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Topology Control Algorithms
Davide Bilòe-mail: [email protected]
What is Topology Control? Mechanisms and algorithms to conserve energy in ad-
hoc radio and sensor networks Primary targets of a topology control algorithm
abandon long-distance communication links prevent the network from being partitioned
Secondary targets each node has “few” neighbors routing path does not have to become non-competitively long
topology control algorithms should find a good tradeoff between
connectivity and sparsness
What does a TC Algorihm do?Let G=(V,E) be an (undirected*) communication graph where V is the set of devices with |V|=n E contains edge (u,v) iff u and v can communicate directly c(u,v) minimum transmission power at which u has to transmit if it wants to send a
msg to v directly (we assume c(u,v)=c(v,u) and c(u,v)pmax)
* As devices are homogeneous, i.e., they have the same characteristics, we can
assume that if u can communicate with v directly if u transmits at power p(u), then
also v can communicate with u directly if v transmits at power p(v)p(u). This implies
that the devices all have the same maximum transmission power pmax.
Running the TC algorithm A on all the nodes yields a graph
GA=(V,EA) which is a subgraph of G
What is the Directed Communication Graph
G in the Euclidean Model?
max( , ) ( , ) ;( , )
d u v d u v pc u v
if
otherwise.
Observe: for every 1, d(u,v)d(u,v) iff d(u,v)d(u,v ).For simplicity, we will assume that =1 even though everything we will
see can be generalized to every 1
The (Directed) Communication Graph G
in the Euclidean Model is a Unit Disk Graph
The transmission range of any node v is the disk centered at v with radius maxp
max 1p
W.l.o.g., we assume that
G has bidirectional symmetric link
Formal Definition of Unit Disk Graph (UDG)
Given a set V of points in the Euclidean plane,
the Unit Disk Graph induced by V is the (undirected) graph G=(V,E)
where E contains edge (u,v) iff d(u,v)1
What does a TC Algorihm do?Let G=(V,E) be an (undirected*) communication graph where V is the set of devices with |V|=n E contains edge (u,v) iff u and v can communicate directly c(u,v) minimum transmission power at which u has to transmit if it wants to send a
msg to v directly (we assume c(u,v)=c(v,u) and c(u,v)pmax)
* As devices are homogeneous, i.e., they have the same characteristics, we can
assume that if u can communicate with v directly if u transmits at power p(u), then
also v can communicate with u directly if v transmits at power p(v)p(u). This implies
that the devices all have the same maximum transmission power pmax.
Running the TC algorithm A on all the nodes yields a graph
GA=(V,EA) which is a subgraph of G
Properties GA should have
Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a neighbor of u in GA
Reason: Asymmetric communications are unpractical
Properties GA should have
Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a neighbor of u in GA
Connectivity: there is a (direct) path from node u to node v in GA iff there is a (direct) path from u to v in G
Connectivity is not enough
A minimum spanning tree algorithm
yields a connected subgraph GMST
Not a good topology because close-by nodes in G might end too far in GMST
Properties GA should have
Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a neighbor of u in GA
Connectivity: there is a (direct) path from node u to node v in GA iff there is a (direct) path from u to v in G
Spanner: if the shortest path from u to v in G w.r.t. some criteria has cost , then the shortest path from u to v in GA w.r.t. the same criteria has cost f(). If f() is bounded from above by a linear function of , then GA is called a spanner
Spanner Connectivity
Properties GA should have Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a
neighbor of u in GA Connectivity: there is a (direct) path from node u to node v in GA iff
there is a (direct) path from u to v in G Spanner: if the shortest path from u to v in G w.r.t. some criteria has
cost , then the shortest path from u to v in GA w.r.t. the same criteria has cost f(). If f() is bounded from above by a linear function of , then GA is called a spanner
Spanner Connectivity
Sparsness: GA is sparse, i.e., |EA|=O(n) Reason: Primary target of a topology control algorithm is to abandon long-distance neighborsSparsness is not enough as sparse graphs may have high degree
Properties GA should have Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a
neighbor of u in GA Connectivity: there is a (direct) path from node u to node v in GA iff
there is a (direct) path from u to v in G Spanner: if the shortest path from u to v in G w.r.t. some criteria has
cost , then the shortest path from u to v in GA w.r.t. the same criteria has cost f(). If f() is bounded from above by a linear function of , then GA is called a spanner
Sparsness: GA is sparse, i.e., |EA|=O(n) Low Degree: Each node in GA has a constant number of neighbors
Spanner Connectivity
Low Degree Sparsness
Properties GA should have Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a
neighbor of u in GA Connectivity: there is a (direct) path from node u to node v in GA iff
there is a (direct) path from u to v in G Spanner: if the shortest path from u to v in G w.r.t. some criteria has
cost , then the shortest path from u to v in GA w.r.t. the same criteria has cost f(). If f() is bounded from above by a linear function of , then GA is called a spanner
Sparsness: GA is sparse, i.e., |EA|=O(n) Low Degree: Each node in GA has a constant number of neighbors Planarity: GA is planar, i.e., it does not have intersecting edges
Reason: we can use geometric routing algorithms on planar graphs
Spanner Connectivity
Low Degree Sparsness
Planar Graphs
A (geometric) graph is planar if it has no intersecting edges(geometric graphs we consider are graphs whose set of vertices are points on the
Euclidean plane, and edges are straight line segments)
Example of planar graph Example of non planar graph
red edges intersect
Intersection point
Properties GA should have
Symmetry: GA is symmetric, i.e., u is a neighbor of v in GA iff v is a neighbor of u in GA
Connectivity: there is a (direct) path from node u to node v in GA iff there is a (direct) path from u to v in G
Spanner: if the shortest path from u to v in G w.r.t. some criteria has cost , then the shortest path from u to v in GA w.r.t. the same criteria has cost f(). If f() is bounded from above by a linear function of , then GA is called a spanner
Sparsness: GA is sparse, i.e., |EA|=O(n) Low Degree: Each node in GA has a constant number of neighbors Planarity: GA is planar, i.e., it does not have intersecting edges
Spanner Connectivity
Low Degree Sparsness
Which TC Algorihm do we need?
