Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the “quality of service” (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.

This paper presents a novel model predictive control (MPC) approach for suppressing circulating currents in MMC-based HVDC systems. The proposed MPC eliminates the need for PI-regulators and pulse width modulators, resulting in improved dynamic response and controllability. The methodology demonstrates exceptional efficacy in controlling output current and addressing voltage ripple concerns associated with sub module (SM) capacitors. An innovative, communication-free fault ride-through (FRT) method is also introduced, eliminating the need for a DC chopper and ensuring rapid recovery following faults. To overcome the computational challenges associated with the traditional MPC algorithm, an aggregate model of the MMC is proposed, significantly reducing predicted states, hardware requirements, and calculations. Simulations validate the robustness of the proposed MPC control algorithm in tracking AC side current, suppressing circulating current, and regulating capacitor voltages under various scenarios. Future research will explore system expansion, integration with renewable energy sources, and hardware-in-loop setup testing for further validation.

In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code–Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., $DOP\le 2$). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation.

Traditional land registry systems have been sub- ject to duplicated entries, manipulation of land documents, and possible risk of legal dispute in urban areas of Sindh, Pakistan. Growing urban population demands preservation of land documentation with efficacy, reliability and transparency. Blockchain based systems have emerged in recent years as a promising solution to ensure immutable and duplication free on- chain storage of records in the form encrypted hashes. In this study, we have demonstrated the integration of blockchain and artificial intelligence for ensuring digitized land record system for province Sindh. We developed the experimental and analytical framework comprising an Ethereum-based smart contract and machine learning based verification mechanism to eradicate prevalent hindrances on land record management. We leverage experimental methodology with real Pakistani naming norms, geographical distributions across ten Sindh districts, utilized the dataset of 2000 residential records, having induced inconsistent records (duplicates: 2.0%, fraudulent entries: 1.85%, ambiguous data: 3.0%). AI layer application was accomplished through a random forest classifier pre-screened records with 95.2% accuracy, preventing dubious entries from being registered on the blockchain. The implementation ensures maintaining immutable records, successfully prevented the duplication and transparency was achieved with public availability of data records. With improved security, transparency, and fraud resistance, the system architecture provides a significant pathway for digitizing urban land records.

In the era of industrial evolution satellites are being viewed as swarm intelligence that do not relies on single system but multiple constellations collaborate autonomously. This has enhanced the potential of the GNSS system to contribute in the improved position, navigation and timing (PNT) services. However, Multipath (MP) and Non-line-of-sight (NLOS) receptions remain the prominent vulnerability for GNSS in harsh environments. The aim of this research is to investigate its impact of MP and NLOS on GNSS performance and then proposed a Received Signal Quality Monitoring (RSQM) algorithm. RSQM works twofold: Initially it performs a signal quality test based on fuzzy inference system. The input parameters are Carrier-to-Noise ratio (CNR), Range Residuals (RR) and Code-Carrier Divergence (CCD) and it computes the membership functions based on mamdani method and classify the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements, while considering the satellite geometry constraints (i.e., DOP≤2).. For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configuration (GPS,BeiDou, GLONASS and Galileo) has been carried out in various operating environments. This RSQM based GNSS receiver has capability to identify signal quality and perform adaptive navigation accordingly to improve the navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4m to 2.3m averagely in the complex urban environment. Accumulating RSQM with GNSS has great potential for future industrial revolution (Industry 5.0) making things automatic and sustainable like autonomous vehicle operation.