Joint Mobility and Routing for Lifetime Elongation in Wireless Sensor Networks †

1. Joint Mobility and Routing for Lifetime Elongation in Wireless Sensor Networks† Jun Luo, Jean-Pierre Hubaux Laboratory of Computer Communications and Applications (LCA) School of Computer and Communication Sciences Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland †This work was supported (in part) by National Competence Center in Research on Mobile Information and Communication Systems (NCCR-MICS), a center supported by the Swiss National Science Foundation under grant number 5005-67322. http://www.terminodes.org 1

2. Outline • Motivations • The longevity of sensor networks is important • Traditional solutions to improving network lifetime • A new trend that we follow • Our approach: Joint mobility and routing • Basic idea • To move or not to move • Optimum mobility strategy • Better routing strategy • Implementation issues • Simulation results

3. Longevity is Important Our view of sensor networks: environmental monitoring Longevity is very important for many reasons: deployment costs, environmental disturbance, ...

4. Traditional Solutions • Basic principle: flow scheduling to balance the load among forwarding nodes • Example – Chang & Tassiulas [1]: linear programming to maximize the time when the first node dies • Problem: only the load among nodes that are at the same distance from the base station is balanced. • Consequence: the nearer a node is from the base station, the higher the load it takes

5. A New Trend – Mobile Base Station • Basic principle: picking up data from nodes with a mobile base station (a mobile relay approach) • Examples: • Shah et al. [15]: Data MULE: unpredictable mobility • Chakrabarti et al. [16]: Predictable observer mobility • Kansal et al. [26]: Controllable mobility • Problem: the latency of data delivery is large. • Consequence: these approaches are limited to certain applications that do not have a stringent latency requirement

6. Outline • Motivations • The longevity of sensor networks is important • Traditional solutions to improving network lifetime • A new trend that we follow • Our approach: Joint mobility and routing • Basic idea and model • To move or not to move • Optimum mobility strategy • Better routing strategy • Implementation issues • Simulation results

7. Basic Idea • Move the base station to distribute over time the role of “hot spots” (i.e., the nodes around the base station) – a complement to the traditional flow scheduling solution • The data collection continues wherever the base station is, so the solution does not sacrifice latency – in opposition to the mobile relay approach

8. Network Model • A set of N nodes of Poisson distribution with intensity within a circle for radius R • Constant data rate  between a node and a base station • An overall energy consumption  of to receive and transmit a unit of data • Fixed transmission and sensing range r • Load-balanced routing

9. Problem Definition • Network Lifetime: the time span from the sensor deployment to the first loss of coverage. • We convert the problem of maximizing network lifetime to a min-max load problem: • because the area with the highest average load will most likely lead to the first lose of coverage (which indicates the end of the lifetime). • Existing solutions to this problem involve only strategies concerned with nodes (e.g., energy conserving routing). • We intend to consider (base station)mobilitystrategy androutingstrategy together.

10. Modeling the Load of Sensor Nodes – I We take w =r We model average load rather than exact load, i.e., • (a) is based on Ganjali & Keshavarzian [23]: • Rectangular envelope of width 2w for routing paths • Two conditions for a node n to be on the way from x to B • (b) is a new model for nodes within the transmission range of the base station

11. Modeling the Load of Sensor Nodes – II We use S3 to represent the average load taken by n. The model is equivalent to the previous one if: • Computing the angle for an arbitrary node is not trivial: it cannot be achieved analytically. • Fortunately, computing  for a node at the center is doable. So we can use this value as an estimation for an arbitrary node

12. To Move or Not to Move – Static Case Conclusion: the nodes around the base station use up their energy much faster than other nodes. Therefore, their lifetime upper bounds the network lifetime.

13. To Move or Not to Move – Mobile Case • Conclusion: mobile base station (even with an arbitrary moving trajectory) does help to balance the load. Further improvements consist in: • Reducing for the hot spot (the center) • Reducing the network size R

14. Optimum Mobility Strategy – I • Searching the trajectory space is not trivial, but the following steps can reduce the space size: • By definingperiodicmobility as recurrent movements with a constant period, we can consideraperiodictrajectory as periodic mobility whose period is the same as the network lifetime. • CLAIM 2:Symmetrictrajectory (rotation symmetry about the center for all degrees) is at least as good as itsnon-symmetricversion. • Finally, we show that, by analytically comparison among all symmetric trajectories,CLAIM 3: The best trajectory is the network periphery (which minimizes ).

15. Optimum Mobility Strategy – II CLAIM 2:Symmetrictrajectory (rotation symmetry about the center for all degrees) is at least as good as itsnon-symmetricversion.PROOF: Conclusion: We only need to consider:(i) movements on concentric circles(ii) identical frequency movements in annuli.

16. Optimum Mobility Strategy – III CLAIM 3: The best trajectory is the network periphery (which minimizes ).

17. Better Routing Strategy Conclusion: the scheme does further improve the network lifetime (see simulations for details), but analytical predication is hard to achieve due to the complicated situation around the base station trajectory. • The ideas: • Although reducing R is impossible, it is possible to reduce the radius of the section where short path routing is applied • We divide the network into two sections and exploit the redundant energy capacity of one section to compensate the other one

18. Implementation Issues • How to move? Robot + Node, see Butler [13] and Kansal et al. [26] for details • How to build routing path? • Pre-computing can be done with a discrete movement that coincides with sensor locations • Periodical querying or routing information exchange builds routing path automatically • What about round routing? Trajectory based forwarding (Niculescu & Nath [31]) • What if a non-circular network? Periphery mobility can be nearly optimum, and it has a practical significance. A joint strategy depends on the shape of the network region

