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Energy-Aware Routing

Energy-Aware Routing. Paper #1: “Wireless sensor networks: a survey” Paper #2: “Online Power-aware Routing in Wireless Ad-hoc Networks”. Robert Murawski February 5, 2008. Energy-Aware Routing. Paper #1 Wireless sensor networks: a survey Focus on Network Layer Energy Aware Sections

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Energy-Aware Routing

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  1. Energy-Aware Routing Paper #1: “Wireless sensor networks: a survey” Paper #2: “Online Power-aware Routing in Wireless Ad-hoc Networks” Robert Murawski February 5, 2008

  2. Energy-Aware Routing • Paper #1 • Wireless sensor networks: a survey • Focus on Network Layer • Energy Aware Sections • Paper #2 • Development of Specific Energy-Aware Routing Algorithm • Online Algorithm, and a Practical Implementation of the Algorithm

  3. Paper #1: Sensor Network Survey • Network Layer Considerations for Sensor Networks: • Power Efficiency • Data Centric Information • Data Aggregation • Attribute Based Addressing / Location Awareness • Focus of this Presentation: • #1: Power Efficiency in Sensor Network Routing

  4. Energy Efficient Routing Approaches for Selecting an Energy-Efficient Route • Maximum Available Power (PA) Route • Select paths containing nodes with the most residual power • Minimum Energy (ME) Route • Select paths that consume the least amount of energy • Minimum Hop (MH) Route • Select paths that utilize the least amount of network hops • Maximum Minimum PA Node Route • Select the route that maximizes the minimum residual energy

  5. Route Selection Example PA: Residual Power of Nodeαi: Energy Required to Transmit a Message through Link Source: “Wireless sensor networks: a survey”, I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci

  6. Available Routes • Four Possible Routes ( T  Sink) • T  B  A  Sink • PA = 4, Total α = 3 • T  C  B  A  Sink • PA = 6, Total α = 6 • T  D  Sink • PA = 3, Total α = 4 • T  F  E  Sink • PA = 5, Total α = 6

  7. Route Selection • Maximum Available Power (PA) • Route 2 (TCBASink) has the largest PA Value • Route 2 is an extension of Route 1 (TBASink) • Must not consider routes extended from other routes by adding additional nodes • Route 4 (TFESink) would be selected

  8. Route Selection • Minimum Energy (ME) • Route 1 (TBASink) • Consumes the Least amount of energy • Minimum Hop (MH) • Route 3 (TDSink) • Lowest Hop Count • Maximum Minimum PA • Route 3 (TDSink) • Minimum PA Value = 3 • Minimum PA for Other Routes = 2

  9. Routing Schemes for Sensor Networks • Small Minimum Energy Communication Network (SMECN) • Given a Network Graph G’ • Weighted Graph G(V,E) • Compute an Energy Efficient Subgraph G: • All nodes in G’ are present also in subgraph G • Number of edges in subgraph G are less than in G’ • All end-to-end node connections in G’ are also in subgraph G • Energy required to transmit a message from node u to node v in subgraph G is less than the power required in graph G’ • For every route u  v in graph G’, there is a Minimum Energy (ME) route u  v in subgraph G • Details on generating subgraph A are not Given, only a description of the subgraph and its use in optimizing energy

  10. SMECN Cont. • Transmission Power: • t is a constant • n : pathloss exponent • d : distance between u and v • Network Path: • Power Required to route a message: • c : receive power • Path r in subgraph G is a minimum energy path if for all routes r’ in graph G’: • Overview of SMECN • Once subgraph G is computed, nodes can easily select the path that requires the least energy consumption from all available routes

  11. More Energy-Efficient Routing Schemes for Sensor Networks • Sensor Protocols for information via negotiation (SPIN) • Limit the amount of information transmitted via the network • Control Message Sequence • ADV Message: • Contains a description of the data to be sent. • REQ Message: • If the neighbor node is interested in the data, send a REQ message back to the source node. • DATA • The source node transmits the data to nodes that request it. Source: “Wireless sensor networks: a survey”, I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci

  12. More Energy-Efficient Routing Schemes for Sensor Networks • Sensor Protocols for information via negotiation (SPIN) • For sensor nodes, the ADV message contains a descriptor of the DATA message • i.e. image, sensor reading descriptions • Nodes only transmit the large DATA packets when necessary • Reducing the amount of large DATA transmissions increases the residual energy of nodes within the network.

