“ Controlled Straight Mobility and Energy-Aware Routing in Robotic Wireless Sensor Networks ”

1. “Controlled Straight Mobility and Energy-Aware Routing in Robotic Wireless Sensor Networks” Rafael Falcon, Hai Liu, Amiya Nayak and Ivan Stojmenovic www.site.uottawa.ca/~ivan

2. Presentation Outline • Motivation and Problem Statement • Existing Solutions • Our Approach • Dynamic Optimal Progress Routing • Depth First Search (DFS)-based Routing • Routing for Disconnected Endpoints • Move Directly (MD) • Experiments • Conclusions and Future Work

3. Problem Statement Assumptions: • Fixed source (S) and destination (D) • Long-term traffic (e.g. video surveillance) • Mobile relays (sensors, robots, vehicles) Problem: • Find a route from S to D. • Move each relay node to an optimal spot. • Minimize TX power and moving distance.

4. Prolonging Network Lifetime Two commonly used methodologies: 1. Controlled Node Mobility 2. Energy-Aware Routing

5. Prolonging Network Lifetime • Our proposal: • A hybrid routing-mobility frameworkfor the optimization of network communications. • Find energy-aware route from S to D. • Move relay nodes from current to optimal positions in straight line while preserving their connectivity along the way.

6. More Assumptions • Common communication radius r. • TX energy cost model is where d is distance. • Each node knows its own location. • Each node learns location of its 1-hop neighbors via periodical “HELLO” messages. • Mobility cost is proportional to moving distance.

7. Existing Solutions Goldenberg, Lin, Morse, Rosen and Yang: “Towards mobility as a network control primitive”, MOBIHOC, pp. 163-174, 2004. [GLMRY04] • First controlled mobility approach. • Finds a route to destination D. • Relay nodes move in rounds (MR) to their optimal spots along the S-D straight line for energy saving.

8. Existing Solutions Greedy (forward to neighbor closest to destination) NP (forward to nearest neighbor with progress to D) The two routing algorithms used in [GLMRY04]

9. Existing Solutions • Each hop iteratively moves to the midpoint of its upstream and downstream neighbors. • Repeat until convergence. Move in Rounds (MR) Illustration of one movement round: D S

10. Existing Solutions • Initial route is not energy-efficient. • Greedy and NP may fail in sparse networks. • Iterative node movement in rounds requires multiple synchronization messages and causes unnecessary zigzag movement. • Large delay and possible communication failures. Problems with [GLMRY04]:

11. Existing Solutions Chen, Jiang and Wu: “Mobility control schemes with quick convergence in WSNs”, IPDPS, pp. 1-7, 2008. [CJW08] • Improve MR with two more advanced mobility schemes (still in rounds) • Straight mobility never considered as it can break path connectivity during node movement.

12. Our Approach • Initial route is built in an energy-efficient way. • Routing algorithm ensures message delivery. • Depth First Search (DFS) routing. • Even target scenarios where S and D are disconnected!! • By collecting k relay nodes and dispatching them to their final locations. • k is optimal hop count.

13. Our Approach • Relay nodes move directly (in straight line) to their optimal locations while preserving the path connectivity as they go. • Path connectivity can be maintained if the relay nodes coordinate among themselves prior to movement. • Two connectivity preservation approaches are presented.

14. Our Approach

15. Power-Aware Routing • Many power-aware routing algorithms in the literature. Liu, Nayak and Stojmenovic: “Localized mobility control routing in robotic sensor wireless networks”, MSN, pp. 19-31, 2007. [LNS07] • Minimum Power-over-Progress Routing (MPoPR): • Optimal Hop Count Routing (OHCR): • round to k • select neighbour with distance closest to d(s,t)/k • More than optimal number of nodes selected

16. Dynamic Optimal Progress Routing (DOPR) • compute the optimal hop count k • Let U be current node and p the current hop count. • Compute dynamic optimal progress as: • Select as next node the neighbor V such that: • DOPR fails is no such neighbor is found.

17. Depth First Search (DFS) Routing • Memorization is required at each node. • How does it work? • Each node sorts its neighbors according to a particular selection criterion. • Packet is sent to top node in the list. • A visited node always rejects the packet • The sender then tries the next node in the list. • DFS fails only when S and D are not connected.

18. Depth First Search (DFS) Routing • We embed DOPR’s selection criterion into the DFS routing machinery. • The resulting power-aware algorithm is DOPR-DFS. • It behaves exactly as DOPR if no greedy failure occurs. • Otherwise, it keeps sending the packet to the remaining neighbors in the local sorted list of the current node. • Other hybrids: MPoPR-DFS, OHCR-DFS.

19. Routing for Disconnected Endpoints • CKNR: Collect k neighbors via DFS routing. • Send them to their final locations along S – D line. • DOPR-CKNR, OHCR-CKNR, MPoPR-CKNR, etc. D S

20. Move Directly (MD) • Moves the relay nodes straight (concurrently, in just one round) to their final energy-saving locations. • MD yields highest profit in spared TX power. • MD yields fastest convergence rate. • But… path connectivity can be broken during concurrent relay node movement. • Yet… it can be maintained if we introduce some degree of inter-relay coordination. • Two different approaches (proofs in the paper).

21. Distance-Free Path Preservation • Hops are initially and finally connected (among themselves and with S and D). • Hops agree on same departure and arrival times. • Hops move at constant, individual speeds. D S

22. Distance-Bound Path Preservation • Hops are initially and finally connected (among themselves and with S and D). • Initial and final internodal separation <= • Hops agree on same departure time. • Hops move at constant speed. D S

23. Experiments • DOPR is more energy-efficient than OHCR, MPoPR, Greedy and NP. • It also yields superior mobility vs. TX power gains.

24. Experiments • Empirical results confirm the feasibility of the proposed path connectivity preservation schemes. • Routing algorithms with larger inter-nodal distances (e.g. Greedy) are more likely to break links among relay nodes during their advance to final locations.

25. Conclusions • A unified routing-mobility framework for the optimization of network communications has been proposed. • DOPR yields increased energy savings among peer routing protocols. • Message delivery is guaranteed in both sparse and dense network topologies via power-aware DFS. • Communication in partitioned networks made possible with CKNR. • Path connectivity was preserved with MD under two different scenarios.

26. Future Work • To target more complex environments (e.g. multiple independent routing flows, multicasting, etc.) • To quantify the coordination overhead required by MD.

27. Wireless Sensor Networks