DYNAMIC ADDRESS ROUTING FOR SCALABLE AD HOC NETWORKS

Dynamic Address Routing For Scalable Ad Hoc Networks, N Niranjan Rao, Sreerag P.S Sushmitha M,
Journal Impact Factor (2015): 8.5041 (Calculated by GISI) www.jifactor.com
www.iaeme.com/ijaret.asp 23 editor@iaeme.com
1,2,3
Visvesvaraya Technological University,
Belagavi, Karnataka, India
ABSTRACT
With today’s rapidly improving link-layer technology, and the widespread adoption of
wireless networking, the creation of large-scale ad hoc networks could be construed as all but
inevitable. Current ad hoc protocol suites do not scale to work efficiently in networks of more than a
few hundred nodes. We believe the main reason behind the lack of scalability is that current
protocols use flat and static addressing. In this paper, we provide an initial design of a routing layer
based on dynamic addressing by providing a separation between address and identity. Each node has
a unique permanent identifier and a transient routing address, which indicates its location in the
network at any given time. We propose mechanisms to implement dynamic addressing efficiently.
Our initial design suggests that dynamic addressing is a promising approach and worth further
examination.
Keywords: AD HOC Networks, Routing, Dynamic Addressing.
I. INTRODUCTION
Scalability is a critical requirement in the use and deployment of ad hoc networks, if we want
this technology to reach its full potential. Ad hoc networking technology is receiving a lot of interest
but it has yet to mature. This is similar to the early stages of the Internet, where very few could
predict its explosive growth. A difference is that in the Internet, scalability was, from the very
beginning, a design constraint. Ad hoc networks research seems to have downplayed the importance
of scalability. In fact, current ad hoc architectures do not scale well beyond a few hundred nodes.
How can we make ad hoc networks scale to thousands, or even millions of nodes? We find this
question fundamental if we want ad hoc technology to be successful in the consumer marketplace.
Already, non-military technology and applications seem to point towards future networks with: a) ad
hoc pockets of connectivity [1], b) consumer-owned networks [2] [3] [4], and c) sensor-net
technologies [5]. All of these applications will place increased scalability demands on ad hoc routing
protocols. Most current research in ad hoc networks focus more on performance and power-
consumption related issues in relatively small networks, and less on scalability. The current routing
protocols and architectures work well only up to a few hundred nodes. We believe the main reason
behind the lack of scalability is that these protocols rely on flat and static addressing. With scalability
DYNAMIC ADDRESS ROUTING FOR SCALABLE AD
HOC NETWORKS
N Niranjan Rao1
, Sreerag P.S2
, Sushmitha M3
Volume 6, Issue 6, June (2015), pp. 23-33
Article ID: 20120150606005
International Journal of Advanced Research in Engineering and
Technology (IJARET)
© IAEME: www.iaeme.com/ijaret.asp
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
IJARET
© I A E M E

