An Analytical Comparison of Factors Affecting the Performance of Ad Hoc Network.
ABSTRACT In this paper an analytical model is proposed to investigate and quantify the effects and interactions of node mobility, network
size and traffic load on the performance of ad hoc networks using AODV in terms of cost, average end-to-end delay and throughput.
The analytical results reveal that contrary to the traditional concept, performance of ad hoc networks is much more sensitive
to traffic load and network size than to node mobility. The capacity of ad hoc networks relies on the collective impact of
all three factors but not any one alone. Furthermore, NS-2 based simulations are carried out to verify the theoretical model.
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ABSTRACT: In this paper we evaluate several routing protocols for mobile, wireless, ad hoc networks via packet‐level simulations. The ad hoc networks are multi‐hop wireless networks with dynamically changing network connectivity owing to mobility. The protocol suite includes several routing protocols specifically designed for ad hoc routing, as well as more traditional protocols, such as link state and distance vector, used for dynamic networks. Performance is evaluated with respect to fraction of packets delivered, end‐to‐end delay, and routing load for a given traffic and mobility model. Both small (30 nodes) and medium sized (60 nodes) networks are used. It is observed that the new generation of on‐demand routing protocols use much lower routing load, especially with small number of peer‐to‐peer conversations. However, the traditional link state and distance vector protocols provide, in general, better packet delivery and end‐to‐end delay performance.Mobile Networks and Applications 01/2000; 5:179-189. DOI:10.1023/A:1019108612308 · 1.50 Impact Factor
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ABSTRACT: An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to the limited transmission range of wireless network interfaces, multiple network "hops" may be needed for one node to exchange data with another across the network. In recent years, a variety of new routing protocols targeted specifically at this environment have been developed, but little performance information on each protocol and no realistic performance comparison between them is available. This paper presents the results of a detailed packet-level simulation comparing four multi-hop wireless ad hoc network routing protocols that cover a range of design choices: DSDV, TORA, DSR, and AODV. We have extended the ns-2 network simulator to accurately model the MAC and physical-layer behavior of the IEEE 802.11 wireless LAN standard, including a realistic wireless transmission channel model, and present the results of simulations of networks of 50 mobile nodes.
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ABSTRACT: In the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users (i.e., a mobility model). This paper is a survey of mobility models that are used in the simulations of ad hoc networks. We describe several mobility models that represent mobile nodes whose movements are independent of each other (i.e., entity mobility models) and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e., group mobility models). The goal of this paper is to present a number of mobility models in order to offer researchers more informed choices when they are deciding upon a mobility model to use in their performance evaluations. Lastly, we present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Specifically, we illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.Wireless Communications and Mobile Computing 08/2002; 2(5). DOI:10.1002/wcm.72 · 1.29 Impact Factor