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Controlled Mobility Wireless Networks

Controlled Mobility Wireless Networks. Eytan Modiano Massachusetts Institute of Technology. Controlled Mobility Wireless Networks. Network of mobile nodes whose mobility can be controlled E.g., miniature autonomous air-vehicles Nodes’ mobility can be used to facilitate communication

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Controlled Mobility Wireless Networks

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  1. Controlled Mobility Wireless Networks Eytan Modiano Massachusetts Institute of Technology

  2. Controlled Mobility Wireless Networks • Network of mobile nodes whose mobility can be controlled • E.g., miniature autonomous air-vehicles • Nodes’ mobility can be used to facilitate communication • Increase network throughput or reduce delays • Maintain network connectivity • Reduce energy consumption

  3. Maintaining Network Connectivity Mobile Backbone Architecture Mobile Backbone Node (MBN) Regular Node (RN) • Arbitrary Node mobility can lead to network disconnection • Mobility dictated by mission • Use dedicated communication nodes to maintain network connectivity • The MBNs primary purpose is to facilitate communication • The MBN’s form a backbone over which communications takes place • Facilitates network management • MBN nodes are moved about to maintain connectivity requirements • Regular nodes (RNs) have unconstrained mobility dictated by their mission • Technical challenges: • Positioning of MBN’s to provide connectivity for RN’s • Various constraints on transmission power, node’s degrees, connectivity requirements • Algorithms for reconfiguring the network as the RN’s move • Real-time, distributed

  4. MBN Placement Problem (MBP) • Disk communications model • RN can communicate up to a distance r • MBN’s can communicate with each other up to a distance R (R > r) • MBP problem can be decomposed into 2 sub-problems: • Geometric Disk Cover (GDC) Problem • Steiner Tree Problem w/ Minimum Number of Steiner Pts. (STP-MSP) • Decomposition Result:Using - and -approximation algorithms for GDC and STP-MSP, yields an (+)-approximation algorithm for the overall MBP • Results • Developed distributed approximation algorithms for GDC problem under arbitrary mobility • Developed a new approach for solving STP-MSP problem • Extended approach to SNR-based communications model • Data rate is a function of received power and interference A. Srinivas, G. Zussman and E. Modiano, “Mobile Backbone Networks – Construction and Maintenance,” ACM Mobihoc, May, 2006.

  5. Incorporating trajectory information • In many scenarios nodes trajectories are known a-priori • E.g., due to pre-planned path or mobility model (e.g., sensors’ trajectory) • Develop algorithms that compute the MBN’s path by incorporating RN trajectory knowledge • Reduce the need for the MBN to move rapidly • MBN’s mobility is constrained (e.g., velocity) • Otherwise could simply find optimal solution in every time step • Problem: Find the optimal MBN path to maximize network connectivity or throughput • Preliminary results: Dynamic Programming algorithms based on discrete formulation (I.e., nodes’ are placed on “grid” points)

  6. C B A C Trajectory Control • Most work on trajectory (path) planning traditionally focuses on physical/geographical constraints • When communication is important, it’s crucial to look at communication requirements in trajectory planning • Example: Maintaining communications connectivity • Vehicle must travel from point A to point B • Vehicle must remain connected to a network (e.g., base-stations) • Find the optimal (minimum time) trajectory from A to B subject to connectivity requirements • Connectivity requirement: hard or soft? • Base-stations static or mobile?

  7. Trajectory-Aware Routing • Question: Can we use trajectory information to design better routing protocols? • Advantage: Reduce routing overhead associated with ad-hoc wireless networks when such overhead cannot be afforded • Issues: • Deterministic vs. stochastic trajectories • Determining the minimum trajectory information that must be exchanged between nodes • Time scales: How long does it take to transmit minimum trajectory information compared to how long it takes to discover which wireless “links” are up? • Trade-off between energy for mobility and energy for communications • For “tiny” air vehicles energy for mobility is of the same order as energy for communications • Communicate over a long distance vs. relocate

  8. Throughput - Delay tradeoffs in mobile ad hoc networks • Cell partitioned network • Nodes move between cells according to an I.I.D mobility model • Nodes can only communicate when they are in the same cell (subject to interference) • Without mobility per user throughput is limited to [Gupta/Kumar] • With mobility[Grossglauser/Tse]: • Throughput-Delay tradeoff [Neely/Modiano] • Reduced delay through redundant transmissions • Question: can throughput/delay be improved if the mobility of certain nodes can be controlled? IEEE Transactions on Information Theory, June 2005

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