Journal of Research and Review: Future Internet and Hyperconnectivity
This is blind peer-reviewed journal that provides a platform which aspires to cover all the aspects Future Internet and Hyperconnectivity. This journal provides a deep insight in advancement and research in the field of Future Internet and Hyperconnectivity. Focus and Scope Hyperconnectivity Network and distributed operating systems Semantic structures Future Internet Centralized Data Centers Information Systems Cyber security compliance Cyber Security
Fitness habit maintenance is significantly limited by low user engagement, lack of real-time motivation support, and high app abandonment rates, leading to widespread fitness goal failures. This project addresses these challenges by implementing "Fitness Tracker", a gamified fitness tracking ecosystem with workout/nutrition logging, 3D progress visualization, and real-time XP tracking. Developed using React with React Three Fiber for 3D body visualization, the system is powered by a MERN stack backend (MongoDB, Express, React, Node.js) with secure JWT authentication, push notifications, and integrated gamification. The methodology follows an Agile MVP approach, resulting in a functional prototype featuring XP progression, daily/weekly quests, rank advancement, and streak management. The implemented system demonstrates significant improvements in user engagement through gamification mechanics including a 7-tier rank progression system, real-time quest tracking, and automated streak management. The 3D body visualization provides immediate feedback on fitness progress, while guild mechanics and social sharing enhance community accountability. The project validates the feasibility of using modern mobile and cloud technologies to build scalable fitness platforms with sustained habit formation through gamified reward systems and community-driven accountability.
Next Gen Security for WSNs: A Lightweight ECC Authentication Model
Wireless Sensor Networks (WSNs) are widely used for monitoring and data collection in resource-constrained environments such as healthcare, agriculture, and surveillance. However, these networks face major challenges related to security, energy consumption, and network congestion. Traditional cryptographic techniques often require large computational power, which increases delay and reduces network lifetime. To overcome these issues, an enhanced authentication scheme based on Elliptic Curve Cryptography (ECC) and Digital Signature Algorithm (DSA) is proposed. The Enhanced Authentication Elliptic Curve Cryptography (EAECC) approach improves security while minimizing computational overhead, bandwidth usage, and energy consumption. Experimental results show that the proposed method performs better than traditional methods like RSA in terms of throughput, encryption time, and decryption time, making it suitable for secure and efficient WSN applications.
Agentic AI: Autonomous Agents to Cognitive Autonomy
Agentic Artificial Intelligence (Agentic AI) represents a paradigm shift from traditional rule-based and task-specific AI systems toward autonomous, goal-driven intelligent systems capable of reasoning, planning, and acting in dynamic environments. Unlike conventional AI agents that operate within predefined constraints, Agentic AI systems leverage foundation models, particularly large language models (LLMs), to achieve adaptive behavior, persistent memory utilization, and multi-agent coordination. This paper provides a comprehensive review of Agentic AI, covering its evolution from symbolic AI to modern generative and agent-based systems. Core architectural components, including perception, memory, planning, execution, and orchestration layers, are analyzed alongside prominent frameworks such as AutoGen, CrewAI, MetaGPT, LangGraph, and Semantic Kernel. A structured taxonomy of agentic systems is presented based on architecture, interaction paradigms, and domain specialization. The paper further examines enabling technologies such as reinforcement learning, multi-agent systems, and neurosymbolic AI, and discusses methodologies including BDI-based reasoning frameworks. Applications across healthcare, finance, manufacturing, and human–AI collaboration are explored to highlight practical impact. Finally, critical challenges including scalability, evaluation limitations, computational complexity, and ethical concerns are analyzed, followed by future research directions focusing on explainability, adaptive learning, and cross-domain generalization. This review aims to provide a coherent and analytical understanding of Agentic AI and its potential to redefine intelligent systems.
Edge AI and DevOps: Orchestrating Intelligent Microservices at Scale
Deploying artificial intelligence at the network edge promises low latency, reduced bandwidth, and data locality, but it breaks the assumptions that conventional DevOps and MLOps pipelines were built on: stable high-bandwidth links and homogeneous, always-connected hardware. This paper presents an orchestration approach for running intelligent microservices across a fleet of distributed edge sites at scale. We combine three ideas. First, a cloud control plane drives a lightweight edge orchestrator (e.g., KubeEdge or K3s) through GitOps desired-state reconciliation, so thousands of intermittently connected nodes converge to a declared configuration. Second, AI workloads are decomposed into containerized microservice pipelines (ingest → inference → post-process) and the inference model is optionally split across edge and cloud, with an optimal split point chosen to minimize end-to-end latency and edge energy. Third, model and code updates roll out progressively (canary) across the fleet with automated anomaly-gated rollback. We formalize the split-point and placement decisions, give the latency model, and report illustrative results, consistent with the literature, showing that collaborative split inference can beat both cloud-only and edge-only modes on latency while bounding edge energy. Public sources are cited throughout.
Sushma Sunkollu Nagaraj Sunkollu Nagaraj
Gamified Training Platforms for AWS/Azure/GCP Certification Preparation
The three major cloud providers each run a gamified training platform to prepare learners for their certifications: AWS (Skill Builder, Cloud Quest, Jam), Microsoft Azure (Microsoft Learn, Cloud Skills Challenge), and Google Cloud (Skills Boost, Qwiklabs, The Arcade). Although these platforms share a common purpose, they differ markedly in their gamification mechanics and in the engineering required to deliver them. This paper takes a platform- and systems-level view rather than an engagement-measurement one. We contribute: (i) a cross-provider comparison of the three platforms and their distinctive mechanics; (ii) a feature taxonomy of gamified cloud-certification platforms; and (iii) a provider-agnostic reference architecture whose defining component is an ephemeral, isolated hands-on-lab and sandbox provisioning layer — the part that is hard to build and that distinguishes real cloud training from quizzes. We analyze the sandbox lifecycle and its guardrails (time limits, budget caps, isolation, auto-teardown), formalize a badge-gated path-completion model and a sandbox-cost bound, and discuss a progression funnel through platform stages. Illustrative results, consistent with public descriptions of these platforms, motivate the design choices. Public sources are cited throughout.
Gamification in Cloud Certification Training Engagement and Outcome Analysis
Cloud certifications — from foundational practitioner credentials to professional architect exams — have become a primary signal of cloud competence, but their preparation is demanding and attrition before exam day is high. Vendors have responded by gamifying certification training: AWS, for example, offers AWS Cloud Quest, AWS Jam, GameDays, and badge programs alongside its Skill Builder exam-prep plan. This article analyzes how gamification affects two linked things that prior general gamification studies often conflate: engagement during preparation, and certification outcomes — first-attempt pass rate, scaled score, and time-to-certification. We propose an engagement–readiness–outcome model in which game mechanics raise sustained study engagement, engagement raises measured exam readiness (practice-exam scores and domain coverage), and readiness predicts the certification result. We formalize the analysis with an engagement index, a logistic pass-probability model linking readiness to outcome, and an effect-size comparison across cohorts. Illustrative results, consistent with vendor-reported figures and the gamification literature, show higher sustained engagement, higher practice-exam readiness, and improved first-attempt pass rates and time-to-certification for gamified cohorts. We discuss the novelty effect, selection bias, and the distinction between engagement and genuine learning. Public sources are cited throughout.