as a partial goal, some efforts have been made in the direction of hierarchical routing and clustering
[6] [7] [8]. These approaches do hold promise, but they do not seem to be actively pursued. It
appears to us as if these protocols would work well in scenarios with group mobility [9], which is
also a common assumption among cluster based routing protocols. Is dynamic addressing a feasible
way of achieving scalable ad hoc routing? This is the question that we address in this work. Dynamic
addressing simplifies routing but introduces two new problems: address allocation, and address
lookup [10] [11].
As a guideline, we identify a set of properties that a scalable and efficient solution must have:
• Localization of overhead: a local change should affect only the immediate neighborhood, thus
limiting the overall overhead incurred due to the change.
• Lightweight, decentralized protocols: we would like to avoid concentrating responsibility at
any individual node, and we want to keep the necessary state to be maintained at each node as
small as possible.
• Zero-configuration: we want to completely remove the need for manual configuration beyond
what can be done at the time of manufacture.
In this paper, we evaluate dynamic addressing and show that it is a promising first step
toward achieving scalability in the order of millions of nodes in ad hoc routing. First, we develop a
dynamic addressing scheme, which has the necessary properties mentioned above. Second, we study
the performance of a new routing protocol, based on dynamic addressing. Our dynamic addressing
based routing scheme provides good network performance. In fact, our results indicate that we would
reliably outperform other routing protocols based on static addresses, in large and actively used
networks.
Our work in perspective
We describe a new approach to routing in ad hoc networks. However, the goal is to show the
potential of this approach and not to provide an optimized protocol. We believe that the address
equals identity assumption used in current ad hoc routing protocols is most likely inherited from the
wireline world, which is much more static and is explicitly managed by specialist system
administrators. DART makes an explicit distinction between node identity and address. The address
of a node reflects its current location in the network at all times. This simplifies routing but
introduces two new challenges. First, we need a node lookup service that will provide the address for
a given a node identifier. Second, DART needs to maintain addresses dynamically: as a node moves,
its address changes to reflect the new location. Although much work remains to be done, we believe
that the dynamic addressing approach is a viable strategy for scalable routing in ad hoc networks.
Although the design presented here is not complete, it provides the backbone and several non-trivial
algorithmic solutions. Furthermore, we expect this work to provoke a constructive reevaluation of
current networking architectures
II. RELATED WORK
In most common IP-based ad hoc routing protocols [12][13] [14], addresses are used as pure
identifiers. Without any structure in the address space, there are two choices: either keep routing
entries for every node in the network, or resort to flooding route requests throughout the network
upon connection setup. Neither of these alternatives scale well. Other protocols [15] [16] use
geographic location information to assist in the routing, and thereby try to achieve scalability.
However, this approach can be severely limiting as location information is not always available and
can be misleading in, among others, non-planar networks. For a survey of ad hoc routing, see [17]. In
the Zone Routing Protocol (ZRP) [18] and Fisheye State Routing (FSR) [19], nodes are treated
differently depending on their distance from the destination. In FSR, link updates are propagated

more slowly the further away they travel from their origin, with the motivation that changes far away
are unlikely to affect local routing decisions. ZRP is a hybrid reactive/ proactive protocol, where a
technique called bordercasting is used to limit the damaging effects of global broadcasts. Some work
has been done on using clustering in ad hoc networks. In multilevel-clustering approaches such as
Landmark [20], LANMAR [9], L+ [21], MMWN [7] and Hierarchical State Routing (HSR) [8],
certain nodes are elected as cluster heads (also called Landmarks). These cluster heads in turn select
higher level cluster heads, up to some desired level. A node’s address is defined as a sequence of
cluster head identifiers, one per level, allowing the size of routing tables to be logarithmic in the size
of the network, but easily resulting in long hierarchical addresses. In HSR, for example, the
hierarchical address is a sequence of MAC adresses, each of which is 6 bytes long.
A problem with having explicit cluster heads is that routing through cluster heads creates
traffic bottlenecks. In Landmark, LANMAR and L+, this is partially solved by allowing nearby
nodes route packets instead of the cluster head, if they know a route to the destination. All of the
above schemes have explicit cluster heads, and all addresses are therefore relative to these, and are
likely to have to change if a cluster head moves away. This reliance on cluster head nodes makes the
above schemes best suited to scenarios involving group mobility, such as troop movements.
Area Routing, as described by Kleinrock and Kamoun in [22], is the method most similar to
the one used in today’s Internet. Here, nodes that are close to each other in the network topology
have similar addresses, without any explicit hierarchy of nodes. Our work is, as far as we know, the
first attempt to use this type of addressing in ad hoc and mesh networks. Tribe [23] is similar to
DART at a high level, in that it uses a two phase process for routing: first address lookup, and then
routing to the address discovered. However, the tree-based routing strategy used in Tribe bears little
or no resemblance to the area based approach in DART. Tree-based routing may under many
circumstances suffer from severe traffic concentration at nodes high up in the tree, and a high
sensitivity to node failure
III.OVERVIEW AND DEFINITIONS
The key idea in DART is the separation of the identity and the address of a node. For now,
we can assume that each DART node has one unique identifier and one unique address. The address
is dynamic and changes with node movement to reflect the node’s location in the network. The ID of
the node remains the same throughout, reliably identifying the node despite address changes. An
integrated distributed node lookup service maps identifiers to addresses.
We distinguish three major functions. First, address allocation maintains one routing address
per network interface, in such a way that the address indicates the node’s relative network location.
Second, routing delivers packets from a node to a given routing address. Third, node lookup is a
distributed lookup table mapping every node identifier to its current network address.
Joining the network. To join a network, a node establishes a physical connection to at least
one node already in the network and requests an address. The receiving node(s) answer(s) with an
available address. The joining node then “registers” its identifier together with the address in the
distributed node lookup service. As a node moves, it requests and receives new addresses from its
new neighbors. On each address change, the node updates its entry in the lookup service.
Packet routing. The sender node only needs to know the identifier of the receiver. Before
sending its first packet to some destination, the sender looks up the current address of the destination
node using the lookup service. The packets contain both the identifier of the destination and the last
known address and routing is done in a Distance Vector fashion, one hop at a time. If the destination
cannot be reached, the lookup table is consulted along the way to find the new address of the
destination.