  13. More Energy-Efficient Routing Schemes for Sensor Networks • Low-energy adaptive clustering hierarchy (LEACH) • Reduce the energy dissipation in the network • Backhaul of data to a base station can be costly • Designate “cluster-head” nodes that backhaul aggregate data from all nodes within the cluster to the base station • Phase 1) Setup Phase • Sensor nodes are randomly chosen to be Cluster-heads • All Cluster heads advertise to all nodes within the network. • Non-cluster head nodes choose their cluster based on signal strength of the advertisement. • Phase 2) Steady State Phase • Non-cluster head nodes send data to their designated cluster-head • Cluster heads backhaul information to the base station.

  14. Paper #2 • Online Power-aware Routing in Wireless Ad-hoc Networks • Paper’s focus: • Development of an “on-line” power-aware routing protocol • On-line: Protocol does not know the sequence of messages to be routed ahead of time • max-min zPmin Algorithm: • Requires knowledge of power availability of all nodes within the network, impractical for large networks • A second algorithm “zone-based” routing • A more practical version of the max-min zPmin theorem

  15. Introduction • Power Consumption in Ad-hoc Networks: • Message Transmission • Message Reception • Node Idle Time • Focus of this paper: • Minimizing power consumption during communication (transmission and reception)

  16. Introduction • Standard metrics for optimizing power-routing • Minimize energy consumed for each message • Minimize variance in each computer power level • Minimize radio of cost/packet • Minimize the maximum node cost • Drawback of these metrics: • Focus on individual nodes, not the system as a whole • Could lead to a system of nodes with high residual power, but with several key nodes depleted of power • This paper: • Focus on maximizing the lifetime of the network • Lifetime: time to the earliest time a message cannot be sent

  17. System Model • Network View: a weighted graph G(V,E) • Vertices are computers within the network • Weight of a vertex corresponds to the residual power of the node. • Edges are pairs of computers within communication range • Weight of an edge is the cost in power of sending a unit message • Large messages are simply multiples of the unit message • Power to transmit a message: • k and c are constants defined by the wireless technology used.

  18. Max-min Path • Intuition: • Route message over paths with the maximum minimum residual energy • Find all possible paths from source to destination • Determine the minimum residual energy node for each path • Choose the path with the maximum residual power for each node • Using the max-min path can have poor performance as seen in the following hypothetical network.

  19. Max-min Path • Assumptions: • Initial power for intermediate nodes = 20 • Initial power for source node = ∞ • Weight of edge on the arc = 1 • Weight of straight edge = 2 • Max-min Path: • Route through the arc: • Residual Power of all nodes after one message • (20 – 1) / 20 = 95% • 20 messages can be sent total. • Optimal Path: • Route messages through the straight paths • Residual Power of intermediate node after transmission through a straight path • (20 – 2) / 20 = 90% • 10 * (n – 4) message can be sent total. • See Right: for a network of 8 nodes, 40 messages can be sent. • Increases with network size

  20. The “z” Parameter • Two extreme solutions to power-aware routing • Compute the path with minimal power consumption: Pmin • Compute the path that maximizes the minimal residual power • Author’s Goal: Optimize for both 1 and 2 • Methodology: • Relax the Pmin requirement by a factor of z. (z ≥ 1) • For z = 2, select a path that consumes no more than twice the minimum possible energy consumption of all possible routes • “Max-min zPmin” Algorithm • Select path that consumes at most z*Pmin while maximizing the minimal residual power fraction

  21. Max-min zPmin Algorithm • Definitions: • P(vi) Initial power level of node vi (at time t=0) • eij weight of the edge between vi and vj (cost of transmission) • Pt(vi) Power of node vi at time t • utij Residual power of node vi after sending message to node j

  22. Max-min zPmin Algorithm • 0) Find the path with the least power consumption, Pmin • 1) Find the path with the least power consumption in the graph • If the power consumption > z*Pmin or no path is found, use the previously computed path and stop. • 2) Find the minimal utij on the path from step 1 (umin) • 3) Find all edges whose residual power fraction utij≤ umin and remove them from the graph. • 4) Goto Step 1

