2 research outputs found
Near Optimal Channel Assignment for Interference Mitigation in Wireless Mesh Networks
In multi-radio multi-channel (MRMC) WMNs, interference alleviation is
affected through several network design techniques e.g., channel assignment
(CA), link scheduling, routing etc., intelligent CA schemes being the most
effective tool for interference mitigation. CA in WMNs is an NP-Hard problem,
and makes optimality a desired yet elusive goal in real-time deployments which
are characterized by fast transmission and switching times and minimal
end-to-end latency. The trade-off between optimal performance and minimal
response times is often achieved through CA schemes that employ heuristics to
propose efficient solutions. WMN configuration and physical layout are also
crucial factors which decide network performance, and it has been demonstrated
in numerous research works that rectangular/square grid WMNs outperform random
or unplanned WMN deployments in terms of network capacity, latency, and network
resilience. In this work, we propose a smart heuristic approach to devise a
near-optimal CA algorithm for grid WMNs (NOCAG). We demonstrate the efficacy of
NOCAG by evaluating its performance against the minimal-interference CA
generated through a rudimentary brute-force technique (BFCA), for the same WMN
configuration. We assess its ability to mitigate interference both,
theoretically (through interference estimation metrics) and experimentally (by
running rigorous simulations in NS-3). We demonstrate that the performance of
NOCAG is almost as good as the BFCA, at a minimal computational overhead of
O(n) compared to the exponential of BFCA
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. 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 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. We compare CALM with three existing
interference estimation metrics, and demonstrate that it is consistently more
reliable. CALM boasts of 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