The Network Layer
DART targets networks consisting of a large number of mobile and stationary nodes,
connected by bidirectional links using any current MAC technology. Node addresses are
dynamically assigned depending on the node’s current position in the network.
We start by presenting two views of the network that we use to describe our approach: a) the
address tree, and b) the network topology.
The Address Tree. In this abstraction, we visualize the network from the address space point
of view as proposed in [10]. Addresses are ℓ-bit binary numbers, a ι-ı, …., aо. The address space can
be thought of as a binary address tree of ℓ + 1 levels, as shown in figure 1. The leaves of the address
tree represent actual node addresses; each inner node represents an address subtree: a range of
addresses with a common prefix. For presentation purposes, nodes are sorted in increasing address
order, from left to right. We stress that the links in the tree do not correspond to physical links in the
network topology. The actual physical links are represented by dotted lines connecting leaves in
figure 1. By selecting node addresses carefully, we guarantee that nodes within a subtree are able to
communicate using only nodes inside that subtree.
Figure. 1. The address tree of a 3-bit binary address space. Leaves represent actual addresses,
whereas inner nodes represent groups of addresses with a common prefix.
Figure. 2. A network topology with node addresses assigned. Dotted enclosures correspond to
subtrees in the address tree.
The Network Topology. This view represents the connectivity between nodes. In figure 2, the
network from figure 1 is presented as a set of nodes and the physical connections between them.
Each solid line is an actual physical connection, wired or wireless, and the sets of nodes from each
subtree of the address tree are enclosed with dotted lines. Note that the set of nodes from any subtree
in figure 1 induces a connected subgraph in the network topology in figure 2. This is not a
coincidence, but a crucial property of our dynamic addressing approach. Intuitively, nodes that are

close to each other in the address space should be relatively close in the network topology. More
formally, we can state the following constraint.
Prefix Subgraph Constraint
The set of nodes that share a given address prefix form a connected subgraph in the network
topology. This constraint is fundamental to the scalability of our approach. Intuitively, this constraint
helps us map the virtual hierarchy of the address space onto the network topology. Based on it, we
constrain the allowed allocation of addresses so that nodes with “similar” addresses are likely to be
close in terms of routing distance.
Let us define two new terms that will facilitate the discussion.
A Level-k subtree of the address tree is defined by an address prefix of (ℓ−k) bits, as shown
in figure 1. For example, a Level-0 subtree is a single address or one leaf node in the address tree. A
Level-1 subtree has a (ℓ−1)-bit prefix and can contain up to two leaf nodes. In figure 1, [0xx] is a
Level-2 subtree containing addresses [000] through [011]. Note that every Level-k subtree consists
of exactly two Level-(k − 1) subtrees.
We define the term Level-k sibling of a given address to be the sibling4 of the Level-k
subtree to which a given address belongs. By drawing entire sibling subtrees as triangles, we can
create abstracted views of the address tree, as shown in figure 3. Here, we show the siblings of all
levels for the address [100] as triangles: the Level-0 sibling is [101], Level-1 is [11x], and the Level-
2 sibling is [0xx]. Note that each address has exactly one Level-k sibling, and thus at most ℓ siblings
in total.
IV. ROUTING
In this work, we use a hierarchical form of proactive distance-vector routing. A distinguishing
difference from previous such schemes is that it makes use of the prefix subgraph constraint, and the
topological meaning that addresses have here.
Each node keeps some routing state, as specified in Listing 1. Routing state about a node’s
Level-i sibling is stored at position i in each of the respective arrays. Intuitively, the routing state for
a sibling contains the information necessary to maintain a route toward a node (any node) in that
subtree. The address field contains the current address of the node, and bit i of the address is referred
to as address[i], where i = 0 for the least significant bit of the address. Arrays nexthop and cost are
self-explanatory. The id array contains the identifier of the subtree in question. As described earlier,
the identifier of a subtree is equal to the lowest out of all the identifiers of the nodes that constitute
that subtree. Finally, route log[i] contains the log of the current route to the sibling at level i, where
bit b of log i is referenced by the syntax route log[i][b].
Figure. 3. Routing entries corresponding to figure 2. Node 100 has entries for subtrees 0xx, 11x
(null entry) and 101.

