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Unformatted text preview: Constrained Random Walks on Random Graphs: Routing
Algorithms for Large Scale Wireless Sensor Networks Sergio D. Servetto Guillermo Barrenechea School of Electrical and Computer Engineering
Cornell Univerisity Lab. de Communications Audiovisuelles
Ecole Polytechnique Federale de Lausanne http://people.ece.cornell.edu/servetto/ [email protected]ﬂ.ch ABSTRACT 1.1 Networks of MicroRouters We consider a routing problem in the context of large scale networks with uncontrolled dynamics. A case of uncontrolled dynamics that has been studied extensively is that of mobile nodes, as
this is typically the case in cellular and mobile adhoc networks.
In this paper however we study routing in the presence of a different type of dynamics: nodes do not move, but instead switch
between active and inactive states at random times. Our interest
in this case is motivated by the behavior of sensor nodes powered
by renewable sources, such as solar cells or ambient vibrations.
In this paper we formalize the corresponding routing problem as a
problem of constructing suitably constrained random walks on random dynamic graphs. We argue that these random walks should
be designed so that their resulting invariant distribution achieves
a certain load balancing property, and we give simple distributed
algorithms to compute the local parameters for the random walks
that achieve the sought behavior. A truly novel feature of our formulation is that the algorithms we obtain are able to route messages
along all possible routes between a source and a destination node,
without performing explicit route discovery/repair computations,
and without maintaining explicit state information about available
routes at the nodes. To the best of our knowledge, these are the
ﬁrst algorithms that achieve true multipath routing (in a statistical
sense), at the complexity of simple stateless operations. Wireless networks span a wide spectrum in terms of their functionality (i.e., what they are used for), organization (i.e., how the
different components are assembled to form a complete working
system), and the technologies used to build them. A longterm
project currently under way at Cornell deals with the design and
prototyping of networks with the following deﬁning characteristics: ¡
¡ Once nodes are deployed, their mobility is very limited (if
there is any mobility at all). Instead, the main source of uncontrolled dynamics in the network is the temporary failure
of individual nodes, typically due to exhaustion of the power
source (and for the duration of the “refueling” period). In this work we refer to the nodes of such a network as microrouters—the network is made up of devices whose functionality
is conceptually that of a traditional Cisco router, with the differences that they communicate over a wireless channel, their size
and throughput is many orders of magnitude smaller, and they may
come equipped with sensors that generate information locally as
well. Networks of microrouters would prove extremely useful in
a variety of very relevant scenarios, such as disaster relief operations, military and surveillance applications, cellsize reduction in
cellular networks, environmental monitoring, etc.
The development of a working network of microrouters requires
solutions to a number of technical challenges (e.g., routing, ﬂow
control, source and channel coding, power control, modem design,
hardware, etc.). Among all these, of particular interest in this paper
is the routing problem, i.e., the problem of moving data among
different network locations. Categories and Subject Descriptors
C.2 [ComputerCommunication Networks]: Network Architecture and Design,Network Protocols; G.3 [Probability and Statistics]: Markov Processes, Probabilistic Algorithms General Terms
Algorithms, Performance, Design, Reliability. 1. The nodes operate under severe power constraints, support
relatively large data transfer rates, and their number and density is large (e.g., about two dozen per square meter, over a
surface of a few square kilometers). INTRODUCTION 1.2 Complexity and Randomness Complexity Considerations in Multipath Routing Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for proﬁt or commercial advantage and that copies
bear this notice and the full citation on the ﬁrst page. To copy otherwise, to
republish, to post on servers or to redistribute to lists, requires prior speciﬁc
permission and/or a fee.
WSNA’02, September 28, 2002, Atlanta, Georgia, USA.
Copyright 2002 ACM 1581135890/02/0009 ...$5.00. Implementing a basic packet forwarding service for a network of
microrouters is a challenging problem, for which we are skeptical
that experiences drawn from routing in other types of communication networks (such as IP, telephone, mobile adhoc, cellular) can
be of much help. Instead, we feel radically new approaches to routing are needed in this context.
The high degree of unreliability of the individual microrouters,
combined with large numbers of nodes, strongly calls for multipath
routing techniques, i.e., techniques in which data ﬂows simultaneously along multiple routes. This is for the simple reason that with Work supported in part by the National Science Foundation under
grant CCR 0227676, and by a gift from the Lockheed Martin Corp. 12 1.3 Random Walks on Random Graphs many errorprone nodes on any individual route, it is almost a certainty that some node along any particular route will fail. However,
this is will not be simple to achieve, for two main reasons: There are three main elements in networks of microrouters that
pose serious challenges in the design of routing algorithms: the
large number of nodes, the lack of structure in the topology of the
network, and the uncontrolled dynamics (ON  OFF transitions) of
nodes. Routing decisions at each node are based on information the
nodes have about the state of other nodes in the network. To
deal with the fact that the network does have some uncontrolled dynamics (such as frequent failure of nodes), we are
primarily interested in routing algorithms whose dependence
of a local decision on the state of other nodes decays with
the distance separating these nodes. In this way, we can effectively limit the scope of local updates. ¡ To deal with the problem of uncontrolled network dynamics,
we are primarily interested in routing algorithms capable of
taking advantage of the vast number of routes (derived from
the size of the network) that would typically exist between
any two nodes. However, because of the size as well, it may
prove computationally unfeasible to explicitly maintain state
information at the nodes describing all these paths—in many
cases, such computations involve NPhard problems [15].
