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USC Computer Science Technical Reports, no. 749 (2001)

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USC Computer Science Technical Reports, no. 749 (2001)

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Content 1 Architectural Framework for Large-Scale Multicast in Mobile Ad Hoc Networks
Ahmed Helmy
Department of Electrical Engineering
University of Southern California
helmy@usc.edu
Emerging ad hoc networks are infrastructure-less
networks consisting of wireless devices with various power constraints, capabilities and mobility characteristics. An
essential capability in future ad hoc networks is the ability
to provide scalable multicast services . This paper presents
a novel adaptive architecture to support multicast services
in large-scale wide-area ad hoc networks. Existing works
on multicast in ad hoc networks address only small size
networks. Our main design goals are scalability,
robustness and efficiency . We propose a self-configuring
hierarchy extending zone-based routing with the notion of
contacts based on the small world graphs phenomenon and
new metrics of stability and mobility. We introduce a new
geographic-based multicast address allocation scheme
coupled with adaptive anycast based on group popularity . Our scheme is the first of its kind and promises efficient
and robust operation in the common case. Also, based on
the new concept of rendezvous regions , we provide a
bootstrap mechanism for the multicast service; a challenge
generally ignored in previous work.
1. Introduction
Ad hoc networks are emerging as very interesting
architectures to support pervasive mobile wireless devices
and are expected to have a significant impact on service
paradigms in various vital fields; such as military, disaster
relief, bio-sensing, and location-based mobile services. Such
networks consist of heterogeneous wireless devices with
various power and mobility characteristics. At the same time,
group communication represents a very important class of
applications in future networks. Multicast is the enabling
technology for efficient group communication. Providing a
scalable architecture for multicast services in large-scale
networks has proven to be a challenge for years in the
networking community [34] [3] . The research challenges are
even greater for ad hoc networks, mainly due to lack of
infrastructure and the highly dynamic nature of wireless
nodes and their unexpected mobility . The developed multicast
service should allow group participants to join and leave at
will, and should impose no restrictions on node mobility. It
should also provide automatic multicast address allocation
and session advertisement. Most existing work on ad hoc
networks ignores such requirement.
In this paper, we design a new scalable architecture for
multicast services support in large-scale ad hoc networks, the
first of its kind. Since ad hoc networks are infrastructure-less , the developed protocols must be self-configuring . Scalability
and robustness should be addressed carefully with growth in
the size of the network and the number of group participants.
Existing approaches for multicast, usually employ flooding or
cores for discovery of group participants. These approaches,
however, only apply to small networks due to cost of
flooding and maintenance of the core tree. Questions are
often asked about which routing mechanisms to use, pro-
active or re-active routing? Hierarchical or flat? It is usually
the case, however, that each of these mechanisms has its
strengths and weaknesses depending on network conditions.
We attempt to leverage strengths of the different approaches
by introducing a hybrid approach that adapts to network
dynamics as a function of mobility and power . Our proposed architectural framework utilizes highly
adaptive mechanisms in the various components of the
architecture. Specifically, we design an adaptive zone-based
hierarchy augmented by the notion of contact nodes to
increase network coverage. In addition, we provide novel
schemes for multicast service support, based on adaptive
anycast for resource discovery. We also introduce
geographic multicast address allocation to map groups into
rendezvous regions.
Unlike existing work on multicast in ad hoc networks
that mainly addresses local areas (with tens or hundreds of
nodes), our work targets large-scale wide-area ad hoc
networks (with tens of thousands of nodes). Issues of
scalability, service model, robustness and efficiency affect the
essence of our architectural design and guide our choice of
multicast model. In order to address these issues we discuss
the design requirements and present an overview of the
architectural components. Then we provide specific
mechanisms to support our design goals.
1.1 Design Requirements
The main factors driving our design are scalability, the
multicast service support, and robustness.
(a) Scalability : Unlike most related work that considers tens
to hundreds of mobile nodes, our architecture should be able
to support large number (tens of thousands) of nodes. We
believe that mobile nodes will be pervasive, replacing PCs
and cellular phones, with tens of new classes of application
supported by mobile wireless devices (e.g., navigation,
location-based services). Flat architectures are known not to
scale well [7] , mainly due to the far-reaching effects of
network dynamics ; mobility, failures and topological
changes. Such effects consume network resources (i.e.,
bandwidth and power), and lead to recovery delays and
increased route oscillations. Hierarchical architectures, on the
other hand, alleviate the above problems, as they tend to
localize and dampen network dynamics, and scale routing
tables using aggregation. Many existing hierarchical
architectures are based on clustering mechanisms, in which a
single node per cluster (called master, cluster-head or parent)
2 is chosen to manage or organize the cluster. Such
architectures suffer from single point of failures, in which the
failure (or movement) of the master may have severe
negative effects on the hierarchy. Furthermore, establishment
and maintenance of clusters requires mechanisms for electing
the master and mechanisms for joining/leaving clusters,
which usually incur a lot of overhead and complexity. We
design an architecture that leverages hierarchical advantages
while alleviating effects of master and hierarchy
maintenance. We adopt a two-level distributed hierarchical
architecture. For the first level of the hierarchy we adopt a
zone-based approach (a variant of the zone routing protocol
ZRP [17] ), in which each node has its own view of a zone. For
the second level, we introduce a novel concept of contacts (based on the concept of small-world [18] [43] ) to enhance a
node’s view, and aid in route and resource discovery. Also
affecting scalability, is the choice of routing protocol. In
general, ad hoc routing protocols are either pro-active (i.e.,
table driven) or re-active (on-demand). Pro-active
protocols [6] exchange periodic messages to keep routes up-
to-date. Pro-active route discovery has low delay, with
significant overhead of periodic route exchange (many of
which may become invalid due to mobility). Re-active
protocols [10] , by contrast, maintain routes on-demand. Re-
active route discovery does not incur periodic overhead, but
incurs more route discovery delay, which usually involves
request broadcasts throughout the network. We believe that
neither protocol perform well in all network conditions. We
attempt to combine the strengths of both protocols using a
hybrid approach. Inside a zone pro-active routing is used to
discover routes to nearby nodes, while re-active routing is
used for discovery of routes faraway. In addition, contacts
extend the notion of zone to enhance route discovery.
