Energy-Efficient Routing with Reliability Constraint

1. Energy-Efficient Routing with Reliability Constraint Team 2 Hojoong Kwon Taehyun Kim Data Communications (Sensor Network)

2. “Routing for Maximum System Lifetime in Wireless Ad-hoc Networks ” Annual Allerton Conference on Communication 1999 J.-H. Chang and L. Tassiulas Data Communications (Sensor Network)

3. Contents • Problem Suggestion • Solutions • Optimal Energy Consumption • FR(Flow Redirection) • MREP(Maximum Residual Energy Path Routing) • Simulation Results • Conclusion • Comments Data Communications (Sensor Network)

4. Problem Suggestion • Many routing algorithms are focused on minimum energy dissipating path. • Nodes in that path will be drained out quickly • To maximize the system lifetime, energy dissipating load should be distributed to all nodes in the network I’m DIEING! Minimum energy path Data Communications (Sensor Network)

5. Solutions • How can we distribute energy dissipating load? • Using multiple paths! • FR (Flow Redirection) • Data route which causes a early system halt is redirected by using other nodes. • Maximum Residual Energy Path Routing • Optimal Energy Consumption • This is computed by linear programming Data Communications (Sensor Network)

6. Optimal Energy Consumption Linear programming problem Data Communications (Sensor Network)

7. Flow Redirection Algorithm • FR is motivated by the following observation • Theorem 1(Necessary optimality condition) - If the minimum lifetime over all nodes is maximized then the minimum lifetime of each path flow from the origin to the destination with positive flow has the same value as the other paths. Data Communications (Sensor Network)

8. Flow Redirection – con’t Taker • Outgoing flow redirection • Procedure • Determine the from which path to which path • Calculate the amount of redirection (εi) • Redirect the flow properly Multihop routes +εi -εi Giver Data Communications (Sensor Network)

9. Flow Redirection – con’t • Firstly, define followings Data Communications (Sensor Network)

10. Flow Redirection – con’t • Determine the from which path to which path • If (node i’s lifetime should be increased), • If , Data Communications (Sensor Network)

11. Flow Redirection – con’t • Calculate the amount of redirection flow (εi) • Constrains Data Communications (Sensor Network)

12. Flow Redirection – con’t • Redirect the flow properly • Add & Subtract ei properly • Adding ei is easy, but subtracting is not easy since there may be some links in the path whose flow is less than ei. • Subtracting procedure • Subtract ei from qig • If qjk (g<j<d, j<k<d) < ei , subtract qjk from node j to node k recursively • Subtract ei -n· qjk Data Communications (Sensor Network)

13. MREP Routing • Maximum Residual Energy Path Routing • Define Lp as a vector whose elements are the reciprocal of the residual energy for each link in the path after the route has been used by a unit flow • Element of Lp for link (j,k) is : Residual Energy : Unit flow • The largest element (the least energy node) comparing Data Communications (Sensor Network)

14. Simulation Results • Performance Measure of algorithm X • Random graph generation • 5x5 square space • 20 nodes(5 origins & 2 destinations) • Initial energy = 1, generation rate Q = 1 • TX range : 2.5 • Energy expenditure per bit TX from i to j is Data Communications (Sensor Network)

15. Simulation Results • MTE(Minimum Transmitted Energy routing) • The shortest path algorithm based on energy expenditures per bit transmission. • The performance comparison Data Communications (Sensor Network)

16. Conclusion • To maximize the lifetime, the traffic is routed so that the energy consumption is balanced. • FR & MREP algorithm are local and amenable to distributed implementation with close to optimal performance. Data Communications (Sensor Network)

17. Comments • Bellman-Ford algorithm is used • Stationary topology is needed to be assumed • Setting up procedure has to be preceded for lifetime & energy consumption calculation. • Is MREP better than FR? • If so, what can we gain by using FR? • Routing overhead is not considered • Maybe it is quite serious. • Author did not mention how to distribute this algorithm Data Communications (Sensor Network)

18. Reliability • Definition • End-to-end, event-to-sink, sink-to-sources • Reliable data transmission, reliable event detection • Management • MAC layer vs. transport layer • hop-by-hop recovery vs. end-to-end recovery • Our approach • Energy-efficient routing algorithm while guaranteeing reliable end-to-end transmission Data Communications (Sensor Network)

19. “Providing Application QoS through Intelligent Sensor Management” WSNA 2003 “Optimal Sensor Management Under Energy and Reliability Constraints” WCNC 2003 M. Perillo and W. B. Heinzelman Data Communications (Sensor Network)

20. Introduction • Maximize lifetime while meeting application-specific QoS (reliability) • Only certain subsets of sensors may satisfy reliability constraint. • Two strategies • Turn off redundant sensors • Energy-efficient routing Data Communications (Sensor Network)

21. Example Problem • Wish to detect the presence of phenomena anywhere in the observation space • Feasible sensor sets • F1 = { S1, S2 } • F2 = { S1, S5, S6 } • F3 = { S2, S3, S4 } Data Communications (Sensor Network)

22. Problem Formulation • Given • Feasible set : • Feasible set makeup • Path makeup • : sensing power and transmission power • : receiving, processing and transmission power ( when routing sensor Sj2’s data ) Sensor Sj is in set Fi Else Sensor Sj1 is included on Sj2‘s lth path Else Data Communications (Sensor Network)

23. Problem Formulation • Find • Total time that the set Fi is used : • Fraction of time that path l is used to route Sj’s data during the time that Fi is used : • Energy constraint • Maximize data sink not in Sj’s tx range otherwise Data Communications (Sensor Network)

24. Maximum Flow Graph Problem R21 Energy P211 S1 F1 Time S2 s F2 d S3 P431 R43 P432 S4 F3 Data Communications (Sensor Network)

25. Maximum Flow Graph Problem S S S 3 3 3 1 1 1 4 4 4 2 2 2 Data Communications (Sensor Network)

26. Energy S1 Time S2 s d S3 P431 R4 P432 S4 F3 Maximum Flow Graph Problem Arc multipliers to convert energy to time Capacities on arcs from initial energy constraint Arc multipliers to normalize time contribution S4 has 2 valid routes Data Communications (Sensor Network)

27. Simulation Results • Lifetime vs. transmission range • Environment dimension : 100m x 100m • Number of sensors : 100 Data Communications (Sensor Network)

28. Simulation Results • Lifetime vs. number of sensors • Transmission range : 25m Data Communications (Sensor Network)

29. Simulation Results • Lifetime vs. field size • Node density : 0.01 node/m2 Data Communications (Sensor Network)

30. Conclusions & Comments • Joint optimization of sensor scheduling and data routing • Optimally balance the tradeoff between application performance and network lifetime • Centralized solution using global information • High computation and signaling cost • It may not be easy to find the feasible sensor sets. Data Communications (Sensor Network)