Journal of Future Internet and Hyperconnectivity (e-ISSN: 3048-9210)

An Autonomous Smart Water Grid Integrating IoT Sensors, Edge AI, Self-Healing Mechanisms, Fair Distribution Algorithms, and Predictive Water Management

B. Ramalakshmi — 2026-06-02

This paper presents an advanced Autonomous Smart Water Grid that integrates Internet of Things (IoT) sensors with artificial intelligence (AI)-based decision systems to enhance water management efficiency, sustainability, and reliability. The proposed system enables real-time monitoring of critical parameters such as water level, flow rate, pressure, and water quality using a network of distributed IoT sensors. Edge AI techniques are employed for localized decision-making, significantly reducing latency and minimizing dependence on continuous network connectivity. Key features of the system include a self-healing mechanism for automatic fault detection, isolation, and rerouting of the water supply, ensuring uninterrupted service delivery. A fair distribution algorithm is incorporated to guarantee equitable water allocation among users, while a water credit system regulates consumption and promotes responsible usage. Additionally, predictive demand mapping and pipe health monitoring modules support proactive planning and preventive maintenance, reducing infrastructure failures. The system effectively minimizes water wastage, detects leakages in real time, prevents unauthorized usage, and optimizes resource allocation. Experimental analysis demonstrates improved operational efficiency, reduced water losses, enhanced scalability, and adaptability, making the proposed solution suitable for both urban smart cities and rural water management applications.

Multimodal Emotion Recognition Through Convolutional Neural Networks and Natural Language Processing

Bhavana B — 2026-06-23

Discerning human affective states from visual expressions, spoken phrases, and written passages is a central challenge in computer vision and natural language processing. This paper presents a fully integrated, lightweight, web-based multimodal emotion recognition system that handles five distinct input pathways: typed text, recorded audio, still images, video files, and live webcam feeds. For visual streams, the system utilizes a Convolutional Neural Network (CNN) trained on the FER-2013 dataset, paired with a Viola-Jones Haar Cascade classifier for rapid face localization. For linguistic streams, a lexicon-based natural language processing approach is implemented using the NRCLex engine, supplemented with a custom emotion vocabulary. Recorded audio files are transcribed using the Google Speech Recognition API prior to text-lexicon matching. Wrapped in a Flask web server, the platform operates entirely on commodity CPUs under a 1 GB RAM footprint, eliminating expensive GPU hardware dependencies. Experimental results demonstrate that the CNN model achieves a validation accuracy of 65 % to 68 % on the noisy FER-2013 benchmark, matching the human agreement rate, while the webcam stream maintains a smooth frame rate of 8 to 12 FPS via a strategic frame-caching technique. The resulting interface displays annotated visual outputs with styled bounding boxes and confidence scores, yielding an explainable and responsive affective computing overlay.

Multi-Agent Clinical Decision Support Systems for ICU Triage: An Agentic AI Framework for Real-Time, Explainable Critical Care Decision-Making

Bipin Sule — 2026-08-08

Intensive Care Unit (ICU) triage requires clinicians to synthesize heterogeneous, rapidly evolving data streams vital signs, laboratory results, medication histories, and unstructured clinical notes under severe time pressure, where errors can directly translate into preventable mortality. In ICU triage, the combination of heterogeneous, rapidly evolving data streams such as vital signs, lab results, medication history, and unstructured clinical notes must be synthesized under severe time constraints, with the potential for errors to have immediate impact on mortality. Traditional Clinical Decision Support Systems (CDSS) use fixed, rule-based scores (such as APACHE II, SOFA) or a single monolithic Machine Learning (ML) model and are unable to change their behavior to fit the context, provide explanations for their behavior, or gracefully degrade when data is incomplete. The proposed multi-agent clinical decision support architecture for triage in an ICU describes a collection of autonomous agents specialized in monitoring vital signs, interpreting laboratory results, reasoning in the context of the patient, verifying medication safety, data integration, and explaining the process results to the human operator to obtain an overall human-interpretable assessment of the patient’s clinical severity. Based on the recent successes of LLM-based multi-agent systems in emergency and intensive care, the study presents the architecture, orchestration protocol, and evaluation methodology of the system and discusses their development within an ethical AI governance framework that highlights the importance of transparency, fairness, and accountability. This modular, auditable approach to clinical reasoning is more clinically credible and readily scalable to full autonomy in the ICU setting, and three open challenges are highlighted that need to be addressed before it can be used in the real world: latency, safety checks, and regulatory clearance.