(b) Multicast Service Support : The multicast service model
defines conditions for joining/leaving groups and specifies
the interaction between the end-nodes (i.e., participants) with
the rest of the network. Multicast participants should be able
to join, leave or send packets to groups at will. We adopt a
model in which participants are not known a priori and are
allowed to move freely during a multicast session. In such a
model the main problems include rendezvous
1 of participants,
service bootstrap and multicast address allocation . We
design a novel adaptive anycast architecture for resource
discovery to facilitate the rendezvous of group participants.
Instead of the traditional rendezvous approaches of broadcast
and prune [25] [5] [31] or rendezvous cores [2] [4] , we introduce
a new multicast paradigm based on ‘ sender push, server
cache, receiver pull’ approach, that better fits large-scale ad
hoc networks. We also design mechanisms for providing
participants with active session information as part of our
bootstrap architecture, along with a new multicast address
allocation scheme based on geographic address allocation.
Our architecture requires minimum configuration of nodes.

1 Rendezvous refers to the problem of senders and receivers meeting by
knowing information to build the distribution tree.
In our scheme, nodes only need to know a well-known
session announcement group address and an algorithmic
mapping function to map groups into rendezvous regions
( RRs) . We do not assume existence of unicast routing. Multi-
sender groups and geographic scoping are also supported.
(c) Robustness : In a highly dynamic environment, such as
ad hoc networks, where mobility and crashes are likely to
occur, robustness is of prime concern. Being able to adapt to
network dynamics to achieve correct behavior and reasonable
performance plays a major role in our design. We incorporate
mobility and stability models into our hierarchy formation to
achieve adaptivity. In addition, our distributed adaptive
resource discovery architecture avoids single point of failure
scenarios and promises continued operation and graceful
recovery during network partitions. We also incorporate path
redundancy mechanisms in our multicast routing protocol.
Multicast trees do not provide sufficient robustness against
mobility and failures. We use mesh structures for robust
delivery. Unlike existing proposals for mesh construction,
however, our mechanisms will be designed to build meshes in
anticipation of movement. Not only does that achieve better
performance, but also provides path redundancy that may be
used in case of failures. In addition, mesh branches are
activated on-demand to reduce overhead without affecting
robustness.
In addition, our mechanisms should be energy-efficient and
self-configuring. Energy-efficiency is one side-effect of
scalability. In addition, we attempt to limit communication
(one main source of energy consumption) by using localized
mechanisms for advertisement and query. Furthermore, in
resource-discovery and anycast (where only a single resource
is sought), localized broadcast techniques are adopted to
reduce overhead. Self- configurability is an inherent feature of
our hierarchy formation and resource discovery mechanisms.
1.2. Brief Architectural Overview
In order to address the above challenging requirements,
we provide an architectural framework based on the
following components: ( i) Self-configuring adaptive
hierarchy formation and adaptation. (ii) The multicast
service architecture consisting of: (a) the multicast model, (b)
multicast routing, (c) adaptive resource discovery and (d)
multicast address allocation.
The rest of this paper is outlined as follows. Section 2
introduces the hierarchy formation and adaptation
mechanisms. Section 3 proposes our new multicast services
architecture. Section 4 discusses related work. Section 5
presents future work and the conclusions.
2. Hierarchy Formation and Adaptation
We provide mechanisms for self-configuring hierarchy
formation, based on zone-based routing, augmented by
contacts , along with description of contact selection
mechanisms. Then we propose hierarchy adaptation
3 mechanisms based on link availability and mobility
estimation models.
2.1 Hierarchy Formation
As was discussed earlier, flat routing architectures do not
scale well for wide-area networks, especially for highly
dynamic ad hoc networks. Also, hierarchical approaches
based on concept of master (or cluster-head ), through which
traffic from the cluster funnels, are undesirable.
One possible approach to consider is to use the master
for infrequent coordination but not for forwarding packets.
