TSA) • Neighbor Discovery • Every node exchange “Hello Message” periodically. – Short timer • Every node updates its neighbor list periodically. – Long timer • Each node learns its 1-hop neighbor information and 2-hop BN neighbor information. • Association Algorithm • Every node that is in a BCN state or RN state attempts to associate with a BN with highest Weight. • The Weight of a node can be based on its ID, degree, congestion level, and a nodal/link stability measure. • If no acceptable neighboring BN is detected, try BCNs; If no BCN either, try RNs (BCN: 3,6) Hello Hello (BCN: 1,3) (BCN: 4,5,7) Hello Hello Hello Hello Hello Hello Hello Hello Hello (BCN: 1,2,5,7) Hello Hello Hello Hello Hello (BCN: 3,4,6) Hello Hello (BCN: 2,3) (BCN: 6,7) Hello Message: ID, Weight, BN Neighbor List

14. BN BN BN The MBN Topology Synthesis Algorithm (TSA) • BCN to BN Conversion Algorithm (1) Client coverage: a BCN that receives an association request from a BCN or RN, converts itself to a BN. (2) Connectivity of the BNet: A BCN node finds that by converting itself to a BN it will upgrade the Bnet connectivity. • BN to BCN Conversion Algorithm (1) All of its BN neighbors have at least one common BN neighbor whose weight is higher than the weight of the underlying BN that is considering to convert. (2) Each of its BCN clients have at least one other BN neighbor. (BCN: 3,6) (BCN: 1,3) (BCN: 4,5,7) (BCN: 1,2,5,7) (BCN: 3,4,6) (BCN: 2,3) (BCN: 6,7)

15. MBN Topology Synthesis Algorithm Convergence Time • The MBN topology synthesis algorithm convergence in constant time, of the order of O(1).

16. Total number of backbone nodes (BNs) in the network • The backbone network (Bnet) size is independent of the number of nodes in the network or the nodal density. • The backbone network (Bnet) size is only proportional to the area size.

17. Control Message Overhead of TSA • The control message overhead of TSA is independent of the number of nodes in the network or the nodal density.

18. Data Delivery Radio of 25 UDP flows

19. Average End-to-end Delay Performance

20. Average Data Path Length

21. Average Path Length • We expect the employment of the MBNR scheme to yield a longer average path length value than that obtained under AODV (since routes are now established only across the backbone network). Interestingly, our simulation results indicate that the MBNR protocol does not always produce longer path lengths. • RREQ packets are transmitted as broadcast packets, when such a packet experiences collision, no MAC layer retransmission takes place. Consequently, if the network is already overwhelmed by RREQ storm, it is likely that a route will not be discovered in time or that a “non-shortest path route” will be selected (a) Stationary network (b) Mobile network

22. QoS based Robust Scalable Routing (MBNR) • MBN based Robust Routing protocols (MBNR) • On-demand routing mechanism that uses selective control packet forwarding (across the MBN) to discover routes • Proactive routing for route establishment in smaller subnets and certain Access Nets • Unique MBN based Flow and Congestion control mechanism (MBNR-FC protocol) to preserve the quality of service (QoS) of established flows and to ensure that, under overloading conditions, only high priority flows are supported at desired QoS • Unique cross physical, MAC and network layer algorithms and protocols to ensure that the realistic nature of the wireless radio environment is dynamically incorporated into communications resource allocations and routing operations. • Effective use of UGV and UAV swarms to establish backbone routes and to distribute control packets • Hybrid backbone and non-backbone routing and flow/congestion control to efficiently utilize resources in areas that are not covered or are away from the mobile backbone and its UGV and UAV agents

23. MBN Routing with Flow Control (MBNR-FC):Delay Jitter Performance Comparison among Different Protocols

24. Network Performance: packet delay and delay jitter • Delay jitter vs. Traffic loading • The delay jitter is reduced as traffic loading rate is increased (when the network is not saturated). Explanation: route discovery produces a larger delay which is different from the delay experienced when the route is available. • When the network is congested, more route discovery attempts take place.

25. Hybrid Routing Strategy • Capacity utilization of pure MBNR-FC • When the number of BCNs is not able to form a backbone to cover the whole network area, backbone-only paths will limit the overall throughput capacity of the network. • Allowing both backbone routing and non-backbone routing could fully utilize the network capacity. • Long-distance traffic vs. Short-distance traffic • Short-distance traffic obtains shorter path lengths by routing through all type of nodes, while long-distance traffic does not. • Long-distance traffic obtains routing overhead reduction by routing through backbone network, while short-distance traffic does not.

26. Delay-throughput performance of MBNR-FC/DA under 2-hop Anets • The delay-throughput performance with distance thresholds equal to 7 hops and 9-hops demonstrate a significant throughput capacity gain compared to that with distance threshold equal to 0-hops (which is obtained by pure MBNR-FC).

