Proposal and Demonstration of Link Connectivity Assessment based Enhancements to Routing in Mobile Ad-hoc Networks Bhaskar Srinivasan and Detlef Clawin
Jatinder Pal Singh, Nicholas Bambos
Robert Bosch Corporation Research and Technology Center, Palo Alto, CA 94304
[email protected] [email protected]
Department of Electrical Engineering Stanford University, CA 94305 {jatinder, bambos}@stanford.edu
Yongchun Yan Department of Electrical & Computer Engineering and Computer Science University of Cincinnati, OH 45220
[email protected] Abstract—Wireless ad-hoc networking can be employed to build inter-vehicle communication based applications. The associated hostile driving environment and high mobility exposes the routing protocol to a more dynamically changing topology. In this work we investigate and demonstrate the application of linkconnectivity assessment to efficient ad-hoc routing. A framework for enhancements is delineated and incorporated in the implementation of OLSR protocol. For performance assessment we deploy a topology consisting of vehicles bearing laptop computers equipped with IEEE 802.11b compliant equipment. Experiments are conducted in a typical environment and routing performance is evaluated based on the UDP packet throughput and round trip time. It is demonstrated that the link quality assessment based enhancements improve the performance of OLSR. The improved scheme will be referred to as SBRS-OLSR (Signal strength assessment Based Routing enhancementS) in this paper. Keywords—Local Area Network, IEEE 802.11b, aggression control, routing protocols.
1.
INTRODUCTION
that reaches the destination first is accepted as the route from source to destination, without considering the time for which the route is expected to last. However, this route may contain weak links, resulting in frequent route failures and throughput degradation. For the work in this paper, Optimized Link State Routing (OLSR) protocol implementation for Linux kernel, developed by INRIA [9] has been used as the basic framework. Being a table driven and proactive protocol, OLSR is adapted to the requirements of a wireless LAN. The salient features of the protocol include the concept of Multipoint Relays (MPRs). Routes contain only MPRs as intermediate nodes between a source destination pair. To introduce link connectivity assessment based enhancement, we inherit the concept of affinity from RouteLifetime Assessment Based Routing (RABR) protocol [2]. The affinity between two nodes m and n, amn, is the time after which node n is anticipated to move out of range of node m. Node m samples the strength of signals received from node n. The rate of change of signal strength between two nodes is used to evaluate affinity.
Robust routing is a challenge in a dynamically changing network of mobile devices [7]. Inter-vehicle communications, with a large scope for potential applications targeting improvement in driving safety and convenience [1,10], perhaps pose the biggest challenge to routing due to associated high mobility and hostile environments. There has been an increasing interest to investigate inter-vehicle communications based on ad-hoc networking [4,5,13].
Wireless software utilities [14] for Linux are used to measure the link quality to 802.11b nodes in the topology. A complete framework within OLSR has been developed and implemented for utilizing the affinity assessment. Based on that, enhancements to MPR selection and route selection algorithms along with a neighbor classification policy for enhanced performance and stability are proposed and implemented.
Various ad-hoc routing protocols like DSR [6], AODV [11], OLSR [3] have been proposed in literature. Intensive evaluation has been done through simulation. To the best knowledge of the authors, only a few protocols propose utilization of link connectivity information to facilitate optimal route selection [12], and no corresponding implementation has been documented. OLSR calculates the routes with least number of hops based on topology information. In DSR and AODV, the path of the route request
We set up an experimental test-bed to analyze routing capabilities of OLSR and SBRS-OLSR. Through the tests we conduct, we investigate the performance achieved by link quality assessment based enhancements to routing.
0-7803-7954-3/03/$17.00 ©2003 IEEE.
2834
The paper is organized as follows. The OLSR protocol and enhancements to OLSR are presented in Section 2. The test set up and experimentation details are discussed in Section 3. The test results are presented in Section 4 and analyzed in
Section 5. The work is concluded in Section 6. Future work is presented in Section 7. 2.