One such approach is the Landmark hierarchy (LMH) [21] . Although designed mainly for wired networks, LMH has the
ability to self-configure dynamically, without relying on
administrative domains and exhibits path lengths and routing
table sizes comparable to conventional cluster-based
hierarchies (e.g., Internet). The traffic from/to a cluster need
not go through the landmark, which adds robustness. LMH,
as presented in [21] , however, was designed mainly for wired
networks, without accounting for mobility dynamics or power
constraints. After examination we identified several
drawbacks of LMH. LMH employs complex promotion,
demotion, and adoption operations for hierarchy
maintenance. Furthermore, effects of mobility on the
hierarchy were found to be drastic, sometimes leading to total
re-configuration of the hierarchy. For example, movement of
a high level landmark triggers re-election in its old region,
then demotion and adoption for the moving node are
triggered (potentially for as many times as the levels of the
hierarchy). This may trigger a chain reaction that consumes
many resources unnecessarily. In addition, sub-optimal paths
may be common due to hierarchical routing. A variant of
LMH (called LANMAR) was presented in [27] . LANMAR,
however, was based on the premise that group mobility will
be dominant in ad hoc networks. Hence, it does not provide
mechanisms to solve LMH drawbacks in the general case.
Another approach that avoids complex coordination for
architectural setup is the zone routing protocol (ZRP) [17]
which defines a zone for every node as the number of nodes
reachable within a radius of R hops away, shown in Figure 1 (a). Inside the zone proactive ( intra-zone) routing is used, so
nodes obtain routes to all nodes within their zone. To
discover nodes outside of the zone reactive (inter-zone)
routing is performed by flooding through periphery or border
nodes of each zone (known as bordercasting ). ZRP routing
overhead depends heavily on the choice of the zone radius . If
the radius is too small the routing overhead is dominated by
reactive overhead, and vice versa. Optimizing such a
parameter is nontrivial. A hybrid min-search and traffic
adaptive approach is used in [40] but requires relative stability
of the network to approach optimality.
ZRP seems appealing, but experiences excessive delays
and overheads in large-scale networks, where much of the
traffic maybe destined out-of-zone. Therefore, we develop a
novel approach that goes beyond the zone while maintaining
similar simplicity and stability. Our approach is based on a
concept we call contacts. Contacts, of a certain node x , are
nodes that previously existed in x’s zone but are drifting out-
of-zone, and hence have a network view beyond that of x or
any of its border nodes
2 . While drifting away gradually, x may maintain route to (some of) these drifting nodes using
low overhead (since these drifting nodes were in x’ s zone and
are close to its border nodes). Figure 1 shows a simple
illustrative example of zoning, contacts and effects of
mobility.
Border Node
Center Node
Internal Node
Mobility
R C 5 1 4 3 6 7 2 Zone Radius
Route
R R R R C 5 1 4 3 6 7 2 contact
contact
contact
(a) ( b)
R R R R C 5 1 4 3 6 7 2 contact
contact
( c)
Figure 1 . Example of zoning, contacts and effect of mobility: (a) Zone for
center node C is shown (with radius R ). Border nodes are numbered (1-7).
Nodes 1,3 and 6 are moving/drifting out of zone. (b) Radii for the drifting
nodes are shown. C stays in contact with the drifting nodes, which enables it
to obtain better network coverage with low overhead. (c) After moving away,
contact nodes drift up to a point where their zones no longer intersect with
C ’s zone. In this example, C maintains contact with those nodes not more
than (2 R +1) hops away, i.e. nodes 3 and 6, and loses contact with node 1 as it
drifts farther than the contact zone.
The concept of contacts relates to the concept of small world graphs, where Watts [18] observes that small world
graphs can have low average path length with high clustering.
In other words, introducing a small number of far away links
(i.e., short cuts) leads to significant reduction in path length.
He also found that picking random far away nodes may lead
to small world graphs, depending on the probability of
choosing the far link. We believe, however, that picking
contacts at random is undesirable, since this may lead to
unpredictable overhead for contact route discovery and
maintenance. In addition, knowing mobility characteristics
and stability of a node helps identify better (more useful)

2 It seems this is one of the few concepts that actually takes advantage of
mobility. Mobility is often viewed as a disadvantage, and for good reasons,
but we think it should also be taken advantage of, when possible.
4 contacts. We take advantage of node mobility and pick
contacts from those nodes drifting away from the zone. The
eventual characteristics of the formed graphs are function of
time and depend on the initial choice of contacts and the node
mobility.
Contacts play an important role in route and resource
discovery in our architecture, as will be explained later. A
node should choose its contacts carefully to attempt to
maintain a relatively long and useful contact as long as the
contact route is kept. The contact list , maintained by a node,
changes adaptively as the network conditions change. We
further discuss how the contact list is chosen in the next
section.
2.2 Hierarchy Adaptation
The architecture presented thus far provides a framework
for hierarchy formation. Such hierarchy should be highly
adaptive to network dynamics and conditions by integrating
concepts of mobility and energy . To achieve this, we use
mechanisms that integrate measures of stability and power.
We use such mechanisms for hierarchy formation and route
selection. In hierarchy formation, the zone radius and contact
selection may be adapted dynamically to establish certain
stability measures for zones and contacts. Furthermore, each
node should adapt its behavior to achieve desirable collective
performance. For example, number of contacts chosen by a
node should be a function of the contacts already established
by other nodes in its zone. Increase in number of contacts
increases bandwidth and must be done only as necessary.
