Cluster-based OLSR Extensions to Reduce Control Overhead in Mobile Ad Hoc Networks Francisco J. Ros
Pedro M. Ruiz
Dept. of Information and Communications Engineering University of Murcia Murcia, Spain E-30100
Dept. of Information and Communications Engineering University of Murcia Murcia, Spain E-30100
[email protected]
[email protected]
ABSTRACT Proactive routing protocols for Mobile Ad Hoc Networks (MANETs) traditionally fail to scale up to large networks, since they generate a big amount of routing overhead. Based on OLSR, a proactive solution specifically designed for dense ad hoc networks, we develop a low overhead protocol called Clustered OLSR (C-OLSR). C-OLSR assumes that somehow the network is partitioned into clusters, and restricts the propagation of topology control messages inside every cluster. The generation and forwarding of inter-cluster topology information is based on the use of Multipoint Relays (MPRs) at the level of clusters. Through a simulation study, we show that C-OLSR outperforms OLSR both in terms of overhead generation and achievable throughput.
Categories and Subject Descriptors C.2.2 [Computer Systems Organization]: Computer Communication NetworksNetwork Protocols[Routing protocols]
General Terms Algorithms, Performance, Design
Keywords Ad Hoc Routing, Scalability, Performance Evaluation
1.
INTRODUCTION AND MOTIVATION
Mobile Ad Hoc Networks (MANET) [1] have been envisioned as the ideal communications technology for scenarios where the network infrastructure is missing, such as military and rescue situations. Because of this, the Internet Engineering Task Force (IETF) developed several routing protocols which suit the challenging demands of such kind of scenarios. In this paper, we focus on the Optimized
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Link State Routing (OLSR) protocol [2], a proactive solution which computes routes to every node in the network. The main drawback of proactive routing protocols like OLSR, is that they generate a big amount of control overhead which consume bandwidth that should be employed by user data traffic instead. The reason is that OLSR requires each node to send its local view of the network (TC messages) to every other node in the network. This leads to a scalability problem as the network size increases in terms of number of nodes. Since topology information must reach the whole network, the generated overhead can jeopardize the overall network performance when the number of nodes is high. In addition, low capacity devices might find unaffordable to store routes to every node in very large-scale ad hoc networks. On the other hand, clustering is a well-known technique, highly used within ad hoc networks research. It is employed to reduce the complexity of protocols by simplifying the problem to smaller groups of nodes (clusters). These nodes organize themselves around another node, named clusterhead. The clusterhead is responsible for the creation and maintenance of the cluster. Thus, routing protocols need to be challenged to deal just with the nodes inside a same cluster, and with the knowledge to reach the remaining clusters. In this paper, we propose a low-overhead protocol, Clustered OLSR (C-OLSR). It makes use of clusters to heavily reduce the generation of topology control messages as well as the size of routing tables. It is independent of the clustering algorithm employed. C-OLSR limits the forwarding of topology control messages within a cluster, and exchanges inter-cluster topology information to reach nodes outside the own cluster. It also leverages the ideas of plain OLSR up to the level of clusters, allowing for overhead savings when both the number of nodes and clusters is high. In addition, the protocol does not impose the existence of higher capacity nodes (obvious candidates for clusterheads), and can be run in a homogeneous MANET. We present three different algorithms which can be employed within C-OLSR to send inter-cluster information. They differ on the node(s) which are responsible for generating the inter-cluster topology information. Through a simulation study, we show that all of them outperform OLSR in terms of generated overhead and achievable throughput. The reminder of the paper is organized as follows: In Section 2, an overview of OLSR is presented. This is needed to understand the optimizations performed by C-OLSR, which
Figure 1: Solid nodes are selected as MPRs by the source. Just four out of eight retransmissions are needed to reach all the nodes. are deeply described in Section 3. The performance comparison between OLSR and C-OLSR is presented in Section 4. Section 5 summarizes the previous work related to the overhead reduction in OLSR. Finally, Section 6 draws some conclusions and describes some future work.
2.
