The integration of the Internet of Things (IoT) with Cloud Computing is ushering in an exciting new era where technology powers smarter, more scalable, and highly secure systems across industries like manufacturing, healthcare, and urban development. This article offers a thorough and insightful review of the IoT-cloud convergence, breaking down how these two powerful technologies intertwine to create seamless, efficient solutions. It highlights the main security hurdles that come with this integration and shares best practices to overcome them, ensuring systems remain robust and trustworthy. Moreover, this piece dives into valuable editorial strategies designed specifically for researchers aiming to publish their findings quickly in prestigious Scopus-indexed journals. It emphasizes practical ways to streamline the peer review process, helping scholars navigate this often-complex journey with confidence. By sharing expert advice on crafting strong submissions and addressing common pitfalls, the article empowers researchers to accelerate the publication of their work, enabling faster sharing of knowledge that can fuel innovation and progress in the rapidly evolving world of IoT and cloud technologies..

Environmental pollution has emerged as a major global challenge, requiring fast, reliable, and scalable monitoring systems to support timely environmental protection efforts. Traditional assessment techniques, though scientifically sound, often lack real-time capability and wide coverage, making them insufficient for today’s rapidly changing environmental conditions. Sensor-based pollution detection systems address this gap by enabling continuous, precise monitoring of air, water, soil, and noise quality across urban and industrial environments. These sensors also support automated control of industrial emissions and facilitate smart environmental management practices. When integrated with IoT, cloud computing, and artificial intelligence, sensor networks strengthen predictive analytics, anomaly detection, and pollution forecasting, thereby improving policy formulation and regulatory enforcement. Although issues such as sensor calibration, environmental interference, and maintenance persist, sensor technologies remain indispensable for advancing sustainable development, protecting public health, and ensuring proactive environmental governance..

The aim of this study was to isolate and characterize the genes responsible for early ripening in tomatoes so as to establish future strategies on how this could be stemmed biotechnologically. About 30 tomato cultivars were collected as seedlings from the Nigerian - Green Agro-Allied Industrial Zone (GAAIZ), Kaduna and cultivated in a nursery bed for four weeks before transferring to a greenhouse. Fruit ripening stages were divided into five stages and total genomic DNA were extracted from these different growth stages using a DNA Plant extraction Kit (Qiagen). Fourteen SSR primers designed for tomato DNA fingerprinting and further molecular analysis were then carried out. For salt stress treatment, the roots of tomato seedlings were submerged in a solution containing 250 mM NaCl for 0, 1, 2, 4, 8, 12 and 24 hours. These were done to know the type of volatile oils expressed from the plant due to environmental factors. Based on the data obtained after the molecular analysis, nine MTases were identified in tomato through Blastp alignment. The open reading frame (ORF) length of these genes varied from 1.1 kb to 4.6 kb and their protein length ranged from 381 to 1559 amino acids. All the deduced polypeptides are hydrophilic. Salt tolerant stress test gave 5 different types of volatile oil. This study shows that series of modifications that transform a mature green fruit into a ripe fruit occur during the ripening stages and involve many different metabolic pathways. The implications of these findings are discussed here..

The convergence of Internet of Things (IoT) and Cloud Computing has led to the creation of powerful, scalable, and intelligent systems that are transforming industries and everyday life. This paper explores advanced concepts in IoT and Cloud Computing, highlighting key trends, technologies, and integration methods that drive innovation in these fields. The discussion focuses on Edge Computing, Fog Computing, and Cloud-Native IoT Architectures, which enable real-time data processing and scalability while reducing latency. It also delves into Cloud-Based IoT Analytics and the use of Machine Learning and Artificial Intelligence to derive actionable insights from vast IoT data streams. Security is a critical concern in IoT ecosystems, and this paper examines end-to-end security strategies, the Zero Trust security model, and the application of blockchain for ensuring device and data integrity. The role of IoT communication protocols, such as MQTT and CoAP, in facilitating seamless data exchange between devices and cloud platforms is also discussed. Furthermore, Digital Twin Technology is explored as a means of simulating real-world systems in the cloud for optimization and predictive analytics. The paper also covers the challenges of data governance, compliance, and interoperability, particularly in relation to IoT device management and the integration of diverse technologies. Case studies in smart cities, Industry 4.0, and connected healthcare demonstrate how cloud-connected IoT systems are enabling transformative applications. Ultimately, the integration of IoT and cloud computing is reshaping the digital landscape, offering new opportunities for efficiency, automation, and innovation across a range of sectors. This paper provides a comprehensive overview of the advanced concepts, use cases, and challenges at the forefront of IoT and Cloud Computing technologies..

The integration of Artificial Intelligence (AI) with Wireless Sensor Networks (WSN) has transformed traditional sensor systems into intelligent, efficient, and highly adaptive networks. AI-driven methods enable advanced data analysis, predictive decision-making, energy-aware routing, and effective anomaly detection, mitigating longstanding WSN challenges such as limited battery power, redundant communication, and security vulnerabilities. Techniques including reinforcement learning, metaheuristic optimization, federated learning, and TinyML empower autonomous operation and real-time on-device processing. Although issues related to model complexity, computational overhead, and heterogeneous sensor data persist, rapid progress in low-power AI chips, edge computing, and 5G/6G communication technologies is continually enhancing system performance. As a result, AI-enabled WSNs are emerging as a foundational technology for smart cities, healthcare, environmental monitoring, and industrial automation..