We devise a mechanism for contact selection in which a
node chooses its contacts with probability p , as a function of
the border’s mobility, the number of zone contacts, energy
and activity. One simple model is to have p proportional to
the energy estimates E est
of the node and the contact, their
relative stability S est , and the activity level of the node A est
measured as rate of discovery requests. Also, p is inversely
proportional to the number of zone contacts Z est . Hence,
est
est est est
Z A S E p a
. These quantities are locally measured and
may be piggybacked on intra-zone pro-active messages. E est includes energy estimates at the node choosing the contact,
and the contact node drifting out of zone . To accommodate
heterogeneous nodes, the estimates should include energy left
E left
, and the drainage DE . Thus, a simple equation for the
energy estimate is:
contact
left
node
left
est
E E E E E ⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
Δ
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
Δ
=
. Stability estimates may be derived from adaptive
availability and mobility models. In [7] a scheme was
proposed to measure link and path availability . The model
used is ( a ,t) , where a is the probability that a link will be
available for time t , and was utilized to build adaptive
clustering schemes for ad hoc networks. The basic idea is to
build stable clusters in which internal route availability is
probabilistically bounded. The proposed model is a random
walk-based model that determines the conditional probability
that the nodes will be within range of each other at time t 0 +t
given that they are located within range at time t 0 . Another
mobility metric may be derived from signal strengths, using a
propagation model ;
d TxPwr
RxPwr
n 1 a
where RxPwr ( TxPwr ) is
received (transmitted) signal power, and d is the distance
between transmitter and receiver. By measuring the signal
strength between two consecutive packets received from the
same transmitter a relative mobility metric is defined as
old
new
RxPwr
RxPwr
log
, if negative high value then nodes are moving
away quickly, and vice versa. This metric was used in [45] to
aid in choosing cluster-heads.
The above adaptive mechanisms are also used in the
multicast service model and resource discovery in section 3.
In such case, contact’s choice should also be affected by the
capabilities of the contact node (e.g., global positioning
system ( GPS) capability, or being a session or sender
discovery server).
3. Multicast Service Architecture
Providing a scalable multicast service architecture is the
focal point of our research. The hierarchical architecture
proposed thus far provides the basis for efficient support of
our multicast service model, presented in this section. As the
mechanisms for our multicast model unfold, the essential role
played by the adaptive hierarchical architecture will become
very clear in support of multicast routing and resource
discovery. In general, existing work on ad hoc multicast
concentrates on multicast routing in small to medium size
networks. Earlier work particularly focused on mechanisms
to establish distribution trees (or meshes) between senders
and receivers in small-scale networks, mainly using periodic
broadcasts (either from the sender [25] or receiver [28] side) or
relying on cores [15] . In this paper, however, we address
multicast in large-scale ad hoc networks, and propose new
schemes for bootstrapping multicast services, by providing
efficient resource discovery using popularity-based adaptive
anycast and geographic multicast address allocation . We are
not aware of existing or on-going work on multicast for wide-
area ad hoc networks. Furthermore, we do not know of other
work for bootstrapping multicast service, resource discovery
or multicast address allocation in ad hoc networks.
Our multicast service architecture consists of three main
components; (a) the multicast model (i.e., how senders and
receivers meet), (b) multicast routing (i.e., establishing
multicast distribution paths), and (c) adaptive resource
discovery architecture using anycast and geographic multicast
address allocation.
3.1. The Multicast Model
The basic premise for scalable multicast is that sources
do not know who/where receivers are a priori. This model
enables any node to join or leave a multicast group at any
point in time. Hence, one of the main components of
5 multicast is a mechanism for group participants to meet or
rendezvous. Traditionally, this problem has been addressed,
in wired and ad hoc networks, using one of two approaches ; broadcast-and-prune , or rendezvous cores . In the
former [5] [31] [25] , a participant (usually the sender)
announces its presence by broadcasting data packets (or
control messages) throughout the network. Network nodes
not interested in the group send prune messages to stop the
flow of packets (or simply do not respond in case of control
messages broadcast). These broadcasts are periodic to capture
network and membership dynamics. It has been shown that
such model is best suited for small to medium size networks
with densely populated groups, but does not scale for wide-
area networks [48] . The rendezvous cores approach, by
contrast, uses explicit join mechanisms to avoid periodic
broadcasts. Participants join (or send packets) towards a
common core, which relays the packets from the senders to
the receivers using a shared tree (or mesh) [2] [15] . This
approach suffers from problems of traffic concentration and
single point of failure scenarios for the core. These problems
may be alleviated by using multiple cores and dynamic core
election mechanisms. We believe, however, that the major
research problem associated with the core approach is the
core bootstrap and consistency problem. How do participants
know the core’s address/location? Senders and receivers need
to maintain a consistent view of the cores in order to meet.
This problem was addressed for wired networks [4] within a
single domain, and uses a flooding to disseminate core-to-
group mapping. This scheme does not scale well for wide-
area networks and its convergence performance degrades
with the size of the network. For ad hoc networks these
problems are exacerbated by network dynamics and node
mobility. Hence, these approaches are not suitable for ad hoc
networks.