Therefore, our algorithms should take advantage of multiple
paths without an explicit listing of them. Our main insight presented in this work is that randomized algorithms [21] for routing can be used to implement multipath routing,
at essentially the cost of having each node implement independent
routing decisions plus some minimal overhead. This is illustrated
with a simple example next. To deal with the size issue, we are primarily interested in
decentralized algorithms: that is, algorithms which operate
based only on local information, and possibly on information
carried by a packet as it moves across the network. In this
way, the complexity of these algorithms is independent of
the size of the network. ¡ ¡ Another is communication complexity: with nodes going up
and down all the time, resulting in routes being created and
destroyed all the time, the communication overhead required
to maintain an accurate picture of even a single route might
be prohibitive, let alone doing this for multiple routes simultaneously. ¡ ¡ One is computational complexity: searching a large space of
possible routes (derived from having a large number of nodes
with high density) may prove computationally prohibitive for
low complexity devices like our microrouters. Randomized Algorithms
In its simplest possible form, the basic principle on which our
routing algorithms are built is as follows. Consider a graph having
nodes with maximum degree bounded by a constant independent
of , each of which stores one bit of information, and let
be a parameter: the goal is to come up with a protocol such that,
at the end of its execution, exactly nodes store 1s, exactly
nodes store 0s, and all possible conﬁgurations of 1s and 0s are
equally likely to occur.
One possible protocol may consist of having a central entity
form a list of the
different possible assignments of 1s and
0s, randomly choose one, and then communicate to each node the
value of the bit to store—this solution however has the drawback
that it requires the existence of that central entity. To simulate
the behavior of the central entity, one could for example create a
bucket with 1s and
0s, and have each of the nodes perform
uniform random sampling without replacement from this bucket—
now, although the protocol can be implemented in a distributed
manner, there is still a substantial overhead in communications to
have nodes synchronize access to the shared bucket. Ideally, what
we would like is for each node to make a decision about the bit to
store independently of the decisions made by any other node: in
that way, all processing is done locally, and no commmunication
with other nodes is required. And it so happens that if we relax
only midly the statement of what needs to be accomplished by the
protocol, such a solution is actually feasible.
Suppose that we are willing to tolerate a fraction of the time
in which the empirical ratio
( is the number of 1s in an actual execution of a candidate protocol) satisﬁes
. But
now we observe that when nodes make independent decisions (e.g.,
each node tosses a coin that lands on with probability ), if the
number of nodes is large enough then the Law of Large Numbers [7, Ch. 3] does provide the sought guarantee. And this is
the basic principle we will exploit when setting up our randomized
routing strategies. We work under the assumption of large scale
networks, and we turn both the size and the unreliability issues that
break down stateful routing algorithms to our advantage. Whereas
in general it will be difﬁcult to predict the behavior of any individual node, the behavior of a large ensemble of nodes is amenable
to analysis. Hence, our main goal in this work will be to deﬁne
routing algorithms executed by a large number of unreliable and
loosely coupled components, which give rise to the desired global
behavior—efﬁcient routing. ©
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We see therefore that the main characteristic of interest to us is
full decentralization—decentralized computations, involving only
local information.
The behavior of largescale, complex systems has been the object
of study in different branches of science for a long time. Among
these, the physical sciences provide many examples: formation of
crystals, ferromagnetic properties of materials, statistical descriptions of gases, etc. Kelly discusses the notion that fundamental
physical/economic concepts such as energy and price can provide
useful insights into the design of routing schemes for communication networks [14]. Among these examples, Kelly considers one
which essentially inspired most of the work reported in this paper:
modeling of interacting particle systems using random walks. At
a microscopic level, the behavior of a particle can be described in
terms of its position and speed, and random walk models are typically used. At a macroscopic level, that same behavior can be described in terms of quantities such as temperature / pressure / voltage / etc. (depending on the type of particles under consideration),
and ﬂuid dynamics equations are typically used. Both descriptions
are equivalent, although at different levels of abstraction.
Now, there are many similarities between the motion of particles
and routing. If we identify particles with data packets, and the network with the medium in which particles move, then the routing
problem becomes a problem of how to “push” a particle from one
location in the medium to another. Therefore, inspired by this analogy, we have chosen to formalize the routing problem as a problem
of constructing suitably constrained random walks on our graphs.
And the main challenge here is to do so under the above speciﬁed
decentralization constraints.
To construct the desired random walks, we need to address the
following issues: 13 ¢ ¡ ¡ We need to specify a desired stationary distribution for the
random walk to be deﬁned. Ideally, we would like two “macroscopic” properties to hold. First, we would like packets to
visit only those nodes which lie on “short” routes between
their source and destination nodes, to ensure low delay. Then,
subject to this constraint, we would also like the number of
packets that visit a node to be independent of the particular
node visited—by spreading out the load evenly over a large
number of nodes, the impact of node failures is minimized. ¡ We also need to deﬁne a distributed algorithm for computing the local parameters of a random walk that results in
the desired stationary load distribution—these are local rules
which, under the given decentralization constraints, yield the
desired macroscopic behavior. ¡ To make routing performance independent of the size of the
network, we require that the algorithm to compute node labels use only: (a) local state information; (b) state information maintained by onehop neighbors; (c) state information
carried by each packet. Ganesan et al. propose two different multipath schemes: one
in which multiple disjoint paths are maintained, another in
which paths are not disjoint, but interleaved. Yet among all
these paths (disjoint or braided), there is one path that is considered “primary”, whereas the other ones are maintained as
backups to deal with node failures. In the context of a different network though, a similar idea of maintaining backup
routes had also been proposed in [20]. In our work, we do
away entirely with the concept of maintaining route information. There exist multiple routes between source and destination, but each node is completely unaware of this, each node
simply randomly chooses one of its neighbors to forward a
packet—how to compute locally the pdf that each node has
to sample is the core of our technical contribution. In work of a similar nature, Ganesan, Krishnamachari et al. also
study the behavior of “epidemic” (typically, ﬂooding) algorithms in
largescale multihop wireless networks [9]. While certainly having data ﬂowing across multiple links in parallel, the focus of that
work is on understanding how these algorithms behave in large networks.