19. Outline • Motivations • The longevity of sensor networks is important • Traditional solutions to improving network lifetime • A new trend that we follow • Our approach: Joint mobility and routing • Basic idea and model • To move or not to move • Optimum mobility strategy • Better routing strategy • Implementation issues • Simulation results

20. Simulation Setting • High level simulator that ignores the MAC effects • About 800 nodes deployed within a circle of 10 unites with density  = 8/π • Transmission range r = 1 unit • Discrete movement (of the base station) consists of several steps • Emulating load-balanced routing by shuffling links weights before searching for a routing path with Dijkstra’s algorithm

21. Static vs. Mobile – II

22. Static vs. Mobile – II • Three reasons for the spikes • Irregular topology • discrete movement • Imperfect emulation of load-balanced routing

23. Optimum Mobility

24. Better Routing

25. Conclusions • Our contribution: using mobile base station to extend the network lifetime • Analytical models to characterize the energy consumption patterns corresponding to certain movement strategies • Opitmality results on the movement strategy • Better routing strategy (than short-path routing) • We also perform high level simulations to evaluate the validity of our analysis. • Future work: • Implementations and field tests • Detail model taking topology into account • Mobile base station helps …

26. Let’s take a short break 

27. Load Balancing through Flow Scheduling The ideas:All possible routes should be exploited How? Off-line scheduling

28. A Linear Program Formulation TQi qik qji i Ni Ei Note: The problem looks like max-flow problem, but it is not because the link cost qik changes with different receivers i eijqik

29. Problems with Off-line Scheduling • The dynamics of a network is not taken into account • The nodes are static does not mean the links are static either • What happens after the first node dies • The energy consumption profile is not correct • The energy consumption is related to nodes instead of links • The overhearing effect should be considered • No existing routing is flow-based (again due to the required adaptability to the network dynamics). So this approach provides only an upper bound on the network lifetime

30. Data MULES: Let the God Collect Data Access points = + MULES Sensor nodes Random walk The problem:only the God knows when the collected data can be delivered to the access points and when the MULEs are going to show up to pick up the next bunch of data

31. Exploiting the Predictability Access points Predictable Observer Sensor nodes Pre-defined route • The problem: • The speed does matter • Why don’t they consider multi-hop? If a multi-hop routing is adopted, how should it behave?

32. Why Controlled Mobility • Adapt topology to network requirements • More adaptation than possible with protocol parameter configuration in static nodes • Increase capacity • Enhanced bandwidth • Energy saving • Repair faults • Connect sparse networks • Other benefits • Improved localization, time synchronization, coverage, calibration, security Slides borrowed from Kansal et al. [26]

33. System Infrastructure Controlled Mobile Element Used to Route Data Access Point Data Sources Mobile Router Slides borrowed from Kansal et al. [26]

34. System Hardware MOTE MOTE Static Node Hardware STARGATE PACKBOT Mobile Router Hardware Slides borrowed from Kansal et al. [26]

35. Energy and Bandwidth Advantages 5 5 D 4 4 4 4 3 3 C Hop distance to base 2 3 3 2 B 2 A 1 1 2 Multihop Routing Mobile Infrastructure • Relay traffic reduced (energy saving) • Number of wireless error-prone hops reduced (enhanced bandwidth) Slides borrowed from Kansal et al. [26]

36. Problems • Sacrificing latency for bandwidth and energy • Complicated control scheme is necessary to adaptively change the moving speed • What happens if nodes do not have enough memory to cache the data • Lack of analytical results, especially the results on the optimality of the movement trajectory • Adaptive mobility requires sophisticated routing protocol

37. Power Consumption Profile ofLow Power Radio– CC1000 radio and B_MAC of Mica2 Motes

38. Common Impressions are WRONG • Tx power consumption is significantly higher than that of Rx • No power consumption if no receiving or transmission happens • A node consumes Rx power only when receiving its own packet

39. Tx Power vs. Rx Power • Tx power determines the transmission coverage • There is no deterministic Tx range for a given Tx power • Rx power is always fixed • The radio can already achieve a remarkable Tx coverage with Tx power < Rx power 30% 60% 90%

40. Zoom in Rx Power – Idle Listening • A power-on radio consumes energy constantly because of idle listening, i.e., listen to an idle channel • It turns out that all transmissions and receptions are free • This result invalidates numerous research efforts • For example

41. Solutions to Reduce Idle Listening • Solution 1: Coordinated Sleeping (S-MAC of UCLA) • Distributed synchronization consumes energy! • Solution 2: Preamble Sampling (B_MAC of Berkeley) Sleep Sleep Listen Listen Listen Listen Sleep Sleep Synchronization between two neighbor nodes Receiver Sleep Sleep Data Rx Sender Data Tx Preamble

42. Zoom in B_MAC – Duty Cycle • Duty cycle (the percentage of radio power-on time) is tunable! • Preamble length ≥ Sleeping time • Long sleeping time trades transmission latency for low power consumption (suitable for sparse transmission) • A long preamble increases the power consumption of all nodes in the sender’s transmission coverage due to overhearing Receiver Data Rx Sleep Sleep Sender Data Tx Preamble CheckInterval

43. Zoom in Rx Power – Overhearing • A node has no knowledge about the destination of a packet before it fully receives the packet! • An unlucky non-receiver spends energy to receive a long preamble and the following packet • Current solution: RTS/CTS Non-receiver Data Rx Sender Data Tx Preamble Non-receiver C Data Rx Receiver Data Tx R Preamble Sender

44. Problem with RTS/CTS • Transmitting RTS/CTS consumes energy, and also increases the complexity of MAC protocol • When the duty cycle is low, the length of preamble is significantly longer than the packet length, so using RTS/CTS does not help too much Maybe you will have a magnificent solution 