As the human population grows, innovations such as virtual patients, vaccines, biotechnological machines, and microneedles offer solutions to global health crises. Deploying smart robots and support systems can help governments reduce public spending on future respiratory viruses, such as a NextGen Respiratory Virus or NeoCorona Virus. This paper presents a robot-based finite-time optimal control model to combat a hypothetical infectious disease, Pandemic-X, capable of causing a global pandemic. The system is optimised for Pandemic-X, integrating vaccination, robotic control, and incidence-rate dynamics to enhance public health emergency responses. Our approach consolidates optimal control strategies, including vaccination and Computational Internet of Things Robotics (CIoTR), in the post-COVID-19 era. Two strategies are proposed: Pontryagin stochastic optimisation for managing disease spread and a CIoTR-based control approach. The model maximises the susceptible and recovered populations while minimising exposed, asymptomatic, and symptomatic cases. The robot operates in three power modes: i) ‘super-active’ for high-computation edge inferencing and vaccination, ii) ‘moderate’ for balanced fallback operations, and iii) ‘sleep’ for idle states. Results show strong alignment between simulated and real data in vaccination, infection reduction, and hospitalisation trends. These findings demonstrate that robotic optimal control strategies can effectively manage pandemic spread, reduce healthcare burdens, and minimise transmission risk.

Legacy field protocols such as Fieldbus and HART provide deterministic communication for traditional process control; however, their limited bandwidth, scalability, and adaptability constrain reliable communication in large-scale, data-intensive Industrial IoT (IIoT) systems, particularly under high node density, mobility, and interference conditions. This paper presents a Hybrid Decode-Amplify-Forward (HDAF) protocol within a Fog-assisted cooperative relay architecture to address these limitations. The proposed HDAF scheme adaptively switches between Decode-and-forward (DF) and Amplify-and-Forward (AF) modes under Nakagami-m fading, leveraging cooperative Fog relay nodes to enable low-latency, and interference-resilient communication. The framework integrates low-power IEEE 802.15.4 (Zigbee) sensor networks with a 5G New Radio (NR) backhaul via dual-radio IoT-Fog gateways, facilitating traffic aggregation and Quality-of-Service (QoS) mapping. The software-defined coupling mechanism further supports real-time analytics and adaptive relay operation. Validation using theoretical analysis, MATLAB simulations, and field experiments conducted on a gas-processing-plant-inspired industrial testbed demonstrates up to a 95.8% reduction in Bit Error Rate (BER) and approximately 96% improvement in channel capacity compared with non-relay schemes. These results confirm that the proposed IoT-Fog HDAF protocol effectively extends coverage, mitigates interference, and provides a scalable and cost-effective solution for high-reliability, low-latency IIoT communications in dense industrial and 5G-enabled environments.

The proliferation of connected objects in the Internet of Things (IoT) ecosystem presents challenges in enabling real-time intelligence while safeguarding data privacy, especially within the computational and energy limitations of edge devices. This study, based on simulation and synthetic data collection methods, addresses these challenges by proposing a lightweight edge AI framework that employs federated learning, enabling model training across distributed Internet of People and Things (IoP) devices without transferring raw data to centralised servers. The framework integrates on-device inference, stochastic local updates, and model compression to ensure low-latency decision-making while adhering to memory and energy constraints. To enhance security, differential privacy mechanisms, encrypted aggregation, and robust outlier detection are utilized to defend against adversarial and Byzantine attacks. The proposed framework offers an effective solution for deploying federated intelligence on resource-constrained IoP devices, enabling responsive, privacy-preserving, and resilient edge AI operations in distributed environments.

Artificial intelligence (AI) and Machine Learning (ML) will play significant roles in 6G networks and beyond, making them intelligent, self-organising, and cost-effective. Network servers will house AI algorithms just like the application servers. 6G networks will be user-centric, leveraging the Internet of Things (IoT) to transform critical infrastructure into smart infrastructure. This study presents a notable use case of AI and IoT for 6G networks, specifically in coordinating peer-to-peer (P2P) energy trading within virtual microgrids (VMGs) of smart grids. The study deploys IoT technologies at the network edge to connect and collect energy trading data from P2P energy prosumers. The model also includes AI agents configured within the mobile edge computing (MEC) servers at the next-generation Node-B (gNB) of the 6G radio access network to adjust the cell size of VMGs, thereby enabling the discovery of more P2P energy prosumers. Results show that deploying AI agents in the 6G networks can minimise the operational expenditure (OPEX) of network operators and energy trading costs for consumers, while maximising the utility of energy prosumers.

