On message delivery through approximate information in
highly dynamic Mobile Ad Hoc Networks
Roberto Beraldi
Dipartimento di Informatica e Sistemistica Universit`a di Roma “La Sapienza”
via Salaria 113, 00198 Roma, Italy e-mail:beraldi@dis.uniroma1.it
Abstract— This paper proposed and analyzed a new strategy to solve the problem of message delivery in Mobile Ad Hoc Networks (MANETs) focusing, in particular, on settings characterized by a high mobility degree. Unlike the classical approaches, in our proposal the message forwarding process is not based on a previously computed path. Rather, the nodes of the network exploit a set of approximate information to discover a path to the destination on the fly.
The main advantage of using such a scheme is providing the whole forwarding process with an intrinsic resilience to topological changes as well as eliminating the need of any network-wide search, as required by the most promising routing protocols. For these reasons, it is particularly suitable for delay sensitive applications. The paper reports a simulation study giving evidence of the effectiveness of the idea.
Keywords— MANET, Routing, Mobile, Delay-sensitive applications.
I. INTRODUCTION
Mobile Ad Hoc Networks (MANETs) are packet-radio based networks that allow to communicate and move at the same time, without the support of any fixed infrastructure. Two nodes of a MANET can communicate directly through a wireless link only when they are neighbors, i.e., they lie within one another’s transmission radius. The network’s diameter, however, is usually much bigger than such a radius, hence making communications multi-hop in nature. The interested reader can refer to [1] for a survey on routing protocols.
The typical approach to solve the problem of message delivery is to calculate an exact path to the destination and then using it to forward a message. The path is exact in the sense that when one of its link is broken the message forwarding process fails.
It is well known that a pure proactive path computation is not suitable for MANETs, especially under frequent topological changes, and in fact many proposals incorporate a reactive compo- nent in their framework (e.g., [9], [11], [2], [8]). When a path is broken, unless it can be locally repaired, such a component triggers a new costly network wide path search. As a consequence, message delivery is temporary interrupted and an appreciable delay perceived by an application [12]. This strategy becomes clearly unsuitable for highly dynamic environments and, even more, for delay-sensitive applications, since the nodes’ mobility causes fre- quent topological changes that require to repair or discover a path often. Multi-path routing, e.g. [10], is an attempt to overcome such limitations. However, it assumes that many paths to the destination are available, thus exacerbating the need of their discovery and maintenance.
This paper proposes a different approach to the problem of message delivery in MANETs, especially suitable for settings with a high mobility degree. The key point of the proposal is to let each node n exploit the set of nodes at distance less than or equal to 2 from itself (referred to as the potential next-hop node set) as approximate next-hop nodes. The nodes in a set are approximate in the sense that they do not provide any guarantee of laying on a
1This work was supported by CNR grant “IS-MANET”, CTB N.CU.03.00002
path towards the destination, but only indirect information useful to estimate their chances of being on the “right” direction. The forwarding process is based on such estimations and it is able to cope with erroneous decisions.
An import issue of the scheme is how estimations are performed.
We found that the time since the last encounter of nodes with the destination, defined in [5] as the encounter age, provides a simple yet effective way to forward correctly a message (a node encounters the destination when it is its neighbor). The encounter age has already been successfully employed in [5] to design a route discovery algorithm, called F RESH; however, the novelty of our proposal is to put such a mechanism at the center of the forwarding process, thus eliminating the need of any network-wide operation.
Moreover, ages are only a possible mean to make estimations, and other sources of information could be exploited by the routing framework as well.
The consequence of using approximations is that a message can follow a path longer than the shortest one. However, the length of a path is clearly not the only aspect to consider when designing a routing algorithm. Our approach provides several advantages compared to other solutions. Due to the lack of global searches, the variable component in the end-to-end message delay a search triggers, is trivially avoided. Another key advantage is that the whole forwarding process is highly resilient to topological changes.