We do not need a global centralized algorithm for sure no central authority in ad-hoc radio and sensor networks
What about a distributed algorithm? better than the centralized one not of practical use in case of mobile devices
We do need a local algorithm each node is allowed to exchange msg’s with its neighbors a few
times and then must decide which links it wants to keep
Topology Control Algorithms for UDG
nodes know their coordinates (for instance, nodes use GPS) Minimum Spanning Tree
distributed but not local symmetry, connectivity, low degree, and planarity
Delaunay Triangulation distributed but not local symmetry, energy-spanner, low degree, and planarity
Gabriel Graph local symmetry, energy-spanner, sparsness, and planarity
nodes can sense signal strength and can perceive from which direction a signal arrives
Cone-based local symmetry, energy-spanner, sparsness, and planarity
(an optional distributed (but not local) second phase) satisfies low degree.
Limitations of the Euclidean Model
signal attenuation is uniform, that is, the Euclidean plane is flat and free of blocking objects
Radio propagation is as in vacuum
vu
u
d(v,u)=d(v,u )
transmission range of v
If we add obstacles…
Euclidean Model does not work in realistic environments
v
u
transmission range of v
obstacle
u
d(v,u)=d(v,u )
Algorithm XTC
R. Wattenhofer and A. Zollinger, XTC: A Practical Topology Control
Algorithm for Ad-Hoc Networks, 4th International Workshop on
Algorithms for Wireless, Mobile, Ad Hoc and Sensor Networks, 2004
download link: http://www.dcg.ethz.ch/members/roger.html
Algorithm XTC works in every environment (i.e., every undirected graph G) nodes do not need to know their coordinates nodes do not need to perceive which direction a signal comes from it is local
and fast (every node communicates with its neighborhood twice) the system can be
asynchronous uniform non-anonymous
satisfies symmetry connectivity low degree planarity energy-spanner (in random UDG’s)
in UDG’s
correctness
efficiency
Algorithm XTC
Three main steps:
1. neighbor ordering
2. neighbor order exchange
3. edge selection (Nu neighborhood of u)
Algorithm XTC Algorithm XTC (description for node u)
1. (neighbor ordering) establish total order <u over u’s neighbors in G
v<uw means that u prefers link (u,v) more than link (u,w),
i.e., link (u,v) is of higher quality than link (u,w)
(for instance, v<uw c(u,v)c(u,w))
Algorithm XTC Algorithm XTC (description for node u)
1. (neighbor ordering) establish total order <u over u’s neighbors in G
2. (neighbor order exchange) broadcast <u to each neighbor in G and
receive orders <v from all neighbors v’s
3. (edge selection (Nu neighborhood of u)) Nu,Ñu:= while (<u contains unprocessed neighbors)
v:= least unprocessed neighbor in <u if (wNuÑu s.t. w<vu) then
Ñu:=Ñu{v} else
Nu:=Nu{v}
i.e., w<uv
Graph Yielded After Execution of Algorithm XTC
Nu is the set of neighbors of u computed by algorithm XTC
GXTC=(V,EXTC)
where
EXTC={(u,v)|u:vNu}
GXTC is Symmetric
Theorem (Symmetry): GXTC is symmetric, i.e., a node u includes v in Nu iff v includes u in Nv.
Proof: Assume u includes v in Ñu.
We show that v includes u in Ñv.
u includes v in Ñu because wNu Ñu with w <uv and w <vu.
When v processes u, wNv Ñv.
Thus, v includes u in Ñv.
From now on, we will tacitly assume that GXTC is symmetric
Some Assumptions Weak Assumption (WA): Neighbor orders are based on
function c, i.e., u, w<uv c(u,w) c(u,v)
Strong Assumption (SA): Every edge (u,v) has a weight l(u,v)=(c(u,v),min{id(u)*,id(v)},max{id(u),id(v)}). Neighbor orders are based on the lexicographic order** of edge weights, i.e.,
u, w<uv l(u,w) < l(u,v)
* id(w) is the identifier of node w. Nodes have distinct identifiers.**(,,)<(,,) (<) or ((=) and (<)) or ((=) and (=) and (< ))
GXTC Satisfies Connectivity
Theorem (Connectivity): Under SA, two nodes u and v are connected in GXTC iff they are connected in G.