OLSR OVERVIEW
OLSR is a proactive link state routing protocol specifically designed to take advantage of dense ad hoc networks. Nodes sense their neighborhood by the periodic exchange of HELLO messages. In this way, nodes learn their local vicinity and the status of the link with each neighbor (i.e., if the link is considered unidirectional or bidirectional). This information is disseminated throughout the whole network via periodic Topology Control (TC) messages. This allows mobile nodes to set up routes to any potential destination present in the network. The former behavior is the typical link state routing algorithm. However, OLSR introduces three optimizations based on the use of partial topology information. A Multipoint Relay set (MPR) is defined as a subset of symmetric (bidirectional) neighbors which provide reachability to all the symmetric 2-hop neighbors. If a message is intended to reach the whole 2-hop neighborhood, only those nodes selected as MPRs by the source are needed to forward the message. Figure 1 shows an example where only four retransmissions are needed to make the message reach all the nodes, instead of eight forwardings that would be needed in the case of blind flooding. Applying the same behavior to bigger networks, only those broadcast messages received from a MPR selector are forwarded. The selection of MPRs is possible because 2-hop neighborhood information is exchanged via HELLO messages. As a second optimization, only those nodes selected as MPR must generate topology information (TC messages). And finally, only the links between a node and its MPR selectors need to be reported in TC messages in order to obtain optimal routes in terms of number of hops.
3.
THE CLUSTERED OLSR PROTOCOL
C-OLSR is a modification of OLSR which makes use of clustering to heavily reduce the protocol overhead. It aims at enabling its use in large-scale ad hoc networks.
Let us assume that a clustering mechanism is being executed in the ad hoc network. The clusterhead is the node responsible for creating and maintaining the cluster, and the nodes which are one hop away from another cluster (different from theirs) are called border nodes. Border nodes can know that they belong to a cluster but have got neighbors from other clusters, because HELLO messages are extended to include cluster membership information. The C-OLSR extension uses regular OLSR inside every cluster, but TC messages are never forwarded within a cluster different from the originator’s one (see Algorithm 1). Border nodes know that the originator of the TC belongs to the same cluster as long as TC messages contain the originator’s membership. In this way, the number of forwardings of TCs is restricted by the extension of the cluster. Since topology information is complete within the cluster, every node can compute host-based routes to every other node in the same cluster. Note that all the optimizations performed via MPRs are still employed. Algorithm 1 TC messages generation and forwarding rules. TC Generation if ∃[CLUSTER(this node)] ∧ SIZE(MPR Selector Set) > 0 then Send TC else Do not send TC end if TC Forwarding if CLUSTER(this node) = CLUSTER(TC.originator) then Use OLSR default forwarding algorithm else Do not forward end if
For the remaining nodes in other clusters, mobile nodes just create routes to every cluster, and not to every other node. This heavily reduces the number of routing entries, which is very useful for memory-constrained devices. Once a data packet arrives at the destination cluster, nodes inside it have the needed information to deliver the packet to the final destination (since they know a host route to every other member of the cluster). In order to achieve this, cluster topology information must be exchanged among the nodes. The approach we have followed is to leverage the same mechanisms of plain OLSR to the level of clusters. So, new C-HELLO and C-TC messages are introduced to emulate the behavior of an OLSR node, but this time are clusters those which come to play. Figure 2 shows the layered architecture of C-OLSR in which MPRs are used within each cluster to reduce the overhead of TC message distribution, and C-MPR clusters are selected at the cluster level to mitigate the overhead of distributing C-TC messages with inter-cluster routes. To support this layered operation we add to C-OLSR additional information repositories for ad hoc nodes: Neighbor Cluster Set Stores the list of clusters which are adjacent to the node’s own cluster. Adjacent Neighbor Cluster Set Records the list of clusters which are adjacent to the node itself. That is, each entry means that the current node has a neighbor (1 hop away) which belong to that particular cluster.
differ in this regard, and their advantages and disadvantages are drawn.
3.1
Clusterhead-based Approach
Obviously, the clusterheads are great candidates to generate clustering topology information, since they are the central element of every cluster. Commonly, at a given time a cluster only has a clusterhead, and therefore the C-HELLO and C-TC messages are centralized on a single generator per cluster. This heavily reduces the protocol overhead. Algorithm 2 shows how C-HELLO messages generated by a clusterhead are only forwarded inside the own cluster, and throughout the adjacent ones. Figure 2: A set of clusters are selected as clusterlevel MPRs to reduce control overhead to distribute C-TC messages Neighbor Cluster 2-hop Set Includes the list of clusters which are neighbors of those in the “Neighbor Cluster set”. Cluster Topology Set Similarly to the OLSR “Topology Set” but at the cluster level, it stores the set of clusters which are visible to the node, plus the neighboring cluster used to reach nodes in that cluster. Cluster MPR Set Holds the clusters which have been chosen as C-MPRs by the node. Cluster MPR Selector Set Holds the clusters which have chosen the own cluster as a C-MPR. C-HELLOs are used to sense the neighboring clusters and compute the Cluster MPR (C-MPR) set. They are propagated through the adjacent clusters of a given one. In this way, the nodes acquire the needed information to create routes to neighboring clusters. C-TCs contain this list of neighboring clusters of a given one, and are relayed to the remaining clusters. We make use of the C-MPR set in order to reduce the number of forwardings of these messages. CTC messages are only relayed through those clusters which have been selected as C-MPR by the previous cluster. If the size of clusters is high, this technique can greatly decrease the overhead caused by the C-TC forwardings. The former description can be seen as if we were deploying OLSR at two different levels. HELLO and TC messages are exchanged, and MPR sets are computed at a node-level (intra-cluster processing); while C-HELLO and C-TC messages are propagated through both levels, and C-MPR sets are computed at a cluster-level (inter-cluster processing). Unlike other approaches [3], which assume the presence of more powerful nodes which can have direct line-of-sight communications, C-OLSR can be used in homogeneous or heterogeneous ad hoc networks. This is achieved by sending CHELLOs and C-TCs at both levels (inter-cluster and intracluster). The question of which node(s) should be responsible for the generation of cluster topology messages (C-HELLOs and C-TCs) still remains. Needless to say, not every node will generate cluster topology information, since this would increase the protocol overhead instead of reducing it. The following subsections explain three different algorithms which
Algorithm 2 C-HELLO messages generation and forwarding rules for the clusterhead-based algorithm. C-HELLO Generation if IS CLUSTERHEAD(this node) then Send C-HELLO else Do not send C-HELLO end if C-HELLO Forwarding if CLUSTER(this node) = CLUSTER(C-HELLO.sender) ∨ CLUSTER(C-HELLO.originator) ∈ Adjacent Neighbor Cluster Set then Use OLSR default forwarding algorithm else Do not forward end if
For the case of C-TCs, the implementation is slightly trickier (Algorithm 3). Once a C-TC arrives at a given cluster, it must be relayed to adjacent clusters only if the previous one selected the current cluster as a C-MPR. However, in case the latter condition does not hold, the C-TC must still be forwarded inside the own cluster. This is needed in order to allow the remaining nodes (others than border nodes) to learn clusters topological information. A simple solution is to dedicate a special flag inside the C-TC, bit D, which is enabled when the message must traverse the current cluster but not leave it. Algorithm 3 C-TC messages generation and forwarding rules for the clusterhead-based and hybrid algorithms. C-TC Generation if IS CLUSTERHEAD(this node) ∧ SIZE(C-MPR Selector Set) > 0 then C-TC.D ← false Send C-TC else Do not send C-TC end if C-TC Forwarding if CLUSTER(this node) 6= CLUSTER(C-TC.sender) ∧ CTC.D then Do not forward else if CLUSTER(this node) 6= CLUSTER(C-TC.sender) ∧ CLUSTER(C-TC.sender) 6∈ C-MPR Selector Set ∧ ¬C-TC.D then C-TC.D ← true Use OLSR default forwarding algorithm else if CLUSTER(this node) = CLUSTER(C-TC.sender) ∨ CLUSTER(C-TC.sender) ∈ C-MPR Selector Set then Use OLSR default forwarding algorithm end if
In this clusterhead-centered algorithm, as a counterpart of the overhead savings, if the network is error-prone then the likelihood that these messages reach all the intended destinations is lower than if a higher number of generators were present.
3.2
Interval
HELLO 2s
TC 5s
C-HELLO 5s
C-TC 10 s
Clustering 5s
Table 1: Intervals used in the simulations for the periodic sending of every type of message.
Distributed Approach
If we want to add redundancy to C-OLSR, C-HELLOs and C-TCs messages can be generated by border nodes. In this way, there is more chance for a node to receive information about a cluster, since more nodes are responsible for generating such information. However, the number of border nodes can be quite high in a large network, and the overhead savings get reduced. As noted in Algorithm 4, C-HELLO messages do not need to be forwarded inside the own cluster. Since they are generated by border nodes, they reach the adjacent ones without the need of traversing the own cluster (as it happened in the clusterhead-based algorithm). The same applies to the C-TC messages (Algorithm 5). Algorithm 4 C-HELLO messages generation and forwarding rules for the distributed and hybrid algorithms. C-HELLO Generation if SIZE(MPR Selector Set) > 0 ∧ SIZE(Adjacent Neighbor Cluster Set) > 0 then Send C-HELLO else Do not send C-HELLO end if C-HELLO Forwarding if CLUSTER(this node) = CLUSTER(C-HELLO.originator) then Do not forward else if CLUSTER(this node) = CLUSTER(C-HELLO.sender) then Use OLSR default forwarding algorithm else if C-HELLO.originator ∈ Symmetric Neighbor Set then Use OLSR default forwarding algorithm end if
Algorithm 5 C-TC messages generation and forwarding rules for the distributed algorithm. C-TC Generation if SIZE(MPR Selector Set) > 0 ∧ SIZE(Adjacent Neighbor Cluster Set) > 0 ∧ SIZE(C-MPR Selector Set) > 0 then Send C-TC else Do not send C-TC end if C-TC Forwarding if CLUSTER(this node) = CLUSTER(C-TC.originator) then Do not forward else if CLUSTER(this node) 6= CLUSTER(C-TC.sender) ∧ C-TC.D then Do not forward else if CLUSTER(this node) 6= CLUSTER(C-TC.sender) ∧ CLUSTER(C-TC.sender) 6∈ C-MPR Selector Set ∧ ¬C-TC.D then C-TC.D ← true Use OLSR default forwarding algorithm else if CLUSTER(this node) = CLUSTER(C-TC.sender) ∨ CLUSTER(C-TC.sender) ∈ C-MPR Selector Set then Use OLSR default forwarding algorithm end if
3.3
Hybrid Approach
As a tradeoff of the previous approaches, a hybrid proposal can be thought of. In this case, C-HELLOs are generated by border nodes in order to provide neighboring clusters with the maximum probability of receiving the cluster topology information. On the other hand, and since C-TCs can provoke a higher overhead in big ad hoc networks, they are generated by the clusterheads to minimize the number of forwardings of such kind of messages.
4.
PERFORMANCE EVALUATION
We have used the version 2.29 of the ns2 Network Simulator [4] with the UM-OLSR implementation [5]. The latter has been also modified to implement C-OLSR. Table 1 shows the intervals used for the periodic sending of control messages. The scenario consists of 100 mobile nodes using 802.11b at 2 Mb/s with a radio range of 250 m. These nodes are placed in a square field of 1000x1000 m2 . In addition, there are from 2 to 5 clusterheads located at fixed locations. This can be seen as if some wireless stations have been previously configured to act as clusterheads. We have adopted this convention in order to have some control onto the clusters topology and therefore be able to interpret the results depending on the scenario. In the simulations with 2 to 4 clusterheads, they are placed 500 m away from each other, centered around the simulation area, and as the vertices of a segment, equilateral triangle and square respectively. For the case of 5 clusterheads, they form a cross and are separated 250 m. A simple clustering algorithm, based on the prefix continuity property [6], has been implemented. Basically, clusterheads announce their presence through restricted flooded messages, and nodes join the cluster which is closer (in number of hops) from them. In order to assess if there are significant differences in the delivery ratio between the approaches, 5 low intensity traffic flows have been simulated. Sources and destinations are randomly chosen. UDP traffic at a constant bit rate of 10 Kbps, with 512 bytes per packet, is generated. Every source begins transmitting data within the first 60 seconds of the simulation, at a randomly chosen time. Mobile nodes follow the well-known Random Waypoint mobility model [7], with a maximum speed of 10 m/s and a pause time of 30 seconds. To avoid accumulation of nodes in the center of the simulation area, we let the nodes initially move for 3600 seconds which are not taken into account. All simulations have been run during 300 seconds. The first 30 seconds have been cut off, to try that the network has reached the steady state. Five different runs have been performed per each scenario. The subsequent values show the average of those 5 simulations.
4.1
Simulation Results
C-OLSR is targeted at reducing the protocol overhead, so this is our main metric of interest. Figure 3 shows the overhead generated by every variant of C-OLSR and plain
140000
6000
C-OLSR(C) C-OLSR(H) C-OLSR(D) OLSR
5950 Throughput (Bytes/s)
Protocol Overhead (#msgs)
160000
120000 100000 80000 60000
C-OLSR(C) C-OLSR(H) C-OLSR(D) OLSR
5900 5850 5800 5750
40000 20000
5700 2
3
4
5
2
No. of Clusters
TC 128114.8 25182.4 24533.6 24351
C-HELLO n/a 6187.6 50074.6 50291.6
C-TC n/a 287.4 277.2 2418.2
4
5
No. of Clusters
Figure 3: Protocol overhead comparison between approaches. The darkest columns show the overhead caused by TC messages.
OLSR C-OLSR(C) C-OLSR(H) C-OLSR(D)
3
Clustering n/a 5752.8 5685.8 5644.6
Table 2: Detailed overhead generation for the scenario with 5 clusters.
OLSR. As shown, C-OLSR always reduce the overhead of OLSR because of the restricted TC forwarding (the number of TCs sent over the network is drawn in the darkest color). As the number of clusters is incremented, TCs are restricted to smaller zones and therefore the overhead decreases. In addition, the use of C-MPRs minimizes the number of inter-cluster topology information messages (CHELLOs and C-TCs) exchange. Therefore our extensions provoke little overhead compared to the TC savings. The clusterhead-based approach of C-OLSR provokes the lowest overhead. Since C-HELLO and C-TC messages are only sent out by the clusterheads, the overhead caused by these messages gets minimized. You can also notice that there is no big differences between the performance of the distributed and hybrid approaches. This is because the clusters topology makes the sending of C-TC messages very scarce. Table 2 shows the detailed overhead in the scenario with 5 clusters, which is the only which needs to generate a significant amount of C-TCs messages. In the same table, you can check that the clustering mechanism overhead is really low compared to improvement we make onto the overall overhead. The great improvement, in terms of overhead, which is made by C-OLSR, translates into an increased throughput (Figure 4). Since our extension limits the number of messages sent by the routing protocol, more bandwidth is available for user data traffic. Again, all the variants of COLSR outperform OLSR. For the most of the scenarios, the clusterhead-based scheme offers the highest throughput because it is the lowest overhead-consuming algorithm. This means that many data drops are due to collisions and contention in the medium access, so it is important to leave
Figure 4: proaches. PDR
Throughput comparison between ap-
OLSR 0.9289
C-OLSR(C) 0.9484
C-OLSR(H) 0.9474
C-OLSR(D) 0.9499
Table 3: PDR of the different approaches in the scenario with 5 clusters. enough bandwidth available for the user data traffic. However, the scenario with 5 clusters generate a non-negligible amount of C-TC messages with important topology information. Such messages are more prone to be lost in the case of the clusterhead-based and hybrid approaches, so that the throughput slightly decreases with respect to the distributed algorithm. However, the packet delivery ratio (PDR) of the protocols still remains very similar (Table 3).