  23. Max-min zPmin • What this algorithm accomplishes: • Step 0: First computes the Pmin • As was shown, the Pmin can perform poorly • Steps 1-4 • Compute the Pmin for the current state of the graph • Determine if this value of Pmin is above the “relaxed” requirement of zPmin • If the remaining graph is within bounds (zPmin), determine the minimum residual power for all nodes within the current Pmin route (umin) • Eliminate edges within the graph that do meet or exceed the umin • Note: Eliminates the current Pmin path found in step 1 • Repeat steps 1 thru 4 for the newly updated version of the graph

  24. Max-min zPmin • What this algorithm accomplishes: • Iteratively finds paths with higher cost, while remaining below the threshold zPmin • Eliminates edges that result in depleted residual power in intermediate nodes. • Resulting path is a trade-off between the pure max-min path and the pure Pmin path

  25. Choosing the Z Parameter • Extremes values of Z • Set Z to 1 • Reduces xPmin algorithm to the purely Pmin path • Set Z to ∞ • Reduces xPmin algorithm to the purely min-max path • Author’s Focus: • Adaptive method for computing the Z parameter that maximizes the network lifetime

  26. Choosing the Z Parameter • 0) Choose initial value for z, and step size δ. • 1) Run max-min zPmin algorithm for some interval T • 2) Compute <1> for all hosts, let the minimal be t1 • 3) Increase z by d, run max-min zPmin for interval T • 4) Compute <1> for all hosts, let the minimal be t2 • 5) If any host is saturated, exit (use this value of z) • 6) If t1 < t2, set t1 = t2, and goto step 3 • 7) If t1 > t2, set δ = - δ /2, t1 = t2, and goto step 3 <1> :

  27. Results for Max-min zPmin • Author’s Claim: • Results show “adaptively selecting z leads to superior performance over the minimal power algorithm (z=1) and the max-min algorithm (z=∞) • Question: • The results do show better results than the max-min algorithm. • The results show little or no improvement over the Pmin algorithm • When z=1, the maximum (or near maximum) value is achieved. • Better resolution graph may be necessary.

  28. Zone-Based Routing • The max-min zPmin algorithm is hard to implement on large scale networks • Accurate knowledge for all node available power is required. • In large networks would result in large control overhead, defeating the purpose of energy-efficient routing • A hierarchical approach to the max-min zPmin algorithm • Group nodes into zones (based on geographic positioning) • Zone hosts direct local routing • Messages are routed through zones based on the power of the zone. • Issues to consider • How zone hosts estimate the power of the zone • How to route messages within a zone • How to route messages between zones

  29. Zone-Based Routing • Zone Power Estimation • Zone Power: Estimate of # of messages that can flow through the zone • Estimation is relative to the direction of the message transition • Zone assumed to be square • neighbors: north, south, east, west • Neighbor zones overlap • Estimation Process • Choose Δ, and P = 0 • Repeat { • Find max-min zPmin for Δ Messages • Send Δ Message through the zone • P = P + Δ • } (Until some nodes are saturated) • Return P

  30. Zone-Based Routing • Global Path Selection • View the Global network as a weighted graph • Each zone has 5 vertices (for square zones) • 1. Zone, and 4. Directions • Zone Weight = Infinite • Direction Weight = Power level for sending message through in this direction • From Previous Slide • There are no edge weights • To Route between zones, us the max-min algorithm on the zone graph • Bias routing for zones with higher power levels (modified Bellman-Ford)

  31. Zone-Based Routing • Local paths are determined using the max-min Pmin Algorithm • To select Zone-edge messages: • There can be multiple nodes within a zone edge • Zone Edge: Overlap between two zones • Example: Route messages between A and C (B in the middle) • Select the highest weight node in the section AB • Select the highest weight node in the section BC • Use min-max zPmin algorithm to compute the best path between the edge nodes.

  32. Zone-Based Routing • Results: • Zone-Base Routing vs. max-min zPmin • Requires Less Control Overhead • Sacrifices Overall Performance • Two Scenarios • 94.5% and 96% lifetime of the max-min zPmin is achieved • Max-min zPmin : 1000 control messages flooded (1000 nodes) • Zone-Based : 24 control messages flooded (24 zones) • After Zone Power Estimation • ~42 Nodes per Zone (assuming even distribution) • Zone-based routing “dramatically reduces” the simulation running time

  33. Conclusion • First Paper: • Overview of power-aware routing • Second Paper • Two algorithms developed for power-aware routing • Max-min zPmin: More effective at the cost of large overhead • Zone-based: ~95% of max-min zPmin is achieved with significantly less overhead

  34. Thank you! Question/Comments?

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