Corollary: Under SA, GXTC is connected iff G is connected.
GXTC Satisfies Connectivity
Theorem (Connectivity): Under SA, two nodes u and v are connected in GXTC iff they are connected in G.Proof:
If u and v are connected in GXTC, then they are connected in G.
(because GXTC is a subgraph of G)
So we have to prove that
Claim: if u and v are connected in G, then they are connected in GXTC.
We prove Claim by contradiction, i.e., we assume that
there exist u and v which are connected in G but not in GXTC.
We use the following scheme:1. we choose the “right” u and v
2. we show that u and v are connected in GXTC
v
u
GXTC Satisfies ConnectivityHow to choose the “right” u and v
Theorem (Connectivity): Under SA, two nodes u and v are connected in GXTC iff they are connected in G.
Proof: …
Let Z be the set of all the pair of nodes u and v which are not connected in GXTC but they are connected in G via a direct edge.
Is Z? YES
w t
w and t are connected in G but not in GXTC
Vw: set of nodes connected to w in GXTC
Vt: set of nodes connected to t in GXTC
VwVt=Vw Vt
G
GXTC Satisfies ConnectivityHow to choose the “right” u and v
Theorem (Connectivity): Under SA, two nodes u and v are connected in GXTC iff they are connected in G.
Proof: …
Let Z be the set of all the pair of nodes u and v which are not connected in GXTC but they are connected in G via a direct edge.
Is Z? YES
u and v is the pair of nodes in Z of minimum value l(u,v)
GXTC Satisfies ConnectivityHow to prove that u and v are connected in GXTC
Theorem (Connectivity): Under SA, two nodes u and v are connected in GXTC iff they are connected in G.Proof: …What we have shown so far: u and v is the pair of nodes of minimum value l(u,v) among those pair of nodes which are not connected in GXTC but connected in G via a direct edge.
u includes v in Ñu because wNu Ñu with w<uv, i.e.,w<uv AND w<vu
l(u,w) < l(u,v) AND l(v,w) < l(u,v)
u and w are connected in GXTC AND v and w are connected in GXTC
u and v are connected in GXTC
GXTC on UDG’s(remember that (u,v) is in G iff c(u,v)=d(u,v)1)
GXTC on UDG’s has Low Degree
Theorem (Low Degree): Under WA, if G is a UDG, then GXTC has degree at most 6.
Proof: Let uV s.t. (u,v),(u,w)EXTC, i.e., d(u,v),d(u,w)1.We prove that the angle /3 by contradiction.So, assume for contradiction that </3.W.l.o.g., assume v<uw, i.e., d(u,v)d(u,w).
Claim: If d(u,v)d(u,w) and </3, then d(v,w)<d(u,w).
d(v,w)1 (v,w) is in G.Moreover, v<wu.Thus, u includes w in Ñu.By Theorem (Symmetry) (u,w)EXTC.
w
v
ucontradicts (u,w)EXTC
Proof of Claim: If d(u,v)d(u,w) and </3, then
d(v,w)<d(u,w)
u
w
v
A
B
A=|d(u,v)-d(u,w)cos |
B=d(u,w)sin
d(v,w)2=A2+B2
=d(u,v)2-2d(u,v)d(u,w)cos +d(u,w)2
(1-2cos )d(u,v)2+d(u,w)2
<d(u,w)2
(use sin2 +cos2 =1)
([0,/3), cos >0.5)
v
GXTC on UDG’s is Planar Theorem (Planarity): Under WA, if G is UDG, then GXTC is planar.
Proof: Let u,v,w,t be any 4-tuple of distinct nodes forming a quadrangle Q as in figure s.t. d(u,w),d(v,t)1.The only two intersecting edges of Q may be (u,w) and (v,t).We prove that (u,w)EXTC or (v,t)EXTC. (This is almost enough as almost every pair of intersecting edges defines a quadrangle)
As the sum of the interior 4 angles of Q is 2, one of them is /2.
W.l.o.g., assume /2. d(u,v),d(w,v)<d(u,w). As d(u,w)1, then (u,v),(w,v) are in G.Moreover, v<uw and v<wu.When u considers w, vNuÑu. As v<wu, then u includes w in Ñu.By Theorem (Symmetry) (u,w)EXTC.
u
wt
Q
v
GXTC on UDG’s is Planar
Theorem (Planarity): Under WA, if G is UDG, then GXTC is planar.
Proof: … to complete the proof, we should consider the case of three aligned points as in figure.
Exercise: Show that (u,w)EXTC.
u w
Experimental Results
Stretch factor of GXTC w.r.t.
energy metric (solid line).
Mean values are plotted in black,
maximum values in gray.
GXTC is an energy-spanner in random UDG’s
Experimental Results
Node degree of GXTC (solid line).
Node degree of G (dotted line).
Mean values are plotted in black,
maximum values in gray.
GXTC has very low degree in random UDG’s
… but we already knew it!!!
A comparison with the Gabriel Graph