This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

The necessity for environmental protection has led to the development of different sensors for monitoring fuel pipelines to avoid explosion, leakage, and corrosion. Distributed optical fiber sensors-based Brillouin scattering have become progressively common because of their novel feature of simultaneously determining temperature, vibration, and strain. The stimulated Brillouin scattering (SBS) threshold power, sensing distance, single-mode fiber cable length, and sensing accuracy are serious parameters to the implementation of Brillouin optical time-domain reflectometry (BOTDR) sensor. In this study, remote sensing of multi-BOTDR fiber optic sensors for fuel pipeline monitoring is designed and presented. The results of simulations and theoretical explainations on suppressing fiber optic nonlinearity are analyzed. Results show that an SBS suppression via 16 dB is required to attain the maximum multi-remote sensing distance of up to 20 km for each network and a perfect frequency shift of 11 GHz. An open eye pattern corresponds to minimal signal distortion with a high Q-factor of 22 for the developed sensor. The highest sensing accuracy of 0.3318 %/K indicating power change (%) is observed from 290–300 K at 193.1 THz operating frequency.

Massive Multiple-Input Multiple-Output (MIMO) is a key enabling technology for fifth-generation (5G) and beyond wireless communication systems because of its ability to greatly enhance both spectral efficiency (SE) and energy efficiency (EE). However, maximizing these two performance metrics simultaneously remains a challenging multi-objective optimization problem due to the conflicting effects of the transmit power, antenna deployment, and circuit power consumption. This paper investigates the EE–SE trade-off in a downlink Massive MIMO system with Minimum Mean Square Error (MMSE) channel estimation (CE) and different linear combining and precoding techniques. A power optimization framework based on transmit power allocation and antenna configuration is analyzed to identify operating points that maximize EE while maintaining high SE. Performance analysis of the number of base station (BS) antennas in MIMO systems, user equipment density, transmit power, and inter-cell interference on system performance is evaluated through numerical simulations. The results demonstrate that appropriately selecting the number of transmit antennas and optimizing the transmit power significantly improve the EE–SE trade-off. Furthermore, although increasing the number of antennas enhances SE, EE exhibits a non-monotonic behavior because of the additional circuit power required by the radio-frequency hardware. The findings confirm that MMSE-based CE provides higher spectral efficiency than the MR, ZF, RZF, and S-MMSE schemes, albeit at increased computational complexity, offering useful design guidelines for energy-efficient Massive MIMO networks.