Wireless Sensor Networks (WSNs) have long been a fundamental technology for data collection in various domains, from environmental monitoring to industrial automation. However, the inherent limitations of WSNs, such as constrained computational power, energy efficiency concerns, and challenges in large-scale data management, have necessitated a shift toward more scalable and efficient solutions. This transition has led to the emergence of Sensor Clouds, which integrate WSNs with cloud computing to enhance data storage, processing, and accessibility.
Sensor Clouds offer a paradigm shift by leveraging cloud-based resources to address the shortcomings of traditional WSNs. By incorporating distributed computing, machine learning, edge computing, and real-time analytics, Sensor Clouds enable seamless scalability, improved data integrity, and intelligent decision-making in resource-constrained environments. These advancements have expanded the scope of WSN applications into areas such as smart cities, healthcare, environmental monitoring, and industrial IoT.

This Research Topic explores the transformation from basic WSNs to sophisticated Sensor Clouds, emphasizing technological, architectural, and practical aspects. By examining key milestones and breakthroughs, researchers can better understand the transformative impact of integrating cloud computing with WSNs, leading to the creation of sensor clouds. Within this collection we explore the technological advancements that have addressed the inherent limitations of WSNs, such as energy constraints, limited processing power, and data integrity issues and welcome submissions that provide a comprehensive review of the evolution of data collection techniques, architectural developments, and application domains. Submissions identifying future opportunities and challenges, such as enhancing sensor network efficiency and scalability, are also welcome.

Topics of interest include (but are not limited to):
o Transition from WSNs to Sensor Clouds
o Key components and architecture of Sensor Clouds
o Comparison of traditional WSNs and modern Sensor Cloud architectures
o Techniques for data aggregation, storage, and real-time processing
o Challenges in data scalability and heterogeneity
o Standards and protocols for seamless integration
o Cross-platform compatibility and interoperability issues
o Strategies for energy management in Sensor Clouds
o Impact of energy harvesting technologies
o Security threats and privacy concerns specific to Sensor Clouds
o Approaches for enhancing data protection and user privacy
o Analysis of key applications in smart cities, healthcare, and industrial IoT
o Emerging trends and future applications
o Integration of machine learning and AI in Sensor Clouds
o Role of edge and fog computing in Sensor Clouds
o Adaptive networking protocols for dynamic Sensor Cloud environments
o Fault tolerance and recovery mechanisms
o Ethical, legal, and sustainability considerations for Sensor Cloud deployments
o Blockchain-based security solutions and data privacy measures

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From Wireless Sensor Networks to Sensor Clouds