Another important body of related work deals with routing problems in mobile adhoc networks (e.g., [12, 24, 25]). In this context,
routing along multiple paths has also been studied (e.g., [22, 23]),
although not as extensively as singlepath routing.
Our interest in random graphs, and more speciﬁcally in connections between random graphs and routing problem, was sparked
by the work of Kleinberg on the algorithmic aspects of the small
world phenomenon [16]. Among other results, of interest to us is
that in that work it is shown how, for one speciﬁc family of random
graphs closely related to those considered in this work, there exist
fully decentralized algorithms of the type we seek to construct here,
that can be very efﬁcient at routing messages. Strogatz presents an
interesting overview on complex networks [30], and Watts provides
an accessible introduction to small worlds [31].
Gupta and Kumar present results on the transport capacity of
wireless networks [10]. Scaglione and Servetto interpret those results in terms of the capacity of ﬂows on graphs, and use that formulation to obtain bounds on the rate/distortion function of the
whole network, to ensure that a broadcast problem of interest in
that work admits a solution [28]. Hajek presents results on how
long will it take for a particle undergoing brownian motion with a
statedependent drift to hit a particular spot [11].
Complexity management techniques for the problem of providing fair bandwidth allocations in large networks have been proposed in [29]. And although they do not deal with routing problems
speciﬁcally, the line of thinking presented in that paper did have a
strong inﬂuence on our own thinking, by pointing out problems
with network algorithms involving complex state and by suggesting approaches to deal with them.
In summary. Routing in a network of devices with the characteristics of our microrouters is a very different problem from more
classical routing problems, such as traditional Internet routing [4,
19]. To the best of our knowledge, ours is the ﬁrst piece of work in
which “stateless routing” (i.e., the idea of routing messages without any notion of discovering / maintaining / repairing explicitly
described routes) is dealt with. In essence, our approach consists of deﬁning random walks with
a drift (so that packets move from the source to the destination),
and whose parameters can be computed under the given decentralization constraints. 1.4 Related Work
The literature on routing is extensive and spans several disciplines, so we will not attempt to be thorough in this compilation
of related work. Instead, we will present a summary of work that
most directly inﬂuenced ours, while at the same time attempting to
keep our list of references at least representative of existing ideas.
Routing using multiple paths has a long history in the context of
highspeed networks [17], where it has been proposed as a way of
reducing queueing delays in a manner analogous to adaptive routing [18], of dealing with transmission errors [6], and of dealing
with system failures [1, 2]. More recently, although a single path
ends up being used for routing, parallel multiple route computations were proposed as a mechanism to provide Quality of Service
(QoS) in adhoc networks [5]—this paper also provides a comprehensive literature survey related to QoS routing.
Recent work by Ganesan et al. proposed energyefﬁcient routing algorithms for sensor networks based on multiple routes, as a
means to combat the unreliability of individual sensors [8]. That
work provided much inspiration for our work presented here, although there are substantial differences worth pointing out:
One is that the type of devices assumed in that paper have
a “ﬁnite lifetime” (they are powered by chemical batteries
that will eventually run out) [26], whereas our microrouters
have “inﬁnite lifetimes”: we envision them being powered
by renewable sources such as ambient vibrations [13], solar
cells, or even new ones being the subject of current research
(http://www.darpa.mil/mto/solicitations/B
AA0109/S/pip.pdf). Therefore energy efﬁciency and
low power operation is important for us to maximize the
number of bits that a node can process/transmit while alive.
But when batteries run out, the node goes into replenishment mode and eventually it comes back up to life—in this
sense, our network never dies, and so the idea that “each bit
transmitted brings the network one step closer to death” (that
seems to be pervasive in much of the previous work on energy efﬁciency for sensor networks) is much less of a concern
in our setup. 1.5 Main Contributions and Organization of
the Paper
The main contribution presented in this paper is the construction
of random walks on one particular family of random graphs that
we have chosen as an abstraction for the behavior of a network of
microrouters, with all the desired decentralization properties men 14 ¡ 0 1 N−1 model [16];1 and (c) the constructions of random walks presented
here naturally precede the construction of random walks on a more
general family of random graphs of interest to us. Observe also
that all the constraints described in Section 1 on suitable routing
algorithms for our application can be translated into constraints on
suitable ’s. For example: 0
1 Vy (c.1) To ensure the avoidance of livelock conditions, we require
that if for some destination node , we have for some
, that
, then
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First we deﬁne some notation.
is a grid as shown in Fig. 1,
. The th diagonal of
is the set of all nodes
of size
such that
, and is denoted by
—to
keep the notation simple we will also write
for a diagonal,
since in this version of the work we will never deal with more than
one grid
. The size of a diagonal is denoted by
.
is the distance from
to the nearest node on the boundary of the
grid. We also divide the network into two regions: gs p of
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ute l
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g S d
eq
re
p
o ¡ In an expansion stage, packets move across diagonals with
an increasing number of nodes and consequently, the density
of packets per node decreases. ¡ In a compression stage, packets move across diagonals with
a decreasing number of nodes and consequently, the density
of packets per node increases. These concepts are illustrated in Fig. 2.