We propose a new multicast model. We refer to our
model simply as sender push, server cache, receiver pull
model. Unlike previous work on ad hoc multicast that
requires periodic broadcasts throughout the entire network,
our scheme incurs less overhead, and only when necessary as
necessary and as localized as possible. We introduce the
notion of sender discovery servers (SDS) to aid in sender
location and information dissemination. As shown in Figure
2 , a sender sends an Advertisement ( Adv ) using localized
broadcast . SDS s receiving the Adv store this information.
Receivers send join requests toward the sender based on
backward learning ; every node forwarding the Adv adds its
address to the message to construct a path back to the source.
Other, farther, receivers not receiving the Adv message
attempt to find a nearby SDS , first by checking in their own
zone ( SDS s advertise in their zone), then by checking with
their contact list. If SDS is found it is queried for group
information and responds with a join reply, with approximate
source location or possible routes (if available at the SDS ).
Depending on the quality of the provided routes (if any), the
querying receiver(s) may opt to use these routes or use
zone/contact search for other routes (eventually, geographic
routing may also be used for route discovery as in LAR [24] , as described later). If SDS is not found, a receiver may send a
localized broadcast to discover other nearby receivers of the
group. If this process fails then the receiver uses a fallback
mechanism, described later in this section. Once the
information about the group/senders is available, the route
discovery/construction is initiated. An illustrative example is
shown in Figure 2 . 3.2 Multicast Routing
Establishing multicast distribution paths for ad hoc
networks has been shown to be more robust using mesh
structures, as opposed to conventional tree structures [16] . For single-source groups or when sources are sparsely
distributed, however, even a mesh does not provide the
desired path redundancy. Local recovery mechanisms [44]
may be used to alleviate such a problem. Conventional
receiver-initiated multicast schemes setup reverse path
forwarding (RPF) trees [2] due to dependence on unicast
routing. For ad hoc networks, however, using RPF paths is
not desirable due to possible path asymmetry due to wireless
channels. We do not use RPF nor do we rely on existence of a
unicast routing protocol.
R 3 SDS 2 R 4 R 1 S SDS 1 R 2 Sender Adv.
Join Query
Join Reply
Join Request
( a)
R 3 R 4 R 1 S R 2 ( b)
Figure 2 . Multicast service scenario. (a) Sender S becomes active and
broadcasts an advertisement (Adv.) locally (in shaded region). Sender
discovery servers SDS 1 , SDS 2 , and receiver R 1 receive the Adv. When new
receivers join the group, they try to find: ( i) a sender discovery server, (ii)
nearby members of the group. Receiver R 2 finds R 1 using local broadcast,
while R 3 and R 4 find SDS 1 and SDS 2 . (b) Once sender information is
obtained, receivers send Join Requests to build multicast distribution paths.
6 We propose to use mesh structures with local recovery
mechanisms, similar to those used in [25] and [44] . In
addition, multiple paths may be selected by the receiver
(during the discovery process) to increase robustness of the
multicast distribution mesh. Depending on the number of
senders in the group and their location (if available), in
addition to the mobility of the receiver, a receiver may opt to
choose several stable paths to join the group (when
propagated, route information includes stability metrics). The
receiver sets an ‘active’ flag in only one join to activate only
one path at a time. Only active paths forward data packets.
This reduces packet transmission overhead (a very significant
factor in ad hoc networks) while maintaining robustness. If
performance or stability of the active path degrades, or local
recovery fails, the receiver may activate another path with
high stability. Also, when the receiver moves, it may activate
another path containing one of the new neighbors, thus
achieving fast handoff using the concept of multicast-based
mobility [1] . Our investigations show that, on average, a
moving node traverses 2.5 hops to reach the nearest point of
the multicast distribution structure in very large networks
(with up to 5000 nodes)
3 . Moreover, mobility prediction [26]
may be used to achieve advance joining, further reducing the
effects of mobility handoff (i.e., mobility of the receiver in ad
hoc networks). Rules for activating/de-activating branches of
the distribution structure should be carefully selected to avoid
black holes. For example, if any member exists downsrtream
a branch then the branch must be activated. In order for a
branch to be inactive, all downstream branches must be
inactive.
So far, we have assumed that the receivers’ efforts in
searching for group information are successful, using the
above localized search techniques. In case of sparse groups,
where participants are far apart, or in case of multi-sender
groups, where information obtained from other group
members may not be complete, a fallback mechanism should
be used. Such mechanism should be efficient, avoiding
frequent global flooding and should be adaptive to
membership dynamics. To achieve this, we introduce a novel
bootstrapping anycast architecture for multicast service in ad
hoc networks, discussed next.
3.3. Resource Discovery and Multicast Address Allocation
The major research challenge for multicast resource
discovery (i.e., discovery of group address, senders), is the
lack of any (centralized or distributed) infrastructure to hold
and distribute such information. Inter-domain multicast for
wired networks [34] [3] utilizes the AS hierarchy of the
Internet and uses BGP extensions to distribute multicast
routes that map group prefixes into root-domains (established
by the multicast address allocation). In turn, receivers join
towards the root-domain and senders send their packets
towards it. Intra-domain multicast is used within the domains
and is built on top of unicast routing. All these infrastructures

3 These results were obtained for Internet topologies. We shall investigate
these measures in the context of ad hoc networks.
