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Dynamic route maintenance for geographic forwarding in mobile ad hoc networks
Available online at www.sciencedirect.com
Computer Networks 52 (2008) 418–431 www.elsevier.com/locate/comnet
Dynamic route maintenance for geographic forwarding in mobile ad hoc networks Chih-Hsun Chou *, Kuo-Feng Ssu, Hewijin Christine Jiau Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Received 3 January 2007; received in revised form 1 October 2007; accepted 1 October 2007 Available online 9 October 2007 Responsible Editor: M. van Steen
Abstract Traditional route maintenance requires mobile nodes periodically exchange beacon messages with their neighbors in geographic forwarding algorithms. The interval at which these nodes broadcast their beacon messages is typically fixed. However, determining an appropriate value for this interval is challenging. A longer interval reduces the number of beacons needed, but may result in significant location errors. Conversely, a shorter interval guarantees more accurate location information, but induces heavier control overheads. Additionally, since a fixed value is assigned to the lifetime of each routing entry, the forwarding algorithm cannot adapt well to different mobility environments. Therefore, this paper presents a dynamic route maintenance algorithm (DRM) for beacon-based geographic routing. In the approach, the mobile nodes dynamically adjust their beacon intervals based on their speed of movement. Moreover, the routing information can be well managed using the mobility prediction. The simulation results show that DRM not only significantly decreased the routing overheads in a low mobility scenario but also guaranteed the high quality packet delivery in high mobility environments. 2007 Elsevier B.V. All rights reserved. Keywords: Geographic routing; Beacon intervals; Delivery rates; Routing performance; Mobile ad hoc networks
1. Introduction In ad hoc networks, mobile nodes can be automatically configured without the need for a centralized mechanism. Geographic routing provides a scalable solution for building mobile ad hoc networks. This mechanism assumes that each mobile node can establish its location information by *
Corresponding author. Tel./fax: +886 6 2374532. E-mail address: [email protected] (C.-H. Chou).
means of a GPS system or some other form of localization techniques [1–5]. Based on a knowledge of the location information, geographic routing makes purely local packet routing decisions and can therefore reduce the routing overheads compared to other ad hoc routing protocols [6–9]. Geographic routing comprises two basic mechanisms, namely a location service and a geographic forwarding strategy. The location service provides a mapping from a node’s identity to its current geographic coordinates, while the geographic forwarding
1389-1286/$ - see front matter 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2007.10.001
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strategy supports a fully any-to-any communication pattern without the need to establish an explicit route. When a source node wishes to communicate with another node, it first requests the location service and then commences its transmission using the geographic forwarding strategy. In general, geographic forwarding algorithms can be classified as either contention-based or beaconbased. In contention-based schemes, the mobile nodes do not maintain route information themselves [10–14]. Instead, a contention process is performed to select the next-hop. The contention process employs suppressing strategies to avoid collisions and to ensure that only one node is selected as the next relay. The contention-based schemes typically involve larger transmission delays as a result of the inevitable contention process required for each relaying step. Beacon-based protocols require that each mobile node should exchange beacon messages with its neighboring nodes in order to maintain its routing table [15–18]. Each node initially sets a fixed transmission interval for its beacon messages. However, determining the optimal beacon interval is difficult in practical implementations. In a low mobility scenario, a longer interval scheme can reduce the number of beacon messages required while still providing sufficiently accurate location information. However, when the mobile nodes travel more rapidly, the location errors can become serious. Therefore, a shorter beacon interval is desirable in higher mobility environments. In general, existing beacon-based geographic forwarding strategies are unable to maintain route information efficiently since their use of a constant lifetime setting for each routing entry leads to excessively stale neighbor information in the routing table. This paper presents a dynamic route maintenance algorithm (DRM) for beacon-based geographic forwarding. DRM maintains both the beacon interval and the route information. The beacon interval management of DRM dynamically adjusts a node’s beacon interval based on the neighboring mobility information, while the route management function ensures the freshness of entries in the routing table. DRM not only reduces unnecessary beacon overheads, but provides accurate location information. The performance of the GPSR-based geographic forwarding strategy with DRM and original static beacon interval (SBI), respectively, was evaluated using the ns2 simulator [15,19]. The results show that the use of DRM reduced the cost of route
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maintenance in low mobility environments and improved the packet delivery rates in high mobility scenarios by dynamically reducing the beacon interval. It is shown that DRM promoted the ability of beacon-based geographic forwarding strategies to adapt to scenarios with varying node mobilities. 2. Related work Both LAR and GRID were previous schemes designed to use location information to facilitate routing decisions in mobile ad hoc networks [20,21]. Unlike traditional on-demand routing protocols, a route request in LAR is flooded in the direction of the target node. A node’s response to the route request depends on whether or not it is in the region approaching the destination. Meanwhile, GRID imposes a grid system on the Earth’s surface and selects a leader in each grid to act as the gateway. Data packets are then forwarded to their destination grid by grid. Both protocols confirm that the availability of location information improves the routing performance. In geographic forwarding schemes, the MostForward-with-fixed-Radius algorithm (MFR) is widely used for next-hop selection [22]. The current relaying node always selects the neighbor closest to the destination as the next relay. However, when the current relay cannot locate any neighbor closer to the packet’s destination, the packet reaches a dead-end. Several solutions have been proposed as a recovery strategy in the event of this dead-end problem. For example, it has been suggested that the node should recursively search its neighbor’s neighbors for another node closer to the destination than itself [23]. In GPSR, the current relay first creates a planar sub-graph using the relative neighborhood graph (RNG) and then routes around the dead-end based on the right-hand rule [24]. In an alternative approach, several studies have proposed the use of intermediate nodes to enable data packets to avoid the dead-end situation [25,26]. Geographic routing relies on the use of a location service to determine the position of each node in the network [27,28]. RLS floods the network with a location request and then waits for a reply from the destination node [29]. DREAM constructs a complete location database based on the information exchanged between the mobile nodes [30]. In the Homezone mechanism, a hash function is used to determine the Homezone of a node and the node’s ID is then hashed to a Homezone center
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within a constant radius [31]. Finally, GLS provides a scalable and elegant approach for distributing the location database across all of the nodes in the network [32].
Upper Layers Network Layer Dynamic Route Maintenance
3. System assumptions The network model considered in this paper is that mobile nodes are uniformly distributed in the network. To identify mobile nodes in the network, a unique permanent identity is assigned to each individual node. Furthermore, it is assumed that each node has a GPS receiver with which to obtain its coordinates and velocity [1,2]. Each node periodically exchanges location information (including geographic coordinates and velocity) with its neighbors in a defined frequency. The presented DRM mechanism can be applied to any beacon-based geographic forwarding strategy. For simplicity, the GPSR [15] scheme is considered in the present investigations. GPSR operates in one of two different modes: either the greedy forwarding mode or the perimeter forwarding mode. In the former, the current relaying node always selects the neighbor closest to the destination as the next relay. However, when the current relay fails to identify a neighbor closer to the destination than itself, it marks the packet as the perimeter mode and records the location (Lp) at which greedy forwarding failed to find a suitable next-hop for the packet. In the perimeter forwarding mode, the current relay uses RNG to create a planar sub-graph and then routes around the dead-end based on the right-hand rule [24]. Perimeter forwarding is only intended as a means of recovering from the dead-end problem. When the packet reaches a location closer to the destination than Lp, the packet resumes a pattern of greedy progress toward the destination.
Beacon Interval Generation Route Management Beacon Interval Adjustment
Forwarding Strategy
MAC / PHY Layer Fig. 1. The protocol stack.
thus can be considered as the time whenever a node just meets a new neighbor. In a mobile environment, however, it is very difficult to calculate the contacting time for two mobile nodes without global information. A beacon interval generation strategy is presented for estimating the appropriate beacon interval using only local neighboring information. Consider a scenario in which a node travelling at a velocity v has n neighbors within its transmission radius, r. As shown in Fig. 2, the node’s position after t seconds can be predicted from a knowledge of its current position and velocity. The new area contacted by the mobile node after t seconds is referred to as the contact area (CA), as shown in Fig. 2a. Since the shaded areas in Figs. 2a and 2b are equal, the area of CA is given by AreaðCAÞ ¼ v t 2r:
ð1Þ
The node density (d) in the node’s transmission area is expressed as nþ1 : pr2
4. Dynamic route maintenance
d¼
The DRM includes three management strategies, Beacon Interval Generation, Beacon Interval Adjustment, and Route Management. The protocol stack in DRM is illustrated schematically in Fig. 1.