The set of potential next-hop nodes can be promptly kept updated (e.g. via beacons); moreover, when a selected next-hop node cannot be reached, a message can readily be sent to another one, i.e., the nodes in a set are used as backups of each others.
The rest of this paper is organized as following. In the next section we discuss the related work. Section III provides a short description of a general framework for routing through approximate information, followed by one of its possible implementations. A simulation study is reported in Section IV and conclusion given in section V.
II. RELATED WORK
The Fisheye State Protocol (F SR) is a proactive algorithm that exploits approximate path information, see [6]. The routing updates are generated by F SR at different rates depending on the scope, where the scope s of a node n is defined as the set of nodes at distance less than or equal to s from n. The lower the scope, the higher the rate. Thus, routing information about a destination node d are progressively more and more accurate as the message gets closer to d. However, the forwarding process fails and the message is discarded when a next-hop node is unreachable.
The meeting likelihood between nodes is exploited in the al- gorithm proposed in [4]. In essence, the algorithm exploits the mobility of nodes (called agents in that context) for storing, carry- ing and forward messages among neighbors. An agent forwards a message to another one if it provides a higher meeting likelihood with the destination. The protocol is designed for networks subject to partitioning and where the relative position of stationary devices
and non-randomness in the movement of agents in the network can be exploited.
The knowledge of the hosts which have recently been noticed is the key ingredient employed in [3] to design an algorithm for the so-called Disconnected Transitive Communication model (DT C) for cross-cluster communications. A host moves an entire message (not a single packet) to another host as close to the destination as possible, where a closer host is defined to be one which is likely to be in contact with the destination earlier than the source. The proposal is a middleware on top of wireless routing protocols.
F RESH, see [5], is a path search algorithm that exploits the times since the last encounter of nodes with the destination to steer a flood-based search in the general direction of the destination, where an encounter between two nodes happens when those nodes are one-hop neighbors. Message forwarding is still based on an underlying reactive routing protocol.
III. EXPLOITING APPROXIMATE INFORMATION
For the sake of simplicity, the following description is given considering a single destination d.
A. System model and definitions
We consider a MANET composed of N mobile devices, here- after also called nodes. Each node is assigned a unique id; it knows the id of all the other nodes in the network as well as the identity of the destination. All the nodes have access to a local time, which is updated to the same nominal rate. A neighbor discovery service is available at each node (two nodes are neighbors if they can reliably send a message to each other).
Let n and i be two nodes and h(i, n) their distance in number of hops. The Potential Next hop set of n is defined as P N H(n) = {i|h(i, n) ≤ 2}. If node i is a neighbor of d until time t, then we say that i encountered the destination at time t. The encounter age, or simply the age of node i, ti, is defined as the time elapsed since the most recent encounter of i with the destination. If i and d are neighbors then ti= 0, while if i has never encountered the destination then ti= ∞.
A path from a source node s to d is a sequence of distinct nodes (n0 = s, n1, .., nk, nk+1= d) with h(ni, ni+1) ≤ 2, 0 ≤ i ≤ k.
A node belonging to the path is called a next − hop node, while the ones used as intermediate nodes between two next-hop nodes are referred to as relay nodes.
B. General description
The framework for routing through approximate information is based on the following two main components:
• A procedure E for computing and advertising the information relevant to the estimation;
• A procedure F (also called the forwarding policy) to forward a message.
The procedure E is triggered by the discovery service when a new neighbor node is detected or when a neighbor is no more reachable. It also maintains any information exploited by F to perform the estimations (e.g., the ages of the nodes belonging to P N H) as well as the P N H set(e.g., the intermediate node required to reach the ones at distance 2).
For each message, the policy F receives as input the information advertised by nodes belonging to P N H and selects the new next- hop node. The policy also defines how to react when the next-hop node is unreachable.
C. Implementing approximate routing
This section describes an algorithm based on the idea described above. The algorithm exploits the ages provided by nodes belong- ing to P N H as information base to make estimations. For a given message, each node can act as next-hop node at most once, while it can relay the same message without any restriction.
