Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Interference-Aware Multicasting in Wireless Mesh Networks Sudheendra Murthy, Abhishek Goswami, Arunabha Sen Networking 2007 1 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 2 Introduction WMN has fueled research work in providing better support to multimedia applications like real-time video transport and VoIP services. Common to these applications is the need for a multicast framework. Early efforts in providing multicast support in WMN ignored to consider the interference effects. We present a novel multicast framework that considers the effects of interference and constructs a multicast structure that provides maximum bandwidth. 3 Introduction Our objective is to identify multicast forwarding group nodes whose transmission includes least amount of interference. The main contributions: We provide a novel formulation of the problem as a graph problem. We prove the NP-completeness of the problem. We provide an Integer Linear Program technique. We provide a centralized heuristic algorithm and describe a distributed implementation. We compare the performance of the proposed heuristic with the optimal, shortest path and minimum Steiner tree solution. 4 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 5 Network Model RT: transmission range. RI: interference range. G(V, E): directed potential communication graph. dist(u, v): Euclidean distance between u and v. One radio/node, one common channel. Any node can be the source node in the multicast tree. RI = q × RT with q ≧ 1. 6 Network Model H(V, F): directed potential interference graph Vertex set corresponds to the mesh routers. (u, v) in H implies that the transmission of u can cause interference at v. Our interference model captures the idea of co-channel interference occurring at the receiving nodes. The conflict graph and potential interference model can be derived from each other. The potential interference graph modeling makes our 7 problem definition more elegant. Graph Definitions and Notations : In-degree : Out-degree N[v]: includes v and the neighbors of v. , neighborhood of a vertex subset S. 〈S〉G : the weakly induced subgraph of graph G on the vertex set S. Including N[S] and E∩(S×N[S]). 9 Problem Definition The maximum data rate is limited by bottleneck link. The data rate of bottleneck link is determined by the amount of interference experienced by the receiver of the link. Our goal is to find the group of forwarding nodes that induce the least interference on the forwarding nodes and the receivers. 10 Problem Definition Given G(V, E), H(V, F), source s, receiver set R, find vertex set S such that The subgraph of G weakly induced by S, i.e., 〈S〉G has directed paths connecting s to each receiver. The maximum in-degree of the vertices of the subgraph of H weakly induced by S is minimized. We term this problem as the Minimum-Degree Weakly Induced Connected Subgraph (MDWICS) problem. => NP-complete. The optimal subset S of the MDWICS problem result in the least interference. 11 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 12 Optimal Solution We provide an ILP formulation to solve the MDWICS problem. Xi,j=1, if edge (i,j) is in the optimal solution, 0 otherwise. fpi,j=1, if there is a flow from s to receiver p through link (i,j) , 0 otherwise. yi,j=1 for edge (i,j) , if node i is the transmitter in the optimal solution, 0 otherwise. Be used to ensure connectivity from s to receiver r. Be used to capture the interference caused by i’s transmission on node j. The objective is to minimize D, The maximum in-degree of al the nodes in the interference subgraph. 13 Constraints 14 Flow Conservation Constraints 15 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 16 S: a feasible solution set of vertices that contain paths from the source to all receivers. W: contains the set of visited vertices. Distributed Protocol Based on the Optimized Link State Routing (OLSR) protocol. OLSR could be used in mesh networks to maintain the state and quality of the links. The TC (Topology control) messages are efficiently dispersed in the network through multipoint relays (MPRs). Each node is uniquely identified by its IP address. The source with its topology information independently computes the set of multicast forwarding group nodes. 18 Distributed Protocol It then disseminates through the MPRs, a MC_FG message consisting of the IP addresses of the forwarding group nodes and a MC_JOIN message to the multicast group members. MC_JOIN message is to inform the multicast group members about the initiation of the session. The session ID is included in every packet forwarded by the multicast source. If a node receiving this packet has the session ID listed in its FG table, it forwards the packet. 19 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 20 Simulation Results We compared the performance of out heuristic with shortest Path Tree (SPT) algorithm, minimum Steiner Tree (MST) approximation algorithm and The optimal solution obtained by solving the ILP formulation. # of nodes was fixed to be 70. The locations of 70 nodes were randomly generated. The transmission radius was fixed at 100m. The multicast source and receivers were selected randomly. The interference-degree is the maximum number of forwarding group mesh nodes that affect any node in the multicast structure. 21 Outline Introduction Problem Formulation Optimal Solution Proposed Algorithms Simulation Environment and Results Conclusion and Comments 24 Conclusion We categorized the interference-aware multicasting problem as a graph problem of finding a weakly induced subgraph of minimum degree. We introduced a new model of interference. We presented an efficient greedy-based heuristic algorithm. The simulation results provide substantial evidence of the superior performance of our heuristic. Future work: Analyzing the performance of the distributed algorithm and Providing approximation bounds to the centralized 25 algorithm. Comments This paper not only provide ILP solution, but also heuristics to approach the optimal solution. Some mechanisms are used to minimize interference in order to maximize bandwidth. However, there is no result about bandwidth or throughput provided. In the distributed protocol, we can not see anything about minimize interference. 26