The new neighbor time (NNT(k)) defines the time required for the node to meet k new neighbors, i.e. neighboring nodes which are not included in its current neighbor list. NNT(k) can be estimated as
4.1. Beacon interval generation
v NNT ðkÞ 2r d ¼ k ) NNT ðkÞ ¼
The purpose of exchanging beacon message is to collect updated location information of neighboring nodes. The optimal timing for sending a beacon
Since a mobile node may not always move with a constant velocity, the node’s historical moving speed (i.e. its speed over the most recent m seconds)
ð2Þ
prk : 2vðn þ 1Þ
ð3Þ
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v*t
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v*t
Contact Area (CA)
CA
Fig. 2. The contact area.
is used to measure a representative value for NNT(k) and (3) can be further calculated as Pi¼0 prk NNT ðkÞ ¼
i¼mþ1 2vi ðnþ1Þ
m
:
ð4Þ
As mentioned, the suitable beacon interval can be selected by calculating the new neighbor time. The dynamic beacon interval (DBI) thus can be set to NNT(k). Note that the value of k can be adjusted to satisfy different application requirements. For instance, a smaller value of k is specified when the node travels with a lower velocity. A performance comparison for various k settings will be presented in Sections 5 and 6. In (4), the beacon interval is calculated based on only the velocity of the sender. However, when nodes’ velocities are quite different, the nodes with high velocities will send beacons more frequently than the low ones. In this situation, the nodes with low velocities are hard to be known by the new neighbors and have larger position error rates. To handle the problem, the velocities of the neighboring nodes are considered during NNT(k) estimation. Assuming vs be the mean moving speed of the sender over the most recent m seconds and vni be the mean moving speed of the neighbor node (ni) over the most recent m seconds, (4) thus can be rewritten as prk NNT ðkÞ ¼ Pj¼n : 2 vs þ j¼1 vnj
Fig. 3. Beacon interval adjustment algorithm.
ð5Þ
4.2. Beacon interval adjustment In our scheme, each mobile node adjusts its beacon interval whenever it needs to send a beacon message. The beacon adjustment algorithm is shown in Fig. 3. Initially, the node establishes its current position and historical velocity. This information is used to generate the DBI based on (5). Both upper and lower bounds are imposed on the
DBI to prevent stationary nodes from setting an infinite DBI and to avoid network congestion as a result of an excessive number of beacon messages. The source node adds the information (including its identity, location, and updated DBI) in the beacon and then broadcasts the beacon message to its neighbors. 4.3. Route management To provide routing information, each node maintains a routing table for recording its neighbors’
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positions. Each neighbor entry in the table contains the neighbor’s identity, coordinates, velocity, message received time, and entry lifetime (see Fig. 4). A beacon message includes the sender’s identity and location information (including geographic coordinates and average velocity), and should be broadcasted to neighbor nodes every DBI seconds. When a node receives a beacon message, it first checks whether or not the sender’s identity exists in the table. If it does, the associated entry is replaced by the new information attached to the beacon message; otherwise, a new entry is created. To ensure the freshness of the route information, each node periodically purges stale information from its routing table by examining the lifetime of each entry. Once a beacon is received, the lifetime of the associated entry is set based on the following procedures. First, the node determines the link expiration time (LET) to the beacon sender according to the positions and relative velocities of the both nodes [33]. Consider the case that two nodes i and j are within the transmission range r of each other. Let (xi, yi) be the coordinate of node i and (xj, yj) be that of node j. Also let vi and vj be the speeds, and hi and hj (0 6hi, hj 6 2p) be the moving directions of node i and j, respectively. The LET thus can be predicted by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðab þ cdÞ þ ða2 þ c2 Þr2 ðad bcÞ LET ¼ ; a2 þ c 2 ð6Þ where a ¼ vi cos hi vj cos hj ; b ¼ xi xj ; c ¼ vi sin hi vj sin hj
and
d ¼ yi yj: Secondly, the node checks whether the sender’s identity exists in the table; if it is the case, the associated entry will be replaced by the information attached to the beacon message and the lifetime will Routing Table Entry 1
Node ID
Coordinates
Velocity
Received time
Entry 2 Entry 3
Fig. 4. Neighbor entries in a routing table.
Lifetime
be replaced by the LET; otherwise, the information and the LET will be inserted into a new entry. 5. Position error analysis This section compares the node position error in forwarding strategies with SBI and DRM, respectively. Fig. 5 shows the SBI and DRM beacon intervals for various moving velocities. The beacon interval remains constant regardless of the moving velocity in SBI. However, in DRM, the beacon interval becomes smaller as the moving speed increases. By reducing the value of k, DRM becomes increasingly sensitive to node mobility. Consider the case where a node sent a beacon message t seconds ago and then moved with a constant velocity v. Let Pt be the node’s position t seconds ago and let P0 be the node’s current position. The distance between Pt and P0 is given by DistðP t ; P 0 Þ ¼ v t:
ð7Þ
Message loss is an important factor in wireless environments. Assume that the message loss rate in the system is k. Since a node sends a beacon message every DBI seconds, the position error function during the beacon interval can be expressed as ! i¼1 X i EðtÞ ¼ v t þ k DBI ; 0 6 t 6 DBI: ð8Þ i¼1
Furthermore, the average position error for the beacon interval duration is given by R DBI EðtÞdt Erroravg ¼ 0 DBI ! i¼1 1 X i þ ¼ k v DBI: ð9Þ 2 i¼1 Assume that 50 nodes are uniformly distributed in an area measuring 1500 * 300 m. The message loss rate (k) is set to 0.05. Each node has a 250-m transmission radius. The velocity different ratio (Rdiff) between the velocity of the node and the mean velocity v of neighboring nodes (vavg) is defined as vavg . With Rdiff of 1.0, the mean position errors caused by the geographic forwarding strategy, are presented in Fig. 6. In SBI, the mobile nodes transmit beacons at a constant rate and therefore the position error is directly related to the nodes’ speed. On the other hand, DRM adjusts the beacon interval dynamically and therefore maintains position accuracy in both low and high mobility scenarios. A forwarding
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10 SBI= 0.5s SBI= 1s SBI= 2s DRMw ith NNT(0.5) DRMw ith NNT(1)
Beacon Interval (s)
8
6
4
2
0
0
10
20
30
40
50
Moving Speed (m/s) Fig. 5. Dynamic beacon interval versus node moving speed.
50
Average Position Error (m)
SBI = 0.5s SBI = 1s
40
SBI = 2s DRM with NNT(0.5) DRM with NNT(1)
30
20
10
0
0
10
20 30 Average Moving Speed (m/s)
40
50
Fig. 6. Average position error versus node moving speed.
strategy with DRM is capable of adapting well to environments with a rapidly changing topology. 6. Performance evaluation The evaluation of the DRM scheme and the conventional static beacon-based algorithm (SBI) is described in this section.
