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46
Stationary Distributions for the Random Waypoint Mobility Model
 IEEE Transactions on Mobile Computing
, 2003
"... In simulations of mobile ad hoc networks, the probability distribution governing the movement of the nodes typically varies over time, and converges to a "steadystate" distribution, known in the probability literature as the stationary distribution. Some published simulation results ig ..."
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Cited by 184 (7 self)
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In simulations of mobile ad hoc networks, the probability distribution governing the movement of the nodes typically varies over time, and converges to a "steadystate" distribution, known in the probability literature as the stationary distribution. Some published simulation results ignore this initialization discrepancy. For those results that attempt to account for this discrepancy, the practice is to discard an initial sequence of observations from a simulation in the hope that the remaining values will closely represent the stationary distribution. This approach is inefficient and not always reliable. However, if the initial locations and speeds of the nodes are chosen from the stationary distribution, convergence is immediate and no data need be discarded. We derive the stationary distributions for location, speed, and pause time for the random waypoint mobility model. We then show how to implement the random waypoint mobility model in order to construct more efficient and reliable simulations for mobile ad hoc networks. Simulation results, which verify the correctness of our method, are included.
Information spreading in stationary markovian evolving graphs
 In Proc. of the 23rd IEEE International Parallel and Distributed Processing Symposium (IPDPS
, 2009
"... Markovian evolving graphs [2] are dynamicgraph models where the links among a fixed set of nodes change during time according to an arbitrary Markovian rule. They are extremely general and they can well describe important dynamicnetwork scenarios. We study the speed of information spreading in the ..."
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Cited by 34 (9 self)
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Markovian evolving graphs [2] are dynamicgraph models where the links among a fixed set of nodes change during time according to an arbitrary Markovian rule. They are extremely general and they can well describe important dynamicnetwork scenarios. We study the speed of information spreading in the stationary phase by analyzing the completion time of the flooding mechanism. We prove a general theorem that establishes an upper bound on flooding time in any stationary Markovian evolving graph in terms of its nodeexpansion properties. We apply our theorem in two natural and relevant cases of such dynamic graphs: edgeMarkovian evolving graphs [24, 7] where the probability of existence of any edge at time t depends on the existence (or not) of the same edge at time t − 1; geometric Markovian evolving graphs [4, 10, 9] where the Markovian behaviour is yielded by n mobile radio stations, with fixed transmission radius, that perform n independent random walks over a square region of the plane. In both cases, the obtained upper bounds are shown to be nearly tight and, in fact, they turn out to be tight for a large range of the values of the input parameters. 1
The Random Trip Model: Stability, Stationary Regime, and Perfect Simulation
, 2006
"... We define "random trip", a generic mobility model for random, independent node motions, which contains as special cases: the random waypoint on convex or non convex domains, random walk on torus, billiards, city section, space graph, intercity and other models. We show that, for this model ..."
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Cited by 22 (0 self)
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We define "random trip", a generic mobility model for random, independent node motions, which contains as special cases: the random waypoint on convex or non convex domains, random walk on torus, billiards, city section, space graph, intercity and other models. We show that, for this model, a necessary and sufficient condition for a timestationary regime to exist is that the mean trip duration (sampled at trip endpoints) is finite. When this holds, we show that the distribution of node mobility state converges to the timestationary distribution, starting from origin of an arbitrary trip. For the special case of random waypoint, we provide for the first time a proof and a sufficient and necessary condition of the existence of a stationary regime, thus closing a long standing issue. We show that random walk on torus and billiards belong to the random trip class of models, and establish that the timelimit distribution of node location for these two models is uniform, for any initial distribution, even in cases where the speed vector does not have circular symmetry. Using Palm calculus, we establish properties of timestationary regime, when the condition for its existence holds. We provide an algorithm to sample the simulation state from a timestationary distribution at time 0 (“perfect simulation”), without computing geometric constants. For random waypoint on the sphere, random walk on torus and billiards, we show that, in the timestationary regime, the node location is uniform. Our perfect sampling algorithm is implemented to use with ns2, and is available to download from
An Efficient CounterBased Broadcast Scheme for Mobile Ad Hoc Networks
"... Abstract. In mobile ad hoc networks (MANETs), broadcasting plays a fundamental role, diffusing a message from a given source node to all the other nodes in the network. Flooding is the simplest and commonly used mechanism for broadcasting in MANETs, where each node retransmits every uniquely receive ..."