Note that other than boundary nodes, for any node
there are
exactly two neighbors on a shortest path from
to
; these neighbors have coordinates
and
. So, in
this particular topology, and under constraint (c.1), a random walk
is deﬁned by a single number , the probability of choosing one of
these two links. By convention, we deﬁne to be the probability of
forwarding a packet to the neighbor that is closer to the boundary
of grid (
being the probability of forwarding to the other). And
then we have the following result: g ( l S d g Sk( l d
e
e
e
e
© 5( © g Sde d g S d
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a uniform distribution on diagonals is g S d
e if that achieves (1) is a node in the expansion stage, and ( © $e l `w %v z
%g f i
g S k
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s
d
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TB £
w q
d
w
5 v t s q i x g f vvv t i V y
x s vvFuFrph`de d We start by designing suitably constrained random walks for a
static graph with a regular structure: a cubic grid, with 4nearestneighbor connectivity only. The resulting topology is illustrated in
Fig. 1.
There were many reasons that prompted us to start our study of
routing algorithms in general random networks with the simple cubic grid: (a) the model is simple enough to allow us to obtain simple
closed form expressions for the sought distributions, yet at the same
time it is rich enough to allow us to explore issues related to scalability / numbers of nodes; (b) the model is a subset of Kleinberg’s g S d
e 7Q
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Problem First ( l $e l `w %v
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1§) tioned above. Such random walks deﬁne a large class of algorithms
for each node in the network to execute, to route packets to any destination. At a given node , let
be the set of
neighbors of . Let
be real numbers such that
,
(a pdf on the neighbors of ). When a packet
reaches node , the next hop is chosen by tossing a die whose th
face occurs with probability , and the packet is forwarded over
. By making different assumptions on the topology
the link
of the underlying network, on its dynamics, and on constraints imposed on the local pdfs, we are able to explore a large and structured
space of possible routing schemes.
The rest of this paper is organized as follows. In Section 2 we
formulate and solve analytically for the local parameters of the
sought random walks in the context of a static, regular network.
Then, we consider increasingly more complex cases that build on
this solution. In Section 3, we give routing algorithms for a static
network that is obtained as a random perturbation of the regular
network considered in Section 2. And in Section 4, we give routing
algorithms for networks which result from timevarying perturbations of the regular network of Section 2. Conclusions are future
work are discussed in Section 5. 2. g f & g f
hA`dh `s . Packets are injected at the source
, and must travel hop by hop to the destination node
. Any interior node
is connected to 4 neighbors:
,
,
, and
; the ﬁrst two are closer to the
source, the latter two are closer to the destination. A completely general
random walk on this grid is speciﬁed by giving four numbers
,
,
, and
, for each node
(except at the boundaries, where the number of neighbors is smaller).
location i fV
£ ag `sy N−1 Figure 1: Cubic grid of size
ps (c.2) To effectively exploit whatever degree of route diversity the
network provides, we require a certain “load balancing” condition of the stationary distribution
induced by the ’s.
and
: we reConsider two nodes
quire that if
, then
. What
this means is that if two nodes are at the same distance from
the source, then these nodes must be visited equally often in
steady state. (2) is a node in the compression stage. The proof in both cases proceeds by induction on the diagonals.
Consider ﬁrst the expansion stage: t Random walks on small world graphs however are beyond the
scope intended for this paper, and will be dealt with elsewhere. 15 0 1 P1 N−1 0 1
D(x,y) P1 1
D(x,y) 1 1
D(x,y) + 1 P2 (x,y) P2 (x,y) P3 P3
1
D(x,y)  1 N−1
0 1 N−1 0 Figure 3: Forwarding probabilities (left: expansion, right: compres 1 sion).