6.1. Simulation setup The performance of the mechanisms was implemented and evaluated using the ns2 simulator [19]. The beacon interval lower bound for DRM was set to 0.25 s to avoid network congestion due to an excessive number of beacon messages. On the other hand, the upper bound of 10 s was set for preventing a stationary node from setting an infinite
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interval. The scheme with SBI adopted the GPSR settings, i.e. the routing entry lifetime was set to a value of three times that of the beacon interval [15]. The GLS was implemented for providing the location service [32]. For each node, the location update process was triggered for every 10 s or when the node moved a threshold distance (100 m) from the last update. When a communication pattern was initiated, the source node should request to the GLS service for obtaining the location information of the destination node. Three hundred nodes were uniformly and randomly placed within a simulated area of 3000 * 1200 m2. The transmission abil-
ity of each mobile node was provided by the 2 Mbps IEEE 802.11 radio, of which the transmission range was 250 m. Each simulation was run for 300 s. Nodes mobility were supported by the Random WayPoint (RWP) model and the Reference Point Group Mobility (RPGM) model [34,35]. In RWP model, the pause time was set to 10 s and mean moving speeds were 5, 10, 15, 20, and 25 m/s, specified in accordance with a normal distribution. On the other hand, average group leader speeds of 5, 10, 15, 20, and 25 m/s were simulated for the RPGM model. Each node deviates its velocity (both speed and direction) randomly from that of the
Invalid routing entry ratio
20% SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec. DRM with NNT(0.5) DRM with NNT(1.0)
15%
10%
5%
0% 5
10
15 Mean speed (m/s)
20
25
Fig. 7. Invalid routing entry rates (RWP model).
15% Invalid routing entry ratio
SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec. DRM with NNT(0.5)
10%
DRM with NNT(1.0)
5%
0%
5
10
15
20
Mean group leader speed (m/s) Fig. 8. Invalid routing entry rates (RPGM model).
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leader. The speed and angle deviation ratio for the RPGM model were, respectively, set to 0.2 and 0.1. Static beacon intervals of 0.5, 1, 3, and 6 s were selected for comparison purposes. The traffic pattern consisted of 30 CBR (constant bit rate) sources sending 128-byte packets at a constant rate of four packets per second. The source nodes initiated their transmissions during a time window of 30 s starting at randomly distributed times between 10 and 270 s of the simulation run. The data points presented in the simulation results below were calculated as the average of 10 simulation runs.
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6.2. Simulation results The following metrics are used to evaluate the performance. • Invalid routing entry rate: The ratio of the number of invalid entries to the number of total entries in each node’s routing table. • Relay success rate: The ratio of the number of packets successfully received by the next-hop to the total number of packets relayed to the nexthop.
Relay success ratio
100%
95%
SBI = 0.5 sec.
90%
SBI = 1 sec. SBI = 3 sec. SBI = 6 sec.
85%
DRM with NNT(0.5) DRM with NNT(1.0)
80% 5
10
15
20
25
Mean speed (m/s) Fig. 9. Average success rate for each relay (RWP model).
Relay success ratio
100%
95% SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec.
90%
DRM with NNT(0.5) DRM with NNT(1.0)
85%
5
10
15 20 Mean group leader speed (m/s)
Fig. 10. Average success rate for each relay (RPGM model).
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• Packet delivery rate: The ratio of the number of data packets successfully delivered to their destinations to the number of data packets sent by the sources. • Path length: The average path length in hop counts for each data packet travelling from the source to the destination. • Beacon overhead: The total packet size of beacon messages transmitted during the simulation. 6.2.1. Invalid routing entry rate Figs. 7 and 8 show the mean invalid routing entry rates for various SBI and DRM schemes. When the beacon interval was higher, routing entries were
held for longer in the nodes’ routing tables and therefore the routing table contained more neighbors. However, some neighbors in the table might leave the transmission range of the current node as it moved. These neighbors thus became unreachable and interfered with the data forwarding. SBI could improve the invalid routing entry rates by reducing its fixed beacon interval. Unlike SBI, DRM used adaptive beacon interval management to maintain the routing information. Invalid routing entry rates were not seriously increased in high mobility scenarios. In addition, with the route management, the stale entries can be efficiently purged from the routing table. Compared to the best SBI
Delivery failure rate
15% SBI (Reason 1)
12%
SBI (Reason 2) DRM (Reason 1)
9%
DRM (Reason 2)
6% 3% 0%
5
10
15 Mean speed (m/s)
20
25
20
25
(a) RWP Model 12%
SBI (Reason 1)
Delivery failure rate
SBI (Reason 2) DRM (Reason 1)
9%
DRM (Reason 2)
6%
3%
0% 5
10
15 Mean group leader speed (m/s)
(b) RPGM Model Fig. 11. Reasons of packet lost versus node moving speed.
C.-H. Chou et al. / Computer Networks 52 (2008) 418–431
scheme, DRM reduced the invalid routing entry rate by approximately 1.5% and 0.9% with the RWP and RPGM models, respectively. 6.2.2. Relay success rate Figs. 9 and 10 present comparisons of the relay success rates obtained from the RWP and RPGM models, respectively. During packet forwarding, the current relaying node selects a neighbor for the next-hop. If an invalid neighbor (i.e. the node has an entry in the routing table, but does not exist
Packet delivery rate (%)
100%
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within the transmission radius) is chosen for the next relay node, the forwarded data packets will be lost. In low mobility scenarios, the invalid routing entry rate was not greatly affected by the beacon interval setting so the probability that an invalid neighbor would be selected for the next relaying node was relatively low. Even when the SBI was set to 6 s, the relay success rate was still higher than 96%. On the other hand, when the nodes moved at higher speeds, the relay failure rates of SBI schemes increased significantly if a large beacon interval was
SBI = 0.5sec.
SBI = 1sec.
SBI = 3sec.
SBI = 6sec.
DRM with NNT (0.5)
DRM with NNT (1.0)
95%
90%
85%
80%
75%
Packet delivery rate (%)
100%
5
10
15 Mean speed (m/s) (a) RWP model
20
25
SBI = 0.5sec.
SBI = 1sec.
SBI = 3sec.
SBI = 6sec.
DRM with NNT (0.5)
DRM with NNT (1.0)
95% 90% 85% 80% 75%
5
10 15 20 Mean group leader speed (m/s)
(b) RPG Mmodel Fig. 12. Packet deliver rate versus node moving speed.
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specified. DRM dynamically changed the beacon interval depending on the node’s speed and used efficiently route management to purge the stale routing entries in time. Therefore, the invalid neighbors can be avoided to be selected as the relaying nodes. Compared to the best SBI scheme, DRM improved relay success rate by approximately 2.1% and 1.3% with the RWP and RPGM models, respectively. 6.2.3. Packet delivery rate The reasons for packet loss can be classified into two categories: (1) the packet arrived at the destination’s position recorded in the location service, but the destination node no longer stayed there; (2)
the packet entered the perimeter mode and encountered a routing loop. Reducing the position error can help to prevent packet loss caused by reason 2. Thus, approximately 54% of the reason 2 packet delivery failures were avoided by DRM (see Fig. 11). On the other hand, the packet loss caused by reason 1 was not affected by DRM. Fig. 12 compares the average packet delivery ratio (confidence interval 95%) for the evaluated schemes. When the simulated scenarios were in low mobility, most packet loss was caused by reason 1 so the average difference between DRM and the best SBI scheme was only about 0.4%. On the contrary, the reason 2 packet loss arose as the moving speed increased.
18
Path length (hops)
SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec.
16
DRM with NNT(0.5) DRM with NNT(1.0)
14
12
10
5
10
15 Mean speed (m/s)
20
25
20
25
(a) RWP Model 16 SBI = 0.5 sec. SBI = 1 sec.
Path length (hops)
15
SBI = 3 sec. SBI = 6 sec.
14
DRM with NNT(0.5) DRM with NNT(1.0)
13 12 11 10
5
10
15 Mean group leader speed (m/s)
(b) RPGM Model Fig. 13. Average path length (hops) versus node moving speed.