A major barrier to the deployment of Virtual Power Plants (VPPs) with energy storage under Demand Response (DR) programs is the absence of a clear dynamic pricing (DP) framework that simultaneously addresses the objectives of all stakeholders (prosumers, VPP aggregator, and the grid) under a bidirectional energy flow. This work investigates a DP regime for a VPP integrating battery storage, aimed at optimizing stakeholder objectives in the day-ahead market. The UK national rolling demand data are employed to capture the time-varying nature of wholesale electricity prices. A Cumulative Performance Index (CPI) is used to quantify the VPP’s contribution to dynamic load leveling. A Genetic Algorithm (GA) is utilized to optimize the transaction of prices and energy exchanges for stakeholders’ welfare maximization. Results show that optimal prices and energy transactions within the DR framework depend strongly on stakeholder objective priorities. The price margin significantly influences the amount of financial rewards received by the stakeholders

Efficient sensing and autonomous decision-making in large-scale environments face challenges such as sensor noise, limited coverage, and dynamic environmental changes, often leading to suboptimal responses. This study proposes an AI-driven framework that integrates multi-sensor data fusion, adaptive learning, and utility-based decision-making to address these issues. The framework combines heterogeneous sensor data using weighted fusion, improving the accuracy and reliability of environmental state estimation. Adaptive learning mechanisms dynamically adjust the learning rate, optimizing system performance by reducing prediction errors and refining model parameters over time. The autonomous decision-making module selects optimal actions based on utility functions, ensuring timely and accurate decisions without human intervention. The system demonstrates significant improvements in sensor coverage efficiency and overall performance, effectively handling complex, data-intensive environments. This work highlights the framework's robustness and its capacity to deliver optimal decision-making and sensing capabilities, validating its applicability in real-world, dynamic scenarios. However, the study is based on simulation, and the results may not fully reflect real-world complexities or limitations. Future work should evaluate the framework's performance in practical, large-scale deployments and address potential scalability and real-time implementation challenges.

Artificial intelligence (AI) has become the game changer in smart grids-an enabler of network autonomy, self-healing, and reconfiguration. This study integrates AI and Internet of Things (IoT) to organise peer-to-peer (P2P) energy prosumers into virtual clusters without altering the physical topology of the power network. The aim is to enable an autonomous, scalable and dynamic virtual microgrids (VMG) by leveraging federated learning, agentic AI, AI agents, IoT, and cluster zooming to optimise P2P energy trading costs for prosumers and operational expenditure (OPEX) for network operators, depending on the number of prosumers available. The study employs a central controller AI to coordinate multiple local AI agents. Each AI agent resides in the network server and monitors energy trading traffic for each long-range wide-area network (LoRaWAN) gateway to optimise trading and OPEX costs via cluster zooming achieved by the spreading factor (SF) via adaptive data rate (ADR) mechanism of LoRaWAN. The agentic AI module in the cloud autonomously selects and adapts the network coverage based on SF, via the AI energy trading agent configured in the LoRaWAN access network server, to zoom the clusters (i.e., VMGs) in grid-connected and island modes. The study formulates an energy trading model connecting the physical (electrical) and virtual (telecom) distances and OPEX in the VMG. With agentic AI-assisted cluster zooming, over 70% of the energy is traded at lower SF. At the same time, the energy costs decrease by 40% in proportion to the network size and the number of prosumers. For the network operator, OPEX reduces by 21% and 38% in base-station power consumption. Ultimately, grid-connected prosumers pay higher charges than their off-grid counterparts. The agentic AI model in this study exemplifies a use case of the 3GPP model of the future 6G network.

5G networks increasingly rely on key enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV), Multi-Access Edge Computing (MEC), and end-to-end network slicing to deliver heterogeneous services with strict quality-of-service (QoS) guarantees. However, programmability, multi-tenancy, and distributed edge–cloud operation significantly expand the attack surface. At the same time, traditional rule-based and reactive security mechanisms remain slow to adapt and may violate latency constraints during mitigation. This paper addresses the problem of QoS-compliant, closed-loop security control for sliced SDN/NFV infrastructures. We propose an AI-assisted, cross-layer security orchestration framework that integrates epoch-wise telemetry with ML-based risk estimation and formalizes mitigation as a Constrained Markov Decision Process (CMDP). The CMDP controller selects enforceable actions— slice isolation, rate limiting, traffic rerouting, and key reconfiguration—while explicitly satisfying latency/overhead constraints, and executes them via SDN flow-rule updates and NFV policy/VNF reconfiguration. Simulation results over 50 decision epochs demonstrate effective response to an injected high-risk event: risk spikes to 0.95 at epoch 15, after which the controller drives risk toward ≈0.10 while maintaining latency below the 40ms QoS bound (with a transient rise during mitigation and subsequent stabilization). The reward trajectory briefly degrades during disruption but recovers and converges to a positive long-term return, indicating stable constraint-aware operation. This work provides (i) a deployable cross-layer orchestration architecture for sliced networks, (ii) a QoS-constrained CMDP decision model that converts risk signals into actionable SDN/NFV controls, and (iii) empirical evidence that adaptive mitigation can reduce security risk without sacrificing service guarantees.