[i,j−1] [i,0]
[0,j] [i−1,j] [i,j] [0,j+1] [i+1,0] Figure 4: The three possible cases of coordinate formation. If a node N−1 has two neighbors whose distance from the source is smaller than his
and
, then
own, and these nodes have lattice coordinates
the coordinates of are
. If has only one
neighbor with smaller distance to the source, this neighbor must have
coordinates of the form either
or
, and then the coordinates
of are either
or
. Finally, the decision of which
node is
and which node is
is made arbitrarily by the source. 7 5 23
4
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9) 9 to the destination
. In the initial expansion stage,
the number of nodes among which to spread the packet load increases,
and therefore the optimal load per node must decrease. After crossing
the longest diagonal (corresponding to nodes with coordinates
)
and entering the compression stage, the number of nodes on diagonals
starts decreasing, and therefore the load per node must increase. 7 h HD4 h 2C Figure 2: The different stages in the path of a packet from the source b 734
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XC h p g eS d y
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( © $e
%g nodes, and the fractional load we want to achieve is
This situation is depicted in Fig. 2.2. For nodes at distance
from the boundary, the corresponding probability
satisﬁes ¡ The ﬁrst diagonal corresponds to the source node, and hence
and
. It follows from eqn. (1) that
, achieving a uniform packet distribution
over second diagonal. i
g (f
© Ai th ~
( i% f
£ i g £ £ d y }g £ w %v t
v t hE g t © Af hEt l hEt
i
(
vvv
t
i
( l
thE g # © Af Wt Gt ht
£i# Let
be a node located on the diagonal
. Assume (by induction) that we have already a uniform packet
distribution over
, and so the fractional load supported by each node is
. The next diagonal has
nodes, and the fractional load we want to achieve is
. The situation is depicted in Fig. 2.2. For nodes
at distance
from the boundary, their
corresponding probability
satisﬁes g
$e l `wv
f Finally, solving this system of equations yields g S d
e
hEt
g
$e l `wv
f l `wv
f %$ f
g
v ( © g Se d yl `w %v i F8
e ( l $e l `w %v ing
%g f
%g f
( }$e l ( `w %v l G $e
t %g
(
# © Af
( l $e l `fw %v i g$e l `w %v
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%
f
(
(
#
h p g eS d y i
¤
¦¤w£ t 2.3 Distributed Computation of the Local Parameters
It is interesting to observe that, should the nodes be aware of
their own lattice coordinates, then they could simply plug those coordinates into the deﬁnition of the optimal ’s above. However, one
of the assumptions we make is that nodes do not know their lattice
coordinates: such coordinates do provide a fair amount of location
information, information that is unreasonable to assume nodes like
our microrouters would have “for free”. Instead, the most we can
assume is that each node comes equipped with a unique identiﬁer
(e.g., burned in at fabrication time), and that position information is
discovered via communication among the nodes. So our next goal
is to give a distributed algorithm for computing lattice coordinates.
And to do this, it is instructive to observe how in this particular grid
coordinates are constrained to take values as illustrated in Fig. 4.
We see from Fig. 4 that all we need to recursively compute these
coordinates is knowledge of distances between nodes. But this is
easily accomplished by computing such distances using the distributed asynchronous version of the BellmanFord algorithm [3]—
all that is required to perform this computation is knowledge of .
.
. ¡
t 5
( l $e
g
l `w %v
f
( %g f
v ( l $e ( l `w %v i
( %g f
%g f
g t F8 © Af $e l ( `w %v l 8 $e l ( `w %v Solving this system of equations, we get %g f
v g S( kl $e $lg e `w`%v w %v i
e d y © % l f
Similarly, in the compression stage, suppose we have the load uniformly distributed over a diagonal
. The fractional load
supported by each node is
. The next diagonal has l `w %v
f 16 . g
$e l `wv
f
hEt 1
0.9 0.8 0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
100 Figure 6: In this irregular topology (with two nodes deleted), if the
load on the marked diagonal with three elements is uniform
,
will result for the marked diagonal with 4
then an uneven load of
elements (two of which are down); but to ensure an even load
on
is required on the ﬁrst.
this second diagonal, an uneven load
Therefore, in this particular example, exactly uniform loads across all
diagonals simultaneously are not feasible. 0 0 20 40 60 t 4 h 20 80 100 1
0.9 th 4 t ¡ 4 t ¡ 60
40 th 4 th
t 4 t 4 t 80 0.8
0.7 beam wider in the expansion stage (by assigning higher probability
to nodes away from the main diagonal), and narrower in the compression stage (by assigning higher probability to nodes close to the
main diagonal). 0.6
0.5
0.4
0.3
0.2
0.1 3. RANDOM WALKS ON IRREGULAR AND
STATIC GRAPHS 0
100
80
60
40
20
0 0 20 40 60 80 100 3.1 Rationale: Regular Model plus Random
Perturbations
The goal for this section is to deﬁne graphs which are less structured than the regular mesh considered in Section 2, and still deﬁne on these graphs random walks whose stationary distributions
achieve the sought load balancing property. And we will do so by
introducing random perturbations to the basic model for connectivity considered above: we delete a random subset of nodes from
the regular grid. Note however that achieving exact load balancing as deﬁned above (uniform distribution on diagonals) will not
be possible in general—this is illustrated in Fig. 6.
So, if we cannot achieve uniform loads across diagonals, what
can we achieve? In the context of irregular networks, what are we
going to require of the random walks we construct? It turns out
that we will still be able to deﬁne suitable random walks. This is
because, with independent decisions made at each node of what is
the next hop to follow, it seems clear that the higher the number
of routes between any two nodes, the harder it becomes to predict
which particular route a given packet will follow—however, if the
number of routes is large, we can still invoke ergodic theorems and
say something about the distribution of where a packet will lie after
hops. And therefore, by choosing appropriate local parameters
for the random walk, we should still be able to control the shape of
this distribution, and steer it to one which is, if not exactly, at least
approximately uniform across diagonals. How to accomplish this,
how to build on this intuition, is what we elaborate on next. Figure 5: Load distribution. Top: a random walk based on tossing
a fair coin among the two feasible neighbors; bottom: a random walk
using the local parameters computed above. The simulation consists of
messages transmitted in a network of size
, injected at
, exiting at
. The axes in the bottom plane denote network
positions (a node
in the cubic grid is represented by a point
in
this plane), and the vertical axis represents number of packets carried
by node
(normalized such that diagonals sum up to 1). 3 03
F3 @ F3 @ 734
865 23
333
FFF3 @ 74
FHDS 2C
74
8FS5F 2 4
5HD6C 74
IED6 2C the source and destination unique identiﬁers (not their coordinates),
and local message exchage, as permitted by our assumptions. So,
once a node discovers its coordinates, routing is performed by applying eqns. (1,2). 2.4 Simulation Results
For illustration purposes, we compare the load distributions induced by the random walks here computed with the load distributions that would be induced by ﬂipping a fair coin to decide which
of the two feasible neighbors on a next hop to pick at each node.
