179 research outputs found
Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster
Reconfigurable intelligent surfaces (RISs) have been introduced to improve
the signal propagation characteristics by focusing the signal power in the
preferred direction, thus making the communication environment "smart". The
typical use cases and applications for the "smart" environment include beyond
5G communication networks, smart cities, etc. The main advantage of employing
RISs in such networks is a more efficient exploitation of spatial degrees of
freedom. This advantage manifests in better interference mitigation as well as
increased spectral and energy efficiency due to passive beam steering.
Challenging environments comprise a range of scenarios, which share the fact
that it is extremely difficult to establish a communication link using
conventional technology due to many impairments typically associated with the
propagation medium and increased signal scattering. Although the challenges for
the design of communication networks, and specifically the Internet of Things
(IoT), in such environments are known, there is no common enabler or solution
for all these applications. Interestingly, the use of RISs in such scenarios
can become such an enabler and a game changer technology. Surprisingly, the
benefits of RIS for wireless networking in underwater and underground medium as
well as in industrial and disaster environments have not been addressed yet. In
this paper, we aim at filling this gap by discussing potential use cases,
deployment strategies and design aspects for RIS devices in underwater IoT,
underground IoT as well as Industry 4.0 and emergency networks. In addition,
novel research challenges to be addressed in this context are described.Comment: 16 pages, 13 figures, submitted for publication in IEEE journa
An accurate RSS/AoA-based localization method for internet of underwater things
Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes’ locations effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the estimation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed estimator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer–Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accuracy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors
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