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 Paper #2 –Development of Specific Energy-Aware Routing Algorithm –Online Algorithm, and a Practical Implementation of the Algorithm

Paper #1: Sensor Network Survey Network Layer Considerations for Sensor Networks: 1.Power Efficiency 2.Data Centric Information 3.Data Aggregation 4.Attribute Based Addressing / Location Awareness Focus of this Presentation: #1: Power Efficiency in Sensor Network Routing

Energy Efficient Routing Approaches for Selecting an Energy-Efficient Route 1.Maximum Available Power (PA) Route –Select paths containing nodes with the most residual power 2.Minimum Energy (ME) Route –Select paths that consume the least amount of energy 3.Minimum Hop (MH) Route –Select paths that utilize the least amount of network hops 4.Maximum Minimum PA Node Route –Select the route that maximizes the minimum residual energy

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

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

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

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

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

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

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

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.

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.

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 zP min 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 zP min theorem

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)

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

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.

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.

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

The “z” Parameter Two extreme solutions to power-aware routing 1.Compute the path with minimal power consumption: P min 2.Compute the path that maximizes the minimal residual power Author’s Goal: Optimize for both 1 and 2 Methodology: –Relax the P min 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 zP min ” Algorithm Select path that consumes at most z*P min while maximizing the minimal residual power fraction

Max-min zP min Algorithm Definitions: –P(v i ) Initial power level of node v i (at time t=0) –e ij weight of the edge between v i and v j (cost of transmission) –P t (v i ) Power of node v i at time t –u tij Residual power of node v i after sending message to node j

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

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

Max-min zP min What this algorithm accomplishes: –Iteratively finds paths with higher cost, while remaining below the threshold zP min –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 P min path

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

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

Results for Max-min zP min 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 P min algorithm When z=1, the maximum (or near maximum) value is achieved. Better resolution graph may be necessary.

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 zP min 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 1.How zone hosts estimate the power of the zone 2.How to route messages within a zone 3.How to route messages between zones

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

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)

Zone-Based Routing Local paths are determined using the max-min P min 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 zP min algorithm to compute the best path between the edge nodes.

Zone-Based Routing Results: –Zone-Base Routing vs. max-min zP min Requires Less Control Overhead Sacrifices Overall Performance –Two Scenarios 94.5% and 96% lifetime of the max-min zP min is achieved Max-min zP min : 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

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

Thank you! Question/Comments?