These plots are shown in Fig. 5.
If we think of the packets as being particles in a beam directed
from the source to the destination, then we see that when using
forwarding probabilities which are independent of the network location, the beam is narrowly conﬁned around the main diagonal
, but as it moves closer to destination the beam becomes more
spread out. Furthermore, since there is only one route from nodes
or
to
, we see
of the form
how this results in a grossly uneven load of these nodes. With the
local parameters deﬁned above, essentially what we do is make the ¢ 3.2 A Generalization of Lattice Coordinates
We introduce ﬁrst a generalization of the concept of a lattice co: is the
ordinate: now we label nodes with pairs of symbols,
number of routes between the source and the labeled node, is the
number of routes between the labeled node and the destination. We g ( © k( © de
e
¢ hAYff
g ¢ 17 g S5( © de
e g ( © d
e ei
~u say that two routes are different if they differ in at least one node.2
And we observe that computation of these labels is again relatively
straightforward using a distributed algorithm as discussed in the
previous section: generalizing the well known result about combinatorial numbers that
,
we recursively compute the number of routes at a node as the sum
of the numbers of routes at the two previous nodes.
The notions of expansion and compression stages admit natural
generalizations to the case when the cubic grid has some missing
nodes, and so do the forwarding probabilities based on these new
. In the regular grid, this
labels. Consider a node with label
node has two neighbors to which it could forward data, with labels
and
. Then, the probability of forwarding a
packet to the node
is deﬁned as: 1 g
I( © ¦ k( ¦¢ ¥£ l g ¦ 5( ¦¢ ¥¥¤g ¦ ¢ ¥£
©f
© f£i
f 1
1 6 2 3 1 1 1 4
1 1 Figure 7: Pascal’s Triangle. Each node is labeled with the number of
routes to the bottomleft node. ):
(3) Similarly, for nodes in the compression stage, we get (4) (7) Therefore, labels based on the number of routes is indeed a meaningful generalization of labels based on lattice coordinates, in that
the packet forwarding probabilities induced are the same. g t t ff
¢ During the expansion stage, we make the forwarding probability proportional to the number of routes between the node and the
source. This is because, if we were successful in spreading the load
evenly in earlier stages, then we would expect the load received by
any node to increase with the number of routes from the source to
that node—more routes mean more ways in which a packet could
reach this node. During the compression stage, we make the forwarding probability proportional to the number of routes between
the node and the destination. Since nodes can distribute the incoming load between all the available routes toward the destination, we
make the supported load proportional to this number of routes. 3.4 An Algorithm for the Irregular Grid
The algorithm for setting forwarding probabilities must be modiﬁed in the case of a grid with possibly missing nodes. At a given
node
, it may happen that: g S d
e (a) both (b) only one of
(c) or neither 3.3 Equivalence with Lattice Coordinates in
the Regular Grid and are on, g ( l S d g Sk( l d
e
e
g ( l S d g S5( l d
e
e
g ( l f d g fk( l d
e
e hl i
h ¨t z
t
¢
hl i
I¢ ¨t ¢ z
h
I¢
): ( © $e l `w %v i h l z
%g f
t
g S §
t i
e d y ¤¢
g t t ff
¢
gh h¢
h§Iff — Compression (when 3 1 g ¢
h5ff — Expansion (when 4 and nor are on, are on. In case (a), locally the network looks like the regular grid, and
therefore we assign probabilities as in the regular case—note that
the probabilities themselves are not identical though, since the number of routes available will depend on which nodes are ON and
which are OFF. In case (b), we assign probability 1 to the active
neighbor. In case (c), we assign probability 1 to a neighbor whose
distance to destination is strictly smaller than the distance from the
current node.
The basic idea of this algorithm is that we let constraint (c.1)
(on the avoidance of livelock conditions) dictate our choice of next
hop: we deal with perturbations to the basic connectivity model
by assigning probability 1 to a neighbor arbitrarily chosen among
those closer to destination. The rationale for this choice is that, if
the process for deleting nodes is homogeneous (in the sense that the
probability of having a node missing is independent of the location
of the node, as is the case when nodes are deleted independently),
then we expect that load imbalances created forwarding data to a
single neighbor in some cases will cancel out. This issue is explored via simulations next. An important property of the labels deﬁned above is that, if applied in the context of the regular cubic grid, the computed forwarding probabilities are identical—it is in this sense that we call these
labels a generalization of the lattice coordinates. That is, eqns. (3,4)
are the same as eqns. (1,2).
Consider the cubic grid shown in Fig. 1. The number of routes
toward the source in the expansion stage represents an instance of
Pascal’s Triangle problem, and hence the number of routes is given
by the combinatorial number
(5) e d y
ª g S §
i ¢
( © $e l `w %v
%g f
© as illustrated in Fig. 7.
To see that these new labels reduce to the standard lattice coordinates in the case of a complete grid, we combine eqns. (3) and (5),
to obtain: 3.5 Simulation Results
For illustration purposes, we repeat the same experiment performed in Fig. 5. The resulting plots are shown in Fig. 8.
It is interesting to see in Fig. 8 how the proposed algorithm does
indeed achieve a marked improvement in terms of load balancing,
especially in comparison to the scheme based on tossing a fair coin.
