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Mobility Tracking for Mobile Ad Hoc Networks
"... Abstract. In mobile ad hoc networks (MANETs), nodes mobility cause network topologies to change dynamically over time, which complicates important tasks such as broadcasting and routing. Mobility tracking is the task to determine a trajectory of the mobile node in time which can facilitate the forwa ..."
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Abstract. In mobile ad hoc networks (MANETs), nodes mobility cause network topologies to change dynamically over time, which complicates important tasks such as broadcasting and routing. Mobility tracking is the task to determine a trajectory of the mobile node in time which can facilitate the forwarding decision in network protocols ’ design. In this paper, we investigate the regularity of mobility patterns and propose comprehensive mobility predication models, that is, not only piecewise linear but also nonlinear models which are based on nodes ’ historical location/speed information. As for historical information, we consider not only periodical update but also conditional update networks. Simulation results validate the accuracy of our proposed tracking schemes. We also compare the performance of those schemes and observe their relationship with parameters of update protocols. 1
1 Distributed Reformation of CoreBased GroupShared Multicast Trees in Mobile Ad Hoc Networks☆
"... This paper proposes a method to reduce the cost of a corebased groupshared multicast tree, where the cost is evaluated by the total bandwidth consumption of multicasting packets among all group members. Due to the broadcast nature of radio transmissions, we find that the challenge of determining m ..."
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This paper proposes a method to reduce the cost of a corebased groupshared multicast tree, where the cost is evaluated by the total bandwidth consumption of multicasting packets among all group members. Due to the broadcast nature of radio transmissions, we find that the challenge of determining minimum cost multicast tree can be approximated by finding the multicast tree with a minimum number of nonleaves (the minimum nonleaf multicast tree problem). However, we also find that the minimum nonleaf multicast tree problem is NPComplete. Thus, a method is proposed to dynamically reduce the number of nonleaves in an existing multicast tree. Experimental results show that our method reduces the cost of the multicast tree in both geometrically and randomly distributed network models and the random waypoint mobility model.
Analysis on a Localized Pruning Method for Connected Dominating Sets
"... While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is present ..."
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While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is presented. Assume N nodes are deployed uniformly and randomly in a square of size LN × LN (where N and LN → ∞); three results are obtained. (1) It is proved that the node degree distribution of such a network follows a Poisson distribution. (2) The expected size of a CDS that is derived by the restricted pruning rulek is a decreasing function with respect to the node density n ˆ. For ˆn ≥ 30. it is found that the expected size is close to N / n ˆ. (3) It is proved that the lower bound on the expected size of a CDS for a Poissonian network of node density ˆn is given by 1 nˆ { − exp ( − ( nˆ − 1))} N. The second result is of paramount importance for practinˆ − 1 nˆ − 1 tioners. It provides the information about the expected size of a CDS when the node density ˆn is between 6 and 30. The data (expected CDS size) for this range can hardly be provided by simulations.
doi:10.1155/2009/958056 Research Article Evaluation of Physical Carrier Sense Based Backbone Maintenance in Mobile Ad Hoc Networks
, 2009
"... Physical carrier sensing has to date mainly been exploited for improving medium access control in wireless networks. Recently, a parallel algorithm striving to extensively exploit physical carrier sensing for constructing and maintaining a connected dominating set (CDS), which is also known as spann ..."
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Physical carrier sensing has to date mainly been exploited for improving medium access control in wireless networks. Recently, a parallel algorithm striving to extensively exploit physical carrier sensing for constructing and maintaining a connected dominating set (CDS), which is also known as spanner, backbone, or overlay network in wireless ad hoc networks with interference ranges larger than transmission ranges has been proposed. Existing evaluations of this algorithm are limited to theoretical asymptotic bounds and simulations of static networks. In this paper, we evaluate the physical carrier sensingbased CDS maintenance for mobile ad hoc networks through discrete event simulations. For a wide range of node speeds and node densities, we evaluate the CDS characteristics and message exchanges required for maintaining the CDS. We find that the algorithm maintains a stable leader set dominating all nodes in the network for a wide range of mobility levels but struggles to maintain connectivity at high mobility levels. We also quantify the portions of the control messages for CDS maintenance that are exchanged through physical carrier sensing. We find that the parallel algorithm manages to greatly reduce the reliance on intact message receptions. Copyright © 2009 Sapna Deval et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.
n̂−1 − n̂n̂−1 exp(−(n̂ − 1))
"... While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is present ..."
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While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is presented. Assume N nodes are deployed uniformly randomly in a square of size LN × LN (where N and LN → ∞), three results are obtained. (1) It is proved that the node degree distribution of such a network follows a Poisson distribution. (2) The expected size of a CDS that is derived by the restricted pruning rulek is a decreasing function with respect to the node density n̂. For n ̂ ≥ 30, it is found that the expected size is close to N/n̂. (3) It is proved that the lower bound on the expected size of a CDS for a Poissonian network of node density n ̂ is given by 1
Analysis on a Localized Pruning Method for Connected Dominating Sets
"... While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is present ..."
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While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is presented. Assume N nodes are deployed uniformly and randomly in a square of size LN × LN (where N and LN → ∞); three results are obtained. (1) It is proved that the node degree distribution of such a network follows a Poisson distribution. (2) The expected size of a CDS that is derived by the restricted pruning rulek is a decreasing function with respect to the node density ˆn. For ˆn ≥ 30, it is found that the expected size is close to N/ˆn. (3) It is proved that the lower bound � on the expected size of a �CDS 1 ˆn for a Poissonian network of node density ˆn is given by ˆn−1 − ˆn−1 exp(−(ˆn − 1)) N. The second result is of paramount importance for practitioners. It provides the information about the expected size of a CDS when the node density ˆn is between 6 and 30. The data (expected CDS size) for this range can hardly be provided by simulations.
Analysis on a Localized Pruning Method for Connected Dominating Sets
"... While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is present ..."
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While restricted rulek has been succeeded in generating a connected dominating set (CDS) of small size, not much theoretical analysis on the size has been done. In this paper, an analysis on the expected size of a CDS generated by such algorithm and its relation to different node density is presented. Assume N nodes are deployed uniformly and randomly in a square of size LN × LN (where N and LN → ∞); three results are obtained. (1) It is proved that the node degree distribution of such a network follows a Poisson distribution. (2) The expected size of a CDS that is derived by the restricted pruning rulek is a decreasing function with respect to the node density n ˆ. For ˆn ≥ 30. it is found that the expected size is close to N / n ˆ. (3) It is proved that the lower bound on the expected size of a CDS for a Poissonian network of node density ˆn is given by 1 nˆ { − exp ( − ( nˆ − 1))} N. The second result is of paramount importance for practinˆ − 1 n ˆ − 1 tioners. It provides the information about the expected size of a CDS when the node density ˆn is between 6 and 30. The data (expected CDS size) for this range can hardly be provided by simulations.