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Cited by 13 (4 self)
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Abstract. In mobile ad hoc networks (MANETs), broadcasting plays a fundamental role, diffusing a message from a given source node to all the other nodes in the network. Flooding is the simplest and commonly used mechanism for broadcasting in MANETs, where each node retransmits every uniquely received message exactly once. Despite its simplicity, it however generates redundant rebroadcast messages which results in high contention and collision in the network, a phenomenon referred to as broadcast storm problem. Pure probabilistic approaches have been proposed to mitigate this problem inherent with flooding, where mobile nodes rebroadcast a message with a probability p which can be fixed or computed based on the local density. However, these approaches reduce the number of rebroadcasts at the expense of reachability. On the other hand, counterbased approaches inhibit a node from broadcasting a packet based on the number of copies of the broadcast packet received by the node within a random access delay time. These schemes achieve better throughput and reachability, but suffer from relatively longer delay. In this paper, we propose an efficient broadcasting scheme that combines the advantages of pure probabilistic and counterbased schemes to yield a significant performance improvement. Simulation results reveal that the new scheme achieves superior performance in terms of savedrebroadcast, reachability and latency.
Intelligent broadcasting in mobile ad hoc networks: Three classes of adaptive protocols
 EURASIP Journal on Wireless Communication and Networking
, 2007
"... Because adaptability greatly improves the performance of a broadcast protocol, we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network (MANET). We chose broadcasting because it functions as a foundation of MANET communication. Unicast, multicast, an ..."
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Cited by 12 (0 self)
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Because adaptability greatly improves the performance of a broadcast protocol, we identify three ways in which machine learning can be applied to broadcasting in a mobile ad hoc network (MANET). We chose broadcasting because it functions as a foundation of MANET communication. Unicast, multicast, and geocast protocols utilize broadcasting as a building block, providing important control and route establishment functionality. Therefore, any improvements to the process of broadcasting can be immediately realized by higherlevel MANET functionality and applications. While efficient broadcast protocols have been proposed, no single broadcasting protocol works well in all possible MANET conditions. Furthermore, protocols tend to fail catastrophically in severe network environments. Our three classes of adaptive protocols are Pure Machine Learning, IntraProtocol Learning, and InterProtocol Learning. In the pure machine learning approach, we exhibit a new approach to the design of a broadcast protocol: the decision of whether to rebroadcast a packet is cast as a classification problem. Each mobile node (MN) builds a classifier and trains it on data collected from the network environment. Using intraprotocol learning, each MN consults a simple machine model for the optimal value of one of its free parameters. Lastly, in interprotocol learning, MNs learn to switch between different broadcasting protocols based on network conditions. For each class of learning method, we create a prototypical protocol and examine its performance in simulation.
Understanding the simulation of mobility models with Palm calculus
 IEEE TRANSACTIONS ON MOBILE COMPUTING ITMMC3
, 2005
"... The simulation of mobility models such as the random waypoint often cause subtle problems, for example the decay of average speed as the simulation progresses, a difference between the long term distribution of nodes and the initial one, and sometimes the instability of the model. All of this has to ..."
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Cited by 10 (3 self)
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The simulation of mobility models such as the random waypoint often cause subtle problems, for example the decay of average speed as the simulation progresses, a difference between the long term distribution of nodes and the initial one, and sometimes the instability of the model. All of this has to do with time averages versus event averages. This is a well understood, but little known topic, called Palm calculus. In this paper we first give a very short primer on Palm calculus. Then we apply it to the random waypoint model and variants (with pause time, random walk). We show how to simply obtain the stationary distribution of nodes and speeds, on a connected (possibly non convex) area. We derive a closed form for the density of node location on a square or a disk. We also show how to perform a perfect (i.e. transient free) simulation without computing complicated integrals. Last, we analyze decay and explain it as either convergence to steady state or lack of convergence.
