The most adverse characteristics of underwater wireless sensor network (UWSN) communications are high propagation delay, high error rate, very low bandwidth, and limited available energy. The energy resources replacement is also more expensive. The proposed clustering-based geographic- opportunistic routing with adjustment of depth-based topology control for communication recovery of void regions (C- GEDAR). The cluster-based GEDAR routes the packet to the surface of sonobuoys with the help of clusters. The void sensor node recovery algorithm is used to recover the void nodes to calculate their new depth. The proposed routing protocol is to be simulated and its performances are evaluated by using an Aquasim simulator. The simulated result shows that C-GEDAR performs better average energy consumption, good packet delivery ratio (PDR) and less end-to-end delay.

In the Underwater Wireless Sensor Networks (UWSN) provide a solution for several aquatic and oceanographic applications. All these UWSN applications are need to be aware of the nodes positioning. In some insecure environment, the misleading data can be transmitted to the sonobuoys or monitoring systems in the network. This may disrupt the functions of the nodes. Thus, the secured localization algorithms are designed to resistant against attack and try to achieve the localization correctly. This paper shows that modified secure localization algorithm based Gradient Descent Algorithm (GDA) to remove the misleading information in the networks. This modified algorithms the normal nodes are cooperate with each other to reduce the localization error and to improve the pruning percentage.

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License plate detection and recognition is the one of the major aspects of applying the image processing techniques towards intelligent transport systems. Detecting the exact location of the license plate from the vehicle image at very high speed is the one of the most crucial step for vehicle plate detection systems. This paper proposes an algorithm to detect license plate region and edge processing both vertically and horizontally to improve the performance of the systems for high speed applications. Throughout the detection and recognition the original images are detected, filtered both vertically and horizontally, and threshold based on bounding box method. The whole system was tested on more than twenty five cars with various license plates in Indian style at different weather conditions. The overall accuracy rate of success recognition is 93% at sunlight conditions, 72% at cloudy, 71% at shaded weather conditions.

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