ST-MAC: Spatial-Temporal MAC Scheduling for Underwater Sensor Networks
Chih-Cheng Hsu, Kuang-Fu Lai, Cheng-Fu Chou, Kate Ching-Ju Lin
IEEE INFOCOM 2009
Outline Introduction Related Work ST-MAC Framework Performance Evaluation Conclusion
Introduction Similar to terrestrial sensors, energy efficienc
y is critical considerations in UWSNs unlike terrestrial sensor utilize acoustic waves propagation is slower than RF
In UWSNs, must consider the locations of the receiver and potential interferers “Spatial-Temporal Uncertainty”
Spatial-Temporal Uncertainty
TDMA-based MAC protocols To utilize time slots efficiently, the vertex
coloring scheme is used for scheduling Propose a novel heuristic algorithm, called
Traffic-based + One-Step Trial Approach (TOTA)
Model the scheduling problem as a Mixed Integer Linear Programming (MILP) model
ST-MAC Framework Most of underwater sensors are deployed to
get data of interest periodically Each node can estimate signal-to-noise-ratio
determining interference relationships measuring the propagation delay
ST-MAC is to compute the schedule each sensor nodes knows when to switch to
sleeping mode
ST-GG Construction Base station can acquire the routing topology
G(V,E), V is a set of sensors E denotes a set of transmission links
Define PD(vi, vj) as the propagation delay between node vi and vj
Spatial-Temporal Conflict Graph Spatial-Temporal Conflict Graph (ST-CG), a
directed graph G(V ,E) V = E and E is the set of conflict relationships bet
ween any two transmissions Conflict relation Conflict(u → v),
exists if transmission of link u affects reception of link v
Example of Conflict
Two Links With Common Node Case 1.1: u.dst = v.dst
Case 1.2: u.src = v.src
Case 1.3: u.src = v.dst
Two Links Without Common Nodes
Two Links Without Common Nodes Case 2.1: ONLY one of INTER(u, v) and INT
ER(v, u) is TRUE cc,d = −3
Case 2.2: both INTER(u, v) and INTER(v, u) are TRUE conflict delays cb,c = −4 and cc,b = −2
Traffic-based One-step Trial ApproachS
MReal
MTest
Traffic-based One-step Trial Approach
MReal
MTest
S
Theoretical Analysis Propose mixed Integer Linear Programming
model solve the new type of the vertex-coloring problem
in ST-CG optimally as a benchmark to quantitatively evaluate the
performance of existing heuristic methods
Propagation Delay Constraint
Modified equations by using the Big-M method
binary variable used to transform disjunction
Inter-frame Constraint Transmission of link j in next frame must not
conflict with the reception of link I
Transmission of link i in the next frame must not conflict with the reception of link j
Minimize Problem
Performance Evaluation All simulations are implemented in NS2 Two different scales
the small topology case: 6 - 12 nodes the large-topology case: 81 - 144 nodes
Small Central-Sink Topology
Large Central-Sink Topology
Large Cluster-Sink Topology
Energy Cost
Unknown Traffic Scenarios
Conclusion Proposes a TDMA-based scheduling to solve
Spatial-Temporal Uncertainty in UWSNs Construct ST-CG that includes the propagatio
n delay information present TOTA, to solve more effectively
Derive a MILP formulation solving the optimal solution of the vertex-colorin
g problem in ST-CG graph