A socio-inspired CALM approach to channel assignment performance prediction and WMN capacity estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time-consuming. There is a conspicuous absence of reliable CA performance prediction metrics to assist in the selection of a high performing CA scheme for a WMN. Popularly used theoretical interference estimation metrics viz., CDAL(cost) and CXLSwt have certain flaws which we discuss in this work. We also elucidate the shortcomings of Total Interference Degree (TID) and propose a hypothesis explaining why it is not a reliable CA performance prediction tool. Besides, these metrics are unable to fulfill our ultimate objective of theoretically predicting the expected network capacity of a CA scheme deployed in a WMN, with high confidence. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of "a sui generis social reality", and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a Mixed Integer Non-linear Programming (MINLP) optimization model to determine the maximal network capacity of a WMN. Since the MINLP model does not run in polynomial time due to non-linear constraints, we design a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes consisting of topology preserving, graph preserving, and graph disrupting CA schemes. We compare CALM with three existing interference estimation metrics and demonstrate that it is consistently more reliable. CALM boasts of an accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing