QUERY LOCALIZATION USING PHEROMONE TRAILS: A SWARM INTELLIGENCE INSPIRED APPROACH. Nupur Kothari, Vartika Bhandari and Dheeraj Sanghi

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1 QUERY LOCALIZATION USING PHEROMONE TRAILS: A SWARM INTELLIGENCE INSPIRED APPROACH Nupur Kothari, Vartika Bhandari and Dheeraj Sanghi Department of Computer Science & Engineering Indian Institute of Technology Kanpur nupurk, vartika, ABSTRACT 2. QUERY LOCALIZATION Query Localization is a recently proposed extension for On-Demand Routing Protocols which reduces overheads of route discovery by restricting the query flood to the vicinity of previously discovered routes. In order to effectively define the request zone in Query Localization, the past connectivity information needs to be utilized to the fullest extent. In this paper we propose a swarm intelligence inspired query localization mechanism which tries to effectively harvest the connectivity information gleaned from previous route discoveries in on-demand routing protocols to restrict query flooding. 1. INTRODUCTION Numerous on-demand protocols have been proposed for ad hoc networks. However, their on-demand nature necessitates repeated route discoveries that flood the network. Query Localization [1] has been proposed to reduce this query flooding by only forwarding route requests in those areas where old paths existed. More comprehensive information regarding past inter-node connectivity would be a valuable asset in determining the request zone. In this paper we propose a new swarm intelligence inspired approach to Query Localization wherein we propose to harness not only information about previously existing paths between two nodes A and B, as in [1] but also other, often implicit information about past connectivity, while incurring minimal overheads. In on-demand protocols, there is network wide flooding of route requests during route discovery, which results in a high overhead. This is a serious problem in case of highly mobile and large networks. Routing overhead can be reduced to a certain extent by using the notion of spatial locality, viz. a mobile node cannot move too far too soon [1]. Therefore there is a high probability of finding a path in the same neighborhood as that of the earlier path(s). Thus localizing the query flood to a limited region of the network, i.e. in the vicinity of recently discovered routes, may reduce the total number of routing packets transmitted in the network. By caching routing history it is possible to estimate a region in the network where there is a high probability of finding the destination. Query localization using such a location unaware topology based mechanism was proposed by Castaneda and Das [1]. Past routing information is maintained in terms of a set of nodes (P old ) that represent an amalgamation of the routes discovered over a certain recent interval. The definition of P old may vary. Query flooding is limited to a region (Request Zone) in the locality of P old. Locality is defined in terms of a parameter k that essentially determines the size of the Request Zone. Two heuristics were used to determine locality in [1] viz. path locality and node locality. The Request Zone defined using node locality subsumes that obtained using path locality.

2 3. SWARM INTELLIGENCE AND PHEROMONE TRAILS Swarm Intelligence [2] is the phenomenon of intelligent collective behavior emerging from the individual actions of comparatively unintelligent agents. In the swarm intelligence paradigm, individuals communicate with each other through their environment, via the mechanism of stigmergy [3]. Swarm intelligence is widely observed in nature, in the form of collective action exhibited by various species, particularly insects like ants, termites, etc. The foraging activity of ants is an excellent example of the same, wherein a forager ant leaves a pheromone trail along the way that serves to direct other ants towards the food source. This pheromone, which is a chemical substance, evaporates at a certain rate. A trail is a superposition of numerous individual trails deposited by different ants traversing the route at different times. Wherever an ant has a choice of direction i.e. when the trail forks, it chooses the branch to follow with a probability that is dependent on the trail strength (pheromone concentration). With each successful round-trip to the food source by an ant, the trail becomes stronger, and more and more ants get attracted towards the source. This paradigm has been widely used in the design of agentbased routing protocols, as in [4]. 4. USING PHEROMONE TRAILS FOR REQUEST ZONE DETERMINATION We propose a definition of P old that captures in a more complete fashion the past routing history of the network. The definition takes inspiration from the pheromone trails laid down by ants. We utilize a mechanism inspired by sign-based stigmergy to capture information about past connectivity rather than past discovered routes. This enhances the information content represented by P old and increases the probability of finding a route within the determined Request Zone The Model We model each node in the network as an ant-nest that is characterized by a unique pheromone. The RREQs/RREPS emanating from this node correspond to the ants belonging to the nest and possess a supply of the nest-pheromone. An RREQ or RREP drops a certain quantity of the source nest pheromone at each node that it visits. This pheromone evaporates (decays) at a certain rate. We now redefine P old in terms of the pheromone levels of the destination. A node belongs to P old if the quantity of the destination pheromone at it is above a certain threshold. Thus we have redefined P old as a subset of nodes that have had recent connectivity with the destination. We now restrict the query flood to a neighborhood of this P old. The assumption is that a region with a stable connectivity history with respect to the destination, may be expected to maintain the same in the near future. As such, there is a high probability of finding a route to the destination in this region. D 1 D 2 Figure 1: Example of Capture of Implicit Route Information: If an RREQ from S1 for dst S2 reaches I, it gets guided along IS2 This approach differs from that proposed in [1] in that we do not merely define P old in terms of past explicitly discovered routes; rather we are willing to consider routes that existed but might not have been discovered, i.e., take into account implicit connectivity information. Fig. 1 depicts one such situation. There have been route discoveries initiated by S 1 for D 1 and by S 2 for D 2. Let us suppose that S 1 now initiates a route discovery for S 2. If the RREQ reaches node I, thereafter it would get propagated in the neighborhood of the pheromone trail of S 2 left by the RREQs from I S 1 S2

