1. Linear Programming & its Applications to Wireless Networks Guofeng Deng IMPACT Lab, Arizona State University

2. MPACT I Arizona State G. Deng2 Outline Linear programming (LP) –Formulation –Solutions –Flow model Applications –Maximizing broadcast lifetime –Optimal role assignments –Multicommodity flow –Energy efficient routing in disaster recovery networks –Cross-layer design for lifetime maximization –Minimum power broadcast tree

3. MPACT I Arizona State G. Deng3 LP Summary LP –Linear objective function –Continuous variables –Linear constraints (equations or inequalities) Solutions –Simplex methods –Interior-point methods Software tools –Cplex, GLPK, Matlab Beyond LP –Integer linear programming (ILP): variables are integers. It is called mixed integer programming (MIP) if not all variables are integers. The problem becomes NP-hard. Approximation methods include branch-and-bound, branch-and-cut. If removing integer constraints, LP provides a lower/upper bound to a minimization/maximization problem. –Nonlinear programming: some constraints or the objective function is nonlinear.

4. MPACT I Arizona State G. Deng4 App1: Maximizing Broadcast Lifetime using Multiple Trees Objective function: Constraint 1: Constraint 2: Summary: -Problem: Given a set of broadcast trees in the form of power consumption of each node, maximizing broadcast lifetime using multiple trees sequentially. -Variables: Duration of each tree being used. We assume duration is indefinitely divisible. -Constraints: For each node, the overall amount of energy that can be consumed in all the trees is limited by its battery capacity. Notations: -K: a set of broadcast trees -  (  ): the duration of tree   K -p i (  ): power of node i on tree  -E i : battery capacity of node i Tree/Node12345Res A1051209  (A) B901350  (B) C1260610  (C) Battery Cap100220150310160

5. MPACT I Arizona State G. Deng5 App2: Bounding the Lifetime of Sensor Networks B 123 3/11 + 5/11 3/11 5/11 3/11 + 3/11 Bhardwaj & Chandrakasan, Bounding the Lifetime of Sensor Networks Via Optimal Role Assignments, INFOCOM’02 Summary: -Problem: Given a pair of source and destination nodes and a set of intermediate nodes, maximize the lifetime, i.e., the amount of packets that is transmitted from source to designation. -Variables: f_ij: the flow from i to j. -Constraints: see below. Notations: -Node 1 is the source and N+1 is the destination. -t: lifetime; e_i: battery capacity of node I Comment: - The formulation was later extended to accommodate multiple source and single sink. For any intermediate node, which does not generate any flow, the amount of incoming flow matches the amount of outgoing flow. This is the total amount of flow injected to the network, i.e., the difference between the amount of flow outgoing from source and that incoming to source.

6. MPACT I Arizona State G. Deng6 App3: Multicommodity Flow Chang & Tassiulas, Energy conserving routing in wireless ad-hoc networks, INFOCOM’00 Chang & Tassiulas, Maximum lifetime routing in wireless sensor networks, TON, Vol.12 No.4, 2004 Sanka & Liu, Maximum lifetime routing in wireless ad-hoc networks, INFOCOM’04

7. MPACT I Arizona State G. Deng7 App4: EE Routing in Disaster Recovery Networks Zussman & Segall, Energy efficient routing in ad hoc disaster recovery networks, Ad Hoc Networks, Vol.1, 2003 \bar{f}_{i,j}: the amount of info transmitted from i to j until time T R: receiver nodes d: destination r_i: the ratio between the rate in which info is generated at badge node i and the maximum possible flow on a link connecting smart badges

8. MPACT I Arizona State G. Deng8 App5: Cross-Layer Design for Lifetime Maximization Madan et al., Cross-layer design for lifetime maximization in interference-limited wireless ad hoc networks, INFOCOM’05 Madan et al., Cross-layer design for lifetime maximization in interference-limited wireless ad hoc networks, IEEE trans. Wireless Communications, Vol.5 No.11, 2006 non-convex! Tv: node lifetime N: number of slots r^n_k: trans rate over link k per unit bandwidth in slot n P^n_k: trans power over link k in slot n P^{max}: maximum trans pwr N_0: noise power

9. MPACT I Arizona State G. Deng9 App6: Minimum Power Broadcast Tree Das et al., Minimum Power Broadcast Trees for Wireless Networks: Integer Programming Formulations, INFOCOM’03 3 4 1 6 8 72 5 Power matrix Reward matrix R_mn(p)=1 if P_mp ≤ P_mn Variables Y_i: power of node i X_ij: =1 if there is a explicit link from i to j X_ijk: =1 if the kth transmission is i to j actual trans implicit trans Defines relation between continuous and binary variables Source node has to transmit to at least one other node Non-source node at most transmits to one other node Defines relation between X_ij and X_ijk. Source has to transmit in the 1 st step. Non-source node is not allowed to transmit in the 1 st step. A non-source node is not allowed to transmit until it is reached actually or implicitly. Source has to transmit in the 1 st step. Each node has to be reached ultimately. At most one transmission in each step.