Note also that now loads on diagonals are not uniform any more—
although these plots suggest that the imbalance is not severe. (6) ( l $e l `w %v
%g f
g S § © $e l `w %v
e d y %g f
ª g S d y
e
ª l e
l ( hg S d y
%
g$e l `fw %v
%g f
$e l `w %v
hl
© i I¢ h ¨t ¢
¢
l e
ª¬( © hg S d y
%g f
$e l `w %v © i i
«
h Note that this is much weaker than requiring the routes to be disjoint, i.e., that all but the ﬁrst and last nodes be different. 18 able to these nodes will change), which in turn may trigger changes
to labels of nodes farther apart. What we need to understand in this
case is how routing performance is affected by the delays in propagating information about updates of labels, and how sensitive this
routing performance is to inaccuracies in the labels. We explore
both issues via simulations next. 1
0.9
0.8
0.7
0.6
0.5 4.3 Simulation Results 0.4 For illustration purposes, we repeat the same experiment performed in Figs. 5 and 8. The resulting plots are shown in Fig. 9. 0.3
0.2
0.1
0
100 1
80
0.9 60
40
20
0 0 20 40 60 80 100 0.8
0.7
0.6 1 0.5 0.9 0.4 0.8 0.3 0.7 0.2 0.6 0.1 0.5 0
100 0.4 80
60 0.3 40
0.2 20
0 0.1
0
100 20 0 40 80 60 100 1
80
0.9 60
40
20
0 0 20 40 60 80 100 0.8
0.7
0.6 Figure 8: Load distribution. Top: a random walk based on tossing 0.5 a fair coin among the two feasible neighbors; bottom: a random walk
using the local parameters computed above. The simulation consists of
messages transmitted in a network of size
, injected
at
, exiting at
. The axes in the bottom plane denote network positions (a node
in the cubic grid is represented by a point
in this plane), and the vertical axis represents number of packets
carried by node
(normalized such that diagonals sum up to 1). In
this simulation, each node is ON with probability 0.95, and OFF with
probability 0.05. 0.4
0.3 3 03
F3 @ F3 @ 0.2
0.1
0
100
80
60
40
20
0 20 0 40 60 80 100 74
IED6 2C
74
IED6 2C
74
8F65F 2 4
5HD6C
74
83S5 23
333
FFF3 @ Figure 9: Load distribution. Top: a random walk based on tossing
a fair coin among the two feasible neighbors; bottom: a random walk
using the local parameters computed above. The simulation consists of
messages transmitted in a network of size
, injected at
, exiting at
. The axes in the bottom plane denote network
positions (a node
in the cubic grid is represented by a point
in
this plane), and the vertical axis represents number of packets carried
by node
(normalized such that diagonals sum up to 1). In this
simulation, the stationary probability of the OFF state is taken to be
(that is, in steady state 5% of the nodes are down), and the probability
.
of an ON OFF transition is 734
8S5 23
333
FFF3 @ We turn our attention ﬁnally to the problem that we were interested in right from the start: routing in random dynamic graphs.
For this purpose, we consider next a timevarying version of the
model considered in Section 3: instead of randomly deleting nodes
from the cubic grid and leaving them deleted for all times, we take
these nodes to switch between ON and OFF states over time, independently from one another, following a Markov rule. 74
IED6 2C
74
8FA5F 2 4.1 Rationale: Regular Model plus Dynamic
Perturbations 4
5ED6C RANDOM WALKS IN DYNAMIC GRAPHS 3 03
F3 @ XF3 @ 74
IED6 2C ¯®
F3 53 ¯± ®
FF3 53 4. ° Interestingly enough, and at ﬁrst surprising to us (although rather
obviously with the beneﬁt of hindsight), is the fact that the load distributions achieved in the context of a network with uncontrolled
dynamics are much closer to uniform than those achieved in an
irregular but static—i.e., more predictable—network. Intuitively
what is happening in this case is that, because of the ergodicity of
the model considered for network dynamics, the load distribution 4.2 Dynamic Labels
The mechanics of the labeling method remain almost unchanged
from the case of an irregular but static network—the only difference is that when a node changes state, this change will affect the
labels of its onehop neighbors (since the number of routes avail 19 0.12 distributed over the a range that goes almost to twice the minimum
time. Alternatively, in the most irregular network considered in
these plots (
ON
OFF
), the delay distribution is
sharply concentrated around a slightly suboptimal value.
We explain this apparent contradiction as follows: fµ
I¨´ ¡ In a relatively stable network (low
ON
OFF ), state
,
transitions are rare effects: in our network of size
ON
OFF
means that on average only one
node per time slot undergoes a state transition. If a packet
never encounters a node with unaccurate information (i.e.,
that underwent a state transition and has not had enough
time yet to update its local state information), this packet will
likely arrive in the minimum number of hops. ¡ However, if a packet does encounter a node that recently underwent a state transition, it will likely get either delayed at
that node, or misrouted, as explained above. ¡ Now, for how long will this condition persist? Recall the dynamics of our routing algorithm: try to route picking next
hops based on the basic model for connectivity, and if none
are available, pick a next hop based on distance maps. So, in
a stable network, the condition for delaying packets is likely
to persist for longer than in a network with nodes going up
and down often: with nodes that seldom change state it is
necessary to wait until the relevant information to update local distance maps arrives, whereas in the network that is in a
“state of ﬂux”, the time it will take for a neighbor of the form
or
to switch back to ON is likely much
smaller than the time it takes for distant updates to arrive. 220 240 260 280 300
Delay 320 340 360 380 400 Figure 10: Transmission delay as a function of the variability of the 3 03
F3 F3 @
333
FFF3 @ @ messages are transmitted in a dynamic network of size
. A network with 5% of the nodes in down in steady state,
and three different chains with
ON
OFF
(the transitions from OFF to ON are adjusted so that in the stationary
distribution, the OFF state occurs 5% of the time).
network. ¯ ® 34 ¯3 ® 3 33 ® 3 c
F3 565FF3 5A4 @ FF3 5'¤ ° ³
² of the dynamic network is essentially the average of the load distributions of many static networks—and it is this averaging effect
what results in smoother, more balanced loads.
