Distributed Topology Control In Wireless Sensor Networks with Assymetric Links Presenter: Himali Saxena

General Outline Introduction Model Algorithm Performance Analysis Simulation

Introduction WSN- collection of power conscious wireless capable sensors without the support of pre existing infrastructure Topology control via per-node Tx power adjustment has been shown to be effective in extending network lifetime and increasing network capacity With reduced Tx range, basic reachability can be jeopardized The problem exacerbated with heterogeneous nodes with different maximum transmission ranges with assymetric power links

Model Nodes are deployed in 2-D plane. Each node is euipped with an omni directional antenna with adjustable Tx power Nodes have different Tx powers & radio range

Pi : Tx Power, Pi max : Max Tx Power Pij : Tx Power required for I to reach j P i max != Pj max : Asymetric link → Pj i> Pj max (impossible for j to reach i) G = (V,L) : Strongly or weakly connected, or disconnected Objective : To derive a minimum power topology G that bis strongly connected, guaranteeing multi hop reachability from any source to any destination in the directed graph

Algorithm Phase I : Establishing the vicinity topology Node i broadcasts IRQ msg using Pi max IRQ includes the location of I & Pi max Set of nodes receive this msg : vicinity nodes of i (Vi) each node j in V i replies to node i with an IRP message, with its location and Pjmax j −→ L ji.

For a node j ∈ Vi, if Pjmax ≥ Pij, j can reach node I via the single-hop link If Pj max < Pij, j must find a multi-hop path to reach i. Having the knowledge of the locations and maximum transmission powers for itself and all its vicinity nodes, it may derive the existence of the vicnity edges For any two nodes j, k ∈ Vi, link Ljk is defined as one of i’s vicinity edges, if Pj max ≥ Pjk Consequently, node i constructs its local vicinity topology that includes all its vicinity nodes, itself and the discovered vicinity edges

Phase II: Deriving the minimum-power vicinity tree 1. With the knowledge of the weighted, directed topology Gi, the weight, Wl, of a directed path l = u0 → u1 →... → uk from node u0 to uk is the sum of edge weights along the Path 2. The minimum power for node i to reach j is min(Wp) for all available paths p 3. Find the shortest path in G from i to j. In this case, node i may execute a single source shortest-paths algorithm to derive the minimum-power vicinity tree Gis = (Vis, Eis) Property 1. Since there does not exist unreachable nodes in the in Gi, we have Vis = Vi, and Eis ⊆ Ei. Property 2. The derivation of Gis depends solely on the edge weights, which does not assume a specific propagation model

Phase 3: Propagation of transmission powers 1. In this phase, node i needs to calculate the transmission power needed for itself and each vicinity node in Vi, to ensure that all its minimum-power paths exist in the final minimum power network topology 2. Specifically, for node i itself and each node in set Vi, the transmission power is assigned as the power required to reach the furthest one-hop downstream nodes in node i’s minimum-power vicinity tree Gis 3. Node i first adopts the minimum power assigned to itself, and then sends the minimum power required for each vicinity node with a explicit PR message

Performance Analysis Scalability : Execution of the algorithm is limited to its vicnity topology, thus scalable to network composed of large no. of nodes Convergence of the algorithm : Network converges to the final mimimum power topology once every node completes the execution of the algorithm Guaranteeing of network connectivity

Simulation N nodes uniformly distributed in 100*100 m network area, where n is in the range of [2,50] Parameters : Reachability, Power Efficiency, and Scalability

Conclusion 1. Algorithm provides a solution to the topology control problem in a network of heterogeneous wireless devices with different maximum transmission ranges 2. The resulting minimum-power topology is shown to guarantee that (a) reachability between any two nodes is guaranteed to be the same as the maximum topology; and (b) nodal transmission range is minimized to cover the least number of surrounding nodes.