SIGNAL STRENGTH ASSESSMENT BASED ENHANCEMENTS TO OLSR
We elaborate the details of SBRS-OLSR in the following subsections. 2.1 SNR measurement and Affinity Estimation We measure SNR by using wireless utilities [14] for wireless LAN standard IEEE802.11b. Each node maintains the history of averaged SNR values to its neighbors. The average rate of change of SNR between node n and its neighbor m is evaluated as follows,
r
= nm, ave
1 N
N −1
∑ n =0
snr t
current
− snr(current+n ) mod N
− t (current+n ) mod N current
(2.1)
The affinity anm is calculated as,
a nm
if
r
nm , ave
>0
otherwise
(2.2)
Route Selection Algorithm
With the support structure defined in previous subsection, enhancements are made to route selection algorithm. Similar to OLSR, the shortest path algorithm is employed for building the routing table from topology table. During this iterative building, it can happen that topology table provides an alternate route (R2) to some destination than what happens to be already existing in the routing table reachable through equal number of hops (say via route R1). In such an event OLSR disregards the new route R2. However SBRS-OLSR, chooses R2 over R1, if 1) R2.affinity is high and R1.affinity is not high, or 2) R2.affinty and R1.affinity are both high, but R2.SNRa > R1.SNRa, or
Where snrthresh is the threshold signal below which link lnm is assumed to be disconnected. 2.2 Software Support Structure for Leveraging Route-lifetime Assessment based Enhancements For introducing affinity based enhancements in OLSR, we have made changes to OLSR implementation framework by INRIA [9]. The implementation maintains a neighbor table wherein each entry stores information pertaining to the neighbors of the node. We add to this neighbor table entry structure, SNR history array field, the corresponding time stamps and SNR smoothing array for averaging the instantaneous SNR values. OLSR maintains a topology table whose entries store information relating to the topology of the network. Topology Control (TC) messages are constructed and dispensed through the network. A node receiving these messages updates the topology table and the routing table accordingly. Additions to topology control (TC) message structure, topology table entry structure, and routing table entry structure include affinity and neighbor’s SNR value. 2.3 Affinity Assessment based Enhancements to OLSR In this subsection we discuss the enhancement policies based on affinity estimation process and software support structure.
0-7803-7954-3/03/$17.00 ©2003 IEEE.
MPR selection algorithm
We believe that if the link between node x and its neighbor n is expected to last long (the affinity is high implying that the nodes are approaching each other) and the SNR between the nodes is above a threshold, then n is a good candidate to be selected as an MPR. However, when the nodes are receding from each other, the node with the maximum of the product of affinity and number of two hop neighbors in connectivity should be selected as MPR. Thus, in the MPR selection procedure, our metric not only considers connectivity of candidate MPR to two-hop neighbors (as in OLSR) but also its affinity to the node under consideration. Taking affinity into consideration, SBRS-OLSR alleviates the problem caused by dynamically changing topology, which usually renders a number of routes stale. 2.3.2
Where rnm,ave is the rate of change of SNR, N is the size of circular linked array snr (the array stores SNR values to neighbor m) and ti is the time stamp array associated with corresponding entry in array snr.
high = snr thresh − snr nm , current r nm ,ave
2.3.1
2835
3) R2.affinity and R1.affinity are both not high and R2.SNRa > R1.SNRa Note that for a routing table entry R, R.affinity denotes the minimum affinity along the route and R.SNRa is the most recent SNR measurement for the corresponding hop. The above algorithm selects the route that passes through an MPR (as the next hop from the given node) that provisions a higher minimum affinity along the route to the destination. 2.3.3
Neighbor classification for improved performance and stability
We define two levels SNR_UPPER_THRESHOLD and SNR_LOWER_THRESHOLD to separately categorize the neighbors that are weakly connected to a node. When a node is initially detected via a HELLO message it is entered in the neighbor table. We categorize it as usable if the SNR to this neighbor is found to be above SNR_UPPER_THRESHOLD. Otherwise the node is categorized as unusable. When any message is received from a neighbor that is classified as unusable, the message is not processed, also such a neighbor is not considered during routing table calculation. In this way, we can avoid the risk of sending data packets to a neighbor to which SNR is below some lower threshold, and having them dropped. Also, control and overhead information from this weakly connected neighbor is not processed. This prevents
calculation of routes that pass through this neighbor and this information being dispensed to other nodes in the network. 3.
DESCRIPTION OF THE TESTS
The tests are conducted in a parking lot surrounding and located in between buildings. There are plantations and trees in the building complex. Laptops running Linux and equipped with IEEE 802.11b ORiNOCO cards are placed in cars and omni-directional 5dBi gain antennas are deployed on top of vehicles. The wireless cards are configured to operate in adhoc mode. The OLSR and SBRS-OLSR daemons are installed on each laptop. We will refer to the laptops as nodes in rest of the paper. The complete test network includes nodes acting as a stationary sender, a mobile receiver and two stationary relays to aid routing packets. Figure 1 shows the arrangement of network nodes. The Netperf application [7] is running on the stationary sender node to send out UDP packets. The mobile receiver node also runs Netperf to receive these packets. The placement of the nodes and the movement path is chosen such that the mobile node moves in and out of the wireless transmission range of UDP sender node. When the mobile receiver is not within the reach of the sender, the other two stationary nodes (relay-1 and relay-2) aid multi-hop routing through them.