(unicast routing, AS hierarchy) do not exist in ad hoc
networks.
An architecture for anycast routing in the Internet was
recently proposed in [20] . Utilizing hierarchical routing and
aggregation. This work provides a scalable mechanism to
discover members of anycast groups that are closer to the
requester than other members. The architecture identifies two
mechanisms, a low overhead mechanism, using default
routes, for non-popular anycast groups (in which case the
request is routed to the home domain, derived from the
anycast address itself), and another mechanism for popular
anycast groups that caches routes for nearby members.
The only global infrastru cture we can probably utilize in
ad hoc networks is geographic location . Based on geographic
multicast address allocation, we devise a new adaptive
anycast architecture as follows. The multicast address space
is broken into prefixes. Each multicast address prefix is
assigned to a geographic region called the rendezvous region
(RR)
4 . Nodes located in the RR have a collective
responsibility of maintaining information about the groups
belonging to the group prefix assigned to their current region.
Since it is ‘collective’ and could be done by only a small
subset of SDS nodes (say 3-7 uncorrelated nodes) we can use
a probabilistic promotion scheme for nodes to become SDS
for the group prefix. Each node decides locally whether it
will become a SDS based on its own configuration (some
nodes maybe configured as servers), capabilities (e.g., GPS),
power and stability estimates. If so, it obtains its
(approximate) geographical location
5 and determines the
group prefix to which its current location maps, using
algorithmic mapping in the general form of f(x
1 to x
2 , y
1 to y
2 ) = G prefix , or similar
6 . At that point, the node acts as a member
of the anycast group of SDS s responsible for G prefix , and
advertises this information in its zone and to its contacts
7 . Other SDS s for the same prefix reply to update the new SDS
(the reply is localized to reduce overhead). As SDS nodes
move out of the RR for the corresponding G prefix , they
advertise their latest group information and leave message to
the RR (using geocast [32] , for example), which increases the
probability of other nodes promoting themselves to become
SDS s. This ensures constant replenishing of the pool of SDS s serving as members of the anycast group for that RR . The above sche me requires approximate knowledge of
geographic location. We do not assume that all nodes are
GPS capable. We do assume, however, that nodes are
heterogeneous; i.e., some nodes are GPS capable, while

4 This does not necessarily imply, however, that these groups are
geographically limited to that region.
5 Geographic location update need not be done frequently, only when a node
moves noticeable distances. Afterall, this information is approximate and is
used for distributed resource discovery (not for forwarding packets).
6 Nodes express their desire to use multicast to their neighbors, zone or
contacts , which will result in a reply with the well-known algorithmic
mapping function. Our scheme allows for changing this mapping to another
well-known mapping (very infrequently, though).
7 Alternatively, a node may advertise this information using localized
broadcast, or geographically scoped broadcast (where only nodes, within a
specific region, broadcast the packets), or Geocast [32] .
7 others use GPS-less techniques [46] [47] to discover their
approximate relative location.
Session Initiation A node initiating a multicast session
is expected (without necessity) to use the above algorithmic
mapping to obtain a multicast address that maps into a
geographical vicinity as its RR . In any case, group/session
initiation requests/updates are sent to the RR to avoid
collisions in multicast address allocation. When a new sender
of a group belonging to G prefix becomes active it performs a
localized broadcast (as described earlier) and, if far from its
RR , issues an update to RR. The requests and updates are sent
using lollipop-LAR (our modified location aided routing
(LAR) [24] ) to improve scalability. In lollipop-LAR, a far
away sender chooses a contact that is closer to the RR . If the
distance between the contact and the RR is less than a limit l , the contact sends the request/update to the RR directly using
LAR, otherwise it chooses one of its contacts closer to the
RR , and so on. An illustration is shown in Figure 3 . SDS SDS SDS SDS SDS: Sender Discovery Server Rendezvous
Region GM: Group Member GM Lollipop - LAR discovery
broadcast Figure 3 . A group member uses algorithmic mapping to obtain the
rendezvous region of the group, then uses lollipop-LAR to reach the region
and contact a sender discovery server to obtain the group information.
When a node joins the group, it first attempts localized
search for SDS . If this fails it sends/ geocasts a join query to
the RR using lollipop-LAR. A join reply is issued by a SDS in
the RR and follows the backward route created by the join
query (this path is only used for the reply so it need not be the
shortest path). Once the receiver has group/sender
information it sends a Join request as described earlier to
establish multicast distribution path(s).
