1 CWCCWC Oulu Determining the Optimal Configuration for the Zone Routing Protocol By M. R. Pearlman and Z. J. Haas Presentation by Martti Huttunen

2 CWCCWC Oulu Introduction n Zone Routing Protocol l A hybrid proactive-reactive protocol l Single configuration parameter: Zone radius n The research problem l To find an optimal value for the zone radius l To use minimal control traffic n Parameters reflecting performance l Node velocity and density, network span and traffic

3 CWCCWC Oulu Classes of routing protocols n Proactive routing l Routing table is updated continuously l Pro: small (and stable) delay l Con: amount of control traffic n Reactive routing l Routes are traced as they are required l Pro: less route queries l Con: highly variable delays

4 CWCCWC Oulu Routing Protocols 1/2 n Distributed Bellman-Ford l Problems: Slow convergence and amount of control traffic l Optimizations such as DSDV do not fully solve problems n Link-state protocols l OSPF: frequent changes in topology result in high control traffic l OLSR: uses multicasting (vs. point-to-point) to reduce control traffic l Global periodic topology updates are not well suited to larger or more dynamic networks

5 CWCCWC Oulu Routing Protocols 2/2 n Wireless Routing Protocol l Each node constructs a minimum spanning tree using its neighbors’ spanning trees l Problem: All nodes must be able to store a full routing table and its construction is costly n Source-initiated protocols l TORA: also destination floods its information – Queries are very costly to the network l AODV: uses source routing to limit flooding – Full path transmission might result in large control packets

6 CWCCWC Oulu The Zone Routing Protocol n A hybrid proactive-reactive protocol l Proactive routing is used in the transmission range of the node l Reactive routing is performed only on selected nodes l Elimination of loops l The configuration is adaptive: traffic is analyzed and zone range modified accordingly

7 CWCCWC Oulu Intrazone Routing n Intrazone routing is proactive, termed IARP l IARP may utilize any proactive algorithm l In this paper, split-horizon version of the distance vector algorithm is used n To achieve functional coverage, a node should have sufficient amount of neighbors n Adjusting zone radius requires the capability of adjusting transmitter power n On the other hand, having a large routing zone might result in excessive control traffic n The amount of proactive control traffic depends only on zone membership vs. network size

8 CWCCWC Oulu Interzone Routing n Intrazone routing is reactive, termed IERP l Improves from flooding algorithms by utilizing the known zone topology – Increases probability of a node being able to provide a route – Helps in estimating propagation in (probable) cases where the destination is not in the current zone l Bordercasting is used – May be implemented via unicasting or selective multicasting – Problem: How to actually derive border information?

9 CWCCWC Oulu Interzone Routing Sequence n Is destination inside zone? l Yes -> Reply n Bordercast a routing request l Peripheral (border) nodes will continue sequence n Once the reply arrives, deliver it to the source n Each node appends its information to the request l Information is used to source-route the reply l A full path is provided n Routing information should be cached if possible n Multiple routes may be discovered l Example selection metrics: number of hops, delay

10 CWCCWC Oulu Interzone Routing Problems n Once the request leaves initial zone, the request may be reflected back l Results in very quick flooding of the network n Early termination process is required l Intermediate nodes must be able to receive bordercasted messages – Intermediate nodes will terminate queries they have already received l Nodes must be able to either eavesdrop routing requests or broadcasting must be applied

11 CWCCWC Oulu Evaluation Procedure n OPNET simulation - network parameters l Number of nodes N Node density  (average nr. of neighbors/node) Relative node velocity (rate of new neighbor entry) n Measurements l Amount of control traffic vs. data The optimal zone radius  (in hops) IARP/node/s = * IARP-update/neighbor ( ,  ) n BER is approximated to increase drastically at certain transmitter range

12 CWCCWC Oulu Evaluation Results – IARP traffic n Procedure consists of delivering topology changes Simulation results show exponential growth  ^ n (roughly) l The n denotes number of neighbors

13 CWCCWC Oulu Evaluation Results – IERP traffic/query Each node receives  packets per query l Data is effectively flooded over network l The early termination process limits traffic Traffic decreases as a mostly linear function of , as zones cover more nodes Simulation shows that with networks  < 6, the average traffic decreases – This is because of network getting partitioned -> queries do not reach all nodes -> networks with  < 5 are ignored (!) l Also, when zones are dense, traffic increases – Detecting redundant queries becomes increasingly difficult IERP/s = IERP-update/query/node ( ,  ) * N * (Rinitial + Rseq) l Rinitial = rate of new route queries, Rseq = Route update rate l Effects of node velocity?

14 CWCCWC Oulu Evaluation Results – total performance As  increases, the rate of IERP queries decreases l Intrazone communication is more dominant n The node density curve is parabolic l As Rused >> Rfailure, the curve gets more steep and the minimum point more evident n The estimation of this information requires specific algorithms l 2 options provided: min searching and traffic adapting

15 CWCCWC Oulu Traffic estimation: Min searching  is adjusted periodically to find optimal value l Requires statistics of transmitted data and control information l These statistics are compared to the previous value – The comparison is used to determine if the direction of change was correct – A history of statistical values is kept per  value l Very sensitive to node velocity as converges slowly n Thresholds in traffic property changes trigger a new estimation

16 CWCCWC Oulu Traffic estimation: Traffic adaptive  is adjusted according to the ratio of IARP/IERP traffic l As zones grow too large, IARP traffic becomes dominant l Does not require “triggering” as such, but is a continuous process – No extra traffic, analysis is based on required transactions Oscillates on small  values n Simulation shows adaptive algorithm to be superior in terms of generated control traffic

17 CWCCWC Oulu Conclusions n The ZRP is a flexible and scalable solution l As IERP is triggered only on demand, the node velocity has little effect on control traffic and on the optimal zone radius l If traffic is periodic (vs. continuous), the performance is less affected by failed routes and node velocity – In these cases zone radius should be smaller l A less dense network with mobile nodes performs better with a larger zone radius

18 CWCCWC Oulu Achievements n Intra- and interzone routing is well applicable to heterogeneous networks l Natural selection of zones: different radio networks n Scales to very large networks n In WLAN-scale solutions is also sufficiently simple

19 CWCCWC Oulu Critique n Bordercasting is vital for IERP efficiency l Performance in less dense networks is questionable l Actual determination of IARP zone depends on the properties of the radio network used l Some IERP queries might be necessary to achieve full intrazone topology l Unicasted bordercasting is not very effective -> multi- or broadcasting should be available n Not very suitable for smallest devices l Various statistics and tables required