Packet forwarding under DART is a matter of looking up the next hop in the routing table. In
our example shown in figures 1-3, node [100] has routing entries for sibling subtrees [0xx], [11x]
and [101]. To route a packet to address [000], node [100] first determines the (sibling) subtree to
which the destination address belongs ([0xx]), and then sends the packet to the neighbor closest to
that subtree ([011]). The process is repeated until the packet has reached the given destination
address.
The hierarchical technique of only keeping track of sibling subtrees rather than complete
addresses has three immediate benefits. One, the amount of routing state kept at each node is
drastically reduced. Two, the size of the routing updates is similarly reduced, and three, it provides
an efficient routing abstraction such that routing entries for distant nodes can remain valid despite
local topology changes in the vicinity of these nodes.
V. NODE LOOKUP
The missing link is: how do we find the current address of a node, if we know its identifier?
We propose to use a distributed node lookup table, which maps each identifier to an address, similar
to what is proposed in [10]. Here, we assume that all nodes take part in the lookup table, each storing
a few <identifier, address> entries. However, this node lookup scheme could potentially be replaced
some other mechanism in the future. For our proposed distributed lookup table, the question now
becomes: which node stores a given <identifier, address> entry? The solution is simple yet elegant,
and reminiscent of consistent hashing.
We use a globally, and a priori, known hash function that takes an identifier as argument and
returns an address where the entry can be found. If there exists a node that occupies this address,
then that node is responsible for storing the entry. If there is no node with that address, then the node
with the most similar address is responsible for the entry. To find this ”most similar” node, we make
a minor change to the routing algorithm for lookup packets: If no route can be found to a sibling
indicated in the address, that bit of the address is ignored, and the packet is routed to the sibling
subtree indicated by the next (less significant) bit. When the last bit has been processed, the packet
has reached its destination. For example, using figure 3 for reference, let’s assume a node with
identifier ID1 has a current routing address of [010]. This node will periodically send an updated
entry to the lookup table, namely <ID1, 010>. To figure out where to send the entry, the node uses
the hash function to calculate an address, like so: hash(ID1). If the returned address is [100], the
packet will simply be routed to the node with that address. However, if the returned address was
instead [111], the packet could not be routed to the node with address [111] because there is no such
node. In such a situation, the packet gets automatically routed to the node with the most similar
address, which in this case would be [101].

VI. DYNAMIC ADDRESS ALLOCATION
To assess the feasibility of dynamic addressing, we develop a suite of protocols that
implement such an approach. Our work effectively solves the main algorithmic problems, and forms
a stable framework for further dynamic addressing research. Although the design has not yet been
optimized for maximum throughput, its scalability properties and predictable performance show
promise. When a node joins an existing network, it uses the periodic routing updates of its neighbors
to identify and select an unoccupied and legitimate address, as specified in Listing 2.
It starts out by selecting which neighbor to get an address from. As illustrated in Listing 3, the
neighbor with the highest level insertion point is selected as the best neighbor. The insertion point is
defined as the highest level for which no routing entry exists in a given neighbor’s routing table.
However, the fact that a routing entry happens to be unoccupied in one neighbor’s routing table does
not guarantee that it represents a valid address choice. We discuss how the validity of an address is
verified in the next subsection. The new node picks an address out of a possibly large set of available
addresses. In our current implementation, we make nodes pick an address in the largest unoccupied
address block.
For example, in figure 3, a joining node connecting to the node with address [100] will pick
an address in the [11x] subtree. There are several ways to choose among the available addresses, and
we have presented only one such method. However, it has turned out that this method of address
selection works well in simulation trials.
Under steady-state, and discounting concurrency, the presented address selection technique
leads to a legitimate address allocation: the joining node is by definition connected to neighbor it got
its new address from, and the new address is taken from one of the neighbors’ empty sibling subtrees,
so the prefix subgraph constraint is satisfied.
Let us see an example of address allocation in action. Figure4 illustrates the address
allocation procedure for a 3-bit address space.