Besides load distributions, another important performance indicator is the delay distribution: how long does it take for a packet
to go from source to destination? In the static case, this question
admits a trivial answer: this is exactly the number of hops on a
shortest route. But in the context of dynamic networks, this delay becomes random: as nodes go up and down, the information
about state transitions needs to propagate throughout the network,
and this propagation takes time. Therefore, inaccurate state information can introduce randomness in transport delay in two forms: g ( l S d
e fµ
Ia´ 200 ¶ 0 ( 5k£ v £ g
££ i 0.02 fµ
§¨´ 0.04 ¶ 0.06 £( £
k£ ¨q g k£ ( Estimated probability 0.08 ¶ 0.1 i
¹
Y£ v £ g 0.0001
0.005
0.05 g Sk( l d
e We intend to make available on the web the simulator we have
developed based on which the plots above were generated—this
will happen by the time we submit a journal paper on this work. 5. CONCLUSIONS ¡ Packets can get delayed at intermediate nodes. This could
happen when both
and
are OFF, and the
current distance estimates from both
and
to destination are greater than that from
. In this case, a
packet at
waits a random amount of time—until either
the map of distances converges (and a new neighbor closer
to destination can be identiﬁed), or until one of
or
turns ON again. 5.1 Summary g d
e
g f5( © d
e
g ( l S d
e g ( © S d
e In this work we presented our work on the design and performance analysis of routing algorithms for large scale wireless sensor
networks. First, we argued that complexity considerations make it
natural to introduce an element of randomization in the problem
formulation, and so we formulated the problem as one of deﬁning
suitable random walks on random dynamic graphs. Then we presented random walk constructions in three different cases: a regular and static grid, an irregular but still static grid, and a dynamic
grid. The basic approach to constructing these random walks consisted of ﬁrst deﬁning a simple basic model for connectivity in the
network (the regular cubic grid), and then introducing random perturbations to the basic model—solve analytically for the optimal
parameters in the basic model, take “greedy shortcuts” around the
random perturbations. Properties of the resulting random walks
were illustrated via simulations. g 5( l d
e g f d
e g fk( l d
e g ( l S d
e Packets can get misrouted. This could happen when both
and
are OFF, and at least one of the current
distance estimates from either
or
to destination is smaller than from
. This case cannot occur in the
static case: with an accurate map of distances, a node
satisfying these conditions would never be reached. However, in the dynamic case, this situation could come up for
short periods of time, while updates to distance maps propagate. g ( © S d
e g S5( © d
e g S d
e 5.2 Future Work g ( l f d
e g S5( l d
e ¡ g S d
e Different delay distributions are shown in Fig. 10, corresponding to
different “degrees of variability” of the network.
It is most interesting to observe in Fig. 10 how networks that are
“more predictable” (i.e., in which state transitions are less frequent)
induce delay distributions with higher variance than networks that
appear to be in a state of ﬂux (i.e., in which state transitions occur more often). Consider the case of
ON
OFF
:
of the packets make it to destination in the
in this case, about
smallest possible number of hops—but conditioned on the delay
being slightly higher than optimal, this delay is almost uniformly There are two lines along which this work could proceed further.
One consists of extending the basic model of connectivity considered in this work (the regular cubic grid) to more general percolation models, such as the random networks analyzed by Gupta and
Kumar [10], Kleinberg’s small world random graph models [16],
etc. Although this is certainly a necessary step, we chose to start
with the regular cubic grid for the simple reason that the main ideas
we wanted to explore, in the case of the cubic grid, could be described using only very elementary mathematics—these models, ( k5£ v £ Pg
££ i 20 ¶ fµ
I¨´ ¸
5· although certainly much more interesting, require the use of more
sophisticated analysis tools. So now that we have a good understanding about how to construct the sought random walks in a simple case, and about their properties, it does make sense to consider
the more general (and more interesting) cases.
In the long term, we will study a number of problems on random
graphs. One of the aspects we feel is part of the beauty of this work
is the existence of a large body of related theory. We intend to: [12] D. B. Johnson and D. A. Maltz. Dynamic Source Routing in
Ad Hoc Wireless Networks. In T. Imielinsky and H. Korth,
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1999. ¡ Explore connections between our routing problem and diffusion theory [27], since we expect the latter may hold the key
to deriving analytical results on the behavior of our routing
algorithms in asymptotically large networks. ¡ Generalize our construction of random walks to random graphs
embedded in arbitrary dimensional manifolds (instead of
the regular grid on a plane). ¡ Extend our construction to the case involving multiple sources
and/or destinations. ¦ 5.3 Acknowledgements
The ﬁrst author would like to thank Raissa D’Souza (for interesting discussions on issues related to this work), and the Institute of Pure and Applied Mathematics (IPAM) of the University of
California, Los Angeles (for travel support to attend their program
on Large Scale Communication Networks, http://www.ipam.
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