Malaria mosquitoes, Anopheles, are well-known for carrying and spreading the malaria pathogens, known as Plasmodium. The public health challenge it brings has remained a global health challenge, of which the most robust control measures include mosquito-treated nets and electronic mosquito killer lamps. Due to health and cost problems, for example, in developing countries, these methods are not suitable for controlling mosquitoes and their plasmodiumic pathogens. In this study, we propose the use of two natural plant (e.g., Petiveria alliacea and Hyptis suavolens leaf) extracts that are cheap, ubiquitous, and effective for the control of mosquitoes, especially in temperate regions such as sub-Saharan Africa. On top of that, the study uses memory, non-locality, and fractal properties of fractal-fractional derivatives from compartmental modeling to capture susceptibility of infected persons, wider coverage, and heterogeneous breeding of mosquitoes, respectively, to evaluate the effectiveness of the two leaf extracts as natural larvicides against Anopheles mosquitoes. To measure the effectiveness of the two plant extracts in controlling malaria, this study develops a basic reproduction number model of Anopheles mosquitoes and evaluates the endemic points of the model. Comparing the results of larvicidal control with those of mosquito-treated nets, the proposed larvicidal control achieved 94.86% efficacy when applied alone and 96.83% efficacy when combined with mosquito nets, each outperforming mosquito nets (83.33%). These findings position compartmental fractal fractional-order modeling as an innovative tool for bioinformatic disease vector control. The study also presents a smart mosquito-net model where data collected from the host nodes on the performance of larvicides in mosquito and malaria control are transmitted via the Internet of Things infrastructure to the edge and cloud servers for computation, processing, artificial intelligence analytics, and policy-making.

The integration of edge-to-cloud infrastructures in smart grid (SG) data center networks requires scalable, efficient, and secure architecture. Traditional server-based SG data center architectures face high computational loads and delays. To address this problem, a lightweight data center network (DCN) with low-cost, and fast-converging optimization is required. This paper introduces a container-based time synchronization model (CTSM) within a spine–leaf virtual private cloud (SL-VPC), deployed via AWS CloudFormation stack as a practical use case. The CTSM optimizes resource utilization, security, and traffic management while reducing computational overhead. The model was benchmarked against five DCN topologies—DCell, Mesh, Skywalk, Dahu, and Ficonn—using Mininet simulations and a software-defined CloudFormation stack on an Amazon EC2 HPC testbed under realistic SG traffic patterns. The results show that CTSM achieved near-100% reliability, with the highest received energy data (29.87%), lowest packetization delay (13.11%), and highest traffic availability (70.85%). Stateless container engines improved resource allocation, reducing administrative overhead and enhancing grid stability. Software-defined Network (SDN)-driven adaptive routing and load balancing further optimized performance under dynamic demand conditions. These findings position CTSM-SL-VPC as a secure, scalable, and efficient solution for next-generation smart grid automation.

Internet of Things (IoT) of networked robots installed at the edges of smart healthcare infrastructure (SHI) can be used to mitigate infectious diseases. Such robots can predict pandemics, and screen, diagnose, treat or perform healthcare nursing for infectious diseases. When equipped with suitable digital technologies, these robots can mitigate epidemics and predict future pandemics more efficiently. This article proposes a co-infection model of infectious diseases, using HIV/AIDS and COVID-19 (or HAC-19) as examples, that can underlie SHI nodes (e.g., robots). The co-infection model benefits from the compartmental applications of fractional derivatives to healthcare problems. Six co-infection control parameters (e.g., awareness, counseling, COVID-19 safety protocol, COVID-19 vaccine, HIV/AIDS therapy, and COVID-19 treatment) are used to evaluate the effectiveness of the proposed model. The HAC-19 model uses a basic reproduction number to indicate the effectiveness of the control measures. When the control parameters are effective, the results show that the HAC-19 co-infection reduces to a minimum in the population. When the control measures are not effective, the HAC-19 co-infection will be endemic. Robots, equipped with IoT at the edge of the SHI, transfer the data from the trials to the outpost network nodes in the hospital and then to the cloud for further analytics and decision-making. The results of real-world trials at three hospital locations strongly agree with the theoretical model.