3 Routing Overheads vs. Number of Conversations per Node Average End-to-end delay vs. Number of Conversations per Node sql with P=1 -sql with P=3 -sql with P=9 -sql with P= sql with P=1 -sql with P=3 -sql with P=9 -sql with R=1 Routing Overheads (in thousand packets) End-to-end Delay (sec) Figure 2: Routing Overhead vs. conv./node Figure 4: Avg. end-to-end delay vs. conv./node.7.6 Packet Delivery Ratio vs. Number of Conversations per Node -sql with P=1 -sql with P=3 -sql with P=1 25 Throughput vs. Number of Conversations per Node -sql with P=1 -sql with P=3 -sql with P=9 -sql with P=1.5 Packet Delivery Ratio.4.3 Throughput (packets/sec) Figure 3: Packet delivery ratio vs. conv./node Figure 5: Throughput vs. conv./node the previous S 2 to D 2 route discovery. Thus implicit information about I S 2 connectivity gets utilized. The swarm intelligence inspired approach has the advantage of being based on a more comprehensive picture of network connectivity. As such, there is a lower probability of route discovery failing due to portions of the existing route(s) lying in the exterior of the request zone. It is also noteworthy that the set P old obtained thus would in general be larger than that obtained using the definition in [1]. 5. PERFORMANCE EVALUATION In order to obtain a preliminary evaluation of the proposed approach, we have implemented the same on [5] using the simulator ns-2, and obtained simulation results. For purposes of nomenclature we shall refer to our extension as -sql. We have simulated as well as -sql for low and high mobility scenarios and obtained graphs for various performance parameters viz. routing overhead, packet delivery ratio, end-to-end delay and throughput, as well as other useful statistics such as number of failed route discoveries, vs. the number of conversations per node The Network Model We have modeled a network comprising 5 nodes spread over a grid of area 1m x 1m. There are two kinds of movement scenarios corresponding to high mobility (nodes have speeds in the range 1- m/s) and low mobility (nodes have speeds in the range -5 m/s). A simple constant bit rate traffic model has been used. We have performed simulations for various values of the pheromone threshold P, keeping the parameter k as 2 and using path locality as in [1] i.e. allowing an RREQ to visit at most k nodes not in P old before getting dropped. The decay factor for the pheromones has been set as 1.5 and decay takes place in discrete time, every one second Results and Analysis We present here the results for simulations carried out on high mobility scenarios. Fig. 2 represents the routing overhead in terms of number of routing packets