————————————————————————————
Procedure E() # Behavior of noden 1. INIT:tn← ∞
2. on detecting a new neighborm 3. if (m = d) #mis the destination 4. tn← 0; # reset local age
5. X ← (0, n, 2); # Create an update vector
7. bcast (X)
8. else #mis not the destination
9. X ← {(t, i, f )|(t, i, i, f )} ∪ (tn, n, 2); # create X
10. send (X) to m
11. on loosing neighborm
12. delete(∗, ∗, m, ∗)fromET # deletem’s entries 13. if (m = d) #mis the destination
14. start increasingtn
15. X ← (−1, n, 2) ∪ (tn, n, 2); # Update my age 16. else #mis not the destination
17. X ← (−1, m, 1) 18. bcast (X)
19. on receiving a vectorX from nodej 20. X0← Ø
21. foreach ((t, i, f ) ∈ X) do #
22. if (t ≥ 0)ET ← ET ∪ {(t, i, j, f − 1)}
23. if (f=2) # propagate the information 24. X0← X0∪ (t, i, f − 1); # update X’
25. endfor
26. if (X06= Ø) bcast (X0)
————————————————————————————
Fig. 1. Pseudocode of E.
1) Implementing the procedure E: The procedure E running at node n maintains the following data structures:
• a local variable, tn, for storing the n’s age.
• an Estimation Table (ET): an entry of ET is a tuple (t, i, j, f ), where t = ti, i is a node, j a relay for i and f the distance of i from n ( j 6= i only when f = 2). The ages are updated according at the nominal rate.
The set P N H corresponds to the first column of ET . The figure 1 shows the pseudocode of E. The variable tnis initialized to ∞, is 0 when n and d are neighbors and starts to increase when E detects that d is no more a neighbor.
ET is maintained by using the control messages received from the nodes belonging to P N H. Each message contains an update vector X whose elements are triples (t, j, f ), where t = tj, j is a node, and f (= 1, 2) the time-to-live associated to the message.
The value t = −1 is used to indicate a delete operation. When E processes X, it updates ET and retransmits only those elements having f = 2.
Suppose that a node, say m, becomes a new neighbor of n (lines 2-10). If m = d, then E sets tn = 0 and broadcasts a control message carrying X = (0, n, 2); otherwise, it sends a control message to m, carrying its own age, tn, as well as the neighbors’ ones.
When a node m ceases to be neighbor, all entries having m as a relay node are deleted (line 12). If m = d then tnstarts increasing and an update vector is created (line 14-15). Otherwise, E builds a suitable update vector for deleting any entry associated to m from the tables of its neighbors (line 17).
2) Forwarding Policy: Each message carries the following con- trol information: the id of the next-hop nodes used to forward the message so far (stored into a vector, say M ) and the id of the new next-hop node (field nhn). M is required to avoid loops: a node in M can’t be next-hop again.
When the node n receives a message, say msg, it activates the procedure F , which behaves as described in figure 2. If n is the
—————————————————————————————- Procedure F(msg) # Behavior of noden
1. if (msg.dst = n) #nis the destination 2. deliver to upper layer(msg) 3. return (1)
4. if (msg.nhn! = n) #nis a relay node 5. send(msg)tomsg.nhn 6. if (send ok) return(1)
7.msg.M ← msg.M ∪ {n}; # update visited node 8.n∗←SelectNext(msg.M )# select the next-hop node 9. if (n∗= −1) return (0); # no candidates then fail 10. repeat
11. msg.nhn = n∗# update next-hop field
12. Lr←RelayNodesOf(n∗)# get list of relay nodes 13. while (Lr6= ∅) do
14. nr←GetNodeFrom(Lr) # get a relay 15. send(msg)tonr
16. if (send ok) return(1)
17. dowhile
18. deleten∗fromP N H# Next-hop node is unreachable 19. n∗←SelectNext(msg.M )# select another node 20.until(n∗=-1)
21.return (0)
———————————————————————————–
Fig. 2. The Pseudocode of the forwarding policy F .
final destination, then msg is delivered to the upper layer (line 1). If n is a relay node (lines 4-6), then the message is sent to the current next-hop node, msg.nhn, (the node nhn must be a neighbor) and, if the transmission succeeds, the procedure terminates successfully.