ABSTRACT Conventional laboratory investigation of rotavirus infection and its antigen in rectal swabs from infected persons uses Electron microscopy (EM) (i.e., non‐acute cases), genome, and antigen‐detecting assays. A recent update involves sorting, trapping, concentrating, and identifying infectious rotavirus particles in clinical samples leveraging activated magnetic microparticles with monoclonal antibodies. However, the routine detection of rotavirus in many specimens using the EM approach is laborious, costly, and requires highly skilled workers. A sustainable healthcare system should leverage the Internet of Things to operate Smart Health Infrastructures (SHI) for predictive control of contagious diseases such as the rotavirus. This paper proposes a biomedical model for predictive control of the virus spread based on Susceptible, Breastfeeding, Vaccinated, Infected, and Recovered (SBVIR) parameters. We introduce breastfeeding, vaccination, and saturated incidence rate variables to deconstruct the transmission dynamics. An efficiency test is conducted using RI control parameters B and V. Applying Lyapunov function analysis, we prove that the global stability of disease‐free and endemic equilibria exists under breastfeeding and vaccination conditions when the primary reproduction number is less than unity. Numerical simulation results show that breastfeeding and vaccination are optimal with SBVIR compared to SVIR, SBIR, and SIR parameters for rotavirus infection control by 99%, 26%, 19%, and 18%, respectively. On top of these, we show that the SBVIR model strongly agrees with real‐world data and can be used to forecast the infected population in a production health facility. Finally, we show multiple Internet of Things applications in SHI to control rotavirus transmission effectively.

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Infectious diseases like coronavirus disease 2019 (COVID-19) have remained a primary public and global health concern. Internet of Things (IoT) of networked robots and physiological intervention can be combined to identify and control the spread of the different variants of COVID-19 disease. With this approach, governments and healthcare institutions can plan for such diseases in the future. This article presents a compact computational model (CCM) to identify and control different COVID-19 variants using IoT-networked robots. The CCM comprises seven physiological variables (PVs) and robotic identification (RI) of infected individuals as alternative intervention strategies. This study uses Market Place Service Robots that correctly identify PV and RI for positively infected individuals. The conditions of the existence and the solution of the deterministic model are derived from a compact flow architecture that we develop. We show that the model has COVID-19-free equilibrium and endemic equilibrium. While PV with appropriate isolation and hospital treatment reduces the COVID-19 disease impact by 19% more than RI alone, this study also shows that combining two PV with RI minimizes the impact better than PV or RI alone, by 36% and 43%, respectively. When the PV control parameters are increased, up to 5, in the presence of IoT and RI, up to 99.99% improvement is seen. With all seven PV control parameters in the presence of IoT and RI, the proposed CCM guarantees an infection-free population.

We propose a biodynamic model for managing waterborne diseases over an Internet of Things (IoT) network, leveraging the scalability of LoRa IoT technology to accommodate a growing human population. The model, based on fractional order derivatives (FOD), enables smart prediction and control of pathogens that cause waterborne diseases using IoT infrastructure. The human-pathogen-based biodynamic FOD model utilises epidemic parameters (SVIRT: susceptibility, vaccination, infection, recovery, and treatment) transmitted over the IoT network to predict pathogenic contamination in water reservoirs and dumpsites in Iji-Nike, Enugu, the study community in Nigeria. These pathogens contribute to person-to-person, water-to-person, and dumpsite-to-person transmission of disease vectors. Five control measures are proposed: potable water supply, treatment, vaccination, adequate sanitation, and health education campaigns. A stable disease-free equilibrium point is found when the effective reproduction number of the pathogens, R0eff<1 and unstable if R0eff>1. While other studies showed a 98.2% reduction in infections when using IoT alone, this paper demonstrates that combining the SVIRT epidemic control parameters (such as potable water supply and health education campaign) with IoT achieves a 99.89% reduction in infected human populations and a 99.56% reduction in pathogen populations in water reservoirs. Furthermore, integrating treatment with sanitation results in a 99.97% reduction in infected populations. Finally, combining these five control strategies nearly eliminates infection and pathogen populations, demonstrating the effectiveness of multifaceted approaches in public health and environmental management. This study provides a blueprint for governments to plan sustainable smart cities for a growing population, ensuring potable water free from pathogenic contamination,in line with the United Nations Sustainable Development Goals #6 (Clean Water and Sanitation) and #11 (Sustainable Cities and Communities).