27. Under Development: Adaptive Scheme for Distance threshold Selection • Adaptive scheme for distance threshold selection • Execute in a distributed manner. • Adjust the distance threshold according to the current traffic distribution. • Procedures: • Each BN collects the congestion information of its own Anet: the number of clients that are not eligible for participating in the route discovery process (i.e.; if they or their neighbors are congested.) • BNs that are within 2 hops from each other exchange their Anet congestion indices. • The obtained congestion information is used by each BN to compute a distance threshold dth which it broadcasts to its Anet clients

28. High Capacity QoS MAC • Power-control spatial-reuse Medium Access Control (MAC) protocols and algorithms • Integrated MAC scheduling, power control and routing leading to significant enhancements in the throughput efficiency of shared radio channels • Provision of quality of service (QoS) by prioritized scheduling and cross layer MAC/Networking operations

29. MAC Mechanisms • Power control spatial reuse (PCSR) Medium Access Control (MAC) layer operations • Scheduling based QoS based MAC mechanisms (such as: PCSR demand assigned TDMA / FDMA / CDMA) • Random access based PCSR techniques providing enhanced performance • Directional and omnidirectional operations • PHY-MIMO driven power control MAC operations • Autonomous power control MAC operations using UAV swarms

30. 4 6 2 5 7 1 BN 3 Power: 1mW 9 Power: 10mW Power: 50mW Power: 100mW 8 Slot 1 Slot 2 Slot 3 Slot 4 Slot 7 Slot 10 Slot 5 Slot 6 Slot 8 Slot 9 BN 3 10mW • 9 50mW • 4 50mW BN 7 10mW • 3 50mW • 1 50mW • 1 10mW • 2 50mW BN 3 10mW BN 7 10mW • 2 10mW • 4 10mW BN 3 10mW • 9 10mW • 1 50mW • 5 50mW • 6 1mW • 7 100mW • 9 50mW • 8 50mW • 1 10mW BN 7 10mW • 4 10mW • 5 50mW • 5 50mW Power Control Spatial-Reuse MAC DA/TDMA large increase in spatial reuse factor

31. Throughput Analysis of our Power Control Scheduling Algorithm (PCSA) and alternative scheme (TPA) (for an illustrative network with 10 active nodes)

32. Uniform Traffic 1000*1000m area, 100 nodes, 30 flows, Fixed Routing In this experiment, we fix the routing in advance so we can focus on understanding purely the characteristics of the 802.11 MAC. DPC offers a significantly better Throughput-delay characteristics compared to low power transmissions (blue) and regular 802.11 with no power control (green).

33. Localized Traffic 400*400m area, 100 nodes, 15 flows, Fixed Routing Benefits of our distributed power control algorithm are especially apparent when traffic patterns are localized.

34. Cross Layer Power Control based Topology Synthesis • What is the optimal number of APs needed for best network performance (in terms of throughput, delay, delay-jitter, packet loss ratio)? • APs should not only be deployed to provide coverage but also to accommodate different capacity needs of nodes • What is the optimal power to operate at? • When is it useful to employ “Cell Splitting” and get new APs or “Soft APs” (a laptop configured to work as an AP) into the network?

35. Adaptation of AP / BN selection to the traffic profile When using power control the number of APs deployed Should depend on the Traffic characteristics in the Network. When the traffic is mostly Long distance, it’s better to Employ a fewer number of APs, and vice versa.

36. On going developments: Simulation Results for Hybrid TDMA/CSMA Experiment with three APs, 9 flows, 3 of which are inter-AP flows. Case 1: Hybrid scheme Case 2: Regular 802.11 We can clearly see that the hybrid scheme delivers significant throughput and delay benefits over the regular, non power controlled IEEE802.11 Note: inter-AP flows can traverse paths that are as long as 3 hops

37. Integrated System Management (ISM) • New paradigm in the design of system management architecture that combines monitoring, control and resource allocations for C4ISR systems • Hierarchical Integrated System Management and control architecture using nodal, subnetwork and system wide monitors and control elements • Monitoring attributes and Management Information Bases (MIBs) for communications, sensing, UV, maneuverable and strike segments • ISM algorithms for joint resource, performance, failure and topology management of MBN based C4ISR systems using UAV swarms

38. Integrated System Management: system configuration

39. Integrated System ManagementIllustration of ISM display of status of communications, sensing and UAV networked systems

40. ISM: Topology Display

41. ISM: Traffic Graph Display

42. On-Going & Planned Research Works • Power control spatial reuse MACs • Hybrid MAC for meshed architectures • Topology Synthesis of the Backbone Networks • Characterization and tuning of the algorithms; performance features and comparisons; stability and efficiency adaptations • MBN based QoS Routing • Development and analysis of the hybrid MBNR-FC/DA scheme

43. outstanding research works • UAV and UGV aided networking • UAV swarms • Cross Layer networking • Distributed cross-layer PCSR MACs • Integrated power control MACs and MBN based QoS routing • Phy / MAC / Link / Network and topology synthesis cross layer protocols and algorithms • Performance analyses and simulations under a multitude of multimedia applications and C4ISR scenarios • Incorporation of QoS oriented network management schemes • Energy aware MBN based networking