An Intelligent Optimization of Agricultural Wireless Sensor Networks via Salp Swarm and Whale Optimization Strategies

DR Kannan Pauliah Nadar; Dr, Shashi Kant; Dr.N.Thilagavathi N

Authors

DR Kannan Pauliah Nadar st joseph university in tanzania Author
Dr, Shashi Kant Author
Dr.N.Thilagavathi Author

Keywords:

cloud computing, DInSAR, distributed storage, Earth observation, P-SBAS, satellite radar interferometry

Abstract

Satellite radar archives now grow more rapidly than conventional workstation and fixed-cluster processing can accommodate. Parallel Small Baseline Subset processing can derive deformation velocity maps and displacement time series from large stacks of synthetic aperture radar observations, but its scalability depends on data placement, shared-storage bandwidth, workflow recovery, and the balance between sequential and parallel stages. This paper proposes a cloud-native distributed P-SBAS framework that replaces a single shared file-system bottleneck with object-based data organization, worker-local scratch space, content-addressed intermediate products, elastic orchestration, and cost-aware scheduling. The framework preserves the scientific stages of established P-SBAS processing while changing how tasks, data, provenance, and failures are managed. A stage-aware scheduler places computation close to required inputs, scales workers according to measured queue pressure and I/O saturation, and avoids allocating nodes that cannot produce useful speedup. Checkpoints and idempotent tasks permit selective recovery instead of restarting an entire stack. A provenance catalog records source scenes, orbit and elevation inputs, software containers, parameters, task lineage, quality indicators, and output checksums. The paper defines correctness, scalability, cost, energy, resilience, and reproducibility metrics and presents a controlled evaluation protocol against centralized cloud and fixed HPC baselines. Because no new experiment was executed for this article, numerical improvements are not claimed; reported values from earlier studies are identified as prior evidence rather than new findings. The proposed design provides a testable path toward operational deformation analytics for sustained Sentinel-class data streams.

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