1,582 research outputs found
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks
Designing high performance channel assignment schemes to harness the
potential of multi-radio multi-channel deployments in wireless mesh networks
(WMNs) is an active research domain. A pragmatic channel assignment approach
strives to maximize network capacity by restraining the endemic interference
and mitigating its adverse impact on network performance. Interference
prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being
a crucial aspect that is seldom addressed in research endeavors. In this
effort, we propose a set of intelligent channel assignment algorithms, which
focus primarily on alleviating the RCI. These graph theoretic schemes are
structurally inspired by the spatio-statistical characteristics of
interference. We present the theoretical design foundations for each of the
proposed algorithms, and demonstrate their potential to significantly enhance
network capacity in comparison to some well-known existing schemes. We also
demonstrate the adverse impact of radio co- location interference on the
network, and the efficacy of the proposed schemes in successfully mitigating
it. The experimental results to validate the proposed theoretical notions were
obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n
environments.Comment: Accepted @ ICACCI-201
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
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