“Controlled Straight Mobility and Energy-Aware Routing in Robotic Wireless Sensor Networks ” Rafael Falcon, Hai Liu, Amiya Nayak and Ivan Stojmenovic

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

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.

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

Prolonging Network Lifetime Our proposal: –A hybrid routing-mobility framework for the optimization of network communications. 1.Find energy-aware route from S to D. 2.Move relay nodes from current to optimal positions in straight line while preserving their connectivity along the way.

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.

Existing Solutions Goldenberg, Lin, Morse, Rosen and Yang: “Towards mobility as a network control primitive”, MOBIHOC, pp , 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. [GLMRY04]

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

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

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]:

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

Our Approach 1. 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.

Our Approach 2.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.

Our Approach

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 , [LNS07] Minimum Power-over-Progress Routing (MPoPR): Optimal Hop Count Routing (OHCR): roundto k select neighbour with distance closest to d(s,t)/k More than optimal number of nodes selected

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.

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.

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.

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. S D

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).

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. S D

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. S D

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

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.

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.

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

Wireless Sensor Networks