of the protocol to changes in receiver environment. From t=1 to t=27 seconds (Phase I), the receiver node moves from sender node location to the location near the relay 1 node (Figure 1). In this phase direct connection exists between the sender node and the receiver node. From t=28 to t=61 seconds (Phase II), the receiver node moves away from relay 1 node, towards relay 2 node. From t=62 to t=71 seconds (Phase III), the receiver node moves away from the relay-2 node deployment position. From t=72 to t=75 seconds, the receiver node drives into no-connectivity zone near the blind corner of the building shown in Figure 1. After t=75 seconds (Phase IV), the sender and receiver nodes are in line of sight. We choose the four time phases to compare the average throughput distribution over 20 test rounds of these two protocols. Figures 4-7 show the average throughput probability distribution in phase I, phase II, phase III and phase IV respectively. In figure 3, we record the standard deviation of average throughput over 20 test rounds.
Mobility pattern of the test was selected to be constant speed of 15 mph for the test node driving through the parking lot. Each driving test round takes a time period of 90 seconds. Twenty rounds for 15mph are conducted for OLSR and SBRS-OLSR respectively. Our observation log includes the routing table records at intervals of one second, the SNR between neighboring nodes, and the throughput at receiver in one-second intervals.
Figure 2. Throughput of 2 sample rounds vs. round time
Figure 3. Distribution of Standard Deviation throughput Figure 1. Test area for driving tests
4.
RESULTS
The results obtained from the experimentation are presented herewith. Figure 2 records the typical throughput of OLSR and SBRS-OLSR during a test round. Four different throughput patterns can be seen in roughly divided time phases. This is in correspondence with our network test topology, where packets are routed through a certain number of relaying nodes to reach the receiver node. Packets from the sender are routed as governed by the routing protocol, and the throughput depends on effectiveness of handoff and reactivity
0-7803-7954-3/03/$17.00 ©2003 IEEE.
2836
5.
RESULT ANALYSIS
In this section, we analyze the throughput results presented in the previous section. The results pertaining to round trip time and SNR measurement are also briefly discussed. 5.1 Throughput Performance Figure 2 shows the improved throughput performance of SBRS-OLSR during certain time periods. The test begins with the receiver node starting from a location near the sender
Figure 6. Average throughput distribution at phase III
Figure 4. Average throughput distribution at phase I
Figure 7. Average throughput distribution at phase IV
Figure 5. Average throughput distribution at phase II
node. There is a direct link from the sender node to the receiver node and communication occurs along one hop. As the receiver node moves further away from the sender node, throughput keeps dropping until t=28s when the relay-1 node is chosen as next hop for routing packets to the receiver node. During phase II, the SNR to relay-2 node increases as the receiver node approaches relay-2 node (which means the affinity of receiver to relay-2 is high). On the other hand the SNR to relay-1 node displays a decreasing trend. The affinity based enhancements in SBRS-OLSR allow the receiver node route to adapt to changing network conditions and accordingly switch between relay-1 and relay-2 nodes as the next hop. For the OLSR case, the throughput remains zero at a stretch during phase II. OLSR fails to take a preemptive action when the receiver is moving away from relay-1 node and approaching relay-2 node. New route calculation at the receiver node occurs when the neighbor table entry corresponding to the sender node times out. In SBRS-OLSR, the choice of a better relay to reach the source node is made in advance and the switch over is handled smoothly. In phase III, at time t=61seconds, the connectivity to relay-1 node is lost and the sender node is not reachable. A three-hop route is selected by SBRS-OLSR, with the receiver selecting relay-2 node as the first hop. Again, this protocol is successful in increasing the number of hops to 3 and results in improved throughput thereafter. Three hop routing continues until time t=71 seconds when the receiver node drives into a blocked area without any connection to the rest of the network. After a direct connection appears at time t=75 seconds, throughput increases as the receiver node approaches the sender node.During one hop routing at phase I and III, SBRS-OLSR
0-7803-7954-3/03/$17.00 ©2003 IEEE.
2837
and OLSR shows similar performance in handling network traffic as can be seen from Figure 4 and 7. As can be seen in Figures 5 and 6, SBRS-OLSR shows much better probability of higher throughput than OLSR, demonstrating performance improvement due to faster responsiveness to SNR variation. In Figure 3, SBRS-OLSR presents lower standard deviation of average throughput, which indicates a uniform connectivity during the test round. The larger standard deviation for OLSR signifies frequent drops in connectivity during the test. This further proves our observations from the previous figures. 5.2 Round Trip Time and SNR Measurement The round trip time between the sender node and the receiver node is plotted for OLSR and SBRS-OLSR. For the sake of brevity these plots are not being included here. The performance in terms of round trip time is noted to be roughly similar for the two protocols. The SNR from the receiver node to other nodes is also plotted versus the time progress. The patterns are observed to be proactively governing the route selection in case of SBRSOLSR as noted in the routing logs at the receiver node. 6.