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DRM schemes maintained the accuracy of the neighboring information in higher mobility scenarios so achieved a superior packet delivery rate. By decreasing the value of k, the delivery rate can be further improved and therefore the packet delivery rate fell only gradually as the mean moving speed increased. Compared to the best SBI scheme, with the RWP and RPGM models, the average delivery ratio improved by DRM was approximately 1.7% and 0.6%, respectively. 6.2.4. Path length As mentioned above, when an invalid neighbor was chosen as the next relay, a relay failure occurred and then the current relaying node should select a
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new candidate. This reselection procedure increases one extra hop to be counted into the routing path length. Fig. 13 displays the average path length for both RWP and RPGM models. Since the invalid routing entries were efficiently purged by the DRM route management, the average path length was significantly reduced especially in a higher mobility scenario. Compared to the best SBI scheme, the average reduction rate for path length came from DRM was approximately 3.3% and 2.6% with RWP and RPGM models, respectively. 6.2.5. Beacon overhead Fig. 14 illustrates the beacon overhead as measured in terms of the packet sizes of total number
Beacon overhead (k bytes)
15000 SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 DRM with NNT(0.5) DRM with NNT(1.0)
12000 9000 6000 3000 0
5
Beacon overhead (k bytes)
15000
10
15 Mean speed (m/s) (a) RWP Model
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25
SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec. DRM with NNT(0.5) DRM with NNT(1.0)
12000 9000 6000 3000 0
5
10
15
20
Mean group leader speed (m/s)
(b) RPGM Model Fig. 14. The total number of beacon messages transmitted.
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of beacons sent during the entire simulation. In the conventional GPSR, the packet size of a beacon is 35 bytes. DRM requires additional two 8-byte fields for representing the velocity information so the beacon size increases to 51 bytes. SBI schemes did not change the beacon interval so the beacon overhead remained at a constant value. In a low mobility scenario, DRM extended the beacon interval to reduce the number of beacons required so it provided significantly beacon overhead savings (see Fig. 14). Conversely, when the mobile nodes travelled at a higher speed, DRM sent more beacons in order to maintain the freshness of routing information. The results demonstrate that the mobile nodes with DRM can adjust their beacon intervals automatically and adaptively based on the environments. 7. Conclusion This paper has presented the dynamic route maintenance algorithm (DRM) for beacon-based geographic forwarding strategies. Unlike previous protocols, the beacon interval is adaptively modified during execution so that mobile nodes can utilize beacon messages more efficiently. Moreover, the enhanced route management function maintains the freshness of the routing information based on the mobility prediction. The results have shown that DRM achieved a better system performance. In lower mobility scenarios, DRM significantly reduced the beacon overhead by dynamically adjusting beacon intervals according to the environments; while in faster mobility scenarios, DRM guaranteed a high quality packet delivery by shortening beacon intervals and using route management to effectively purge the stale information. Acknowledgments We are pleased to thank the anonymous reviewers for their valuable comments and suggestions. This research was supported in part by the Taiwan National Science Council under contracts NSC 952221-E-006-074 and 95-2221-E-006-092-MY2. References [1] G. Dommety, R. Jain, Potential Networking Applications of Global Positioning Systems (GPS), Technical Report TR-24, Department of Computer Science, The Ohio State University, April 1996.
[2] E.D. Kaplan, Understanding GPS: Principles and Applications, Artech House, Boston, 1996. [3] T. He, C. Huang, B. Blum, J. Stankovic, T. Abdelzaher, Range-free localization schemes in large scale sensor networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), September 2003, pp. 81–95. [4] L. Hu, D. Evans, Localization for mobile sensor networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), September 2004, pp. 45–57. [5] K.F. Ssu, C.H. Ou, H.C. Jiau, Localization with mobile anchor points in wireless sensor networks, IEEE Transactions on Vehicular Technology 54 (3) (2005) 1187– 1197. [6] C.E. Perkins, E.M. Royer, Ad-hoc on-demand distance vector routing, in: Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications, Feb. 1999, pp. 90–100. [7] J. Broch, D.B. Johnson, D.A. Maltz, The dynamic source routing protocol for mobile ad hoc networks, IETF Internet Draft (1998). [8] V.D. Park, M.S. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, in: Proceedings of the Annual IEEE Conference on Computer Communications (INFOCOM), April 1997, pp. 1405–1413. [9] E.M. Royer, C.K. Toh, A review of current routing protocols for ad hoc mobile wireless networks, IEEE Personal Communications (1999) 46–55. [10] M. Zorzi, R.R. Rao, Geographic random forwarding (GeRaF) for ad hoc and sensor networks: multihop performance, IEEE Transactions on Mobile Computing 2 (4) (2003) 337–347. [11] M. Zorzi, R.R. Rao, Geographic random forwarding (GeRaF) for ad hoc and sensor networks: energy and latency performance, IEEE Transactions on Mobile Computing 2 (4) (2003) 349–365. [12] H. Fu¨ßler, J. Widmer, M. Ka¨semann, M. Mauve, H. Hartenstein, Contention-based forwarding for mobile adhoc networks, Ad Hoc Networks Journal 1 (4) (2003) 351– 369. [13] H. Fu¨ßler, J. Widmer, M. Mauve, H. Hartenstein, A novel forwarding paradigm for position-based routing (with implicit addressing), in: Proceedings of the IEEE Annual Workshop on Computer Communications, October 2003, pp. 194–200. [14] M. Heissenbttel, T. Braun, A novel position-based and beaconless routing algorithm for mobile ad-hoc networks, in: Proceedings of the IEEE Workshop on Applications and Services in Wireless Networks (ASWN), July 2003, pp. 1970– 210. [15] B. Karp, H.T. Kung, GPSR: greedy perimeter stateless routing for wireless networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), August 2000, pp. 120–130. [16] P. Bose, P. Morin, I. Stojmenovic, J. Urrutia, Routing with guaranteed delivery in ad hoc wireless networks, ACM Wireless Networks 7 (6) (2001) 609–616. [17] M. Mauve, J. Widmer, H. Hartenstein, A survey on positionbased routing in mobile ad-hoc networks, IEEE Network Magazine 15 (6) (2001) 30–39.
C.-H. Chou et al. / Computer Networks 52 (2008) 418–431 [18] D. Son, A. Helmy, B. Krishnamachari, The effect of mobility-induced location errors on geographic routing in mobile ad hoc and sensor networks: analysis and improvement using mobility prediction, IEEE Transactions on Mobile Computing 3 (3) (2004) 233–245. [19] K. Fall, K. Varadhan, The ns Manual (ns Notes and Documentation). The VINT project. (February 2002). [20] Y.B. Ko, N.H. Vaidya, Location-aided routing (LAR) in mobile ad hoc networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), October 1998, pp. 66–75. [21] W.H. Liao, Y.C. Tseng, J.P. Sheu, GRID: a fully locationaware routing protocol for mobile ad hoc networks, Telecommunication Systems 18 (13) (2001) 37–60. [22] T.C. Hou, V. Li, Transmission range control in multihop packet radio networks, IEEE Transactions on Communications 34 (1) (1986) 38–44. [23] G.G. Finn, Routing and Addressing Problems in Large MetropolitanScale Internetworks, Technical Report ISI/RR87-180, Information Sciences Institute, University of Southern California, March 1987. [24] G. Toussaint, The relative neighborhood graph of a finite planar set, Pattern Recognition 12 (4) (1980) 261–268. [25] L. Blazevic, J.Y.L. Boudec, S. Giordano, A location-based routing method for mobile ad hoc networks, IEEE Transactions on Mobile Computing 4 (2) (2005) 97–110. [26] D.S.J. De Couto, R. Morris, Location Proxies and Intermediate Node Forwarding for Practical Geographic Forwarding, Technical Report MIT-LCS-TR-824, MIT Laboratory for Computer Science, June 2001. [27] T. Camp, J. Boleng, L.Wilcox, Location information services in mobile ad hoc networks, in: Proceedings of the IEEE International Conference on Communications (ICC), April 2002, pp. 3318–3324. [28] C.H. Chou, K.F. Ssu, H.C. Jiau, A distributed location service for reducing query failures in geographic ad hoc routing, in: Proceedings of the IEEE Pacific Rim International Symposium on Dependable Computing (PRDC), December 2005, pp. 333–340. [29] M. Ka¨semann, H. Fu¨ßler, H. Hartenstein, M. Mauve, A Reactive Location Service for Mobile Ad Hoc Networks, Technical Report TR-02-014, Department of Computer Science, University of Mannheim, November 2002. [30] S. Basagni, I. Chlamtac, V.R. Syrotiuk, B.A. Woodward, A distance routing effect algorithm for mobility (DREAM), in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), October 1998, pp. 76–84. [31] S. Giordano, M. Hamdi, Mobility Management: The Virtual Home Region, Technical Report TR-SSC/1999/037, EPFL, Lausanne, Switzerland, October 1999. [32] J. Li, J. Jannotti, D.S.J. De Couto, D.R. Karger, R. Morris, A scalable location service for geographic ad hoc routing, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), August 2000, pp. 120–130. [33] W. Su, S.J. Lee, M. Gerla, Mobility prediction in wireless networks, in: Proceedings of the IEEE International Con-
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ference on Military Communications, October 2000, pp. 491–495. [34] X. Hong, M. Gerla, G. Pei, C.C. Chiang, A group mobility model for ad hoc wireless networks, in: Proceedings of the ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems, August 1999, pp. 53–60. [35] F. Bai, N. Sadagopan, A. Helmy, IMPORTANT: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks, in: Proceedings of the Annual IEEE Conference on Computer Communications (INFOCOM), April 2003, pp. 825– 835.
