The rapid proliferation of Internet of Things (IoT) devices has led to a growing ecosystem of Cloud Platforms designed to manage, process, and analyze IoT data. Selecting the optimal IoT Cloud Platform is a critical decision for businesses and developers, yet it presents a significant challenge due to the diverse range of features, pricing models, and architectural nuances. This manuscript presents a comprehensive, feature-driven review of twelve prominent IoT Cloud Platforms, including AWS IoT Core, IoT on Google Cloud Platform, and Microsoft Azure IoT Hub among others. We meticulously analyze each platform across nine key features: Security, Scalability and Performance, Interoperability, Data Analytics and AI/ML Integration, Edge Computing Support, Pricing Models and Cost-effectiveness, Developer Tools and SDK Support, Compliance and Standards, and Over-The-Air (OTA) Update Capabilities. For each feature, platforms are quantitatively scored (1–10) based on an in-depth assessment of their capabilities and offerings at the time of research. Recognizing the dynamic nature of this domain, we present our findings in a two-dimensional table to provide a clear comparative overview. Furthermore, to empower users in their decision-making process, we introduce a novel, web-based tool for evaluating IoT Cloud Platforms, called the “IoT Cloud Platforms Selector”. This interactive tool allows users to assign personalized weights to each feature, dynamically calculating and displaying weighted scores for each platform, thereby facilitating a tailored selection process. This research provides a valuable resource for researchers, practitioners, and organizations seeking to navigate the complex landscape of IoT Cloud Platforms.

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT connectivity, but the degree to which their embedded cryptographic mechanisms satisfy formal cybersecurity certification schemes remains underexplored. This work draws primarily on recent peer-reviewed publications and major conference proceedings to rigorously evaluate Thread, Zigbee, and Z-Wave against the Common Criteria (CC) Functional Requirements for Cryptography (FCS) as specified in CC:2022 and the EU cybersecurity certification scheme on Common Criteria (EUCC). The assessment focuses on essential CC cryptographic components, including key generation (FCS_CKM.1), secure key distribution (FCS_CKM.2), agreement protocols (FCS_CKM_EXT.7), cryptographic operations (FCS_COP.1), and random bit generators (FCS_RBG.1). The analysis reveals that Thread demonstrates the strongest alignment with CC requirements by leveraging Advanced Encryption Standard—Counter with CBC-MAC mode (AES-CCM) authenticated encryption and Elliptic Curve Diffie-Hellman (ECDH)-based key exchange within a decentralized trust framework. Zigbee matches this cryptographic strength at the primitive level, but its dependency on a centralized Trust Center for key management complicates full compliance with key lifecycle and distribution controls. Z-Wave, especially through its S2 Security framework, improves by incorporating authenticated ECDH exchanges, though proprietary constraints and limited protocol transparency remain obstacles to independent assurance. This comparative study concludes that while all three protocols provide a baseline of robust cryptographic security, only Thread currently aligns with CC and EUCC certification schemes. Zigbee and Z-Wave will require additional protocol hardening and enhancement of cryptographic key lifecycle management to achieve comparable assurance levels. Ensuring conformance with formal cybersecurity standards is imperative for building trust and resilience across critical IoT infrastructures.

Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures.

Managing firmware updates in large-scale IoT deployments presents significant challenges regarding security, reliability, and operational cost. This paper presents the design and implementation of “IOTAfy”, a new, open-source, comprehensive, Over-The-Air (OTA) firmware management platform tailored for ESP32-based IoT devices. “IOTAfy” distinguishes itself through its efficient database design and asynchronous update mechanism, enabling scalable deployments. The proposed system comprises a device-side solution for ESP32 microcontrollers, incorporating a custom bootloader, an application firmware boilerplate code, and an OTA library, alongside a web-based management interface developed using PHP, SQLite3, and Bootstrap. This integrated approach facilitates secure and reliable OTA application firmware updates. The platform enables centralized monitoring of device status, scheduled firmware updates, and robust version control for multiple IoT devices. OTA updates are implemented directly on the ESP32, eliminating the need for physical access or intervention. The web interface provides administrators with features for group upgrades, rollback capabilities, real-time update status monitoring and alerts. The system was tested in a controlled environment with 50 ESP32 devices, achieving a 98% success rate for OTA updates. The results demonstrate the potential for significant cost savings and reduced maintenance time compared to manual update processes. The architecture of the “IOTAfy” platform, leveraging an efficient database design and asynchronous update mechanism, facilitates scalability to hundreds or thousands of devices. This work offers a practical and scalable solution for managing firmware in large-scale IoT deployments, contributing to enhanced device security, improved reliability, and reduced operational expenditures.

The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integrated dashboard visualization. This research addresses these gaps by investigating and evaluating an IoT framework leveraging lightweight communication and real-time visualization for improved healthcare monitoring. Drawing primarily on recent peer-reviewed journals and reputable conference proceedings, we evaluate an IoT architecture that securely integrates wearable biometric data into a cloud-based dashboard. The system utilizes encrypted advertising packets (e.g., AES-128-CCM) to broadcast biometric signals, eliminating the need for permanent device pairing and minimizing energy consumption. These packets are captured by our prototype ESP32-based (Espressif Systems, Shanghai, China) gateway node, decrypted and forwarded to a secure cloud environment that ensures persistent storage and accessibility. The cloud-based dashboard provides medical staff and end-users with real-time insights and long-term data tracking. Emphasis was placed on evaluating the system’s low latency performance, energy efficiency and data confidentiality. System evaluation demonstrates that encrypted advertising packets can securely transmit biometric signals, while drastically reducing energy consumption and latency. System evaluation demonstrates that encrypted BLE advertising serves as a superior alternative to traditional pairing-based methods for long-term medical monitoring. By implementing a dual-optimization strategy that balances data confidentiality with power efficiency, the proposed system achieved a 33-fold increase in operational autonomy compared with standard permanent BLE connections. These results represent a significant advancement in battery longevity for the IoMT ecosystem, providing a scalable solution for continuous, secure biometric signal transmission with minimal energy overhead.