4 Number of Packets Dropped due to Congestion vs. Number of Conversations per Node Throughput vs. Pheromone Threshold P Packets Dropped due to Congestion sql with P=1 -sql with P=3 -sql with P=9 -sql with R=1 Throughput (packets/sec) sql Value of P Figure 6: Congestion drops vs. conv./node Figure 8: Variation of Throughput with P Number of Failed Route Discoveries vs. Number of Conversations per Node Number of RERRs vs. Pheromone Threshold P 25 -sql with P=1 -sql with P=3 -sql with P=9 -sql with P= sql No. of failed Route Discoveries 15 1 Number of RERRs Value of P Figure 7: Failed route disc. vs. conv./node Figure 9: Variation of RERRs with P propagated in the network, that show a consistent reduction with an increase in the pheromone threshold P. The PDR as shown in Fig. 3 increases with increasing value of P and then stabilizes at a certain value. This result is surprising at first glance, since one expects PDR to be lower when query flooding is restricted, as certain route discoveries would fail even if a route does exist. However this may be explained by the fact that the results pertain to a highly mobile scenario wherein link breakages would be frequent. In such a scenario, our proposed approach, apart from reducing routing overheads and thereby also reducing congestion, appears to have the added positive effect of restricting route discovery to regions of the network with comparatively stable connectivity history. As such the discovered routes are less likely to fail in the middle of a conversation and packet loss is less. This analysis is borne out by the fact that the number of routing errors (RERRs) reduces as P increases and then stabilizes (Fig. 9). The average end-to-end delay (Fig. 4) increases as P increases, and -sql shows consistently higher delay than at low to moderate traffic. At very high traffic, the delay in is higher than in -sql. This can be explained by the fact that congestion in the network is generally higher at high traffic and is even more so in (due to higher number of routing packets). This may cause higher delays due to longer queues. Throughput (Fig. 5) shows trends similar to P.D.R. as is expected. We have obtained detailed trends regarding variation of throughput with threshold P (Fig. 8). An observation that we made, was that in the simulated scenarios, even for extremely high P throughput remained high, despite the fact that P old had become an empty set. In such a situation, RREQ flooding would be limited to a region of radius k+1 (in this case 3) hops, centred at the source. Generally we would expect lower PDR and throughput in such a case. However in these particular scenarios, we might expect a large number of nodes to lie within k+1 hops of each other, as the grid size is 1m x 1m, and the radio range for each node is set as 25m. The

5 final stable value might therefore change with change in radio range. This possible variation has not been explored in these simulations that only look at routing layer issues. The number of data packets dropped due to congestion in the queues consistently decreases with increasing value of P. As P increases, the number of routing packets in the network decreases leading to lower congestion. This explains the above observed trend. The number of failed route discoveries increases with increasing P. Once again, this is an expected trend, since higher values of P result in the set P old having a lower cardinality. This results in certain existent routes not being found as they were partially outside the Request Zone and hence the number of route discovery failures increases. 6. FUTURE WORK The swarm intelligence inspired approach to query localization requires further and more detailed study and analysis of performance in low mobility scenarios and the effect of change in radio range on the performance. A performance comparison of the proposed approach with the approach outlined in [1] is also to be undertaken. In fact there are possibilities of evolving variants of the mechanism outlined in this paper. We propose to study the possibility of using community-aware ants that possess limited memory of nests visited and disseminate this information along their path. protocols. REFERENCES [1] Robert Castaneda and Samir R. Das, Query localization techniques for on-demand routing protocols in ad-hoc networks, in Proc. of ACM Mobicom 99, Aug [2] G. Beni and J. Wang, Swarm intelligence in cellular robotic systems, in Proc. of Advanced NATO Workshop on Robots and Biological Systems 89, [3] P. P. Grasse, La reconstruction du nid et les coordinations inter-individuelle chez bellicoitermes natalenis et cubitermes sp. la theorie de la stigmergie: Essai d interpretation des termites constructeurs, Insect Societies, Vol. 6, [4] G. Di Caro and M. Dorigo, Antnet: distributed stigmergetic control for communications networks, Journal of Artificial Intelligence Research, vol. 9, [5] Charles E. Perkins, Elizabeth M. Royer, and Samir R. Das, Ad-hoc on-demand distance vector routing (draft-ietf-manet-aodv-12.txt), IETF Mobile Ad Hoc Networking Working Group INTER- NET DRAFT, Nov CONCLUSIONS After carrying out detailed simulation and analysis of query localization using pheromone trails in high mobility scenarios in MANETs, we believe that this approach has the potential to substantially improve network performance. However further study of the same is required in order to determine the optimal values of various parameters including P, k, decay factor of P etc. We also need to ascertain the kinds of scenarios in which a substantial performance gain would be obtained, to justify the proposed modification to existing