Analysis of TCP performance over mobile ad hoc networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Characterizing the interaction between routing and MAC protocols in ad-hoc networks
Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Energy concerns in wireless networks
IEEE Wireless Communications
Factor interaction on service delivery in mobile ad hoc networks
IEEE Journal on Selected Areas in Communications
MATH'08 Proceedings of the 13th WSEAS international conference on Applied mathematics
Locally proactive routing protocols
ADHOC-NOW'10 Proceedings of the 9th international conference on Ad-hoc, mobile and wireless networks
A variable strength interaction test suites generation strategy using Particle Swarm Optimization
Journal of Systems and Software
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The performance of mobile ad hoc networks can be influenced by numerous factors, including protocol design at every layer; parameter settings such as retransmission limits and timers; system factors such as network size and traffic load; as well as environmental factors such as channel fading. In this work, we are concerned with understanding the functional relationship between these influential factors and performance of mobile ad hoc networking systems. We show how a systematic statistical design of experiments (DOE) strategy can be used to analyze network system and protocol performance, leading to more objective conclusions valid over a wide range of network conditions and environments. Using a DOE strategy and a 2k factorial design, we quantify the main and interactive effects of five factors (i.e., network density, node mobility, traffic load, network size, and medium access control scheme) on two response metrics (i.e., packet delivery ratio and end-to-end delay). Using these effects measures, we then develop two first-order linear regression models that define the functional relationship between the influential factors and two performance metrics.