In the other cases, the node n is a next-hop node. In particular, n is promoted to next-hop node when it is a relay node, but the current next-hop node is unreachable. The node n is thus added to the vector M (line 7).
Then, the procedure calls the function SelectN ext which re- turns the new next-hop node, say n∗, which is in charge of continuing forwarding the message towards the destination. The implementation of the function is discussed later in this section. In general, the procedure fails and the message discarded if no nodes are available for this purpose (n∗= −1).
The repeat-until loop (lines 10-20) contains the recovery logic exploited when a node cannot be reached. First, since more than one node can act as a relay, the list Lr, containing the relay nodes for n∗, is calculated. The procedure tries to send the message to one node belonging to Lr (while loop). If none of the node can be reached, n∗ is deleted from P N H and a new next-hop node selected (line 18-19). If the selection function fails, the procedure fails too (lines 20-21), otherwise the external loop is repeated again.
Two different greedy heuristics are considered for the implemen- tation of SelectN ext, giving rise to the Optimistic Policy (OP ) and the Conservative Policy (CP ). As in [5], the rational is to assume that:
ti < tj ⇒ P r(h(i, d) < h(j, d)) < P r(h(i, d) ≥ h(j, d)), i.e., given any two nodes, say i and j, if the age of i, ti, is smaller than the j’s one, tj, then the probability that i is closer than j to the destination is higher than the probability that j is closer than i to the destination; thus, i it preferred.
The function SelectN ext called by n returns the node n∗, determined as following (ties are broken at random):
• n∗= j : tj= min{ti|i ∈ P N H(n), i 6∈ V, tj< tn} (CP );
• n∗= j : tj= min{ti|i ∈ P N H(n), i 6∈ V } (OP ).
The idea behind the conservative policy is simple: to select a sequence a nodes with decreasing age. If the selection never fails then this rule trivially assures that the destination is reached.
Fig. 3. An example of message forwarding.
The optimistic policy, on the other hand, picks the node with the lowest age in the set regardless the value at the selecting node, in the hope that subsequent selections are able to correct any wrong estimation.
Figure 3 shows a network topology at a given instant of time and the path selected by OP when a message destined to d is generated by s (the dashed line in the figure). The figure also shows the nodes of P N H, together with their ages. For the sake of simplicity, it is assumed that the topology doesn’t change during forwarding.
The source node s sends the message to node 2, tagged as node n1 in the figure, as it provides the lowest age. Note that the node 1 in this case just relays the message and thus it is not added to M . Node 2 then sends the message to node 1. Now node 1 selects node 7, since the node 2 has been already used as next-hop, and the node 7 sends the message to the node 12, whose age is zero.
3) Recovery from a link breakage: Suppose that the link be- tween the nodes 4 and 7 in the figure 3 is broken. Node 4 uses 5 as next-hop node (the nodes 1 ,s and 2 cannot be used again), which then sends the message to node 7.
IV. PERFORMANCEANALYSIS
A. Simulation model and performance metrics
The purpose of the simulation study was to analyze the properties of the forwarding policies independently from any particular tech- nology employed or implementation dependent aspects. We thus assumed an ideal MAC layer (collisions and interferences are not modelled).
We observed N nodes moving into a square region (with edge 1 Km) according to a modified random waypoint mobility model. A node always moves at a constant speed v, between points selected uniformly at random in the region. The speed is constant in order to overcome the problem reported in [13]. A message can be forwarded at most 20 times.
