How to make a sensor network live longer?

1. How to make a sensor network live longer? Presentator: Yibo Sun Course prof.: Kyoung-Don Kang

2. Agenda • The definition of Lifetime of sensor networks • To make the whole network live longer -- Energy balancing strategy • To make an application live longer -- Energy optimization for target area

3. What is the lifetime? • The time till all nodes die out? • The average of nodes’ lifetime? • The time for the system to work properly? • The time for an application to work properly?

4. Some definition of Lifetime • [Blough,et al. 02], defines the Lifetime of the sensor network as min {t1,t2,t3} • t1 is the time it takes for the cardinality of the largest connected components to drop below c1*n(t), where n(t) is the number of alive nodes at time t • t2 is the time it takes for n(t) to drop below c2*n(0) • t3 is the time it takes for the area covered to drop below c3*L^d • 0 <= c1 ,c2, c3 <= 1

5. setting c1 =0 and c2 = 1 corresponds to defining lifetime as the time it takes for the first node to die • setting c1 =1 and c2 =0 corresponds to defining lifetime as the time to network disconnection.

6. E.G • Node#1 is the only sink • Node#6’s lifetime = The Lifetime

7. Definition by coverage ratio • By setting c1=c2=0, and c3=q, then we obtain the definition of network lifetime given in [Zhang,et al. 04] , which defines lifetime as: • the entire interval in which at least q portion of the region R is covered by at least one sensor node. (q =1 indicates full coverage)

8. Definition by target node/area • [Duarte-Melo, et al. 02] defines the lifetime of a sensor network as the expected lifetime of any given sensor in the network. In a densely deployed sensor network this definition is extend to be the time until a certain percentage of the sensors died. • In [Ye, et al. 02] The lifetime is defined as the time it takes for the coverage (defined as the ratio of the area covered by working nodes to the total area) to drop below, and never exceed again, a pre-determined threshold.

9. “The Lifetime” • Hence, different application has different lifetime definition • “The Lifetime” here is defined by how long the target function unit works properly. • i.e. The lifetime of a subgraph G’(V) in whole graph G(V)

10. Target Function Unit • The nodes near the event together with the notes on the route (red line) • The target nodes’ lifetime = The lifetime

11. How to make a whole network live longer? • Main ideas: • Reduce the total power consumption • Efficiently transmit the data packets • Synchronously consuming all nodes’ energy • [Joongseok, et al, 2005] Maximum lifetime is NP-hard!

12. Minimum Energy vs. Maximum Lifetime • Lifetime under minimum energy routing is 67% of that under maximum lifetime routing

13. Some assumptions • Energy consumption model: • consider packet sending and receiving only • Simplified Radio Model: • Radio transceiver, • Micro Controller, • Energy Source

14. In fixed transmit radio power level, energy used to send/receive one bit is • Transmiting a k-bit packeta distance d costs • Receive a k-bit packet costs • In real propagation model, E = k*dc, 2<c<4

15. An experiment on lifetime of routing protocols of different category • Direct communication protocol • Minimum-transmission-energy routing protocol • Clustering

16. Direct communication • can be generated to protocols based on multiple base stations or anchor nodes • Energy used on sending one k-bit message is:

17. MTE • Shortest path • GPSR • Energy used on sending one k-bit message is: • Each node consumes:

19. Clustering • Energy consumed when sending a k-bit message: • Each node consumes:

21. The applicationdies long before the last node dies!

22. How about energy awaring routing? • Minimum Total Transmission Power Routing (MTPR) • Attempts to reduce the total transmission power per packet. • Prefers routes with more hops with short transmission ranges than few hops with longer transmission rage. • Problem: cost more extra energy , delay, not scalable • Min-Max Battery Cost Routing (MMBCR) • Consider the remaining battery power of nodes as metric. • Nodes with high residual capacity participate in routing more often. And prefers to choose a path whose weakest node has the maximum remaining power.

23. Conditional MMBCR • Set a parameter as threshold, if no node in the chosen route with MMBCR algorithm, whose battery capacity is lower than , MMBCR applied, else, use MTPR.

24. Seems end-to-end optimization is not practical.

25. Hop-to-Hop Optimization • Take energy consumption in routing metric • NADV [Seungjoon et al, MobiHoc’05] • Select neighbors with optimal trade-off between proximity and link cost

26. Advance: advantage by greedy option • Normalized Advance: • Cost(n) = fraction of successful data transmission to neighbor n

27. GEAR (Geographically and Energy Aware Routing) • Each node maintains a neighbor table • Energy levels and locations of each neighbor • Cost to transmit to each neighbor • Packet is forwarded to neighbor with smallest cost

28. Why not balance the energy? • Make clusters to be balanced in member • Balanced k-clustering [Soheil, et,al.Sensors 2002] • Make every node to be key node • Low-Energy Adaptive Clustering Hierarchy (LEACH) • Combine direct communication and MTE • [Martin Haegnni, ISCAS '03 ]

29. Balanced K-clustering • Minimum cost flow question, O((n+k)3)

30. LEACH • Using randomization to distribute the energy load evenly • Break up operation into rounds • Set-up phase • Cluster-head Advertisement • Cluster Set-Up • Transmission schedule creation • Steady-state phase • Data transmission to cluster head • Signal prosessing (Data fusion) • Data transmission to the base station

32. Combine direct communication and MTE • Node i transmits locally generated packet to next neighbor with probability ai ,and directly to the sink with bi = 1 - ai . • Incoming packet always forward to the neighbor • Goal: choose ai to achieve energy balancing

33. Assume distance between node is the same • By solving • In 5 nodes: b1…b5 are 0.0301,0.0438,0.0694,0.1250,0 • In 10 nodes: 14% lifetime increased with an extra energy consumption: 60%~80% • To make all the nodes live same shorter???

34. How to make an application live longer? • Here, “an application” implies it cares more about a set of nodes instead of all • Turn off the redundant nodes down or make them to sleep…can be a choice • Set priority to different nodes and consider as a factor in routing. Thus to divide a subgraph from the graph.

35. An application-oriented GPSR version • Set a VIP-rate p (0<=p<=1) to every node, initially 0 • Set a threshold h (not carefully considered!) • When event arises, nodes near the spot are set their priority to a higher value, say 0.8 • And it send a VIP-awareness packet to its neighbor

36. When an intermediate node want forward a packet • if there exists a greedy option, it compares the VIP-rates • If higher, then do greedy forward • If lower, then do primeter forward • If no greedy option, follow right hand rule

37. Maintenance • all nodes maintain a single-hop neighbor table • At source: • mode = greedy • Intermediate node: • if (mode == greedy) { greedy forwarding; if (no_greedy_option||greedy_option_VIPrate - this.VIPrate>h) mode = perimeter; } if (mode == perimeter) { if (have left local maxima && local maxima’s VIPrate - this.VIPrate<h) mode = greedy; else (right-hand rule); }

38. Original Networks

39. Earthquake happen What we need is to optimize lifetime of this subgraph

40. Spread the VIPrate or not? This node still suffers from large traffic

41. Earthquake moves… What we need is to optimize lifetime of this subgraph

42. After optimization Work in lower cycle

43. A analysis by hand • Before optimization Receive 1 packet Forward 1 packet Send 1 pakcet Send 1 packet Energy consume: 55 Whole energy: 92

44. After optimization Energy consume: 49 Whole energy: 100 Save for subgraph: 6 Extra energy: 8

45. Energy consume: 32 Whole energy: 98 Save for subgraph: 24 Extra energy: 6

46. Thanks