TY - JOUR
T1 - A socio-inspired CALM approach to channel assignment performance prediction and WMN capacity estimation
AU - Kala, Srikant Manas
AU - Sathya, Vanlin
AU - Kumar Reddy, M. Pavan
AU - Lala, Betty
AU - Tamma, Bheemarjuna Reddy
N1 - Funding Information:
Interference estimation is an NP-hard problem which puts certain limitations on the role of a theoretical interference estimate. At best, an interference estimate may exhibit high conformance to the expected correlation with experimentally observed performance of a CA deployed in a WMN. Further, due to the three-dimensional nature of interference which includes the unpredictable temporal characteristics, it is not feasible to predict CA performance with absolute confidence. In this context, CALM proves to be a reliable CA prediction and interference estimation metric, by offering accuracy of over 90% with respect to network capacity, over a large CA test-set of forty CA schemes. Its probabilistic link-conflict estimation mechanism, combined with an algorithm design inspired by sociological theory, enables it to produce fairly reliable estimates even for CA schemes that disrupt graph topology. Reduction in prediction errors and high accuracy of CALM substantiates the two corollaries proposed in Subsection 5.2.2 . It also validates the proposed Sociological Ideas Borrowing Mechanism , as the performance of CALM demonstrates a successful application of sociological concepts in wireless networks. Further, CALM enables NETCAP to generate an accurate estimate of network capacity as NETCAP offers above 90% accuracy on an average, in two out of three test-cases. Thus, CALM and NETCAP create a robust and reliable framework for interference estimation and CA performance prediction, that works for all types of CA schemes. It simplifies and streamlines the task of appropriate CA selection for a WMN, and also offers an estimate of expected network capacity. Our work also paves the way for a greater and simplified borrowing of abstract ideas from Sociology, to wireless network paradigms in particular, and computer networks in general. In this work, we have tested CALM and NETCAP on a planned GWMN environment, and we plan to extend this framework to a random or unplanned WMN. We also intend to introduce ideas of fairness through the use of Link-Weights generated by CALM. Although CDAL cost does offer a measure of absolute fairness in channel allocation and is used to test a fairness index ( Qu, 2018 ), absolute fairness is not truly reflective of network performance ( SongYe, 2005 ). Thus, we plan to assess Qualified Fairness of a CA scheme wherein fairness in channel allocation to links is dovetailed with the impact of interference. We also plan to improve upon CALM and create a topology independent CAPP metric ( Manas Kala et al., 2018 ). Further, In a 5G network, the cellular operators make use of the unlicensed spectrum, i.e., LAA/LTE-U in the 5 Ghz band to serve the indoor users. Hence, we intend to use both CALM and NETCAP efficiently in 5G deployments as well. Srikant Manas Kala received his M.Tech degree in Computer Science and Engineering from Indian Institute of Technology, Hyderabad (IIT-H). Soon after, he co-founded Publicator, a platform aimed at encouraging research projects at the undergraduate level. He has earlier worked as a Mainframe developer with Infosys Technologies Ltd. At present, he is working as a trustee in the PDGRB trust, a non-profit organization that supports the education of the girl child in Uttarakhand, India. Being an alumni of the prestigious Rashtriya Indian Military College, he is a mountaineering and adventure sports enthusiast. He has been awarded the IIT-Hyderabad Research Excellence Award for two consecutive years in 2016 and 2017, and Employee Excellence Award by In- fosys in 2012. He won the best-paper award in ICACCI-2015. His research interests lie in the domains of wireless mesh networks, proximity centric mobile networks, and application of social theory in wireless networks. Vanlin Sathya is currently a post-doctoral researcher at University of Chicago, USA. From August 2016 to October 2018, he was a project officer in the Department of Computer Science and Engineering at IIT Hyderabad, India and he obtained his PhD degree from the same Institute. He received his Master of Engineering in Mobile and Pervasive Computing (2011) from Velammal Engineering College (Anna Univ) and Bachelor of Engineering in Computer Science (2009) from St Peters Engineering College (Anna Univ), India. Being a part of the Networked Wireless Systems (NEWS) Lab in IITH, his research interests includes Interference Management, handover in Heterogeneous LTE Network, Device to Device communication in Cellular Network, Cloud Base Station and Phantom cell (LTE-B), and LTE in unlicensed (LTE-U). Currently his focus is to design an efficient resource allocation and power control algorithm to boost the indoor data rate and spectral efficiency in enterprise building environment. He is a member of IEEE and he has published over 25 paper in IEEE conferences. M. Pavan Kumar Reddy is a B.Tech graduate from Indian Institute of Technology, Hyderabad (IIT-H). Since graduation, he has been working with Qualcomm India. He was the runner up in Sir C.V. Raman Talent test. He has held the office of Sports Secre-tary, Student Gymkhana at IIT-H. His research interests are in the areas of wireless mesh networks and Android development. Betty Lala is currently a research scholar at Department of Energy and En-vironmental Engineering (EEE), Interdisciplinary Graduate School of Engi-neering Sciences (IGSES), Kyushu University. She received a Masters degree in architecture from Indian Institute of Technology, Roorkee. She has carried out research work at The Hermann Rietschel Institute (HRI), Department of Building Energy Systems at the Technical University of Berlin, Germany. She worked as a professional Architect for 2 years, prior to joining the Faculty of Architecture, Manipal University, as Assistant Professor, where she taught for over 3 years. Her interests include architectural design, thermal comfort in buildings, vernacular architecture and technology, architectural history, and social theory. Dr. Bheemarjuna Reddy Tamma is an Associate Professor in the Dept. of Computer Science and Engineering at IIT Hyderabad. He obtained his Ph.D. degree from IIT Madras, India in 2007 and then worked as a post-doctoral fellow at the University of California San Diego (UCSD) division of California Institute for Telecommunications and Information Technology (CALIT2) prior to taking up faculty position at IIT Hyderabad, India in 2010. His research interests are in the areas of Converged Cloud Radio Access Networks, 5G, SDN, IoT/M2M, and Green ICT and network secu-rity. He has published over 100 articles in refereed international journals and conferences. Dr. Reddy is a recipient of Visvesvaraya Young Faculty Research Fellowship at IIT Hyderabad and iNautix Research Fellowship for his Ph.D. tenure at IIT Madras. He is a co-recipient of Top Cited Article Award from Elsevier publishers and Best Paper award at IEEE ICACCI 2015 conference. He is a member of IEEE and served as a General co-chair for National Conference on Communications (NCC) 2018, TPC co-chair for IEEE ANTS 2015, a TPC vice chair for IEEE ANTS 2014 and a Ph.D. student forum co-chair for IEEE ANTS 2013 conferences. He was a Co-PI of MEITY (Ministry of Electronics & IT, Govt. of India) funded research projects: Cyber Physical System Innovations Hub and Converged Cloud Communication Technologies at IIT Hyderabad. He also led some industry (Saankhya Labs, Bangalore, Uurmi systems, Hyderabad, India and KDDI Labs, Japan) funded consultancy/research projects on Wireless Networks as the PI at IIT Hyderabad.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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., CDALcost, 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.
AB - 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., CDALcost, 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.
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U2 - 10.1016/j.jnca.2018.10.002
DO - 10.1016/j.jnca.2018.10.002
M3 - Article
AN - SCOPUS:85055339855
VL - 125
SP - 42
EP - 66
JO - Journal of Network and Computer Applications
JF - Journal of Network and Computer Applications
SN - 1084-8045
ER -