Figure. 4. Address tree for a small network topology. The numbers 1-3 show the order in which
nodes were added to the network.
Node A starts out alone with address [000]. When node B joins the network, it observes that
A has a null routing entry corresponding to the subtree [1xx], and picks the address [100]. Similarly
when C joins the network by connecting to B, C picks the address [110]. Finally, when D joins via A,
A’s [1xx] routing entry is now occupied. However, the entry corresponding to sibling [01x] is still
empty, and so, D takes the address [010].
The characteristics of the address allocation can have a major impact on the performance of
the network. For example, if an available address can not be found, the request is refused. However,
with a large enough address space and with efficient address tree maintanance, this is unlikely to
happen. Two issues are critical for the address tree: a) we want to keep the address tree balanced, and
b) we want to maximize the connectivity within an subtree. These two objectives may at times be
conflicting, and we are currently evaluating techniques to find a good balance between tree balancing
and inter-area connectivity
Address tree balancing. We need a way to balance the tree while maintaining the DART subgraph
invariant. If a particular subgraph becomes congested, using up all locally available address space,
new nodes that try to obtain an address may be unable to do so. Thus, in order to alleviate cases of
local congestion in the tree, we would like nodes to proactively migrate in the tree in order to balance
it. Migrating in this case, means simply to select a new address; without affecting connectivity and
within the constraints of the subtree invariant.
Maximizing the intra-subgraph connectivity. We want to select addresses in such a way that
nodes within a subtree are well connected by physical links. This improves the routing performance
and tolerance to link failures, and is especially desirable in mobile networks.
VII. INTERPRETATION OF RESULTS
Figure1 shows the generation of dynamic address, which is used to allocate addresses to
every incoming nodes in the network.

When an incoming node joins a network, it receives an address which is dynamic and
changes with node movement to reflect the node’s location in the network. In figure 2,when node 1
joins the network by sending a request message, it receives the address 000.
As shown in figure 3, the joining node then “registers” its identifier together with the address.
Note that as a node moves, it requests and receives new addresses from its new neighbors. On each
address change, the node updates its entry in the lookup service.
When a node is disconnected from the network, then at the same time if another node tries to
communicate with the disconnected node, then the sender will get an error icon that the destination is
disconnected, as shown in Fig 4.

In the following figure 5, at the sender side node 1 is trying to communicate with another
node, say node 3 by sending a message.
As shown in figure 6, when the message arrives at the destination node 3 receives the
message.
VIII. CONCLUSION
In this paper, we propose dynamic addressing as a building block for scalable ad hoc routing.
We outline the novel challenges involved in a dynamic addressing scheme, and proceeded to
describe efficient algorithmic solutions. We show how our dynamic addressing can support a
scalable routing scheme.
There are two fundamental and complementary novelties in DART. First, there is a
distinction between the identity and address of a node. This distinction enables us to handle mobility
in a novel way, improving the scalability of the system. Specifically, the effect of node mobility is
confined to the neighborhood of a moving node in most cases. Second, DART supports routing at the
network layer. Critical functions like address allocation and routing are addressed in a distributed
and cooperative fashion using a per node state of O (logN). In addition, DART requires no manual
configuration, and is fully distributed.
The motivation behind this work was to challenge the status quo in ad hoc routing. We
believe that dynamic addressing has the potential to bring ad hoc routing to the point where it can be
used in massive ad hoc and mesh networks.

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pp. 194 - 202, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.

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