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Immense development of Micro Electro Mechanical Systems (MEMS) made an incredible advancement in wireless technology. The Wireless Sensor Network (WSN) has created many opportunities for the development of various applications in the fields of military, research, medical, engineering, etc. In this research article, a novel trust-based energy-aware clustering protocol is proposed. The clustering algorithm concentrates on reducing the time spent on cluster formation, controlling redundant data forwarding, and prolonging the network's lifespan. In this model, clustered nodes are classified into three levels like Cluster heads (CH), secondary CHs, and sensor nodes (SN) are used to sense the environmental changes and report to the Base Station (BS). An extension of the lifetime of a WSN is possible by the use of secure multi-hop routing with an aggregation technique to forward data from a cluster to the BS. Compared to relevant works on clustering with the routing protocol, the simulation result showed improved energy efficiency and network lifetime.

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.