Chih-Hsun Chou received the BS in Computer Science and Information Engineering from Chung Yuan Christian University, Taiwan, in 2001. He is a PhD candidate in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. His research interests include mobile computing, dependable systems, and wireless sensor networks.
Kuo-Feng Ssu received the BS in Computer Science & Information Engineering from National Chiao Tung University and PhD in Computer Science from the University of Illinois at Urbana-Champaign. He is an Associate Professor in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. His research interests include mobile computing, dependable systems, and distributed systems. He is a member of the IEEE, the ACM, and the Phi Tau Phi honor scholastic society.
Hewijin Christine Jiau received the BS in Electrical Engineering from National Cheng Kung University, MS in Electrical Engineering & Computer Science from Northwestern University, and PhD in Computer Science from the University of Illinois at Urbana-Champaign. She is an Associate Professor in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. Her research interests include software reuse, object technologies, information integration, data mining, and database applications on the Internet. She is a member of the IEEE and the ACM.
Computer Networks 52 (2008) 418–431 www.elsevier.com/locate/comnet
Dynamic route maintenance for geographic forwarding in mobile ad hoc networks Chih-Hsun Chou *, Kuo-Feng Ssu, Hewijin Christine Jiau Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan Received 3 January 2007; received in revised form 1 October 2007; accepted 1 October 2007 Available online 9 October 2007 Responsible Editor: M. van Steen
Abstract Traditional route maintenance requires mobile nodes periodically exchange beacon messages with their neighbors in geographic forwarding algorithms. The interval at which these nodes broadcast their beacon messages is typically fixed. However, determining an appropriate value for this interval is challenging. A longer interval reduces the number of beacons needed, but may result in significant location errors. Conversely, a shorter interval guarantees more accurate location information, but induces heavier control overheads. Additionally, since a fixed value is assigned to the lifetime of each routing entry, the forwarding algorithm cannot adapt well to different mobility environments. Therefore, this paper presents a dynamic route maintenance algorithm (DRM) for beacon-based geographic routing. In the approach, the mobile nodes dynamically adjust their beacon intervals based on their speed of movement. Moreover, the routing information can be well managed using the mobility prediction. The simulation results show that DRM not only significantly decreased the routing overheads in a low mobility scenario but also guaranteed the high quality packet delivery in high mobility environments. 2007 Elsevier B.V. All rights reserved. Keywords: Geographic routing; Beacon intervals; Delivery rates; Routing performance; Mobile ad hoc networks
1. Introduction In ad hoc networks, mobile nodes can be automatically configured without the need for a centralized mechanism. Geographic routing provides a scalable solution for building mobile ad hoc networks. This mechanism assumes that each mobile node can establish its location information by *
Corresponding author. Tel./fax: +886 6 2374532. E-mail address: [email protected] (C.-H. Chou).
means of a GPS system or some other form of localization techniques [1–5]. Based on a knowledge of the location information, geographic routing makes purely local packet routing decisions and can therefore reduce the routing overheads compared to other ad hoc routing protocols [6–9]. Geographic routing comprises two basic mechanisms, namely a location service and a geographic forwarding strategy. The location service provides a mapping from a node’s identity to its current geographic coordinates, while the geographic forwarding
1389-1286/$ - see front matter 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2007.10.001
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strategy supports a fully any-to-any communication pattern without the need to establish an explicit route. When a source node wishes to communicate with another node, it first requests the location service and then commences its transmission using the geographic forwarding strategy. In general, geographic forwarding algorithms can be classified as either contention-based or beaconbased. In contention-based schemes, the mobile nodes do not maintain route information themselves [10–14]. Instead, a contention process is performed to select the next-hop. The contention process employs suppressing strategies to avoid collisions and to ensure that only one node is selected as the next relay. The contention-based schemes typically involve larger transmission delays as a result of the inevitable contention process required for each relaying step. Beacon-based protocols require that each mobile node should exchange beacon messages with its neighboring nodes in order to maintain its routing table [15–18]. Each node initially sets a fixed transmission interval for its beacon messages. However, determining the optimal beacon interval is difficult in practical implementations. In a low mobility scenario, a longer interval scheme can reduce the number of beacon messages required while still providing sufficiently accurate location information. However, when the mobile nodes travel more rapidly, the location errors can become serious. Therefore, a shorter beacon interval is desirable in higher mobility environments. In general, existing beacon-based geographic forwarding strategies are unable to maintain route information efficiently since their use of a constant lifetime setting for each routing entry leads to excessively stale neighbor information in the routing table. This paper presents a dynamic route maintenance algorithm (DRM) for beacon-based geographic forwarding. DRM maintains both the beacon interval and the route information. The beacon interval management of DRM dynamically adjusts a node’s beacon interval based on the neighboring mobility information, while the route management function ensures the freshness of entries in the routing table. DRM not only reduces unnecessary beacon overheads, but provides accurate location information. The performance of the GPSR-based geographic forwarding strategy with DRM and original static beacon interval (SBI), respectively, was evaluated using the ns2 simulator [15,19]. The results show that the use of DRM reduced the cost of route
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maintenance in low mobility environments and improved the packet delivery rates in high mobility scenarios by dynamically reducing the beacon interval. It is shown that DRM promoted the ability of beacon-based geographic forwarding strategies to adapt to scenarios with varying node mobilities. 2. Related work Both LAR and GRID were previous schemes designed to use location information to facilitate routing decisions in mobile ad hoc networks [20,21]. Unlike traditional on-demand routing protocols, a route request in LAR is flooded in the direction of the target node. A node’s response to the route request depends on whether or not it is in the region approaching the destination. Meanwhile, GRID imposes a grid system on the Earth’s surface and selects a leader in each grid to act as the gateway. Data packets are then forwarded to their destination grid by grid. Both protocols confirm that the availability of location information improves the routing performance. In geographic forwarding schemes, the MostForward-with-fixed-Radius algorithm (MFR) is widely used for next-hop selection [22]. The current relaying node always selects the neighbor closest to the destination as the next relay. However, when the current relay cannot locate any neighbor closer to the packet’s destination, the packet reaches a dead-end. Several solutions have been proposed as a recovery strategy in the event of this dead-end problem. For example, it has been suggested that the node should recursively search its neighbor’s neighbors for another node closer to the destination than itself [23]. In GPSR, the current relay first creates a planar sub-graph using the relative neighborhood graph (RNG) and then routes around the dead-end based on the right-hand rule [24]. In an alternative approach, several studies have proposed the use of intermediate nodes to enable data packets to avoid the dead-end situation [25,26]. Geographic routing relies on the use of a location service to determine the position of each node in the network [27,28]. RLS floods the network with a location request and then waits for a reply from the destination node [29]. DREAM constructs a complete location database based on the information exchanged between the mobile nodes [30]. In the Homezone mechanism, a hash function is used to determine the Homezone of a node and the node’s ID is then hashed to a Homezone center
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within a constant radius [31]. Finally, GLS provides a scalable and elegant approach for distributing the location database across all of the nodes in the network [32].