During a simulated period, the network is observed every second and the connectivity graph, G = (V, L), is calculated. V is the set of mobile devices and L a set of node pairs representing communication links. (n, m) ∈ L if the Euclidian distance between n and m is less than the transmission radius R. A path from the source to the destination is then calculated by applying a specific forwarding policy to G. Finally, the age variables and the estimation tables are updated according to the new graph.
Each point reported in the following figures is the mean of five independent runs of duration 2000 s. The system’s statistics are collected after 90% of nodes have a finite age. If not differently specified the parameters are: N = 100, R = 200 m and v = 10 m/s.
The following metrics are estimated during a simulation:
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
RS-1 RS-2 CP-1 OP-1 CP-2 OP-2
Forwarding Policy
Delivery Probability
Fig. 4. Delivery probability for all policies.
• delivery probability, the probability that a message sent by the source reaches the destination. It is calculated as the ratio of the total number of times a path to the destination is found to the total number of observations. The cases when the destination cannot be reached due to a network partition are not included in the computation.
• normalized overhead, the number of control messages gener- ated on the average by a single node. For messages sent over multiple hops each single hop is counted as one transmission.
The traffic generated by the discovery service is not included.
• average path length, the average length of the path found by a policy, given in number of hops.
• topological distance (Dmin), the length of the shortest path.
B. Numerical results and discussion
1) Forwarding policies studied: We have studied the following policies:
• Conservative Policy (CP -2);
• Optimistic Policy (OP -2);
• Conservative Policy with next-hop at distance 1 (CP -1);
• Optimistic Policy with next-hop at distance 1 (OP -1);
• next-hop at distance 1, picked uniformly at random (RS-1);
• next-hop at distance ≤ 2, picked uniformly at random (RS-2).
Under CP -1 and OP -1 the next-hop node is forced to be a neighbor, and thus a message is not allowed to visit the same node two times. These two policies are introduced to gain some insight on the capability of the forwarding process to recover from wrong estimations. The policies RS are used as baselines.
2) Preliminary results: As a sanity check we have first com- pared the proposed policies against the random schemes that blindly selected the next-hop node. We observed N = 100 nodes moving at speed v = 10 m/s. The delivery probabilities are reported in Figure 4. Note that using RS-2 a message reaches the destination with a high probability. This is because each node acts as a next-hop at most once for a given message, so that at each forwarding step the message is forced to visit at least a new node. When OP -2 is used, messages are always delivered, while forcing the next-hop to be at distance 1 reduces such a probability.
In this case in fact, the forwarding process reduces its flexibility (see also Figure 4). CP exhibits a similar behavior.
Figure 5 shows the average path length for all the policies. As expected, RS-2 provides the highest value, which is almost six times the topological distance, Dmin. Interestingly, both CP and OP provide a much shorter distance, which is also close to Dmin. Figure 6 shows the topological distance as well as the path length for OP -1 and OP -2, as a function of the time. A point is reported in the figure only when a path is found, while a negative value indicates a network partition. At time 680 s, the network becomes partitioned. Before partitioning, it is very likely that the number of paths between the source and the destination decreases gradually with the time. Thus, the chances of recovering from a wrong forwarding are reduced, and this explains why the path length increases before the partitioning. Please note how OP -2
0 2 4 6 8 10 12
RS-1 RS-2 Dmin CP-1 OP-1 CP-2 OP-2 Forwarding policy
Average length [hop]
Fig. 5. Average path length for all different policies
-1 2 5 8 11 14 17 20
0 200 400 600 800 1000
Simulation Time [s]
Path Length [hop]
Dmin OP-1 OP-2
Fig. 6. Path length as a function of the time.
is able to track the destination when it ceases to be neighbor of the source (this happens roughly at time t = 180 s). This is not the case if OP -1 is used.
3) Varying the number the nodes.: The effect of varying the number of nodes is studied for N in the range 50-200, v = 10m/s and R = 200 m.
a) Delivery Probability: Figure 7 portraits the delivery prob- ability as a function of N for OP -k and CP -k (k = 1, 2). The delivery probability increases with N . Since R is fixed, the higher N the higher the number of nodes belonging to P N H. Thus, both the selection and recovery mechanisms become more effective.