5.
RELATED WORK
The scalability problem of OLSR has been addressed in previous works. Clausen [8] proposed the integration of fisheye routing techniques [9] into OLSR. Fisheye routing consists of frequently forwarding topology information to nearby nodes, while reducing the frequency as the destination is farther away. The rationale is that a node does not necessarily need to know the exact location of a far destination. It suffices to have a vague idea of where the node is, and as the packet goes on, nearer nodes can successfully route the packet because they possess more accurate information. Through a simulation study, he showed that Fisheye OLSR heavily improves the scalability of OLSR in large ad hoc networks. In [10], Adjih et al. define the condition that a routing protocol must satisfy in order to fit the scaling property outlined by Gupta and Kumar [11]. They show that OLSR is not able to scale up to large ad hoc networks, since the generated overhead makes the links between neighbors unoperational. On the other hand, the same model is also employed to show that Fisheye OLSR can reach the scalability bound. However, in practice, the algorithm needed to adapt the behavior of the protocol to a given large MANET, in order to reach the scalability bound while maintaining enough connectivity, is not assessed. A different approach is taken by Hierarchical OLSR (HOLSR) [3]. H-OLSR creates a hierarchical topology because
it assumes that some nodes are equipped with better communication capabilities. So, nodes at level 1 have a single interface, while nodes at level 2 own two different interfaces: one to communicate with nodes at level 1, and another with longer transmission range to communicate with nodes at the same level. The same criteria applies if there are more levels in the hierarchy. The point of the protocol is that nodes with higher capacities automatically become clusterheads of some lower-level nodes. The topology information exchange is restricted to every cluster, and direct communication between the clusterheads of a same level is used to exchange the local nodes within the cluster. In this way, routing traffic is greatly reduced and nodes can set up routes to other same-level same-cluster nodes. However, to reach any other destination, data packets must traverse the clusterhead (which knows enough topology information to route the packet). This leads to suboptimal routes when source and destination are close but belong to different clusters. When compared to C-OLSR, the latter does not assume the existence of higher capacity nodes, although if they exist, both approaches could coexist. This would be appropriate if the number of higher capacity nodes is small compared to the total number of devices in the network. In addition, C-OLSR does not route packets through the clusterheads, but directly to their intended destinations.
6.
CONCLUSIONS AND FUTURE WORK
This paper has presented the C-OLSR protocol, an OLSRbased routing protocol which relies on an underlying clustering algorithm to heavily reduce the control overhead. This feature makes the protocol suitable for large ad hoc networks. Besides, C-OLSR reduces the amount of routing entries stored in every network device, since routes to nodes outside the own cluster are aggregated. The exchange of information about inter-cluster routing follows rules which extend the optimizations performed by OLSR, leveraging the same concepts up to the level of clusters. This further reduces the protocol overhead. Depending on the node(s) responsible for generating such messages, three different algorithms have been introduced. Through a simulation study, all the algorithms have been shown to outperform regular OLSR in terms of overhead generation and achievable throughput. In particular, the clusterhead-based approach (in which clusterheads are the only nodes which generate clusters topology control messages) greatly reduces the sending of protocol messages, what in turn improves the throughput of the network. As future work, we will evaluate the scalability of the protocol as the network becomes larger. In addition, a comparison with other overhead-reduction techniques (such as Fisheye OLSR) will be performed. In fact, the ideas behind fisheye routing can be easily incorporated into C-OLSR, what opens up a modification of the protocol which may worth being evaluated as well. Finally, the impact of the clustering algorithm should be analyzed, since mechanisms which allow for more stable clusters are more suitable for C-OLSR.
7.
ACKNOWLEDGMENT
This work has been partially funded by the Spanish MEC by means of the “Ramon y Cajal” work programme and the SMART (TIN2005-07705-C02-02) project.
8.
REFERENCES
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