Delay-Minimized Route Design for Wireless Sensor-Actuator Networks
Edith C.-H. Ngai1, Jiangchuan Liu2, and Michael R. Lyu1
1Department of Computer Science and Engineering, The Chinese University of Hong Kong 2School 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
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
Characteristics1. 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.
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 Wi will be visited by Wi*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 Aavg 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
(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
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