ABSTRACT:An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data.

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The problem of extending the lifespan of wireless sensor networks (WSN) based on the Internet of Things (IoT) has been widely investigated over the last 20 years. This paper proposes an Optimized J-RMAC (optimized joint routing and media access control protocol) to guarantee the network lifetime in IoT-based WSN. Initially, all sensor nodes report their position and coverage information to the sink, which uses this information to pick a list of active nodes based on energy usage and active time. Then, the k-covered network is formed to execute the routing task by selecting the active nodes with the largest sensing areas. A multi-objective seagull optimization algorithm (MO-SOA) represents routing paths between the source and destination by considering two objective functions: energy consumption cost and end-to-end delay of a routing path. After that, the contention window of the nodes in the routing path is adjusted using a new iterative adaptive adjustment process of the contention window with adjustment parameters (IAACW-AP) to avoid message conflicts. The proposed protocol is simulated in the NS2 simulator. The performance of the proposed protocol will be compared with existing strategies in terms of network lifetime, packet delivery ratio, communication overhead, energy consumption, and delay.

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