Popularity-based Dynamic Adaptation If used for the
common case, geocasting to the RR may incur a lot of
overhead to maintain group information. Hence, our multicast
service and resource discovery paradigm should adapt to
dynamics of membership, to achieve better performance for
popular groups. As explained above, joining or sending to
groups entail local advertisement ( Adv) or query. This gives
indications of the popularity of the group in the vicinity of the
participants. Nodes receiving Advs and queries, those that are
willing to become SDS for that group (based on their
configuration, stability and capability), estimate the
popularity of the group. The initial estimate is based on
Advs/queries heard. If this initial estimate exceeds a
threshold pop
query-th
a local group query is sent by the
candidate- SDS to its own zone (using pro-active routing
updates readily sent) and to its contacts. Response to this
group query gives a better estimate of group popularity
Grp
est
, in addition to information about existing SDS s nearby
SDS
est . Popularity estimate pop
est is be obtained
as
est
est
est
SDS
Grp
pop a
. If pop est > pop th where pop
th is the
popularity threshold, then a node advertises itself as SDS for
the group to its zone and contacts, and contacts the RR SDS
for updates (using lollipop-LAR). Future nearby join queries
for the group reach the local SDS and are answered locally,
reducing overhead and delay. Furthermore, this adaptive
mechanism also achieves better robustness and continued
operation during network partitions, when RR is unreachable.
Illustration of popularity-based adaptation is shown in Figure 4 SDS SDS SDS SDS Rendezvous Region GM GM GM GM Local
SDS Figure 4 . If the group becomes popular in a region away from the rendezvous
region a subgroup is formed and a local sender discovery server is chosen to
contact the rendezvous region.
Discussion We note that the probability of success of the
localized search is affected mainly by two factors. The first is
the group address, obtained during session initiation, which
decides the location of the RR , and in turn determines the
location of the RR- SDSs . The second factor is the nearby
popularity of the group, which decides the promotion of
nearby SDSs. Many of the offered services are expected to be
location-based services, meaning it is targeted to a specific
location. Hence, these groups will tend to be popular within
certain locations more than others . In addition, initiators are
expected to choose group addresses that have RR in the
geographical vicinity. Both these factors increase the
probability of success for localized search, and lead us to
believe that, in the common case, our architecture is capable
of high performance, with low overhead and low delays. Our
schemes work for global as well as locally- scoped groups.
One essential question to ask here is ‘how do participants
know about newly initiated sessions and their properties?’
This can be provided using the same scheme provided above,
as follows. When multicast participants express their interest
in multicast service, they obtain the algorithmic mapping
function (described above) as well as a well-known session
advertisement group address. Like other groups, this well-
known group has its own RR . As groups are initiated, they are
updated at the RR-SDS and the local SDSs (if any) , then
information about new sessions is obtained as above. This
provides a bootstrapping mechanism essential for providing
the multicast service.
4. Related Work
Related work lies in the areas of ad hoc routing (unicast
and multicast), hierarchy and cluster formation, anycast
architectures, inter-domain multicast, and geocasting. In the
8 area of unicast ad-hoc routing, protocols are generally
classified as either pro-active (or table-driven) or re-active (or
on-demand) protocols. Pro-active protocols include
DSDV [6] , CGSR [8] , and WRP [9] , and rely upon routing
updates to maintain consistency of route information. Re-
active protocols include AODV [10] , DSR [11] , TORA [12] , ABR [13] , and SSA [14] , and create routes only when required
by the source node. One feature of SSA is that it selects
routes based on the signal strength between nodes. Fisheye
state routing (FSR) [49] is used to reduce routing update
overhead of link state routing. For a node, the route update
frequency to a certain destination is inversely proportional to
the distance (in hops) of the destination. That is, routes to
nodes within a small distance are sent to neighbors with
higher frequency than routes to far away nodes. This reduces
route overhead (not table size) and reduces accuracy of
routing with distance. Routing efficiency decreases and
delays increase, however, with dynamics of mobility, and
routing table size grows linearly with network size [27] . CEDAR [29] uses a simple approximate algorithm for
building a core graph consisting of the minimum dominating
set of nodes. The mechanism was designed for a network of
small (10s of nodes) to medium (100s) size network. A
variant of CEDAR [30] was used for multicast by joining to
the core graph. The effects of mobility and concentration on
the core graph, however, were not clear in the study. Other
works on multicast ad-hoc routing in [15] [16] generally
extend existing multicast routing for the Internet, such as
PIM-SM [2] . Other recently proposed multicast ad hoc routing
protocols include tree-based and mesh-based protocols. Tree-
based protocols include AMRoute [22] and AMRIS [23] . AMRoute creates a bi-directional shared core-based tree
using unicast tunnels, it uses virtual mesh links for tree
creation and needs unicast, but incurs temporary loops and
chooses sub-optimal routes with mobility. AMRIS uses a
shared tree and an ID number per node, does not need
unicast, broadcasts new-session messages and uses beacons
to detect disconnection and re-joins to potential parents.
However, it uses the expanding ring search mechanism for
branch re-construction due to node failure, which does not
scale well. Mesh-based protocols include ODMRP [25] and
CAMP [35] . CAMP uses a shared mesh, and all nodes keep
membership, routing and packet information. New members
use expanding ring search to find other member neighbors.
