Wireless Network Design: Optimization Models and Solution ...

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Chapter 11 Improving Network Connectivity in Ad Hoc Networks Using Particle Swarm Optimization and Agents Abdullah Konak, Orhan Dengiz, and Alice E. Smith Abstract In a mobile ad hoc network (MANET) the nodes can serve as both routers and hosts and can forward packets on behalf of other nodes in the network. This functionality allows the MANET to form an instant, autonomous telecommunication network without an existing infrastructure or a central network control. This chapter introduces a dynamic MANET management system to improve network connectivity by using controlled network nodes called agents. Agents have predefined wireless communication capabilities similar to the other nodes in the MANET. However, the agents’ movements, and thus their locations, are dynamically determined to optimize network connectivity. A particle swarm optimization (PSO) algorithm is used to choose optimal locations of the agents during each time step of network operation. 11.1 Introduction Mobile wireless ad hoc networks (MANET) are instantaneous, autonomous telecommunication networks that provide service to users wherever and whenever the service is needed. The communication depends on wireless links that are formed between the mobile nodes. A link is established between two nodes if they are within each other’s wireless communication range. A MANET topology is dynamic and ex- Abdullah Konak Department of Information Sciences and Technology, Penn State Berks, Tulpehocken Road P.O. Box 7009, Reading, PA 19610-6009, USA, e-mail: auk3@psu.edu Orhan Dengiz DnD Technical Solutions, Tunus Cad. 87-5, Kavaklidere Ankara 06680, Turkey, e-mail: orhan.dengiz@gmail.com Alice E. Smith Department of Industrial and Systems Engineering, 3301 Shelby Center, Auburn University, Auburn, AL 36849, USA, e-mail: smithae@auburn.edu J. Kennington et al. (eds.), Wireless Network Design: Optimization Models and Solution Procedures, International Series in Operations Research and Management Science 158, DOI 10.1007/978-1-4419-6111-2_11, © Springer Science+Business Media, LLC 2011 247
248 Abdullah Konak, Orhan Dengiz, and Alice E. Smith pected to change often and unpredictably due to arbitrary movements of its nodes. Therefore, a MANET might be disconnected intermittently during its mission time. This chapter introduces an approach to maintain global connectivity in MANETs by using controlled MANET nodes, called agents. The function of an agent node is to augment network connectivity. During a mission time of a MANET, the user nodes are assumed to move freely. However, their current and past location data are available, and a mobility prediction model can be used to predict their future locations. Based on the predicted locations of user nodes, the agent nodes are directed to move to new locations to improve connectivity among the user nodes. Although networks operate in continuous time, to make the optimization tractable, the continuous time operation is sliced into T discrete time steps. Let Gt(Nt,Et) denote a MANET with node set Nt and arc set Et at time t. Node set Nt includes two types of nodes: user nodes (UNt) and agent nodes (ANt). User nodes are nodes that demand network services. Agent nodes are responsible for helping the user nodes experience the best network service possible. At the beginning of time t, it is assumed that the current and past locations (xik,yik) are known for all nodes i ∈ UNt and k = 1,...,t. The decision problem at time t is to determine the best new location (x i(t+1),y i(t+1)) for each agent node i ∈ ANt to maximize a measure of network connectivity. Note that it will take some time for agent nodes to move from their current locations at the beginning of time t to their new locations, and during this time user nodes may also move to new positions. Between time t and t +1, each agent node i moves to its new location (i.e., (x i(t+1),y i(t+1))). It is assumed that at the beginning of each time period, new location data becomes available, and the new best locations of agent nodes should be determined again. Therefore, the decision problem should be solved as quickly as possible when new location data becomes available at the beginning of each time period, and the decisions about agents’ next locations should be propagated to them. This chapter presents a particle swarm optimization (PSO) algorithm to direct the motion of the mobile agents in MANETs where the global state of the network can be tracked using a GPS tracking system. In GPS tracking systems, each node is equipped with a GPS receiver, and the location data is periodically transmitted to a central location using low frequency radio or a satellite modem embedded in the node [17]. The proposed approach in this chapter includes a mobility prediction model to predict the future locations of the nodes, and the PSO algorithm determines the new locations of the agent nodes based on this prediction. The proposed PSO algorithm is tested under dynamic scenarios using a simulation environment. Among application areas of the proposed approach are network-centric warfare, fleet tracking, and search and rescue operations. 11.2 Background In the literature, several approaches have been proposed to address the challenges in MANETs due to unpredictable user node movements. One of the major problems
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International Series in Operations
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Editors Jeff Kennington Eli Olinick
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vi Preface assistance. The work of
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viii Contents 3.4.1 Experiments to
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x Contents 8.3.2 The Solution Proce
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xii Contents References . . . . . .
