Narrow Results

This paper develops an approach for detecting landslide using IoT. The simulation of IoT is the preliminary step that helps to collect data. The suggested Water Particle Grey Wolf Optimization (WPGWO) is used for the routing. The Water Cycle Algorithm (WCA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) are combined in the suggested method (WPGWO). The fitness is newly modeled considering energy, link cost, distance, and delay. The maintenance of routes is done to assess the dependability of the network topology. The landslide detection process is carried out at the IoT base station. In feature selection, angular distance is used. Oversampling is used to enrich the data, and Deep Residual Network (DRN) — used for landslide identification — is trained using the proposed Water Cycle Particle Swarm Optimization (WCPSO) method, which combines WCA and PSO. The proposed WCPSO-based DRN offered effective performance with the highest energy of 0.049J, throughput of 0.0495, accuracy of 95.7%, sensitivity of 97.2% and specificity of 93.9%. This approach demonstrated improved robustness and produced the global best optimal solution. For the proposed WPGWO, WCA, GWO, and PSO are linked to improve performance in determining the optimum routes. When comparing with existing methods the proposed WCPSO-based DRN offered effective performance.

Plant health monitoring is a very significant task in any agriculture-based environment. The Internet of Things (IoT) plays an important role in the monitoring of plant diseases. IoT is required to obtain data through sensor nodes for finding soil moisture and heat level. Even though different methods are available to monitor the health of plants, observing heat level and soil moisture still results a complex task. Thus, this paper introduces a novel chimp shuffled shepherd optimization (ChSSO) by the integration of chimp optimization algorithm (ChOA) and shuffled shepherd optimization (SSOA) to perform the selection of cluster head (CH) and routing process. The proposed ChSSO is trained using the deep LSTM which is developed for predicting soil moisture and heat level conditions in IoT network to monitor the health of plants. The proposed method obtained higher performance by the metrics, like testing accuracy and precision of 0.937, and 0.926 for 100 nodes and the values of 0.940, and 0.940 for 150 nodes using the LDAS dataset.