However, CAMP needs a special unicast protocol for its
proper operation. ODMRP floods packets within mesh, but
follows an on-demand policy for establishment and update of
the mesh. It uses request and reply phases, broadcasts source
announcements, and does not require unicast routing. The
mesh is created when join requests from multiple receivers
are sent to multiple-sources. Hence, for sparse groups or
single-sender groups ODMRP may not be robust. A local
route recovery scheme [44] may be used to address this
problem. In our routing protocol, we utilize the concept of
mesh construction and local recovery, but we attempt to
avoid floods for resource discovery, using a contact-based
query approach. Moreover, we allow on-demand-activated
multiple paths to be constructed to a single source, to increase
robustness and achieve better handoff performance during
mobility. Multi-path routing was proposed [37] for parallel
data distribution. We will leverage mechanisms provided for
multi-path discovery, but we use multiple-paths for multicast
differently, by activating on path at a time on-demand.
Recently, a scalable anycast architecture (discussed in
Section 3.3) was proposed for the Internet [20] , and was
discussed earlier. Work on location-based routing was
presented in location-aided routing LAR [24] , Geocast [32] [33] . We use concepts of geocasting for route and
resource discovery. We modify it for scalability using
lollipop shaped regions with the aid of contacts. We also
leverage work on GPS-less positioning [46] [47] to determine
relative approximate positions of the nodes, assuming GPS
capability in some nodes.
One of the earliest works on self-configuring hierarchical
architectures includes work on the landmark hierarchy. Each
node has a level in the hierarchy and a radius r associated
with that level. Each node advertises information about itself
to nodes within r hops. So, a node receives advertisements
from nearby nodes that are the lowest level of the hierarchy,
and faraway nodes that are at higher levels of the hierarchy,
and so on. In [38] landmark hierarchy is used to form an
object location architecture for sensor networks. Hierarchy
levels are self-configuring and may be adapted using a
promotion/demotion scheme. Drawbacks of landmark
hierarchy were discussed in Section 2.1. LANMAR [26] [27]
uses the landmark hierarchy concepts to establish hierarchy in
ad hoc networks. However, landmarks are used for sets of
nodes moving together as a group to reduce routing
information exchange. Other hierarchical ad hoc routing
include the zone-based hierarchical link state (ZHLS) [39] . ZHLS is a GPS-based routing protocol for ad hoc networks,
where a network is divided into non-overlapping zones. A
node only knows node connectivity within its zone and the
zone connectivity for the network. This architecture does not
use cluster head to mitigate traffic concentration, reduce
routing protocol control/message exchange overhead and
avoid single point of failure. It uses zone ID and node ID for
routing. However, the zone map is defined by design for
interzone routing, and hence does not adapt to network
changes and dynamics. Another protocol called the zone
routing protocol (ZRP) [17] [40] was discussed in Section 2.1.
In [19] the ZRP approach is coupled with geographic
(geodesic) routing for remote routing. In [7] [41] [42] the link
availability model is proposed (discussed in Section 2.2). The
authors suggest to use it with a cluster based approach, in
which a parent is selected based on the availability model to
increase the lifetime of the cluster. Parent selection and
cluster dynamics may complicate our architecture. Instead,
we propose to incorporate the availability model with our
modified ZRP approach and to use it for determining zone
size, and in choosing contacts.
9 In the Internet, Hierarchical PIM [34] was proposed is an
inter-domain architecture based on the PIM-SM protocol. It
suggests a hierarchy of rendezvous points (equivalent to
cluster heads) to communicate between multicast domains.
The BGMP architecture [3] was proposed for hierarchical
inter-domain multicast. It uses a bi-directional shared tree and
the notion of a root domain. The problem of multicast address
allocation is coupled with BGMP for the choice of the root
domain. The same study proposes the MASC scheme for
multicast address allocation. Such problem is still active in
research.
5. Conclusion and Future Work
We have presented the first architecture for multicast
service support in large-scale ad hoc networks. Our
architecture is based on the zone-based routing concept, but
extends it using our novel concept of contacts to increase
zone coverage and reduce route and resource discovery
overhead . Our mechanisms are self-configuring and highly
adaptive to network dynamics and mobility , which renders
our architecture more robust, efficient and scalable . We also
provide the first architecture for adaptive anycast in ad hoc
networks and the first scheme for geographic based multicast
address allocation based on our new concept of rendezvous
regions. We hope that these mechanisms can potentially
provide the resource discovery component for a wide-array of
future applications and middle-ware. Our future work
includes thorough evaluation of the architecture and fine-
tuning of the mechanistic parameters presented herein.
More specifically, we plan to study the characteristics of
the resulting network/small-world graphs due to the use of
contacts and the probability p of choosing a contact. These
characteristics, we expect, will be function of time and
depend on the initial choice of contacts and the node
mobility. Furthermore, our plans include thorough analysis of
the performance of the architecture as function of the
popularity threshold pop
th and other popularity-based
parameters. Our hope is that our work provides a framework
for further research in the area of multicast (and other areas)
in large-scale ad hoc networks.
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Ahmed Helmy. "Architectural framework for large-scale multicast in mobile ad hoc networks." Computer Science Technical Reports (Los Angeles, California, USA: University of Southern California. Department of Computer Science) no. 749 (2001).

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Core Title USC Computer Science Technical Reports, no. 749 (2001)

Alternative Title Architectural framework for large-scale multicast in mobile ad hoc networks (title)

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