An Approximate Model for Analyzing Real-World Wireless Network Scalability

We present a framework for non-asymptotic analysis of real-world multi-hop wireless networks that captures protocol overhead, congestion bottlenecks, traffic heterogeneity and other real-world concerns. The framework introduces the concept of symptotic 1 scalability to determine the number of nodes to which a network scales, and a metric called change impact value (CIV) for comparing the impact of underlying system parameters on network scalability. A key idea is to divide analysis into generic and specific parts connected via a signature – a set of governing parameters of a network scenario – such that analyzing a new network scenario reduces mainly to identifying its signature. Using this framework, we present the first closed-form symptotic scalability expressions for line, grid, clique, randomized grid and mobile topologies. We model both TDMA Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-09-20053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce & distribute reprints for Government purposes notwithstanding copyright notation here on.. Ram Ramanathan Raytheon BBN Technologies E-mail: [email protected] Ertugrul Ciftcioglu, Tom La Porta Pennsylvania State University E-mail: (enc118 | tlp)@psu.edu Abhishek Samanta Northeastern University E-mail: [email protected] Rahul Urgaonkar IBM Research E-mail: [email protected] 1 The word “symptotic” is not part of the English lexicon. We borrow from the commentary of Euclid’s Elements by Proclus [1] “.. some are asymptotic, namely, those which however far extended never meet, and others that do intersect are symptotic..” and 802.11, as well as unicast and broadcast traffic. We compare the analysis with discrete event simulations and show that the model provides sufficiently accurate estimates of scalability. We show how our impact analysis methodology can be used to progressively tune network features to meet a scaling requirement. We uncover several new insights, for instance, on the limited impact of reducing routing overhead, the differential nature of flooding traffic, and the effect real-world mobility on scalability.