Upper Layers Network Layer Dynamic Route Maintenance
3. System assumptions The network model considered in this paper is that mobile nodes are uniformly distributed in the network. To identify mobile nodes in the network, a unique permanent identity is assigned to each individual node. Furthermore, it is assumed that each node has a GPS receiver with which to obtain its coordinates and velocity [1,2]. Each node periodically exchanges location information (including geographic coordinates and velocity) with its neighbors in a defined frequency. The presented DRM mechanism can be applied to any beacon-based geographic forwarding strategy. For simplicity, the GPSR [15] scheme is considered in the present investigations. GPSR operates in one of two different modes: either the greedy forwarding mode or the perimeter forwarding mode. In the former, the current relaying node always selects the neighbor closest to the destination as the next relay. However, when the current relay fails to identify a neighbor closer to the destination than itself, it marks the packet as the perimeter mode and records the location (Lp) at which greedy forwarding failed to find a suitable next-hop for the packet. In the perimeter forwarding mode, the current relay uses RNG to create a planar sub-graph and then routes around the dead-end based on the right-hand rule [24]. Perimeter forwarding is only intended as a means of recovering from the dead-end problem. When the packet reaches a location closer to the destination than Lp, the packet resumes a pattern of greedy progress toward the destination.
Beacon Interval Generation Route Management Beacon Interval Adjustment
Forwarding Strategy
MAC / PHY Layer Fig. 1. The protocol stack.
thus can be considered as the time whenever a node just meets a new neighbor. In a mobile environment, however, it is very difficult to calculate the contacting time for two mobile nodes without global information. A beacon interval generation strategy is presented for estimating the appropriate beacon interval using only local neighboring information. Consider a scenario in which a node travelling at a velocity v has n neighbors within its transmission radius, r. As shown in Fig. 2, the node’s position after t seconds can be predicted from a knowledge of its current position and velocity. The new area contacted by the mobile node after t seconds is referred to as the contact area (CA), as shown in Fig. 2a. Since the shaded areas in Figs. 2a and 2b are equal, the area of CA is given by AreaðCAÞ ¼ v t 2r:
ð1Þ
The node density (d) in the node’s transmission area is expressed as nþ1 : pr2
4. Dynamic route maintenance
d¼
The DRM includes three management strategies, Beacon Interval Generation, Beacon Interval Adjustment, and Route Management. The protocol stack in DRM is illustrated schematically in Fig. 1.
The new neighbor time (NNT(k)) defines the time required for the node to meet k new neighbors, i.e. neighboring nodes which are not included in its current neighbor list. NNT(k) can be estimated as
4.1. Beacon interval generation
v NNT ðkÞ 2r d ¼ k ) NNT ðkÞ ¼
The purpose of exchanging beacon message is to collect updated location information of neighboring nodes. The optimal timing for sending a beacon
Since a mobile node may not always move with a constant velocity, the node’s historical moving speed (i.e. its speed over the most recent m seconds)
ð2Þ
prk : 2vðn þ 1Þ
ð3Þ
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v*t
421
v*t
Contact Area (CA)
CA
Fig. 2. The contact area.
is used to measure a representative value for NNT(k) and (3) can be further calculated as Pi¼0 prk NNT ðkÞ ¼
i¼mþ1 2vi ðnþ1Þ
m
:
ð4Þ
As mentioned, the suitable beacon interval can be selected by calculating the new neighbor time. The dynamic beacon interval (DBI) thus can be set to NNT(k). Note that the value of k can be adjusted to satisfy different application requirements. For instance, a smaller value of k is specified when the node travels with a lower velocity. A performance comparison for various k settings will be presented in Sections 5 and 6. In (4), the beacon interval is calculated based on only the velocity of the sender. However, when nodes’ velocities are quite different, the nodes with high velocities will send beacons more frequently than the low ones. In this situation, the nodes with low velocities are hard to be known by the new neighbors and have larger position error rates. To handle the problem, the velocities of the neighboring nodes are considered during NNT(k) estimation. Assuming vs be the mean moving speed of the sender over the most recent m seconds and vni be the mean moving speed of the neighbor node (ni) over the most recent m seconds, (4) thus can be rewritten as prk NNT ðkÞ ¼ Pj¼n : 2 vs þ j¼1 vnj
Fig. 3. Beacon interval adjustment algorithm.
ð5Þ
4.2. Beacon interval adjustment In our scheme, each mobile node adjusts its beacon interval whenever it needs to send a beacon message. The beacon adjustment algorithm is shown in Fig. 3. Initially, the node establishes its current position and historical velocity. This information is used to generate the DBI based on (5). Both upper and lower bounds are imposed on the
DBI to prevent stationary nodes from setting an infinite DBI and to avoid network congestion as a result of an excessive number of beacon messages. The source node adds the information (including its identity, location, and updated DBI) in the beacon and then broadcasts the beacon message to its neighbors. 4.3. Route management To provide routing information, each node maintains a routing table for recording its neighbors’
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positions. Each neighbor entry in the table contains the neighbor’s identity, coordinates, velocity, message received time, and entry lifetime (see Fig. 4). A beacon message includes the sender’s identity and location information (including geographic coordinates and average velocity), and should be broadcasted to neighbor nodes every DBI seconds. When a node receives a beacon message, it first checks whether or not the sender’s identity exists in the table. If it does, the associated entry is replaced by the new information attached to the beacon message; otherwise, a new entry is created. To ensure the freshness of the route information, each node periodically purges stale information from its routing table by examining the lifetime of each entry. Once a beacon is received, the lifetime of the associated entry is set based on the following procedures. First, the node determines the link expiration time (LET) to the beacon sender according to the positions and relative velocities of the both nodes [33]. Consider the case that two nodes i and j are within the transmission range r of each other. Let (xi, yi) be the coordinate of node i and (xj, yj) be that of node j. Also let vi and vj be the speeds, and hi and hj (0 6hi, hj 6 2p) be the moving directions of node i and j, respectively. The LET thus can be predicted by qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðab þ cdÞ þ ða2 þ c2 Þr2 ðad bcÞ LET ¼ ; a2 þ c 2 ð6Þ where a ¼ vi cos hi vj cos hj ; b ¼ xi xj ; c ¼ vi sin hi vj sin hj
and
d ¼ yi yj: Secondly, the node checks whether the sender’s identity exists in the table; if it is the case, the associated entry will be replaced by the information attached to the beacon message and the lifetime will Routing Table Entry 1
Node ID
Coordinates
Velocity
Received time
Entry 2 Entry 3
Fig. 4. Neighbor entries in a routing table.
Lifetime
be replaced by the LET; otherwise, the information and the LET will be inserted into a new entry. 5. Position error analysis This section compares the node position error in forwarding strategies with SBI and DRM, respectively. Fig. 5 shows the SBI and DRM beacon intervals for various moving velocities. The beacon interval remains constant regardless of the moving velocity in SBI. However, in DRM, the beacon interval becomes smaller as the moving speed increases. By reducing the value of k, DRM becomes increasingly sensitive to node mobility. Consider the case where a node sent a beacon message t seconds ago and then moved with a constant velocity v. Let Pt be the node’s position t seconds ago and let P0 be the node’s current position. The distance between Pt and P0 is given by DistðP t ; P 0 Þ ¼ v t:
ð7Þ
Message loss is an important factor in wireless environments. Assume that the message loss rate in the system is k. Since a node sends a beacon message every DBI seconds, the position error function during the beacon interval can be expressed as ! i¼1 X i EðtÞ ¼ v t þ k DBI ; 0 6 t 6 DBI: ð8Þ i¼1
Furthermore, the average position error for the beacon interval duration is given by R DBI EðtÞdt Erroravg ¼ 0 DBI ! i¼1 1 X i þ ¼ k v DBI: ð9Þ 2 i¼1 Assume that 50 nodes are uniformly distributed in an area measuring 1500 * 300 m. The message loss rate (k) is set to 0.05. Each node has a 250-m transmission radius. The velocity different ratio (Rdiff) between the velocity of the node and the mean velocity v of neighboring nodes (vavg) is defined as vavg . With Rdiff of 1.0, the mean position errors caused by the geographic forwarding strategy, are presented in Fig. 6. In SBI, the mobile nodes transmit beacons at a constant rate and therefore the position error is directly related to the nodes’ speed. On the other hand, DRM adjusts the beacon interval dynamically and therefore maintains position accuracy in both low and high mobility scenarios. A forwarding
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10 SBI= 0.5s SBI= 1s SBI= 2s DRMw ith NNT(0.5) DRMw ith NNT(1)
Beacon Interval (s)
8
6
4
2
0
0
10
20
30
40
50
Moving Speed (m/s) Fig. 5. Dynamic beacon interval versus node moving speed.