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List of Contributors Khaldoun Al Ag
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List of Contributors xvii Nitin Sal
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Chapter 1 Introduction to Optimizat
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1 Introduction to Optimization in W
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1 Introduction to Optimization in W
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Part I Background
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10 Dinesh Rajan The goal of this ch
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12 Dinesh Rajan The source encoder
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14 Dinesh Rajan ping (FH) CDMA, and
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16 Dinesh Rajan of SNR is given in
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18 Dinesh Rajan 2.3 Information The
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20 Dinesh Rajan Perror ≤ e −nE
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22 Dinesh Rajan For instance, for t
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24 Dinesh Rajan 2.4.1 Block Codes I
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26 Dinesh Rajan B(z) = 1 � (1 + z
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28 Dinesh Rajan layers. Two of the
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30 Dinesh Rajan nel can support a p
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32 Dinesh Rajan 2.5.2 Physical laye
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34 Dinesh Rajan matched filter for
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36 Dinesh Rajan codes) leads to bet
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38 Dinesh Rajan a length N Inverse
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40 Dinesh Rajan the transmission of
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42 Dinesh Rajan setting, a node may
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44 Dinesh Rajan weighting of the si
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46 Dinesh Rajan 29. Oestges, C., Cl
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48 K. V. S. Hari terrain of operati
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50 K. V. S. Hari fading is calculat
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54 K. V. S. Hari Fig. 3.1 Normalize
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56 K. V. S. Hari Table 3.3 Impulse
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58 K. V. S. Hari where m antennas a
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60 K. V. S. Hari 3.5.2.4 Dominant R
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62 K. V. S. Hari March, 1984-13 Sep
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64 K. V. S. Hari 47. Van Rees, J.:
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Part II Optimization Problems for N
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102 Eli Olinick carrying capacity.
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104 Eli Olinick 93 85 160 16 38 21
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106 Eli Olinick Amaldi et al. [7] p
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110 Eli Olinick gmℓ ∑ ∑ m∈M
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112 Eli Olinick Using bk to denote
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114 Eli Olinick 5.3.5 Additional De
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118 Eli Olinick ficult optimization
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122 Eli Olinick solution times. Cai
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124 Eli Olinick 27. Glover, F.: Tab
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Chapter 6 Optimization Based WLAN M
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6 Optimization Based WLAN Modeling
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Chapter 7 Spectrum Auctions Karla H
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7 Spectrum Auctions 149 full value.
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7 Spectrum Auctions 151 also decide
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7 Spectrum Auctions 153 disclosed t
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7 Spectrum Auctions 157 bidders to
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7 Spectrum Auctions 159 in the regi
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7 Spectrum Auctions 163 pays, since
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7 Spectrum Auctions 167 and constra
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7 Spectrum Auctions 171 bidder on a
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7 Spectrum Auctions 175 21. Dunford
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Chapter 8 The Design of Partially S
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8 The Design of Partially Survivabl
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298 Hang Jin and Li Guo (ISI) as lo
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300 Hang Jin and Li Guo ARQ (HARQ)
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302 Hang Jin and Li Guo 13.3 Radio
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304 Hang Jin and Li Guo Table 13.2
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Chapter 14 Cross Layer Scheduling i
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14 Cross Layer Scheduling in Wirele
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15 Major Trends in Cellular Network
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