CONCLUSIONS
In this work we have demonstrated that signal strength measurements can be used to enhance routing algorithm functionality in a mobile ad-hoc network. We discuss the concept of affinity between two nodes and propose enhancements to the OLSR protocol utilizing the affinity concept for route selection, MPR selection, and strongly connected neighbor classification.
Through actual test runs we assess the performance of SBRS-OLSR vis-à-vis OLSR, to demonstrate the effectiveness of proposed enhancements. It is noted that the enhanced protocol is more responsive to variations in network connectivity and can take preemptive actions in choosing stable and optimal routes, thereby maintaining relatively uniform connectivity between communicating nodes. SBRS-OLSR successfully attempts to incorporate in the routes, those hops that have high affinity and hence are anticipated to last long. Also, route-switching decisions are made in advance when a hop is foreseen to be broken due to mobility or hostile surrounding. It has been proven that IEEE 802.11b successfully provides wireless connectivity in ad-hoc multi-hop scenario. Versions of IEEE 802.11 can hence be employed as backbones for ad-hoc inter-vehicular communication. We suggest the SBRS-OLSR scheme for further consideration as a standard routing layer for 802.11 based adhoc networks. This scheme proves the benefits of crossing the layer boundaries of physical layer, MAC layer and routing layer for improved overall performance. 7.
FUTURE WORK
There are several potential areas for extension to this work. We intend to conduct experiments in dense networks, with higher mobility and realistic mobility patterns representing actual driving scenarios. The benefit of signal strength assessment can be analyzed for other ad-hoc routing protocols like DSR and AODV.
0-7803-7954-3/03/$17.00 ©2003 IEEE.
2838
REFERENCES [1] O. Andrisano and R. Verdone, “Intellingent Transportation Systems: The Role of Third-Generation Mobile Radio Networks”, IEEE Communications Magazine, Vol. 38, No. 9, Sept. 2000. [2] Sulabh Agarwal, Ashish Ahuja, Jatinder Pal Singh and Rajeev Shorey, “Route-Lifetime Assessment Based Routing (RABR) Protocol for Moblile Ad-Hoc Networks”, International Conference on Communications 2000, vol. 3, pp.1697 - 1701, New Orleans. [3] T. Clausen, P. Jacquest, A. laouiti, P. Muhlethaler, A. Qayyum ET L. Viennot, “Optimized Link State Routing Protocol”, IEEE National MultiTopic Conference (INMIC 2001), 2001. [4] R. Dube, C.D. Rais, K. Wang and S.K. Tripathi, “Signal Stability Based Adaptive Routing for Ad-hoc Mobile Networks”, IEEE Personal Communications, Feb. 1997. [5] H. Hartenstein, B. Bochow, A. Ebner, M. Lott, M. Radimirsch and D.Vollmer, “Position-Aware Ad Hoc Wireless Networks for Inter-V ehicle Communications: the Fleetnet Project”, Poster, The ACM Symposium on Mobile Ad Hoc Networking & Computing. [6] D. Jhnson and D. Maltz, “Dynamic Source Routing in Ad-hoc Wireless Networks”, Internet Draft, Dec. 1998 [7] H. Lundgren, E. Nordström, C. Tschudin, “Coping with Communication Gray Zones in IEEE 802.11b based Ad hoc Networks”, WoWMoM 2002. [8] Netperf, http://www.netperf.org/netperf/NetperfPage.html [9] OLSR implementatio (Inria) http://menetou.inria.fr/olsr/ [10] K.Paul, S. Bandyopadhyay, A. Mukherjee and D. Saha, “Communication Aware Mobile Hosts in Ad-hoc Wireless Network”, IEEE International Conference on Personal Wireless Communications (ICPWC), Feb. 17 – 19, Jaipur, India, 1999 [11] C. Perkins and E. Royer, “Ad-hoc On Deman Distance Vector (AODV) Routing”, Internet Draft, Dec. 1998 [12] Jatinder Pal Singh, Ashish Ahuja and Sulabh Agarwal, “Link Connectivity Assessment based Applications for Mobile Ad- hoc Networks”, 42nd Annual Technical Convention of The Institution of Electronics and Telecommunication Engineers, September 2000, New Delhi, India. [13] Jatinder Pal Singh, Nicholas Bambos, Bhaskar Srinivasan and Detlef Clawin, “Wireless LAN Performance under Varied Stress Conditions in Vehicular Traffic Scenarios”, IEEE Vehicular Technology Conference, Fall 2002, vol. 2, pp. 743 - 747, Vancouver. [14] Wireless Extensions for Linux, http://www.hpl.hp.com/personal/Jean_Tourrilhes/Linux/Linux.Wireless.Ex tensions.html