50
Average Position Error (m)
SBI = 0.5s SBI = 1s
40
SBI = 2s DRM with NNT(0.5) DRM with NNT(1)
30
20
10
0
0
10
20 30 Average Moving Speed (m/s)
40
50
Fig. 6. Average position error versus node moving speed.
strategy with DRM is capable of adapting well to environments with a rapidly changing topology. 6. Performance evaluation The evaluation of the DRM scheme and the conventional static beacon-based algorithm (SBI) is described in this section.
6.1. Simulation setup The performance of the mechanisms was implemented and evaluated using the ns2 simulator [19]. The beacon interval lower bound for DRM was set to 0.25 s to avoid network congestion due to an excessive number of beacon messages. On the other hand, the upper bound of 10 s was set for preventing a stationary node from setting an infinite
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interval. The scheme with SBI adopted the GPSR settings, i.e. the routing entry lifetime was set to a value of three times that of the beacon interval [15]. The GLS was implemented for providing the location service [32]. For each node, the location update process was triggered for every 10 s or when the node moved a threshold distance (100 m) from the last update. When a communication pattern was initiated, the source node should request to the GLS service for obtaining the location information of the destination node. Three hundred nodes were uniformly and randomly placed within a simulated area of 3000 * 1200 m2. The transmission abil-
ity of each mobile node was provided by the 2 Mbps IEEE 802.11 radio, of which the transmission range was 250 m. Each simulation was run for 300 s. Nodes mobility were supported by the Random WayPoint (RWP) model and the Reference Point Group Mobility (RPGM) model [34,35]. In RWP model, the pause time was set to 10 s and mean moving speeds were 5, 10, 15, 20, and 25 m/s, specified in accordance with a normal distribution. On the other hand, average group leader speeds of 5, 10, 15, 20, and 25 m/s were simulated for the RPGM model. Each node deviates its velocity (both speed and direction) randomly from that of the
Invalid routing entry ratio
20% SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec. DRM with NNT(0.5) DRM with NNT(1.0)
15%
10%
5%
0% 5
10
15 Mean speed (m/s)
20
25
Fig. 7. Invalid routing entry rates (RWP model).
15% Invalid routing entry ratio
SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec. DRM with NNT(0.5)
10%
DRM with NNT(1.0)
5%
0%
5
10
15
20
Mean group leader speed (m/s) Fig. 8. Invalid routing entry rates (RPGM model).
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leader. The speed and angle deviation ratio for the RPGM model were, respectively, set to 0.2 and 0.1. Static beacon intervals of 0.5, 1, 3, and 6 s were selected for comparison purposes. The traffic pattern consisted of 30 CBR (constant bit rate) sources sending 128-byte packets at a constant rate of four packets per second. The source nodes initiated their transmissions during a time window of 30 s starting at randomly distributed times between 10 and 270 s of the simulation run. The data points presented in the simulation results below were calculated as the average of 10 simulation runs.
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6.2. Simulation results The following metrics are used to evaluate the performance. • Invalid routing entry rate: The ratio of the number of invalid entries to the number of total entries in each node’s routing table. • Relay success rate: The ratio of the number of packets successfully received by the next-hop to the total number of packets relayed to the nexthop.
Relay success ratio
100%
95%
SBI = 0.5 sec.
90%
SBI = 1 sec. SBI = 3 sec. SBI = 6 sec.
85%
DRM with NNT(0.5) DRM with NNT(1.0)
80% 5
10
15
20
25
Mean speed (m/s) Fig. 9. Average success rate for each relay (RWP model).
Relay success ratio
100%
95% SBI = 0.5 sec. SBI = 1 sec. SBI = 3 sec. SBI = 6 sec.
90%
DRM with NNT(0.5) DRM with NNT(1.0)
85%
5
10
15 20 Mean group leader speed (m/s)
Fig. 10. Average success rate for each relay (RPGM model).
25
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• Packet delivery rate: The ratio of the number of data packets successfully delivered to their destinations to the number of data packets sent by the sources. • Path length: The average path length in hop counts for each data packet travelling from the source to the destination. • Beacon overhead: The total packet size of beacon messages transmitted during the simulation. 6.2.1. Invalid routing entry rate Figs. 7 and 8 show the mean invalid routing entry rates for various SBI and DRM schemes. When the beacon interval was higher, routing entries were
held for longer in the nodes’ routing tables and therefore the routing table contained more neighbors. However, some neighbors in the table might leave the transmission range of the current node as it moved. These neighbors thus became unreachable and interfered with the data forwarding. SBI could improve the invalid routing entry rates by reducing its fixed beacon interval. Unlike SBI, DRM used adaptive beacon interval management to maintain the routing information. Invalid routing entry rates were not seriously increased in high mobility scenarios. In addition, with the route management, the stale entries can be efficiently purged from the routing table. Compared to the best SBI
Delivery failure rate
15% SBI (Reason 1)
12%
SBI (Reason 2) DRM (Reason 1)
9%
DRM (Reason 2)
6% 3% 0%
5
10
15 Mean speed (m/s)
20
25
20
25
(a) RWP Model 12%
SBI (Reason 1)
Delivery failure rate
SBI (Reason 2) DRM (Reason 1)
9%
DRM (Reason 2)
6%
3%
0% 5
10
15 Mean group leader speed (m/s)
(b) RPGM Model Fig. 11. Reasons of packet lost versus node moving speed.
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scheme, DRM reduced the invalid routing entry rate by approximately 1.5% and 0.9% with the RWP and RPGM models, respectively. 6.2.2. Relay success rate Figs. 9 and 10 present comparisons of the relay success rates obtained from the RWP and RPGM models, respectively. During packet forwarding, the current relaying node selects a neighbor for the next-hop. If an invalid neighbor (i.e. the node has an entry in the routing table, but does not exist
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within the transmission radius) is chosen for the next relay node, the forwarded data packets will be lost. In low mobility scenarios, the invalid routing entry rate was not greatly affected by the beacon interval setting so the probability that an invalid neighbor would be selected for the next relaying node was relatively low. Even when the SBI was set to 6 s, the relay success rate was still higher than 96%. On the other hand, when the nodes moved at higher speeds, the relay failure rates of SBI schemes increased significantly if a large beacon interval was
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specified. DRM dynamically changed the beacon interval depending on the node’s speed and used efficiently route management to purge the stale routing entries in time. Therefore, the invalid neighbors can be avoided to be selected as the relaying nodes. Compared to the best SBI scheme, DRM improved relay success rate by approximately 2.1% and 1.3% with the RWP and RPGM models, respectively. 6.2.3. Packet delivery rate The reasons for packet loss can be classified into two categories: (1) the packet arrived at the destination’s position recorded in the location service, but the destination node no longer stayed there; (2)
the packet entered the perimeter mode and encountered a routing loop. Reducing the position error can help to prevent packet loss caused by reason 2. Thus, approximately 54% of the reason 2 packet delivery failures were avoided by DRM (see Fig. 11). On the other hand, the packet loss caused by reason 1 was not affected by DRM. Fig. 12 compares the average packet delivery ratio (confidence interval 95%) for the evaluated schemes. When the simulated scenarios were in low mobility, most packet loss was caused by reason 1 so the average difference between DRM and the best SBI scheme was only about 0.4%. On the contrary, the reason 2 packet loss arose as the moving speed increased.