TradeSphere: A High-performance Web-based Financial Trading Platform for Retail Investors

Manjushree Nayak — 2026-08-08

The rapid democratization of global financial markets has created an urgent demand for accessible, high-performance trading interfaces suited to retail investors. Legacy desktop platforms such as Meta Trader 4/5 impose prohibitive complexity, platform dependency, and steep learning curves that deter novice participants. This paper presents Trade-Karo, a browser-native, single-page financial trading platform engineered using React.js v19.x, TradingView Lightweight Charts, and the Framer Motion animation library. The system delivers professional-grade candlestick charting at a sustained 60 frames-per-second (fps) via hardware-accelerated HTML5 Canvas rendering, a fully simulated margin-trading engine with configurable leverage (1:1–1000:1), and a glassmorphic dark-themed user interface. Performance benchmarks demonstrate a 70% reduction in initial JavaScript bundle size through React lazy-loading and code-splitting, achieving a Time-to-Interactive of 1.6 seconds and a Lighthouse Performance Score of 94/100. Rigorous testing validates the mathematical accuracy of margin, equity, and pip-value calculations. Trade-Karo constitutes a compelling proof-of-concept that modern web browsers are fully capable of serving as professional financial terminals, eliminating the need for desktop installation and providing a zero-cost, risk-free environment for financial education.

Next-generation Cloud Computing: From Virtualization to AI-driven Cloud Services

Siddhi Sunil Patil — 2026-08-08

Cloud computing is evolving from infrastructure virtualization toward intelligent, automated, and adaptive service management. Although conventional cloud platforms provide elastic access to computing, storage, networking, and software resources, highly variable workloads create persistent challenges in resource allocation, autoscaling, service-level agreement (SLA) compliance, operational cost, and energy efficiency. This paper presents a structured AI-driven cloud resource management framework that integrates monitoring and AIOps, workload prediction, intelligent resource allocation, dynamic orchestration, and closed-loop feedback. The framework is complemented by a task-oriented taxonomy that maps artificial intelligence techniques to cloud-management functions and measurable objectives. A critical review of recent studies on deep reinforcement learning for scheduling, machine-learning-based autoscaling, adaptive resource prediction, load balancing, and energy-aware management is used to identify gaps in fragmented decision-making and inconsistent evaluation. The proposed architecture is designed to support virtual machines, containers, serverless services, edge–cloud deployments, and multi-cloud environments. A reproducible evaluation methodology is defined using response time, throughput, resource utilization, cost, energy consumption, prediction error, and SLA violations. The paper does not claim unmeasured performance improvements; instead, it provides an implementable framework and validation protocol that can be experimentally tested using public workload traces or a controlled cloud testbed.

UrbanPulse: A Multi-tenant Intelligent Bike Routing System using OpenStreetMap, Live Traffic Data, and a Modified A* Routing Algorithm

Arathi H L — 2026-08-13

With rapid global urbanization and a critical shift toward sustainable micro-mobility, the urgency for intelligent, bike-centric navigation has reached a pivotal point. Traditional routing systems primarily focus on motor vehicles, often utilizing static graphs that prioritize the shortest distance while neglecting cyclist-specific variables like traffic congestion, air quality, and safety. This paper provides an extensive literature survey (2018–2025) exploring the evolution of routing algorithms, specifically the transition from monolithic to microservice-oriented Geographic Information Systems (GIS). The study analyzes “UrbanPulse,” a system designed to overcome the limitations of current static routing by leveraging a modified A* algorithm that integrates live traffic data. The significance of the proposed multi-tenant architecture lies in its ability to serve diverse user groups from individual commuters to delivery fleets within a single, scalable cloud-native framework while ensuring strict data isolation. By synthesizing recent advancements, this survey identifies critical research gaps in multi-tenant scalability and bike-specific traffic integration. The analysis demonstrates that the future of urban navigation depends on adaptive systems that prioritize the cyclist’s experience, providing a foundational blueprint for developing resilient, intelligent transportation solutions that respond dynamically to the pulse of the city.