Based on the characteristics of the 5 G standard defined in Release 17 by 3GPP and that of the emerging Beyond 5 G (or the so-called 6 G) network, cyber-physical systems (CPSs) used in smart transport network infrastructures, such as connected autonomous vehicles (CAV), will significantly depend on the cellular networks. The 5 G and Beyond 5 G (or 6 G) will operate over millimetre-wave (mmWave) bands. These network standards require suitable path loss (PL) models to guarantee effective communication over the network standards of CAV. The existing PL models suffer heavy signal losses and interferences at mmWave bands and may not be suitable for cyber-physical (CP) signal propagation. This paper develops an Agile Gravitational Search Algorithm (AGSA) that mitigates the PL and signal interference problems in the 5G–NR network for CAV. On top of that, a modified Okumura-Hata model (OHM) suitable for deployment in CP terrestrial mobile networks is derived for the CAV-CPS application. These models are tested on the real-world 5 G infrastructure. Results from the simulated models are compared with measured data for the modified, enhanced model and four other existing models. The comparative evaluation shows that the modified OHM and AGSA performed better than existing OHM, COST, and ECC-33 models by 90%. Also, the modified OHM demonstrated reduced signal interference compared to the existing models. In terms of optimisation validation, the AGSA scheme outperforms the Genetic algorithm, Particle Swarm Optimisation, and OHM models by at least 57.43%. On top of that, the enhanced AGSA outperformed existing PL (i.e., Okumura, Egli, Ericson 999, and ECC-33 models) by at least 67%, thus presenting the potential for efficient service provisioning in 5G-NR driverless car applications.

This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems.

The quest for novel antioxidant and anti-inflammatory medications from medicinal plants is crucial since the plants contain bioactive compounds with a better efficacy and safety profile than orthodox therapy. This study harnesses the capabilities of mechatronics-driven Agilent Gas Chromatography, deploying in vitro, in vivo, and in silico models to unravel the antioxidant and anti-inflammatory attributes within Combretum paniculatum ethanol extract (CPEE). Employing gas chromatography-mass spectroscopy (GC-MS), our analysis efficiently segregates and evaluates volatile compound mixtures, a technique renowned for identifying organic compounds, as exemplified by its success in detecting fatty acids in food and resin acids in water. Using gas chromatography-mass spectrometry (GC-MS) and GC-FID analyses, this paper ascertains the comprehensive phytochemical composition of CPEE. Also, Molecular interactions of identified compounds with cyclooxygenase (COX-2) implicated in inflammatory urpsurge is verified. GC-MS and GC-FID analyses unveil 41 phytoconstituents within CPEE. Based on the in vitro research, CPEE demonstrated potential in inhibiting thiobarbituric acid-reactive substances, nitric oxide, and phospholipase lipase A2 with inhibition rates of 2.284, 6.547, and 66.8 μg/mL respectively. In vivo experiments confirm CPEE's efficacy in inhibiting granuloma tissue formation, lipid peroxidation, and neutrophil counts compared to untreated rats. Moreover, CPEE elicited a significant (P < 0.05) increase in the activities of SOD, CAT, and GSH concentrations while decreasing C-reactive protein, signifying promising therapeutic potential. Highlighting interactions between top-scoring phytoligands (epicatechin, catechin, and kaempferol) and COX-2, the findings underscore their drug-like characteristics, favorable pharmacokinetics, and enhanced safety toxicity profiles. Results from in vitro, in vivo, and in silico studies, highlights CPEE remarkable antioxidant and anti-inflammatory potentials.

The transportation industry, followed by the energy sector, emits the highest amount of carbon, which negatively impacts the 2030 United Nations Sustainable Development Goals and slows down the 2050 Net-Zero target. In the UK, 78% of every household owns at least one car, thus worsening the carbon footprint statistics, including the health risks associated with the frequent use of cars. Braking of such vehicles loses the kinetic energy environment. Sustainable transport, such as cycling on pedal bicycles, can be strenuous, leading to exhaustion, fainting, accidents, or deaths. Electric- powered bicycles (or E-bikes) can mitigate these problems as the users can engage in moderate exercise void of the risks above. However, E-bikes expend the energy stored in batteries and may not suffice for distant travel. Regenerative electric braking (REB) in E-bikes can store the would-have-lost braking energy in supercapacitors to extend the travel range, battery life and efficiency. Our experimental study shows a 4.14% net gain in energy efficiency when supercapacitors are used with REB. On top of that, we show that a lightweight, low-volume REB system could be constructed at a low cost, showing that future E-bikes with a similar system could be financially viable.