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DRM schemes maintained the accuracy of the neighboring information in higher mobility scenarios so achieved a superior packet delivery rate. By decreasing the value of k, the delivery rate can be further improved and therefore the packet delivery rate fell only gradually as the mean moving speed increased. Compared to the best SBI scheme, with the RWP and RPGM models, the average delivery ratio improved by DRM was approximately 1.7% and 0.6%, respectively. 6.2.4. Path length As mentioned above, when an invalid neighbor was chosen as the next relay, a relay failure occurred and then the current relaying node should select a
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new candidate. This reselection procedure increases one extra hop to be counted into the routing path length. Fig. 13 displays the average path length for both RWP and RPGM models. Since the invalid routing entries were efficiently purged by the DRM route management, the average path length was significantly reduced especially in a higher mobility scenario. Compared to the best SBI scheme, the average reduction rate for path length came from DRM was approximately 3.3% and 2.6% with RWP and RPGM models, respectively. 6.2.5. Beacon overhead Fig. 14 illustrates the beacon overhead as measured in terms of the packet sizes of total number
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of beacons sent during the entire simulation. In the conventional GPSR, the packet size of a beacon is 35 bytes. DRM requires additional two 8-byte fields for representing the velocity information so the beacon size increases to 51 bytes. SBI schemes did not change the beacon interval so the beacon overhead remained at a constant value. In a low mobility scenario, DRM extended the beacon interval to reduce the number of beacons required so it provided significantly beacon overhead savings (see Fig. 14). Conversely, when the mobile nodes travelled at a higher speed, DRM sent more beacons in order to maintain the freshness of routing information. The results demonstrate that the mobile nodes with DRM can adjust their beacon intervals automatically and adaptively based on the environments. 7. Conclusion This paper has presented the dynamic route maintenance algorithm (DRM) for beacon-based geographic forwarding strategies. Unlike previous protocols, the beacon interval is adaptively modified during execution so that mobile nodes can utilize beacon messages more efficiently. Moreover, the enhanced route management function maintains the freshness of the routing information based on the mobility prediction. The results have shown that DRM achieved a better system performance. In lower mobility scenarios, DRM significantly reduced the beacon overhead by dynamically adjusting beacon intervals according to the environments; while in faster mobility scenarios, DRM guaranteed a high quality packet delivery by shortening beacon intervals and using route management to effectively purge the stale information. Acknowledgments We are pleased to thank the anonymous reviewers for their valuable comments and suggestions. This research was supported in part by the Taiwan National Science Council under contracts NSC 952221-E-006-074 and 95-2221-E-006-092-MY2. References [1] G. Dommety, R. Jain, Potential Networking Applications of Global Positioning Systems (GPS), Technical Report TR-24, Department of Computer Science, The Ohio State University, April 1996.
[2] E.D. Kaplan, Understanding GPS: Principles and Applications, Artech House, Boston, 1996. [3] T. He, C. Huang, B. Blum, J. Stankovic, T. Abdelzaher, Range-free localization schemes in large scale sensor networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), September 2003, pp. 81–95. [4] L. Hu, D. Evans, Localization for mobile sensor networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), September 2004, pp. 45–57. [5] K.F. Ssu, C.H. Ou, H.C. Jiau, Localization with mobile anchor points in wireless sensor networks, IEEE Transactions on Vehicular Technology 54 (3) (2005) 1187– 1197. [6] C.E. Perkins, E.M. Royer, Ad-hoc on-demand distance vector routing, in: Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications, Feb. 1999, pp. 90–100. [7] J. Broch, D.B. Johnson, D.A. Maltz, The dynamic source routing protocol for mobile ad hoc networks, IETF Internet Draft (1998). [8] V.D. Park, M.S. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, in: Proceedings of the Annual IEEE Conference on Computer Communications (INFOCOM), April 1997, pp. 1405–1413. [9] E.M. Royer, C.K. Toh, A review of current routing protocols for ad hoc mobile wireless networks, IEEE Personal Communications (1999) 46–55. [10] M. Zorzi, R.R. Rao, Geographic random forwarding (GeRaF) for ad hoc and sensor networks: multihop performance, IEEE Transactions on Mobile Computing 2 (4) (2003) 337–347. [11] M. Zorzi, R.R. Rao, Geographic random forwarding (GeRaF) for ad hoc and sensor networks: energy and latency performance, IEEE Transactions on Mobile Computing 2 (4) (2003) 349–365. [12] H. Fu¨ßler, J. Widmer, M. Ka¨semann, M. Mauve, H. Hartenstein, Contention-based forwarding for mobile adhoc networks, Ad Hoc Networks Journal 1 (4) (2003) 351– 369. [13] H. Fu¨ßler, J. Widmer, M. Mauve, H. Hartenstein, A novel forwarding paradigm for position-based routing (with implicit addressing), in: Proceedings of the IEEE Annual Workshop on Computer Communications, October 2003, pp. 194–200. [14] M. Heissenbttel, T. Braun, A novel position-based and beaconless routing algorithm for mobile ad-hoc networks, in: Proceedings of the IEEE Workshop on Applications and Services in Wireless Networks (ASWN), July 2003, pp. 1970– 210. [15] B. Karp, H.T. Kung, GPSR: greedy perimeter stateless routing for wireless networks, in: Proceedings of the Annual International Conference on Mobile Computing and Networking (MobiCom), August 2000, pp. 120–130. [16] P. Bose, P. Morin, I. Stojmenovic, J. Urrutia, Routing with guaranteed delivery in ad hoc wireless networks, ACM Wireless Networks 7 (6) (2001) 609–616. [17] M. Mauve, J. Widmer, H. Hartenstein, A survey on positionbased routing in mobile ad-hoc networks, IEEE Network Magazine 15 (6) (2001) 30–39.
C.-H. Chou et al. / Computer Networks 52 (2008) 418–431 [18] D. Son, A. Helmy, B. Krishnamachari, The effect of mobility-induced location errors on geographic routing in mobile ad hoc and sensor networks: analysis and improvement using mobility prediction, IEEE Transactions on Mobile Computing 3 (3) (2004) 233–245. [19] K. Fall, K. Varadhan, The ns Manual (ns Notes and Documentation). The VINT project.
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ference on Military Communications, October 2000, pp. 491–495. [34] X. Hong, M. Gerla, G. Pei, C.C. Chiang, A group mobility model for ad hoc wireless networks, in: Proceedings of the ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems, August 1999, pp. 53–60. [35] F. Bai, N. Sadagopan, A. Helmy, IMPORTANT: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks, in: Proceedings of the Annual IEEE Conference on Computer Communications (INFOCOM), April 2003, pp. 825– 835.
Chih-Hsun Chou received the BS in Computer Science and Information Engineering from Chung Yuan Christian University, Taiwan, in 2001. He is a PhD candidate in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. His research interests include mobile computing, dependable systems, and wireless sensor networks.
Kuo-Feng Ssu received the BS in Computer Science & Information Engineering from National Chiao Tung University and PhD in Computer Science from the University of Illinois at Urbana-Champaign. He is an Associate Professor in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. His research interests include mobile computing, dependable systems, and distributed systems. He is a member of the IEEE, the ACM, and the Phi Tau Phi honor scholastic society.
Hewijin Christine Jiau received the BS in Electrical Engineering from National Cheng Kung University, MS in Electrical Engineering & Computer Science from Northwestern University, and PhD in Computer Science from the University of Illinois at Urbana-Champaign. She is an Associate Professor in the Department of Electrical Engineering, National Cheng Kung University, Taiwan. Her research interests include software reuse, object technologies, information integration, data mining, and database applications on the Internet. She is a member of the IEEE and the ACM.