Delay-Minimized Route Design for Wireless Sensor-Actuator Networks Edith C.-H. Ngai 1, Jiangchuan Liu 2, and Michael R. Lyu 1 1 Department of Computer Science and Engineering, The Chinese University of Hong Kong 2 School of Computer Science, Simon Fraser University, Vancouver, BC, Canada IEEE Wireless Communication & Networking Conference 2007

Outline Introduction Related Work Route Design Problem (RDP) Formulation MST-Based Route Design Algorithm Performance Evaluation Conclusion and Future Work

WSN Distributed and large-scale like the Internet A group of static sensors resource constrained wireless communications

WSAN Collection of sensors and actuators Sensors numerous resource-limited and static devices monitor the physical world Actuators resource-rich devices equipped with more energy, stronger computation power, longer transmission range, and usually mobile make decisions and actuate adaptively in response to the sensor measurements

Applications

Motivation Given Each static sensor has a limited buffer Non-uniform data generation rates among the sensors Sensor stores locally sensed data and uploads the data until some actuator approaches Strategy Actuator visits locations with higher importance (i.e. higher data rate) more frequently Question How to minimize the inter-arrival time from the actuator to the static sensors??? => Route Design Problem (RDP)

Related Work Mobile elements to carry data in wireless networks Architecture using moving entities (Data Mules) to collect sensor data [Shah et. al. SNPA’03] Mobile sinks with predictable and controllable moving pattern [Chakrabarti et al. IPSN’03, Kansal et al. Mobisys’04] Mobile sinks can find the optimal time schedule to stay at appropriate sojourn points [Wang et al. HICC’05] Message ferry (MF) approach to address the network partition problem in sparse ad hoc network [Zhao et al. Mobihoc’04]

Related Work (cont.) Joint mobility and routing algorithm with mobile relays to prolong the network lifetime [Luo et al. Infocom’05] Partitioning-based algorithm to schedule the movement of mobile element (ME) to avoid buffer overflow and reduce min. required ME speed [Gu et al. Secon’05] Vehicle routing problem (VRP) Considers scheduling vehicles stationed at a central facility to support customers with known demands Minimize the total distance traveled Variations Capacitated VRP (CVRP) VRP with time windows (VRPTW)

Problem Formulation WSAN consists of multiple actuators and a set of static sensors Actuators move in the sensing field along independent routes Each static sensor has a limited buffer to accommodate locally sensed data When an actuator approaches, the sensor can upload the data to the actuator and free the buffer Sensors are assigned with different weights Wj according to their data rate, type, or importance

System Parameter

Route Design Problem (RDP)

Characteristics 1. The sensors are of different weights, according to their data generation rates and importance. Sensor locations with higher weights will achieve lower average actuator inter-arrival times. 2. Sensors upload data to actuators through wireless communications Data transmission is possible only when the distance between the sensor and actuator is within a communication range Rs. 3. It is not necessary for each route to pass through the depot (or the base station) Actuators generally can interact with the base station by wireless communications.

Definition and Property

Route Design Algorithm Design independent routes for multiple actuators Utilize multiple minimum spanning trees (MSTs) Construct M routes with equal period where highly weighted sensors will be visited more frequently A sensor location with weight W i will be visited by W i *M actuators (routes) E.g. Wi = 0.75, M=4 => Ni = 3 If all routes have the same period T, from property (2), the average inter-arrival time A avg will be T/3

(1) Clustering with MSTs Ni = ceil (Wi * M) Locations with the same Ni belong to the set Si Our algorithm builds M spanning trees Tk, where k = 1, …, M Locations with highest Ni=M will be included in all trees Then, the locations with the next highest Ni will be assigned to Ni trees with lowest costs The process repeats until there is no remaining locations

Example

(2) Form a TSP Solution The M spanning trees result in M groups of nodes to be walked through by distinct actuators The route design problem can be reduced to traveling salesman problem (TSP) for each group of nodes In literature, several algorithms to calculate the TSP paths are provided, such as the nearest neighbor, LKH, and some polynomial approximation schemes We adopt the Approx-TSP-Tour algorithm here, which use MST to create a tour and perform a preorder traversal on the tree to obtain a Hamiltonian cycle

(3) Determine the Locations of Actuators It is more efficient for a sensor to have short waiting time Maximum inter-arrival time Amax may also be an important consideration other than Aavg We focus on the sensor locations with the highest Wi and select it as reference point pr Each actuator k will be assigned to the point after travelling for time T*k/M from pr on its own route Encourage more even inter-arrival time of the actuators

Performance Evaluation Parameters

Average Inter-arrival Distance under Uniform Random Sensor Distribution N=50, M=5N=100, M=7

Average Inter-arrival Distance under Non-uniform Sensor Distribution N=50, M=5N=100, M=7

Distribution of Average Inter-arrival Distance N=50, M=5N=100, M=7

Conclusion and Future Work We focused on WSN with multiple actuators and their route design We demonstrated the problem is NP-hard and proposed an effective MST-based approximation algorithm It aims at minimizing the overall inter-arrival time of the actuators It differentiates the visiting frequency to sensor locations with different weights Simulation results suggested that the algorithm remarkably reduces the average inter-arrival time Future work: Improve the performance of the route design algorithm and consider the cooperation among the actuators

Thank you!