Note that for N > 75, OP -2 always delivers a message, while the delivery probability is close to 1 under CP -2 only for 200 nodes. Again, the limitation on the selection of the next-hop node produces a severe performance degradation. The policies CP -1 and OP -1 will be not further considered.
b) Average path length: Figure 8 shows the path length and Dminas a function of N . OP -2 provides a distance higher than CP -2 for N small, while as N is increased they are the same.
These results are consistent with the higher delivery probability that characterizes the optimistic policy (see also figure 7) and reflect its higher capacity to route a message. As expected, the length is longer than Dmin.
c) Protocol overhead: As far as the protocol overhead is concerned, the following table reports the average number of control messages generated by a node per second.
0,5 0,6 0,7 0,8 0,9 1
50 100 150 200
Number of nodes
Delivery probability
OP-1 CP-1 OP-2 CP-2
Fig. 7. Delivery probability as a function of the number of nodes.
0 1 2 3 4 5 6
50 100 150 200
Number of nodes
Average length [hop]
OP-2 CP-2 Dmin
Fig. 8. Average path length as a function of nodes.
0,5 0,6 0,7 0,8 0,9 1
150 200 250 300
Transmission radius [m]
Delivery probability
CP-2 OP-2
Fig. 9. Delivery probability as a function of R
N 50 75 100 150 200
msg/s 0,79 1,11 1,39 1,89 2,36
The overhead increases with N since the topological change rate (the number of new link creation or deletion per unit of time) is increased with N . Recall that we are considering a single source-destination pair. Clearly, the overhead of the algorithm is independent from the number of sources. This feature is very interesting in settings where a few hot destinations in the network exist (like for client/server applications).
4) Varying the speed: In this set of experiments N = 100 and R = 200 m while the speed v vaires in the range [2..50] m/s. As already pointed out in [5], varying the nodes’ speed corresponds to a rescaling of time, and therefore of all the encounter ages. In this way, the selections of the next-hop nodes are not influenced by the speed and thus the delivery probability, as well as the path length, don’t change with v. This is not the case for the protocol overhead, since the number of control messages is determined by the topological change rate. The following table reports the overhead measured during the experiments.
v 2 5 10 15 20
msg/s 0,35 0,81 1,39 1,89 2,34
5) Varying the transmission radius: In this set of experiments we observed N = 100 nodes moving at v = 10 m/s with different transmission radiuses. The effect of decreasing R is twofold: (a) the age provided by nodes on the average increases, and therefore the distance-age correlation is weakened (see also [5]); (b) the number of nodes belonging to P N H decreases, and this reduces the chances of recovering from the incorrect selections.
a) Delivery probability: Figure 9 shows the delivery proba- bility as a function of the transmission radius. When the radius is decreased, the policy CP -2 often fails to deliver a message, while OP -2 is unable to deliver only a small fraction of messages if R = 150 m. This confirms CP -2 as the best heuristic.
b) Average path length: Figure 10 shows the average path length as a function of R. Again, the figure shows Dmin to facilitate the comparison. An incorrect next-hop node is selected with higher probability for R small, and therefore the length of the route is increased.
c) Protocol overhead: The protocol overhead depends on the topological change rate as well as on the number of nodes in
0 1 2 3 4 5 6 7 8
150 200 250 300
Transmission radius [m]
Average length [hop] CP-2
OP-2 Dmin
Fig. 10. Average path length as a function of R
P N H. Both values increase with R, and thus, as expected, the higher R the higher the overhead, as reported numerically in the following table.
R 150 175 200 250 300
msg/s 2,03 2,36 2,36 3,04 3,09 V. CONCLUSION AND FUTURE WORK
This paper proposed and analyzed a new strategy to solve the problem of message delivery in mobile networks. Instead of using pre-computed paths, a message is forwarded using a set of approximate next-hop nodes, which are determined by elaborating the information exchanged locally between nodes. One key advan- tage of the proposed method is the elimination of network-wide operations, and thus all their side effects.
