Chivalry: Signal Processing for WMSNs Humanoid vertical lace up boot where the lacing up shoes are made of suede or synthetic leather. Yet, the current research on WSNs shows limitations such as much energy consumption, scalability issues, security risks and no multi-environmental integration. To tackle those challenges, this research proposes an enhanced, optimized WSN framework integrative of blockchain-encrypted security, artificial intelligence (AI)-based adaptive routing, and hybrid edge–cloud computing. Their proposed system consisting of terrestrial, aerial and underwater WSNs ensures high-quality integration for continuous environmental monitoring, due to its seamless, energy-efficient and scalable nature. Also, the long-range communication protocols (LoRaWAN & NB-IoT) are included to boost data sending from distant regions. In this paper, we introduce a new UAV-assisted, solar-powered sensor network solution for coverage extension and operational cost reduction. This research further investigates AI-based energy-conscious data aggregation methods to reduce power utilization and enhance sensor lifespan. We perform real-world validation to show the practicality of proposed system to monitor air pollutants, water quality and climate parameters. As a result, the recommended framework greatly improves data reliability, scalability, and real-time decision-making capacities, therefore it can serve as an applicable model for modern environmental monitoring.
R et al. (Tue,) studied this question.