A Visualization and Analysis Tool for NS2 Wireless Simulations: iNSpect ∗
"... The Network Simulator 2 (NS2) is a popular and powerful simulation environment, and the number of NS2 users has increased greatly in recent years. Although it was originally designed for wired networks, NS2 has been extended to work with wireless networks, including wireless LANs, mobile ad hoc n ..."
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Cited by 9 (0 self)
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The Network Simulator 2 (NS2) is a popular and powerful simulation environment, and the number of NS2 users has increased greatly in recent years. Although it was originally designed for wired networks, NS2 has been extended to work with wireless networks, including wireless LANs, mobile ad hoc networks (MANETs), and sensor networks; however, the Network Animator (NAM) for NS2 has not been extended for wireless visualization. In this paper, we discuss a new visualization and analysis tool for use with NS2 wireless simulations. Visual analysis of a wireless environment is important for three areas of NS2 based simulation research: (1) validating the accuracy of a mobility model’s output and/or the node topology files used to drive the simulation; (2) validation of new versions of the NS2 simulator itself; and (3) analysis of the results of NS2 simulations. Our iNSpect program handles all three of these areas quickly and accurately. We’ve made our iNSpect program available for other researchers in order to improve the accuracy of their simulations. 1. Introduction and Related
An Efficient Approach to Distributed Information Dissemination in Mobile Ad Hoc Networks
 Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM
, 2004
"... In order to ease the challenging task of information dissemination in a MANET, we employ a legend: a data structure that is passed around a network to share information with all the nodes. Our motivating application of the legend is sharing location information. Previous research shows that a locati ..."
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Cited by 8 (1 self)
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In order to ease the challenging task of information dissemination in a MANET, we employ a legend: a data structure that is passed around a network to share information with all the nodes. Our motivating application of the legend is sharing location information. Previous research shows that a location service using a legend performs better than other location services in the literature. To improve the legendbased location service, we evaluate three methods for the legend to traverse a network in this paper and compare their performance in simulation. We also explore several improvements to the traversal methods, and describe our way of making the legend transmission reliable.
Improvements to locationaided routing through directional count restrictions
 In Proceedings of the International Conference on Wireless Networking (ICWN
, 2004
"... Abstract — We present an effective way to improve the quality of unicast routes determined by the LAR Box method. Our method uses the location information acquired by LAR itself to determine relay nodes in the forwarding zone. The improvements are twofold: (1) shorter, moredirect routes are discov ..."
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Cited by 8 (3 self)
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Abstract — We present an effective way to improve the quality of unicast routes determined by the LAR Box method. Our method uses the location information acquired by LAR itself to determine relay nodes in the forwarding zone. The improvements are twofold: (1) shorter, moredirect routes are discovered; and (2) less overhead is needed to find those routes. Because more direct routes last longer, higher delivery ratio and lower endtoend delays are produced by our improvements. The higher the network density, the greater the effect of our improvements. I.
A Visualization and Animation Tool for NS2 Wireless Simulations: iNSpect
 In Proceedings of the 13th Annual Meeting of the IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS
, 2004
"... The Network Simulator 2 (NS2) is a popular and powerful simulation environment, and the number of NS2 users has increased greatly in recent years. Although it was originally designed for wired networks, NS2 has been extended to work with wireless networks, including wireless LANs, mobile ad ho ..."
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Cited by 8 (1 self)
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The Network Simulator 2 (NS2) is a popular and powerful simulation environment, and the number of NS2 users has increased greatly in recent years. Although it was originally designed for wired networks, NS2 has been extended to work with wireless networks, including wireless LANs, mobile ad hoc networks, and sensor networks. The Network Animator (NAM) for NS2 has not been extended for wireless visualization, however. In this paper, we discuss a new visualization and animation tool for use with NS2 wireless simulations. Visual analysis of a wireless environment is important for three areas of NS2 based simulation research: (1) validating the accuracy of a mobility model's output and/or the node topology files used to drive the simulation; (2) validation of new versions of the NS2 simulator itself; and (3) analysis of the resulting NS2 trace files. Our iNSpect program can handle all three of these areas quickly and accurately. We've made our iNSpect program available for other researchers in order to improve the accuracy of their simulations. See http://toilers.mines.edu for information on obtaining iNSpect.