We have exploited the correlation between the time elapsed since a node has most recently seen the destination and its distance from the source (the encounter age) to drive the forwarding decision at each node, and then shown by numerical results that the approach is suitable for medium-size networks (50-200 nodes).
The topic discussed in this paper still needs further study. It is clear that the time-distance correlation becomes weaker and weaker as we move outwards the destination. Therefore, if the area of the network is increased, the forwarding policy is likely to fail when the source is very far from the destination.
One straightforward solution to overcome this potential limita- tion to the scalability of the protocol, is to increase the diameter of the potential next-hop node set, and consequently use a routing algorithm for reaching the nodes in the set. However, we argue that this strategy doesn’t adhere to the philosophy of the approach, and other solutions should be devised; for example, when the estimations are considered not accurate enough (e.g., all ages are above a given threshold), one copy of the same message could be sent to different nodes, forcing each message to explore different zones in the network.
REFERENCES
[1] M. Abolhasan, T. Wysocki and E. Dutkiewicz, “A review of routing protocols for mobile ad hoc networks”, Ad Hoc Networks, Volume 2, Issue 1, Jan 2004, Elsevier.
[2] Charles E. Perkins and Elizabeth M. Royer. “Ad hoc On-Demand Distance Vector Routing”, Proc. of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, February 1999, New Orleans, LA.
[3] X. Chen, A.L. Murphy, “Enabling Disconnected Transitive Communicaiton in Mobile Ad Hoc Networks”, in Workshop on Principles of Mobile Computing, colocated with PODC’01, August 2001 Newport, RI (USA).
[4] James A. Davis et al., “Wearable Computers as Packet Transport Mechanisms in Highly-Partitioned Ad-Hoc Networks”, Proc. of ISWC 2001, Oct 7-9 2001, Zurich.
[5] H. Dubois-Ferriere, M. Grossglauser, M. Vetterli “Age matters: efficient route discovery in mobile ad hoc networks using encounter ages”, Proc of ACM MobiHoc2003, June 1-3 2003, Annapolis, MD.
[6] M. Gerla, “Fisheye State Routing Protocol (FSR) for Ad Hoc Networks”, IETF MANET Internet Draft, Jun 2002 (work in progress).
[7] M. Grossglauser and M. Vetterli, “Locating nodes with ease: Mobility diffusion of last encounters in ad hoc networks”, Proc. of IEEE INFOCOM ’2003, March 30 - April 3 2003, San Fransisco.
[8] P. Jacquet, P.Muhlentharm A.Qayyum, “Optimized Link State Routing Protocol”, Internet Draft, draft-ietf-manet- oslr-02.txt. July 2000 (Work in progress)
[9] D.B. Johnson, D.A. Maltz, “Dynamic Source Routing in Ad Hoc Wireless Networking”, In Mobile Computing, chapter 5, Kluwer Academic, 1996.
[10] W.H. Liao et al., “A Multi-path QoS Routing Protocol in a Wireless Mobile Ad Hoc Network”, Proc of IEEE ICN 2001, July 9-13 2001, Colmar, France.
[11] V. Ramasubramanian et al., “SHARP: a hybrid adaptive routing protocol for mobile ad hoc networks”, Proc of MobiHoc 2003, June 01 - 03, Annapolis, Maryland.
[12] Q. Xue, A. Ganz, “Ad hoc QoS on-demand routing (AQOR) in mobile ad hoc networks”, Journal of Parallel and Distributed Computing, Volume 63, Issue 2, Feb 2003, Academic Press.
[13] J. Yoon, M. Liu, B. Noble, “Random Waypoint Considered Harmful”, In Proc of IEEE INFOCOM ’2003, March 30 - April 3 2003, San Fransisco.
